so very freaking close to done
This commit is contained in:
Nicky Case
parent
dc6b1abcea
commit
9a1b9d6be3
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@ -5,8 +5,11 @@ body{
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font-weight: 100;
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line-height: 1.7em;
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}
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b, strong{
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font-weight: bold;
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}
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article > p, article > ul{
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article > p, article > ul, article > ol, article > h1, article > h2, article > h3{
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width: 640px;
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margin:1em auto;
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}
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@ -37,4 +40,34 @@ iframe{
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border:2px solid #eee;
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display: block;
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margin:0 auto;
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}
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icon{
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display: inline-block;
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width: 1em;
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height: 1em;
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position: relative;
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top:0.1em;
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background-size: 100% 100%;
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}
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icon[s]{
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background-image: url(../icons/s.png);
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}
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icon[e]{
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background-image: url(../icons/e.png);
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}
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icon[i]{
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background-image: url(../icons/i.png);
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}
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icon[r]{
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background-image: url(../icons/r.png);
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}
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p > img{
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width: 100%;
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border: 1px solid #ddd;
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margin: 0.5em auto;
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}
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sub{
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line-height: 0;
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}
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752
index.html
752
index.html
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@ -38,7 +38,7 @@
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<input class="sim_checkbox" type="checkbox" id="c_recovery">
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Becomes <icon r></icon> in <span id="label_p_recovery">N</span> days
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<br>
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<input class="sim_input" type="range" id="p_recovery" min="1" max="30" step="1" value="11">
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<input class="sim_input" type="range" id="p_recovery" min="1" max="30" step="1" value="10">
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</div>
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<div id="label_c_waning">
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@ -60,9 +60,6 @@
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<br>
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<span id="label_s">
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<br>
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But R is changed by...
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<br>
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% of people who are <i>NOT</i> <icon s></icon>
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<input class="sim_input" type="range" id="p_s" min="0" max="1" step="0.001" value="0" disabled>
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<div class="herd"></div>
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@ -96,10 +93,6 @@
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<br>
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<input class="sim_input recordable" type="range" id="p_masks" min="0" max="1" step="0.001" value="0">
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<br>
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Deep Cleaning
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<br>
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<input class="sim_input recordable" type="range" id="p_cleaning" min="0" max="1" step="0.001" value="0">
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<br>
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</span>
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<span id="int_block_4">
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Summer
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@ -121,9 +114,9 @@
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</span>
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<span id="hospital_capacity">
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Hospital capacity at <span id="label_p_hospital">N</span>%
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ICU capacity at <span id="label_p_hospital">N</span>%
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<br>
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<input class="sim_input recordable" type="range" id="p_hospital" min="0" max="500" step="1" value="100">
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<input class="sim_input recordable" type="range" id="p_hospital" min="100" max="1000" step="1" value="333">
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</span>
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<hr id="divider">
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@ -171,32 +164,32 @@
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</div>
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<div id="legend">
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<span id="label_susceptible">
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<span id="label_susceptible" class="lines">
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<icon s></icon> Susceptible<span id="show_percent_s"></span>
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<!--<br>-->
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<br>
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</span>
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<span id="label_exposed">
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<span id="label_exposed" class="lines">
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<icon e></icon> Exposed<span id="show_percent_e"></span>
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<!--<br>-->
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<br>
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</span>
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<span id="label_infectious">
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<span id="label_infectious" class="lines">
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<icon i></icon> Infectious<span id="show_percent_i"></span>
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<!--<br>-->
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<br>
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</span>
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<span id="label_removed">
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<span id="label_removed" class="lines">
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<icon r></icon> Removed<span id="show_percent_r"></span>
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</span>
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<br>
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<br class="lines">
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<span id="label_herd_immunity">
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- - - Herd Immunity
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</span>
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<!--<br>-->
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<br class="lines">
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<span id="label_capacity">
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––– Healthcare Capacity
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––– ICU Capacity
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</span>
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</div>
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@ -244,11 +244,20 @@ bbDOM.onclick = ()=>{
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let defaultParams = [
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["p_transmission", 4],
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["p_exposed", 3],
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["p_recovery", 11],
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["p_recovery", 10],
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["p_waning", 1],
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["p_hospital", 100],
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["p_hospital", 333],
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["p_years", 2],
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["p_speed", 30],
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["p_non_s", 0],
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["p_hygiene", 0],
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["p_distancing", 0],
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["p_isolate", 0],
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["p_quarantine", 0],
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["p_cleaning", 0],
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["p_masks", 0],
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["p_summer", 0],
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];
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sbDOM.onclick = ()=>{
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@ -301,7 +310,7 @@ let _showAllControls = ()=>{
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hofp.style.position = "absolute";
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hofp.style.top = "-1000px";
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setTimeout(()=>{
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let newHeight = hofp.getBoundingClientRect().height;
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let newHeight = hofp.getBoundingClientRect().height + 10;
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hofp.style.position = "";
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hofp.style.top = "";
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hofp.style.height = originalHeight+"px";
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@ -27,7 +27,7 @@ let interventionStrengths = [
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['isolate', 0.4],
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['quarantine', 0.5],
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['cleaning', 0.1],
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['masks', 0.5], // 3.4 fold reduction (70%) (what CI?), subtract points for... improper usage? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/ // cloth masks...
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['masks', 0.35], // 3.4 fold reduction (70%) (what CI?), subtract points for... improper usage? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/ // cloth masks...
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['summer', 0.333] // 15°C diff * 0.0225 (Wang et al)
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];
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@ -120,7 +120,7 @@ let updateModel = (days, fake)=>{
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if(I>1) I=1;
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// Susceptible & Re
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if(!fake && s_dom.disabled){
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if((!fake || params.FROZEN_IN_TIME) && s_dom.disabled){
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s_dom.value = 1 - S;
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}
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re = newlyExposed/newlyRecovered;
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@ -451,9 +451,9 @@ let draw = ()=>{
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});
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// ICU bed capacity
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// Actually... just make it a generous 1%.
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// 0.6%
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if(params.p_hospital){
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y = (1-((params.p_hospital/100)*0.02))*canvas.height;
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y = (1-((params.p_hospital/100)*0.006))*canvas.height;
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h = 2;
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ctx.fillStyle = "#000000";
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ctx.fillRect(0,y,w,h);
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330
sim/js/Stages.js
330
sim/js/Stages.js
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@ -18,11 +18,11 @@ int-2a hygiene & distancing
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int-2 Flatten Curve / Herd Immunity
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int-3 Lockdown for a while
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int-4 Intermittent Lockdown "second & third waves"
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int-5 Lockdown, then Test & Trace...
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int-5b and with Vaccination!
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int-4a Calc trace
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int-4b Calc vaccinate
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int-5 Lockdown, then Test & Trace.. then Vaccination!
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int-6a Masks
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int-6b Deep Cleaning
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int-6c Summer
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int-6b Summer
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int-7 Test+Trace+Masks + One Circuit Breaker
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yrs-1 Decay of Recovered
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@ -299,7 +299,7 @@ const STAGES = {
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],
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SHOW_ALL_AT_START: true,
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PLAY_RECORDING: [
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["p_distancing",0.344,84], ["p_hygiene",1,84],
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["p_distancing",0.275,84], ["p_hygiene",1,84],
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["p_distancing",0,340], ["p_hygiene",0,340],
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],
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SIR: [0.999995,0.000005,0]
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@ -323,7 +323,7 @@ const STAGES = {
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SHOW_ALL_AT_START: true,
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PLAY_RECORDING: [
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["p_distancing",1,84], ["p_hygiene",1,84],
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["p_distancing",0,234], //["p_hygiene",0,234]
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["p_distancing",0,234], ["p_hygiene",0,234]
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],
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SIR: [0.999995,0.000005,0]
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},
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@ -345,27 +345,74 @@ const STAGES = {
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],
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SHOW_ALL_AT_START: true,
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PLAY_RECORDING: [
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["p_distancing",1,85], ["p_hygiene",1,85],
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["p_distancing",0,85+58],
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["p_distancing",1,85+58+33],
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["p_distancing",0,85+58+33+58],
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["p_distancing",1,85+58+33+58+36],
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["p_distancing",0,85+58+33+58+36+58],
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["p_distancing",1,85+58+33+58+36+58+48],
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["p_distancing",0,85+58+33+58+36+58+48+58],
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["p_distancing",1,85+58+33+58+36+58+48+58+60],
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["p_distancing",0,85+58+33+58+36+58+48+58+60+58],
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["p_distancing",1,85+58+33+58+36+58+48+58+60+58+80],
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["p_distancing",1,90], ["p_hygiene",1,90],
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["p_distancing",0,90+68],
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["p_distancing",1,90+68+54],
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["p_distancing",0,90+68+54+73],
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["p_distancing",1,90+68+54+73+73],
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["p_distancing",0,90+68+54+73+73+73],
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["p_distancing",1,90+68+54+73+73+73+87],
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["p_distancing",0,90+68+54+73+73+73+87+58],
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["p_distancing",1,90+68+54+73+73+73+87+58+108],
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],
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SIR: [0.999995,0.000005,0]
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},
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"int-4a": {
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hide: [
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"section_dynamics",
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"section_meta","label_c_waning","c_recovery",
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"label_c_exposed",
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/*"int_block_0",
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"int_block_1","int_block_2",*/"int_block_3","int_block_4","int_block_5","hospital_capacity",
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"graph",
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//"label_s","label_re",
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"sim_controls",
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"divider"
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],
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checkboxes: [
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["c_recovery", true]
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],
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inputs: [
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["p_hygiene",1],
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["FROZEN_IN_TIME", true],
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],
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disabled:[
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["p_s", false]
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],
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SHOW_ALL_AT_START: true
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},
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"int-4b": {
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hide: [
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"section_dynamics",
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"section_meta","label_c_waning","c_recovery",
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"label_c_exposed",
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"int_block_0",
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"int_block_1","int_block_2","int_block_3","int_block_4",/*"int_block_5",*/"hospital_capacity",
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"graph",
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//"label_s","label_re",
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"sim_controls",
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"divider"
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],
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checkboxes: [
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["c_recovery", true]
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],
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inputs: [
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["FROZEN_IN_TIME", true],
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],
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/*disabled:[
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["p_s", false]
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],*/
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SHOW_ALL_AT_START: true
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},
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"int-5": {
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hide: [
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"section_dynamics",
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"label_c_waning","c_recovery","c_exposed",
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"section_meta_years",
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/*"int_block_2",*/"int_block_3","int_block_4","int_block_5","hospital_capacity"
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/*"int_block_2",*/"int_block_3","int_block_4",/*"int_block_5",*/"hospital_capacity"
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],
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inputs: [
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["p_years",2],
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@ -378,45 +425,24 @@ const STAGES = {
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SHOW_ALL_AT_START: true,
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PLAY_RECORDING: [
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["p_distancing",1,84], ["p_hygiene",1,84],
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["p_distancing",0,175], ["p_quarantine",0.65,175], ["p_isolate",0.65,175],
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],
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SIR: [0.999995,0.000005,0]
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},
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"int-5b": {
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hide: [
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"section_dynamics",
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"label_c_waning","c_recovery","c_exposed",
|
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"section_meta_years",
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/*"int_block_2",*/"int_block_3","int_block_4",/*"int_block_5",*/"hospital_capacity"
|
||||
],
|
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inputs: [
|
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["p_years",2],
|
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["p_speed",10],
|
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],
|
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checkboxes: [
|
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["c_recovery", true],
|
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["c_exposed",true]
|
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],
|
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SHOW_ALL_AT_START: true,
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PLAY_RECORDING: [
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["p_distancing",1,84], ["p_hygiene",1,84],
|
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["p_distancing",0,175], ["p_quarantine",0.65,175], ["p_isolate",0.65,175],
|
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["p_hygiene",0,550], ["p_quarantine",0,550], ["p_isolate",0,550],
|
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["p_vaccines",0.64,550],
|
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["p_vaccines",0.61,550],
|
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["p_vaccines",0,580],
|
||||
],
|
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SIR: [0.999995,0.000005,0]
|
||||
},
|
||||
|
||||
"int-6c": {
|
||||
"int-6a": {
|
||||
hide: [
|
||||
"section_dynamics",
|
||||
"section_meta",
|
||||
"label_c_waning","c_recovery",
|
||||
"label_c_exposed",
|
||||
/*"int_block_0",
|
||||
"int_block_1","int_block_2","int_block_3","int_block_4","int_block_5",*/
|
||||
"int_block_1","int_block_2","int_block_3",*/"int_block_4","int_block_5",
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"hospital_capacity",
|
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"graph",
|
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//"label_s","label_re",
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||||
|
|
@ -428,6 +454,37 @@ const STAGES = {
|
|||
],
|
||||
inputs: [
|
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["FROZEN_IN_TIME", true],
|
||||
["p_hygiene", 1],
|
||||
["p_isolate", 0.516],
|
||||
["p_quarantine", 0.515],
|
||||
],
|
||||
disabled:[
|
||||
["p_s", false]
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
_HACK_MAKE_TIME_KEEP_GOING: true,
|
||||
},
|
||||
|
||||
"int-6b": {
|
||||
hide: [
|
||||
"section_dynamics",
|
||||
"section_meta",
|
||||
"label_c_waning","c_recovery",
|
||||
"label_c_exposed",
|
||||
"int_block_0",
|
||||
"int_block_1","int_block_2","int_block_3",/*"int_block_4",*/"int_block_5",
|
||||
"hospital_capacity",
|
||||
"graph",
|
||||
//"label_s","label_re",
|
||||
"sim_controls",
|
||||
"divider"
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true]
|
||||
],
|
||||
inputs: [
|
||||
["FROZEN_IN_TIME", true],
|
||||
["p_summer", 1],
|
||||
],
|
||||
disabled:[
|
||||
["p_s", false]
|
||||
|
|
@ -460,15 +517,17 @@ const STAGES = {
|
|||
// Lift
|
||||
["p_distancing",0,175],
|
||||
["p_hygiene",0.66,84],
|
||||
["p_quarantine",0.33,175], ["p_isolate",0.33,175], ["p_masks",0.33,175],
|
||||
["p_quarantine",0.4,175],
|
||||
["p_isolate",0.4,175],
|
||||
["p_masks",0.17,175],
|
||||
|
||||
// Circuit Breaker
|
||||
["p_distancing",1,60+283],
|
||||
["p_distancing",0,60+283+60],
|
||||
["p_distancing",1,365],
|
||||
["p_distancing",0,365+60],
|
||||
|
||||
// Vaccine!
|
||||
["p_hygiene",0,550], ["p_quarantine",0,550], ["p_isolate",0,550], ["p_masks",0,550],
|
||||
["p_vaccines",0.64,550],
|
||||
["p_vaccines",0.6,550],
|
||||
["p_vaccines",0,580],
|
||||
|
||||
],
|
||||
|
|
@ -483,111 +542,153 @@ const STAGES = {
|
|||
"yrs-1": {
|
||||
hide: [
|
||||
/*"section_dynamics",*/
|
||||
"section_r",
|
||||
"c_waning","c_recovery","c_exposed",
|
||||
"section_meta_years",
|
||||
"int_block_0","int_block_1",
|
||||
"int_block_2","int_block_3",/*"int_block_4",*/"int_block_5","hospital_capacity"
|
||||
"label_herd_immunity","label_capacity"
|
||||
],
|
||||
inputs: [
|
||||
["p_years",10],
|
||||
["p_speed",20],
|
||||
["p_years",5],
|
||||
["p_speed",5],
|
||||
["p_hospital", 0],
|
||||
["DO_NOT_SHOW_HERD_IMMUNITY", true],
|
||||
["_HACK_SHOW_SI_PERCENTS",3],
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_exposed",true],
|
||||
["c_waning", true],
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
SIR: [0.999995,0.000005,0]
|
||||
//SHOW_ALL_AT_START: true,
|
||||
SIR: [0,0,1],
|
||||
SHOW_HAND: "tutorial_1"
|
||||
},
|
||||
|
||||
/*
|
||||
|
||||
"12": {
|
||||
hide: ["section_r","section_meta","label_transmission","label_c_recovery","c_waning"],
|
||||
inputs: [
|
||||
["p_years",5],
|
||||
["p_speed",10]
|
||||
],
|
||||
checkboxes: [
|
||||
["c_waning", true]
|
||||
],
|
||||
SIR: [0,0,1]
|
||||
},
|
||||
|
||||
"13": {
|
||||
"yrs-2": {
|
||||
hide: [
|
||||
"section_meta","c_waning","c_recovery",
|
||||
//"section_dynamics",
|
||||
"c_waning","c_recovery","c_exposed",
|
||||
"section_meta_years",
|
||||
"c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3","int_block_4","int_block_5","hospital_capacity"
|
||||
],
|
||||
inputs: [
|
||||
["p_years",5],
|
||||
["p_years",10],
|
||||
["p_speed",20],
|
||||
//["TIME_DELTA", 0.2],
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_exposed",true],
|
||||
["c_waning", true]
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
//SIR: [0.09,0.01,0.9]
|
||||
},
|
||||
|
||||
"13b": {
|
||||
"yrs-3": {
|
||||
hide: [
|
||||
"section_dynamics",
|
||||
"section_meta","c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3","int_block_4","int_block_5",
|
||||
//"section_dynamics",
|
||||
"c_waning","c_recovery","c_exposed",
|
||||
"section_meta_years",
|
||||
"c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3",/*"int_block_4",*/"int_block_5","hospital_capacity"
|
||||
],
|
||||
inputs: [
|
||||
["p_years",5],
|
||||
["p_speed",20],
|
||||
//["TIME_DELTA", 0.2],
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_waning", true]
|
||||
],
|
||||
SIR: [0.09,0.01,0.9]
|
||||
},
|
||||
|
||||
"14": {
|
||||
hide: [
|
||||
"section_dynamics",
|
||||
"section_meta","c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3","int_block_5",
|
||||
],
|
||||
inputs: [
|
||||
["p_years",5],
|
||||
["p_years",10],
|
||||
["p_speed",20],
|
||||
["p_summer",1],
|
||||
//["TIME_DELTA", 0.2],
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_exposed",true],
|
||||
["c_waning", true]
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
//SIR: [0.09,0.01,0.9]
|
||||
},
|
||||
|
||||
"15": {
|
||||
|
||||
"yrs-4": {
|
||||
hide: [
|
||||
"section_dynamics",
|
||||
"section_meta","c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3",
|
||||
//"c_waning","c_recovery","c_exposed",
|
||||
"section_meta_years",
|
||||
"c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3",/*"int_block_4","int_block_5",*/"hospital_capacity"
|
||||
],
|
||||
inputs: [
|
||||
["p_years",5],
|
||||
["p_years",10],
|
||||
["p_speed",20],
|
||||
["p_summer",1],
|
||||
//["TIME_DELTA", 0.2],
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_exposed",true],
|
||||
["c_waning", true]
|
||||
],
|
||||
SIR: [0.09,0.01,0.9]
|
||||
PLAY_RECORDING: [
|
||||
|
||||
// Vaccine!
|
||||
["p_vaccines",0.62,365-60],
|
||||
["p_vaccines",0.0,365],
|
||||
["p_vaccines",0.62,2*365-60],
|
||||
["p_vaccines",0.0,2*365],
|
||||
["p_vaccines",0.62,3*365-60],
|
||||
["p_vaccines",0.0,3*365],
|
||||
["p_vaccines",0.62,4*365-60],
|
||||
["p_vaccines",0.0,4*365],
|
||||
["p_vaccines",0.62,5*365-60],
|
||||
["p_vaccines",0.0,5*365],
|
||||
["p_vaccines",0.62,6*365-60],
|
||||
["p_vaccines",0.0,6*365],
|
||||
["p_vaccines",0.62,7*365-60],
|
||||
["p_vaccines",0.0,7*365],
|
||||
["p_vaccines",0.62,8*365-60],
|
||||
["p_vaccines",0.0,8*365],
|
||||
["p_vaccines",0.62,9*365-60],
|
||||
["p_vaccines",0.0,9*365],
|
||||
["p_vaccines",0.62,10*365-60],
|
||||
["p_vaccines",0.0,10*365]
|
||||
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
//SIR: [0.09,0.01,0.9]
|
||||
},
|
||||
|
||||
"yrs-5": {
|
||||
hide: [
|
||||
"section_dynamics",
|
||||
//"c_waning","c_recovery","c_exposed",
|
||||
"section_meta_years",
|
||||
"c_waning","c_recovery",
|
||||
"int_block_0","int_block_1","int_block_2","int_block_3",/*"int_block_4",*/"int_block_5",
|
||||
//"hospital_capacity"
|
||||
],
|
||||
inputs: [
|
||||
["p_years",10],
|
||||
["p_speed",20],
|
||||
["p_summer",1],
|
||||
//["TIME_DELTA", 0.2],
|
||||
],
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_exposed",true],
|
||||
["c_waning", true]
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
//SIR: [0.09,0.01,0.9]
|
||||
PLAY_RECORDING: [
|
||||
|
||||
// Hospital
|
||||
["p_hospital",500,365],
|
||||
["p_hospital",750,365*2],
|
||||
["p_hospital",1000,365*3]
|
||||
|
||||
]
|
||||
},
|
||||
*/
|
||||
|
||||
//////////////////////////////////////////
|
||||
// SANDBOX ///////////////////////////////
|
||||
|
|
@ -596,8 +697,10 @@ const STAGES = {
|
|||
"SB": {
|
||||
checkboxes: [
|
||||
["c_recovery", true],
|
||||
["c_waning", true]
|
||||
]
|
||||
["c_waning", true],
|
||||
["c_exposed",true],
|
||||
],
|
||||
SHOW_ALL_AT_START: true,
|
||||
},
|
||||
|
||||
|
||||
|
|
@ -638,6 +741,7 @@ let setStage = (stageID)=>{
|
|||
|
||||
// Sliders
|
||||
stage.inputs = stage.inputs || [];
|
||||
changeSliders(defaultParams);
|
||||
changeSliders(stage.inputs);
|
||||
|
||||
// Checkboxes
|
||||
|
|
@ -690,4 +794,26 @@ let setStage = (stageID)=>{
|
|||
};
|
||||
|
||||
let stageParams = new URLSearchParams(location.search);
|
||||
if(stageParams.has('stage')) setStage(stageParams.get('stage'));
|
||||
if(stageParams.has('stage')) setStage(stageParams.get('stage'));
|
||||
|
||||
if(stageParams.has('format')){
|
||||
|
||||
if(stageParams.get('format')=='calc'){
|
||||
document.body.style.overflow = 'hidden';
|
||||
$('#sandbox').style.margin = '0';
|
||||
}
|
||||
if(stageParams.get('format')=='lines'){
|
||||
$all('.lines').forEach((dom)=>{
|
||||
dom.style.display = 'none';
|
||||
});
|
||||
}
|
||||
if(stageParams.get('format')=='sb'){
|
||||
$('#legend').style.display = 'none';
|
||||
$('#sandbox').style.margin = '0';
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
if(stageParams.has('height')){
|
||||
$('#sandbox').style.height = stageParams.get('height')+'px';
|
||||
}
|
||||
|
|
@ -14,7 +14,7 @@ div{
|
|||
|
||||
#sandbox{
|
||||
width: 800px;
|
||||
height: 640px;
|
||||
height: 540px;
|
||||
overflow: hidden;
|
||||
background: #fff;
|
||||
position: absolute;
|
||||
|
|
@ -264,7 +264,7 @@ icon[r]{
|
|||
transition: height 0.5s ease-in-out;
|
||||
overflow: hidden;
|
||||
height:0px;
|
||||
padding-right: 2px;
|
||||
/*padding-right: 2px;*/
|
||||
}
|
||||
|
||||
#month_names{
|
||||
|
|
|
|||
743
words/words.html
743
words/words.html
|
|
@ -12,548 +12,701 @@
|
|||
|
||||
<body>
|
||||
|
||||
<h1 id="toc_0">What Happens Next?</h1>
|
||||
<p><strong>{WIP, DON'T SHARE YET THX!}</strong></p>
|
||||
|
||||
<h2 id="toc_1">COVID-19 Possibilities, Explained With Playable Simulations</h2>
|
||||
<p>"The only thing to fear is fear itself" was stupid advice.</p>
|
||||
|
||||
<p>"The only thing to fear is fear itself" is stupid.<sup id="fnref1"><a href="#fn1" rel="footnote">1</a></sup></p>
|
||||
<p>Sure, don't hoard toilet paper – but if policymakers fear fear itself, they'll downplay dangers to us to avoid "mass panic". Fear's not the problem, it's how we <em>channel</em> our fear. Fear gives us energy to deal with dangers now, and prepare for dangers later.</p>
|
||||
|
||||
<p>Sure, don't hoard toilet paper. But if someone's so scared to think about scary things, that they deny danger <em>when it's already here</em>, then they've got more problems<sup id="fnref2"><a href="#fn2" rel="footnote">2</a></sup> than toilet paper.</p>
|
||||
|
||||
<p>The problem's not fear, but how we <em>use</em> our fear. Taiwan and South Korea <em>bravely used their fear</em> (from SARS) to invest in "pandemic insurance", and it paid off in controlling COVID-19! Fear gives you energy to deal with present dangers & plan for future dangers – <em>if</em> you know how to channel your fear.</p>
|
||||
|
||||
<p>So, we (Marcel & Nicky) have channeled our COVID-19 fears into making these playable simulations – so that <em>you</em> can channel <em>your</em> fear into gaining a deep, intuitive understanding of:</p>
|
||||
<p>Honestly, we (Marcel, epidemiologist + Nicky, art/code) are worried. We bet you are, too! That's why we've channelled our fear into making these <strong>playable simulations</strong>, so that <em>you</em> can channel your fear into understanding:</p>
|
||||
|
||||
<ul>
|
||||
<li><strong>The Last Few Months</strong> (epidemiology 101, SIR model, R0 & Rt)</li>
|
||||
<li><strong>The Next Few Months</strong> (lockdowns, contact tracing, masks)</li>
|
||||
<li><strong>The Next Few Years</strong> (vaccines, summers, loss of immunity)</li>
|
||||
<li><strong>The Last Few Months</strong> (epidemiology 101, SEIR model, R & R<sub>0</sub>)</li>
|
||||
<li><strong>The Next Few Months</strong> (lockdowns, contact tracing, masks?)</li>
|
||||
<li><strong>The Next Few Years</strong> (loss of immunity? no vaccine?)</li>
|
||||
</ul>
|
||||
|
||||
<p>Note: We're publishing this on April 30th, 2020. Still the early days. As humanity learns more about COVID-19, our plans will and <em>should</em> change – but we hope this post will address 90%+ of all future possibilities!</p>
|
||||
<p>This guide (published April 30th, 2020<sup id="fnref1"><a href="#fn1" rel="footnote">1</a></sup>) is meant to give you hope <em>and</em> fear. To beat COVID-19 <strong>in a way that also protects our mental & financial health</strong>, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, <em>“The optimist invents the airplane and the pessimist the parachute.”</em></p>
|
||||
|
||||
<p>Honestly, some of the possibilities are scary. And some are hopeful! But preparing for the scary possibilites is what <em>creates</em> the hopeful possibilites. You don't get to save the prince/ss without facing the dragon.</p>
|
||||
<p>So, buckle in: we're about to experience some turbulence.</p>
|
||||
|
||||
<p>Let's bravely use our fear, and face this dragon.</p>
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Last Few Months</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<hr>
|
||||
<p>Pilots use flight simulators to learn how not to crash planes.</p>
|
||||
|
||||
<h1 id="toc_2">The Last Few Months</h1>
|
||||
<p><strong>Epidemiologists use epidemic simulators to learn how not to crash humanity.</strong></p>
|
||||
|
||||
<p>...has been a real worldwide cram-school in Epidemiology 101.</p>
|
||||
<p>So, let's make a simple "epidemic flight simulator"! In this simulation, <icon i></icon> Infectious people can turn <icon s></icon> Susceptible people into more <icon i></icon> Infectious people:</p>
|
||||
|
||||
<p>Pilots use flight simulators to learn how not to crash planes. <strong>Epidemiologists use epidemic simulators to learn how not to crash humanity.</strong></p>
|
||||
<p><img src="pics/spread.png" alt=""></p>
|
||||
|
||||
<p>So, let's set up an epidemic "flight simulator"! First, we need some simulation rules.</p>
|
||||
<p>It's estimated that, <em>at the start</em> of a COVID-19 outbreak, the virus jumps from an <icon i></icon> to an <icon s></icon> <em>approximately</em> every 4 days.<sup id="fnref2"><a href="#fn2" rel="footnote">2</a></sup></p>
|
||||
|
||||
<p>Let's say you have some Infected (i) people and not-yet-infected Susceptible (s) people. One (i) infects a (s), those 2 (i) infect another 2 (s), those 4 (i) infect another 4 (s), and so on:</p>
|
||||
<p>If we simulate "double every 4 days" <em>and nothing else</em>, on a population starting with just 0.001% <icon i></icon>, what happens? </p>
|
||||
|
||||
<p>// pic</p>
|
||||
<p><strong>Click "Start" to play the simulation! You can re-play it later with different settings:</strong> (technical caveats: <sup id="fnref3"><a href="#fn3" rel="footnote">3</a></sup>)</p>
|
||||
|
||||
<p><em>On average</em>, COVID-19 jumps from an (i) to a (s) every 4 days.<a href="source">1</a> The average # of days it takes for an (i) to infect an (s) is called the <strong>"generation time"</strong><a href="serial%20interval">2-note</a>. (Click the gray circles for sources, and the blue squares for side-notes!)</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-1" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p><em>Rule #1: The more (i)s there are, the faster (s)s become (i)s.</em></p>
|
||||
<p>This is the <strong>exponential growth curve.</strong> Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create <em>mass graves in rich cities</em>". </p>
|
||||
|
||||
<p>// pic - rule</p>
|
||||
<p><img src="pics/exponential.png" alt=""></p>
|
||||
|
||||
<p>If we simulate <em>just this rule and nothing else</em>, here's what it looks like over 3 months, starting with 99.9% (s) and just 0.1% (i):</p>
|
||||
<p>But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others <em>already</em> have the virus:</p>
|
||||
|
||||
<p><strong>Click "Start" play the simulation! You can then change the "generation time", and see how that changes the simulation:</strong></p>
|
||||
<p><img src="pics/susceptibles.png" alt=""></p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>The more <icon i></icon>s there are, the faster <icon s></icon>s become <icon i></icon>s, <strong>but the fewer <icon s></icon>s there are, the <em>slower</em> <icon s></icon>s become <icon i></icon>s.</strong></p>
|
||||
|
||||
<p>Starts small ("it's just a flu"), then explodes ("oh right, flus don't break hospitals in rich countries"). This is the "J-shaped" <strong>exponential growth curve</strong>.</p>
|
||||
<p>How's this change the growth of an epidemic? Let's find out:</p>
|
||||
|
||||
<p>But this simulation is wrong. There are things that prevent an (i) from infecting someone else – like if that other person is <em>already</em> an (i):</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-2" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>// pic - 100% spread, 50% spread, 0% spread</p>
|
||||
<p>This is the "S-shaped" <strong>logistic growth curve.</strong> Starts small, explodes, then slows down again.</p>
|
||||
|
||||
<p><em>Rule #2: The fewer (s)s there are, the slower (s)s become (i)s.</em></p>
|
||||
<p>But, this simulation is <em>still</em> wrong. We're missing the fact that <icon i></icon> Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.</p>
|
||||
|
||||
<p>// pic - rule</p>
|
||||
<p>For simplicity's sake, let's pretend that all <icon i></icon> Infectious people become <icon r></icon> Recovered. (Just remember that, in reality, some of them are dying.) <icon r></icon>s can't be infected again, and let's pretend – <em>for now!</em> – that they stay immune for life.</p>
|
||||
|
||||
<p>Now, what happens if we simulate <em>both</em> these rules?</p>
|
||||
<p>With COVID-19, it's estimated you're <icon i></icon> Infectious for <em>approximately</em> 10 days.<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> Let's simulate a population starting at 100% <icon i></icon>, most of whom recover after 10 days, then most of the remainder recover after another 10 days, then most of <em>that</em> remainder recover after another 10 days, etc:</p>
|
||||
|
||||
<p><strong>Again, click Start to play the simulation!</strong></p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-3" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>This is the opposite of exponential growth, the <strong>exponential decay curve</strong>.</p>
|
||||
|
||||
<p>Starts small, explodes, then slows down again. This is the "S-shaped" <strong>logistic growth curve.</strong></p>
|
||||
<p>Now, what happens if you simulate S-shaped logistic growth <em>with</em> recovery?</p>
|
||||
|
||||
<p>Still, this simulation predicts 100% of people will get the virus, and even the most pessimistic COVID-19 simulations don't predict <em>that</em>. </p>
|
||||
|
||||
<p>What we're missing: You stop being infectious for COVID-19 when you recover... or die.</p>
|
||||
|
||||
<p>For the sake not making these simulations too depressing, let's only simulate Infected (i) becoming (r) Recovered. (The math works out the same.) And let's assume <em>(for now!!!)</em> that (r)s can't get infected again. So, new rule:</p>
|
||||
|
||||
<p><em>Rule #3: (i)s eventually become (r)s.</em> </p>
|
||||
|
||||
<p>// pic - rule</p>
|
||||
|
||||
<p>Let's have (i)s become (r)s after 14 days, <em>on average</em>.<a href="technical%20notes">3-note</a> This means some (i)s will recover <em>before</em> 14 days, and some recover <em>after</em>! This is closer to real life.</p>
|
||||
|
||||
<p>To show <em>only</em> Rule #3, here's a simulation starting with 100% (i):</p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>This is the "flipped-J-shaped" <strong>exponential decay curve.</strong></p>
|
||||
|
||||
<p>Now, what happens if you simulate all 3 rules at once? What happens when you combine an S-shaped logistic curve with a flipped-J exponential decay curve?</p>
|
||||
|
||||
<p>// pic</p>
|
||||
<p><img src="pics/graphs_q.png" alt=""></p>
|
||||
|
||||
<p>Let's find out:</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>And <em>that's</em> where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten.</p>
|
||||
|
||||
<p>// pic: 3 rules</p>
|
||||
<p>This is the the <strong>SIR Model</strong><sup id="fnref5"><a href="#fn5" rel="footnote">5</a></sup> – <icon s></icon><strong>S</strong>usceptible <icon s></icon><strong>I</strong>nfectious <icon s></icon><strong>R</strong>ecovered – the second-most important idea in Epidemiology 101:</p>
|
||||
|
||||
<p>This is the <strong>SIR Model</strong>, the <em>second</em>-most important idea in epidemiology.</p>
|
||||
<p><img src="pics/sir.png" alt=""></p>
|
||||
|
||||
<p><strong>NOTE:</strong> The simulations you've been hearing in the news are <em>far</em> more complex than the ones you're seeing here! But the sims you'll play with here reach the same general conclusions, even if missing the nuances.</p>
|
||||
<p>NOTE: The simulations that inform policy are <em>far</em> more sophisticated than this! But the SIR Model can still explain the same findings, even if missing the nuances.</p>
|
||||
|
||||
<p>One nuance you could add is the <strong>SIRS Model</strong>, where the final "S" also stands for (s) Susceptible – this is when people recover, are immune for a bit, <em>then lose that immunity and can be infected again.</em> (We'll consider this in the Next Few Years section)</p>
|
||||
<p>Actually, let's add one more nuance: before an <icon s></icon> becomes an <icon i></icon>, they first become <icon e></icon> Exposed. This is when they have the virus but can't pass it on yet – infect<em>ed</em> but not yet infect<em>ious</em>.</p>
|
||||
|
||||
<p>Another nuanced version is the <strong>SEIR Model</strong>, where the "E" stands for (e) Exposed, a brief period of time <em>after</em> you've been infected, but <em>before</em> you can infect others. This is called the <strong>"latent period"</strong>, and for COVID-19 it's around 3 days.<a href="">4</a></p>
|
||||
<p><img src="pics/seir.png" alt=""></p>
|
||||
|
||||
<p>Here's what happens if you simulate that:</p>
|
||||
<p>(This variant is called the <strong>SEIR Model</strong><sup id="fnref6"><a href="#fn6" rel="footnote">6</a></sup>, where the "E" stands for <icon e></icon> "Exposed". Note this <em>isn't</em> the everyday meaning of "exposed", when you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Science terminology is bad.)</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>For COVID-19, it's estimated that you're <icon e></icon> infected-but-not-yet-infectious for <em>approximately</em> 3 days.<sup id="fnref7"><a href="#fn7" rel="footnote">7</a></sup> What happens if we add that to the simulation?</p>
|
||||
|
||||
<p>Doesn't change much, so let's stick to the vanilla SIR model. We brought (e)s up because the exact timing of contagiousness is important in "contact tracing", which we'll explain in the Next Few Months section.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Oh! But almost forgot, the <em>first</em>-most important idea in epidemiology:</p>
|
||||
<p>Not much, actually! How long you stay <icon e></icon> Exposed changes the ratio of <icon e></icon>-to-<icon i></icon>, and <em>when</em> the peak of current cases (<icon e></icon>+<icon i></icon>) happens... but the <em>height</em> of that peak, and the total % of people infected in the end, stays the same.</p>
|
||||
|
||||
<p><strong>"R"</strong></p>
|
||||
<p>Why's that? Because of the <em>first</em>-most important idea in Epidemiology 101:</p>
|
||||
|
||||
<p>Which is short for "Reproduction Number". It's the <em>average</em> number of people an (i) will infect <em>before</em> they recover:</p>
|
||||
<p><img src="pics/r.png" alt=""></p>
|
||||
|
||||
<p>// pic - R>1 R=1 R<1</p>
|
||||
<p>Short for "Reproduction number". It's the <em>average</em> number of people an <icon i></icon> infects <em>before</em> they recover (or die).</p>
|
||||
|
||||
<p><strong>R0</strong> (pronounced R-nought) is the Reproduction Number for a virus <em>at the very beginning of an outbreak, before we have immunity or interventions</em>. (Also called "Basic Reproduction Number")</p>
|
||||
<p><img src="pics/r2.png" alt=""></p>
|
||||
|
||||
<p><strong>Rt</strong> (the 't' stands for time) is the Reproduction Number <em>right now</em>, after we have some immunity or interventions. (Also called "Re", e standing for "Effective Reproduction Number". Also called just "R", to... confuse people)</p>
|
||||
<p><strong>R</strong> changes over the course of an outbreak, as we get more immunity & interventions.</p>
|
||||
|
||||
<p>// pic of R0 and Rt over time for the Famous Curve – with peak for inflection!</p>
|
||||
<p><strong>R<sub>0</sub></strong> (pronounced R-nought) is what R is <em>at the start of an outbreak, before immunity or interventions</em>. R<sub>0</sub> more closely reflects the power of the virus itself, but it still changes from place to place. For example, R<sub>0</sub> is higher in dense cities than sparse rural areas.</p>
|
||||
|
||||
<p>(A lot of news outlets confuse these two Rs! They're different!)</p>
|
||||
<p>(Most news articles – and even some scientific papers! – confuse R and R<sub>0</sub>. Again, science terminology is bad)</p>
|
||||
|
||||
<p>The R0 for the flu<a href="more">6</a> is around 1.3. The R0 for COVID-19 is somewhere between 2 and 5.<a href="source">7</a> The huge uncertainty is because R0 depends on exactly how quickly new people are infected ("generation time") vs how quickly people recover<a href="technical%20note">8</a>:</p>
|
||||
<p>The R<sub>0</sub> for "the" seasonal flu is around 1.28<sup id="fnref8"><a href="#fn8" rel="footnote">8</a></sup>. This means, at the <em>start</em> of a flu outbreak, each <icon i></icon> infects 1.28 others <em>on average.</em> (If it sounds weird that this isn't a whole number, remember that the "average" mom has 2.4 children. This doesn't mean there's half-children running about.)</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>The R<sub>0</sub> for COVID-19 is estimated to be around 2.2<sup id="fnref9"><a href="#fn9" rel="footnote">9</a></sup>, though a not-yet-finalized CDC study estimates it was 5.7(!) in Wuhan.<sup id="fnref10"><a href="#fn10" rel="footnote">10</a></sup></p>
|
||||
|
||||
<p>Rt for COVID-19 depends on the interventions we do (or don't) have, as well as how many people <em>aren't</em> (s) Susceptible. (because they're (r) Recovered, currently (i) Infected, or... dead.)</p>
|
||||
<p>In our simulations – <em>at the start & on average</em> – an <icon i></icon> infects someone every 4 days, over 10 days. "4 days" goes into "10 days" two-and-a-half times. This means – <em>at the start & on average</em> – each <icon i></icon> infects 2.5 others. Therefore, R<sub>0</sub> = 2.5. (caveats:<sup id="fnref11"><a href="#fn11" rel="footnote">11</a></sup>)</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p><strong>Play with this R<sub>0</sub> calculator, to see how R<sub>0</sub> depends on recovery time & new-infection time:</strong></p>
|
||||
|
||||
<p>Note that when (s)% is low enough, you can get Rt<1 – <em>containing the virus!</em> This is called <strong>the "herd immunity" threshold</strong>. "Herd immunity" is a terrible <em>policy</em> (TODO: explain why), but it's important for understanding epidemiology.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-6a&format=calc" width="285" height="255"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Now, let's run the same SIR model simulation again, but this time showing 1) Rt changing over time, and 2) the herd immunity threshold:</p>
|
||||
<p>But remember, the fewer <icon s></icon>s there are, the <em>slower</em> <icon s></icon>s become <icon i></icon>s. The <em>current</em> reproduction number (R) depends not just on the <em>basic</em> reproduction number (R<sub>0</sub>), but <em>also</em> on how many people are no longer <icon s></icon> Susceptible. (For example, by recovering & getting natural immunity.)</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-6b&format=calc" width="285" height="390"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Note how total cases ((i)+(r)) <em>overshoots</em> the herd immunity threshold! And the <em>exact</em> moment it does this is when infections peak <em>and</em> when Rt drops below 1!</p>
|
||||
<p>When enough people have natural immunity, R < 1, and the virus is contained! This is called <strong>herd immunity</strong>, and while it's <em>terrible</em> policy (we'll explain why later – it's not for the reason you may think!), it's essential to understanding Epidemiology 101.</p>
|
||||
|
||||
<p>If there's only one lesson you take away today, here it is, in big shiny letters:</p>
|
||||
<p>Now, let's play the SEIR Model again, but showing R<sub>0</sub>, R over time, and the herd immunity threshold:</p>
|
||||
|
||||
<h1 id="toc_3">Rt>1 = bad</h1>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-7" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<h1 id="toc_4">Rt<1 = good</h1>
|
||||
<p>Note: Total cases (gray curve) does not stop at herd immunity, but <em>overshoots</em> it! And it does this <em>exactly when</em> current cases (pink curve) peaks. (This happens no matter how you change the settings – try it for yourself!)</p>
|
||||
|
||||
<p><strong>NOTE: We do not need to catch all transmissions, or even nearly all transmissions, to stop COVID-19.</strong></p>
|
||||
<p>This is because when there are more non-<icon s></icon>s than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.</p>
|
||||
|
||||
<p>It's a paradox – COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 72% of infections. 72%?! That's, like, a C– grade. But if R0 = 3.5, then reducing that by 72% will make Rt < 1 = good.</p>
|
||||
<p><strong>If there's only one lesson you take away from this guide, here it is</strong> – it's an extremely complex diagram so please take time to fully absorb it:</p>
|
||||
|
||||
<p>(And even if worst-case, R0=5, you "only" need to stop 80%. That's a B–.)</p>
|
||||
<p><img src="pics/r3.png" alt=""></p>
|
||||
|
||||
<p><em>Every</em> COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're <em>all</em> doing the same thing:</p>
|
||||
<p><strong>This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!</strong></p>
|
||||
|
||||
<p>Reducing Rt.</p>
|
||||
<p>It's a paradox. COVID-19 is extremely contagious, yet to contain it, we "only" need to stop more than 60% of infections. 60%?! If that was a school grade, that's a D-. But if R<sub>0</sub> = 2.5, cutting that by 61% gives us R = 0.975, which is R < 1, virus is contained!<sup id="fnref12"><a href="#fn12" rel="footnote">12</a></sup></p>
|
||||
|
||||
<p>Let's see how we can get Rt<1 – in a way that protects not just our physical health, but also our mental health, social health, <em>and</em> financial health!</p>
|
||||
<p><img src="pics/r4.png" alt=""></p>
|
||||
|
||||
<hr>
|
||||
<p>(If you think R<sub>0</sub> or the other numbers in our simulations are too low/high, that's good you're challenging our assumptions! There'll be a "Sandbox Mode" at the end of this guide, where you can plug in your <em>own</em> numbers, and simulate what happens.)</p>
|
||||
|
||||
<h1 id="toc_5">The Next Few Months</h1>
|
||||
<p><em>Every</em> COVID-19 intervention you've heard of – handwashing, social/physical distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're <em>all</em> doing the same thing:</p>
|
||||
|
||||
<p>...could have been worse.</p>
|
||||
<p>Getting R < 1.</p>
|
||||
|
||||
<h3 id="toc_6">Scenario 0: Do Absolutely Nothing</h3>
|
||||
<p>So now, let's use our "epidemic flight simulator" to figure this out: How can we get R < 1 in a way <strong>that also protects our mental health <em>and</em> financial health?</strong></p>
|
||||
|
||||
<p>For COVID-19, 1 in 20 (i)s need to be hospitalized. In rich countries like the US and UK, there's 1 hospital bed for every 1000 people. Therefore: a rich country can handle a maximum of 20 (i)s per 1000 people – or, a maximum of 2% of the population being simultaneously sick.</p>
|
||||
<p>Brace yourselves for an emergency landing...</p>
|
||||
|
||||
<p>Here's the same simulation from before, but with the "2%" threshold drawn:</p>
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Next Few Months</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>...could have been worse. Here's a parallel universe we avoided:</p>
|
||||
|
||||
<p>It's not good.</p>
|
||||
<h3 id="toc_0">Scenario 0: Do Absolutely Nothing</h3>
|
||||
|
||||
<p>That's the same thing the March 16th Imperial College report found: if we do nothing, hospitals break. Almost everyone gets infected. Even with a low 0.5% infection fatality ratio, 80% of people infected in a large country like the US still means over a million dead... <em>IF</em> we did nothing.</p>
|
||||
<p>Around 1 in 20 people infected with COVID-19 need to go to an ICU (Intensive Care Unit).<sup id="fnref13"><a href="#fn13" rel="footnote">13</a></sup> In a rich country like the USA, there's 1 ICU per 3400 people.<sup id="fnref14"><a href="#fn14" rel="footnote">14</a></sup> Therefore, the USA can handle 20 out of 3400 people being <em>simultaneously</em> infected – or, 0.6% of the population.</p>
|
||||
|
||||
<p>(A lot of news & social media chose to report the scary bit, <em>without</em> "IF WE DO NOTHING". Fear was channeled into clicks, not understanding. <em>Sigh.</em>)</p>
|
||||
<p>Even if we <em>more than tripled</em> that capacity to 2%, here's what would've happened <em>if we did absolutely nothing:</em></p>
|
||||
|
||||
<h3 id="toc_7">Scenario 1: Flatten The Curve</h3>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-1&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Handwashing was discovered in ____ by the doctor _______, when he realized that by getting his staff to wash their hands, child deaths in his hospital were cut by <em>90%!</em></p>
|
||||
<p>Not good.</p>
|
||||
|
||||
<p>Doctors around the world immediately hailed his life-saving discovery, and ha ha just kidding they committed him to an asylum where he was beat to death by guards.</p>
|
||||
<p>That's what <a href="http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/">the March 16 Imperial College report</a> found: do nothing, and we run out of ICUs with 80%+ of the population infected.</p>
|
||||
|
||||
<p>In any case, frequent handwashing reduces your chances of catching influenza by 50%[9]() And if we combine this with other hygiene tips – cough into your elbow, don't touch your face – let's guess-timate that 100% compliance (which we will <em>NOT</em> get) will result in a 60% reduction in new infections, in Rt:</p>
|
||||
<p>Even if only 0.5% of infected die – a generous assumption when there's no more ICUs – in a large country like the US, with 300 million people, 0.5% of 80% of 300 million = still 1.2 million dead... <em>IF we did nothing.</em></p>
|
||||
|
||||
<p>// controls</p>
|
||||
<p>(Lots of news & social media reported "80%+ will be infected" <em>without</em> "IF WE DO NOTHING". Fear was channelled into clicks, not understanding. <em>Sigh.</em>)</p>
|
||||
|
||||
<p>It can't get Rt<1, but it <em>does</em> reduce it! How does that affect the epidemic?</p>
|
||||
<h3 id="toc_1">Scenario 1: Flatten The Curve / Herd Immunity</h3>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>The "Flatten The Curve" plan was touted by every public health organization, while the United Kingdom's original "herd immunity" plan was universally booed. They were <em>the same plan.</em> The UK just communicated theirs poorly.<sup id="fnref15"><a href="#fn15" rel="footnote">15</a></sup></p>
|
||||
|
||||
<p>That's a... <em>better</em> catastrophe.</p>
|
||||
<p>Both plans, though, are horribly flawed.</p>
|
||||
|
||||
<p>Contrary to many news & social media posts, "flattening the curve" <em>does also reduce total cases</em>. But as long as Rt is still above 1, our hospitals will still most likely shatter.</p>
|
||||
<p>First, let's look at the two main ways to "flatten the curve": handwashing & physical distancing.</p>
|
||||
|
||||
<p>That's what the Imperial College report also found: any attempt at mere <strong>"mitigation"</strong> (Reduce Rt, but still Rt>1 = bad) will fail, and the only way out is <strong>"suppression"</strong>. (Reduce Rt, so that Rt<1 = good!)</p>
|
||||
<p>Increased handwashing cuts flus & colds in high-income countries by ~25%<sup id="fnref16"><a href="#fn16" rel="footnote">16</a></sup>, while the city-wide lockdown in London cut close contacts by ~70%<sup id="fnref17"><a href="#fn17" rel="footnote">17</a></sup>. So, let's assume handwashing can reduce R by <em>up to</em> 25%, and distancing can reduce R by <em>up to</em> 70%:</p>
|
||||
|
||||
<p><em>Crush</em> the curve, not just flatten it. For example, by doing a...</p>
|
||||
<p><strong>Play with this calculator to see how % of non-<icon s></icon>, handwashing, and distancing reduce R:</strong> (this calculator visualizes their <em>relative</em> effects, which is why increasing one <em>looks</em> like it decreases the effect of the others.<sup id="fnref18"><a href="#fn18" rel="footnote">18</a></sup>)</p>
|
||||
|
||||
<h3 id="toc_8">Scenario 2: Months-Long Lockdown (we are here)</h3>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-2a&format=calc" width="285" height="260"></iframe>
|
||||
</div>
|
||||
|
||||
<p>There's different degrees of "physical distancing". (previously called "social distancing") At the mildest, avoiding crowds. At the strongest, a full city-wide lockdown.</p>
|
||||
<p>Now, let's simulate what happens to a COVID-19 epidemic if, starting March 2020, we had increased handwashing but only <em>mild</em> physical distancing – so that R is lower, but still above 1:</p>
|
||||
|
||||
<p>London's full lockdown reduced Rt by 70%.<a href="">11</a> So, let's guess-timate that as the maximum effect for distancing.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-2&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Here's how hygiene & distancing together change Rt:</p>
|
||||
<p>Three notes:</p>
|
||||
|
||||
<p>// calc</p>
|
||||
<ol>
|
||||
<li><p>This <em>reduces</em> total cases! Lots of folks think "Flatten The Curve" spread outs cases without reducing the total. This is impossible in <em>any</em> Epidemiology 101 model. But because the news reported "80%+ will be infected" as inevitable, folks thought total cases will be the same no matter what. <em>Sigh.</em></p></li>
|
||||
<li><p>Due to the extra interventions, current cases (pink curve) peaks <em>before</em> herd immunity is reached. And in fact, total cases doesn't overshoot, but <em>goes to</em> herd immunity – the UK's plan! At that point, R < 1, you can let go of all other interventions, and COVID-19 stays contained! Well, except for one problem...</p></li>
|
||||
<li><p>You still run out of ICUs. For several months. (and remember, we <em>already</em> tripled ICUs for these simulations)</p></li>
|
||||
</ol>
|
||||
|
||||
<p>That's Rt<1 = good!</p>
|
||||
<p>That was the other finding of the March 16 Imperial College report, which convinced the UK to abandon its original plan. Any attempt at <strong>mitigation</strong> (reduce R, but R > 1) will fail. The only way out is <strong>suppression</strong> (reduce R so that R < 1).</p>
|
||||
|
||||
<p>Let's see what happens if we <em>crush</em> the curve with a lockdown for 3 months, then finally, <em>finally</em> return to normal life:</p>
|
||||
<p>// pic: difference </p>
|
||||
|
||||
<p><strong>Remember, you can re-play the simulation, and change the sliders <em>WHILE</em> it's running, to simulate your own COVID-19 strategy! You can also pause & slow down the simulation:</strong></p>
|
||||
<p>That is, don't merely "flatten" the curve, <em>crush</em> the curve. For example, with a...</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<h3 id="toc_2">Scenario 2: Months-Long Lockdown</h3>
|
||||
|
||||
<p>Let's see what happens if we <em>crush</em> the curve with a 5-month lockdown, reduce <icon i></icon> to nearly nothing, then finally – <em>finally</em> – return to normal life:</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-3&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Oh.</p>
|
||||
|
||||
<p>Right, as soon as you remove the lockdown, Rt>1 again, and so you get a spike in cases that's almost as bad as if you'd done <em>nothing at all.</em></p>
|
||||
<p>This is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover <icon i></icon> (or imported <icon i></icon>) can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing.</p>
|
||||
|
||||
<p><strong>A lockdown isn't a cure, it's just a restart.</strong></p>
|
||||
|
||||
<p>So, what, do we just lockdown again & again?</p>
|
||||
|
||||
<h3 id="toc_9">Scenario 3: Intermittent Lockdown</h3>
|
||||
<h3 id="toc_3">Scenario 3: Intermittent Lockdown</h3>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>This solution was first suggested by the Imperial College report, and later again by a Harvard paper<sup id="fnref19"><a href="#fn19" rel="footnote">19</a></sup>.</p>
|
||||
|
||||
<p>This was one solution suggested by the March 16 Imperial College report, and analyzed again by Marc Lipsitch ______ etc. [https://science.sciencemag.org/content/early/2020/04/14/science.abb5793?]</p>
|
||||
<p><strong>Here's a simulation:</strong> (After playing the "recorded scenario", you can try simulating your <em>own</em> lockdown schedule, by changing the sliders <em>while</em> the simulation is running! Remember you can pause & continue the sim, and change the simulation speed)</p>
|
||||
|
||||
<p>This <em>would</em> keep hospitals below capacity! You just have to... shut everything down for 2 months, every 3 months, until a vaccine is available in 18 months. That's... one year total out of 18 months.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Look, it's all well & good to draw a line on a graph saying "healthcare capacity", but there's lots of important things we <em>can't</em> simulate here. Like:</p>
|
||||
<p>This <em>would</em> keep cases below ICU capacity! We'd just need to... shut everything down for few months, open up for a few, shut down for a few, open up for a few... and repeat until a vaccine is available. (And if there's no vaccine, repeat until herd immunity is reached... in 2022.)</p>
|
||||
|
||||
<p>Mental Health) Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as negatively associated with an early death as smoking 15 cigarettes a day.</p>
|
||||
<p>Look, it's nice to draw a line saying "ICU capacity", but there's lots of important things we <em>can't</em> simulate here. Like:</p>
|
||||
|
||||
<p>Financial Health) "What about the economy" sounds like you care more about dollars than lives, but "the economy" isn't just stocks: it's people's ability to provide food & shelter for their loved ones, to invest in their kids' futures, and enjoy arts, foods, videogames – the stuff makes life worth living. And besides, poverty <em>itself</em> has horrible impacts on mental and physical health.</p>
|
||||
<p><strong>Mental Health:</strong> Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as associated with an early death as smoking 15 cigarettes a day.<sup id="fnref20"><a href="#fn20" rel="footnote">20</a></sup></p>
|
||||
|
||||
<p>Not saying we should rule out intermittent lockdowns! But it's not ideal.</p>
|
||||
<p><strong>Financial Health:</strong> "What about the economy" sounds like you care more about dollars than lives, but "the economy" isn't just stocks: it's people's ability to provide food & shelter for their loved ones, to invest in their kids' futures, and enjoy arts, foods, videogames – the stuff makes life worth living. And besides, poverty <em>itself</em> has horrible impacts on mental and physical health.</p>
|
||||
|
||||
<p>Wait, didn't we say Taiwan & South Korea "bravely used their fear" to control COVID-19? For 4 whole months? How?</p>
|
||||
<p>Not saying we <em>shouldn't</em> lock down again! We'll look at "circuit breaker" lockdowns later. Still, it's not ideal.</p>
|
||||
|
||||
<h3 id="toc_10">Scenario 4: Test, Trace, Isolate</h3>
|
||||
<p>But wait... haven't Taiwan and South Korea <em>already</em> contained COVID-19? For 4 whole months, <em>without</em> long-term lockdowns?</p>
|
||||
|
||||
<p>You may be thinking:</p>
|
||||
<p>How?</p>
|
||||
|
||||
<p><em>Sure, we *could* have done what Taiwan + South Korea did at the start, but it's too late now. We missed the start.</em></p>
|
||||
<h3 id="toc_4">Scenario 4: Test, Trace, Isolate</h3>
|
||||
|
||||
<p>But that's exactly it! A lockdown isn't a cure, it's just a restart – <strong>and a fresh start is what we need.</strong> (TODO: Actually, South Korea started late!)</p>
|
||||
<p><em>"Sure, we *could've* done what Taiwan & South Korea did at the start, but it's too late now. We missed the start."</em></p>
|
||||
|
||||
<p>The lockdown will let us reduce (i) cases, and buy time to copy what Taiwan & South Korea are already successfully doing: isolating COVID-19 cases, <em>and finding out who've they been in extended close contact with</em> (<strong>"contact tracing"</strong>) <em>and quarantining them too</em>.</p>
|
||||
<p>But that's exactly it! “A lockdown isn't a cure, it's just a restart”... <strong>and a fresh start is what we need.</strong></p>
|
||||
|
||||
<p>(Pedantic note: <strong>"isolate"</strong> is for infected cases, <strong>"quarantine"</strong> is for contacts)</p>
|
||||
<p>To understand how Taiwan & South Korea contained COVID-19, we need to understand the exact timeline of a typical COVID-19 infection<sup id="fnref21"><a href="#fn21" rel="footnote">21</a></sup>:</p>
|
||||
|
||||
<p>Why do we need to quarantine the contacts? Because they could have been (e) Exposed & caught the virus, but not know it yet:</p>
|
||||
<p><img src="pics/timeline1.png" alt=""></p>
|
||||
|
||||
<p>// timeline</p>
|
||||
<p>If cases only self-isolate when they know they're sick (that is, they feel symptoms), the virus can still spread:</p>
|
||||
|
||||
<p>If you <em>only</em> isolate the cases, the virus can still spread:</p>
|
||||
<p><img src="pics/timeline2.png" alt=""></p>
|
||||
|
||||
<p>// timeline</p>
|
||||
<p>And in fact, 44% of all transmissions are like this: <em>pre</em>-symptomatic! <sup id="fnref22"><a href="#fn22" rel="footnote">22</a></sup></p>
|
||||
|
||||
<p>But if you <em>also</em> quarantine the contacts, you stop the spread, by staying one step ahead!</p>
|
||||
<p>But, if we find <em>and quarantine</em> a symptomatic case's recent close contacts... we stop the spread, by staying one step ahead!</p>
|
||||
|
||||
<p>// timeline</p>
|
||||
<p><img src="pics/timeline3.png" alt=""></p>
|
||||
|
||||
<p>(TODO: 30 min+ exposure)</p>
|
||||
<p>This is called <strong>contact tracing</strong>, and it's a core part of Taiwan & South Korea's successful strategies.</p>
|
||||
|
||||
<p>Contact tracing was how they contained Ebola in (where?) Africa! And that was just good ol' fashioned "ask people who they met" contact tracing.</p>
|
||||
<p>Traditionally, contact tracing is done with in-person interviews, but that's too slow for COVID-19's ~48 hour window. That's why on March 31st, <a href="https://science.sciencemag.org/content/early/2020/04/09/science.abb6936">an Oxford study</a> recommended helping contact tracers with <em>contact tracing apps</em>.</p>
|
||||
|
||||
<p>...which, unfortunately, will not work for COVID-19. Interviews are too slow and human memory is too unreliable. [MARCEL'S SOURCE] There's only <em>3 days</em> between being exposed to the virus (e) and being able to infect others (i).</p>
|
||||
<p>Does that mean giving up privacy, giving in to Big Brother? Heck no! <a href="https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing">DP-3T</a>, a team of epidemiologists & cryptographers (including one of us, Marcel Salathé) is <em>already</em> making a contact tracing app that reveals <strong>no info about your identity, location, who your contacts are, or even <em>how many contacts</em> you've had.</strong></p>
|
||||
|
||||
<p>So, regrettably, some countries have resorted to privacy-invasive techniques, like grabbing loads of citizens' phone location data. But does protecting human lives mean surrendering to Big Brother?</p>
|
||||
<p>Here's how it works:</p>
|
||||
|
||||
<p>HECK NO</p>
|
||||
<p><img src="pics/dp3t.png" alt=""></p>
|
||||
|
||||
<p><strong>Here's a short comic we made, explaining how you can do digital contact tracing in a privacy-protecting way.</strong> And when we say "privacy-protecting", we mean that <em>even if the central server was hacked and all its data stolen</em>, the hacker would learn <em>nothing</em> about people's identities, locations, or who met who.</p>
|
||||
<p>(<a href="https://ncase.me/contact-tracing/">Here's the full comic</a>, and <a href="">here's a video adaptation by 3Blue1Brown</a>)</p>
|
||||
|
||||
<p>(And here's a 3Blue1Brown video adaptation of our comic! Thanks Grant!)</p>
|
||||
<p>Along with similar teams like <a href="https://github.com/TCNCoalition/TCN#tcn-protocol">TCN Protocol</a> and <a href="https://pact.mit.edu/">MIT PACT</a>, they've inspired Apple & Google to bake privacy-first contact tracing <a href="https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/">directly into Android/iOS</a>. Next month, your local public health agency may ask you to download an app. If it's privacy-first & open-source, please do!</p>
|
||||
|
||||
<p>And this isn't just "in theory". Several apps are <em>already</em> being developed for this. The European council vote (FILL IN). And Google/Apple's new announcement specifically supports the privacy-protecting protocol as described in our comic above. (Don't trust Google/Apple? Neither do we! The beauty of the protocol is that <em>it doesn't rely on trust.</em>)</p>
|
||||
<p>But what about folks without smartphones? Or infections through doorknobs? Or "true" asymptomatic cases? Contact tracing apps can't catch all transmissions... <em>and that's okay!</em> We don't need to catch <em>all</em> transmissions, just 60%+ to get R < 1.</p>
|
||||
|
||||
<p>Okay okay, enough tooting our own horn. How does isolating cases & quarantining contacts reduce Rt?</p>
|
||||
<p>(rant about the confusion about pre-symptomatic vs. "true" asymptomatic:<sup id="fnref23"><a href="#fn23" rel="footnote">23</a></sup>)</p>
|
||||
|
||||
<p>University of Oxford study estimates that:</p>
|
||||
<p>Anyway, isolating cases would reduce R by up to 40%, and quarantining their contacts would reduce R by up to 50%<sup id="fnref24"><a href="#fn24" rel="footnote">24</a></sup>:</p>
|
||||
|
||||
<ul>
|
||||
<li>(i) Symptomatic <strong>cases</strong> account for 40% of new infections. So by isolating cases, <strong>you reduce Rt by up to 40%.</strong></li>
|
||||
<li>(e) Pre-symptomatic & A-symptomatic <strong>contacts</strong> account for 50%. So by quarantining contacts, <strong>you can reduce Rt by up to 50%.</strong></li>
|
||||
<li>Stuff like doorknobs accounts for the rest, 10%.</li>
|
||||
</ul>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4a&format=calc" width="285" height="340"></iframe>
|
||||
</div>
|
||||
|
||||
<p><a href="">Loong note about "pre" vs "a" symptomatic & how the media screwed it up AGAIN</a></p>
|
||||
<p>Thus, we can get R < 1 <em>without a lockdown!</em> Much better for our mental & financial health. (As for the cost to folks who have to self-isolate/quarantine, <em>governments should support them</em> – subsidized paid leave, job protection, etc. Still way cheaper than intermittent lockdown.)</p>
|
||||
|
||||
<p>So, combined, isolating cases & quarantining contacts can get Rt comfortably below 1, <em>even with NO physical distancing!</em></p>
|
||||
<p>We then keep R < 1 until we have a vaccine, which turns susceptible <icon s></icon>s into immune <icon r></icon>s. Herd immunity, the <em>right</em> way:</p>
|
||||
|
||||
<p>// calc</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4b&format=calc" width="285" height="230"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Remember: <strong>we do not need to catch all transmissions, or even nearly all transmissions, to stop COVID-19.</strong> So the fact that not everybody is able (or willing) to download a privacy-protecting contact tracing app isn't a dealbreaker.</p>
|
||||
<p>Okay, enough talk. Here's a simulation of:</p>
|
||||
|
||||
<p>We don't need to catch <em>all</em> contacts, isolate <em>all</em> cases, or even wash <em>all</em> the hands. Just enough to get that C– grade of 72%, to get Rt<1 = good.</p>
|
||||
<ol>
|
||||
<li>A few-month lockdown, until we can...</li>
|
||||
<li>Switch to "Test, Trace, Isolate" until we can...</li>
|
||||
<li>Vaccinate enough people, which means...</li>
|
||||
<li>We win.</li>
|
||||
</ol>
|
||||
|
||||
<p>(do wash your hands, though)</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-5&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Alright, enough chat. Here's a simulation of using a lockdown as reset, then switching to "Test, Trace, Isolate":</p>
|
||||
<p>So that's it! That's how we make an emergency landing on this plane.</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>That's how we beat COVID-19.</p>
|
||||
|
||||
<p>And here it is again, with a vaccine at 18 months, which converts (s) into an immune (r), without having to become a (i). This gives us "herd immunity" the <em>right</em> way, and we can <em>finally</em> stop all other interventions.</p>
|
||||
<p>...</p>
|
||||
|
||||
<p>(actually, keep washing your hands. come on, a doctor was beaten to death in an asylum.)</p>
|
||||
<p>But what if things <em>still</em> go wrong? Things have gone horribly wrong already. That's fear, and that's good! Fear gives us energy to create <em>backup plans</em>.</p>
|
||||
|
||||
<p>// sim </p>
|
||||
<p>The pessimist invents the parachute.</p>
|
||||
|
||||
<p>So that's it!</p>
|
||||
<h3 id="toc_5">Scenario 4+: Masks For All, Summer, Circuit Breakers</h3>
|
||||
|
||||
<p>That's currently the best working plan, recommended by several independent teams of epidemiologists & policymakers from across the political spectrum. (LINKS) Lockdown to get a fresh start, switch to Taiwan & South Korea's strategy later.</p>
|
||||
<p>What if R<sub>0</sub> is way higher than we thought, and the above interventions, even with mild distancing, <em>still</em> aren't enough to get R < 1?</p>
|
||||
|
||||
<p>But...</p>
|
||||
|
||||
<p>...you may be feeling a knot in your stomach. Things have <em>already</em> gone horribly wrong, more stuff could <em>still</em> go horribly wrong with this plan, right?</p>
|
||||
|
||||
<p>You're dang right it could. Let's channel that fear... into making some <em>backup plans:</em></p>
|
||||
|
||||
<h3 id="toc_11">Scenario X: Other Interventions & Backup Plans</h3>
|
||||
|
||||
<p>If handwashing + case isolation + contact quarantining <em>still</em> isn't enough to get Rt<1... we can supplement it with three things:</p>
|
||||
|
||||
<p><strong>Deep Cleaning:</strong></p>
|
||||
|
||||
<p>Remember we said "stuff like doorknobs" accounts for 10% of new infections? The technical jargon for things that can pass a virus from one human to another is a <strong>"fomite".</strong></p>
|
||||
|
||||
<p>10% means frequent deep cleanings of public spaces – subways, libraries, and malls – can reduce Rt by up to 10%. Which sounds useless, but if it reduces Rt from 1.05 to 0.95... that's Rt<1 = lives saved.</p>
|
||||
|
||||
<p>// calc?</p>
|
||||
<p>If so, here's a few supplements:</p>
|
||||
|
||||
<p><strong>Masks For All:</strong></p>
|
||||
|
||||
<p>[small brain] Correlation implies causation!</p>
|
||||
<p><em>"Wait,"</em> you might ask, <em>"I thought face masks don't stop you from getting sick?"</em></p>
|
||||
|
||||
<p>[normal brain] Correlation doesn't imply causation, you need Randomized Controlled Trials (RCTs) to prove things.</p>
|
||||
<p>You're right. Masks don't stop you from getting sick... they stop you from getting <em>others</em> sick.</p>
|
||||
|
||||
<p>[large brain] Actually, under Bayes' Theorem, <em>all</em> correlations are evidence for causation, because the likelihood of {seeing a correlation, given causation} is greater than the likelihood of {seeing a correlation, given <em>no</em> causation}. It's just not 100% proof, because <em>nothing</em> in science is 100% proof, not even RCTs (hence the replication crisis). <strong>Evidence isn't 0% or 100%, they have a full range of "weights".</strong> And though correlational evidence has a lower "weight" than an RCT, it <em>is still evidence.</em> (See this 3Blue1Brown video for a visual explanation of Bayes' Theorem)</p>
|
||||
<p><img src="pics/masks.png" alt=""></p>
|
||||
|
||||
<p>What we're trying to say is:</p>
|
||||
<p>(sources for the comic: <sup id="fnref25"><a href="#fn25" rel="footnote">25</a></sup> <sup id="fnref26"><a href="#fn26" rel="footnote">26</a></sup> <sup id="fnref27"><a href="#fn27" rel="footnote">27</a></sup> <sup id="fnref28"><a href="#fn28" rel="footnote">28</a></sup>)</p>
|
||||
|
||||
<p>There aren't any RCTs (yet) testing "Cloth masks prevent COVID-19 spread" <em>specifically</em>. But there's lots of <em>suggestive</em> evidence, if of lower "weight":</p>
|
||||
<p>Still, in science, one should only publish a finding if you're 95% sure of it. (...<em>should.</em><sup id="fnref29"><a href="#fn29" rel="footnote">29</a></sup>) Admittedly, the current evidence for face masks on COVID-19 <em>specifically</em>, rather than "just" colds and flus, is less than "95% sure".</p>
|
||||
|
||||
<ul>
|
||||
<li>Staff in hospitals without masks are more likely to die of COVID-19</li>
|
||||
<li>Taiwan & South Korea have widespread public wearing of masks</li>
|
||||
<li>For colds & flus, masks reduce droplets/aerosols from the <em>wearer</em>.</li>
|
||||
</ul>
|
||||
<p>But, pandemics are like poker. <strong>Make bets only when you're 95% sure, and you'll lose everything at stake.</strong> We <em>have</em> to make cost/benefit analyses under uncertainty.<sup id="fnref30"><a href="#fn30" rel="footnote">30</a></sup> Like so:</p>
|
||||
|
||||
<p>Pandemics are like poker. Act only when you "have enough info", and you'll lose everything at stake. You'll never have enough info, just cost/benefit analyses under uncertainty. Like so:</p>
|
||||
<p>Cost: If homemade cloth masks, same as the cost of all that soap for handwashing. If surgical masks, more expensive but still pretty cheap.</p>
|
||||
|
||||
<p>Cost of cloth masks (certain): Small. Same as handwashing.</p>
|
||||
<p>Benefit: Even if it's a 50–50 chance of surgical masks reducing transmission by 0% or 70%<sup id="fnref31"><a href="#fn31" rel="footnote">31</a></sup>, the average "expected value" is still 35%, same as a half-lockdown! So let's guess-timate that surgical masks reduce R by up to 35%. (Again, you can challenge our assumptions by turning the sliders up/down)</p>
|
||||
|
||||
<p>Benefit of cloth masks (uncertain): They probably don't stop <em>the wearer</em> from getting COVID-19, but they probably stop a pre-symptomatic wearer from <em>spreading</em> COVID-19. Let's guess masks reduce Rt by 0% to 20%. <em>Even though "0%" is still likely</em>, the average "expected value" is <em>halfway</em> between 0% and 20% – that is, 10%, same as deep cleaning, but at minuscule cost.</p>
|
||||
<p><strong>Here's a calculator of how masks reduce R! You can switch between cloth & surgical:</strong> (assumes cloth masks are half as effective as surgical masks<sup id="fnref32"><a href="#fn32" rel="footnote">32</a></sup>)</p>
|
||||
|
||||
<p>Analysis: If someone offered you a coin flip, where tails = nothing happens, and heads = 1000s of lives saved... and the price for playing this game is a rag and two rubber bands... even though "nothing" is as likely as "lives saved", you should do it. </p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-6a&format=calc" width="285" height="380"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Cloth masks for all: do it!</p>
|
||||
<p>(other arguments for/against masks:<sup id="fnref33"><a href="#fn33" rel="footnote">33</a></sup>)</p>
|
||||
|
||||
<p>// calc?</p>
|
||||
<p>Masks <em>alone</em> won't get R < 1. But if handwashing & "Test, Trace, Isolate" only gets us to R = 1.10, having just 2/3 of people wear <em>cloth</em> masks would tip that over to R < 1, virus contained!</p>
|
||||
|
||||
<p><strong>Summer:</strong></p>
|
||||
|
||||
<p>Okay, this is not an "intervention" we have control of, but it <em>does</em> help reduce Rt!</p>
|
||||
<p>Okay, this isn't an "intervention" we can control, but it will help! Some news outlets report that summer won't do anything to COVID-19. They're half right: summer won't get R < 1, but it <em>will</em> reduce R.</p>
|
||||
|
||||
<p>For every extra 1° Celsius (2.2° Fahrenheit), Rt drops by ___%. The average difference between winter & summer in New York is 15°C (60°F), so summer will make Rt drop by _%.</p>
|
||||
<p>For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.<sup id="fnref34"><a href="#fn34" rel="footnote">34</a></sup> The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.</p>
|
||||
|
||||
<p>Many news sites (wrongly) report summer won't slow COVID-19. They're probably trying not to get your hopes up: with R0=3.5, a _% reduction is Rt=_, still above 1.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-6b&format=calc" width="285" height="220"></iframe>
|
||||
</div>
|
||||
|
||||
<p>But still, it's <em>something</em>. If we have limited resources, we can scale back some interventions in the summer – so we can scale them higher in the winter.</p>
|
||||
|
||||
<p>// calc? over time</p>
|
||||
<p>Summer alone won't make R < 1, but if we have limited resources, we can scale back some interventions in the summer – so we can scale them <em>higher</em> in the winter.</p>
|
||||
|
||||
<p><strong>A "Circuit Breaker" Lockdown:</strong></p>
|
||||
|
||||
<p>And if all that <em>still</em> isn't enough to get Rt<1... we can do another lockdown.</p>
|
||||
<p>And if all that <em>still</em> isn't enough to get R < 1... we can do another lockdown.</p>
|
||||
|
||||
<p>But because Rt was reduced dramatically, we wouldn't have to do a 2-month-lockdown-every-3-months! Probably just <em>one</em> more 1-month lockdown, between now and when we have a vaccine.</p>
|
||||
<p>But we wouldn't have to be 2-months-closed / 1-month-open over & over! Because R is reduced, we'd only need one or two more "circuit breaker" lockdowns before a vaccine is available. (Singapore had to do this recently, "despite" having controlled COVID-19 for 4 months. That's not failure: this <em>is</em> what success takes.)</p>
|
||||
|
||||
<p>Here's a simulation of that (with sliders for <em>ALL</em> the interventions):</p>
|
||||
<p>Here's a simulation a "lazy case" scenario:</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<ol>
|
||||
<li>Lockdown, then</li>
|
||||
<li>A moderate amount of hygiene + "Test, Trace, Isolate" + <em>cloth</em> "Masks For All", then...</li>
|
||||
<li>One more "circuit breaker" lockdown before a vaccine's found.</li>
|
||||
</ol>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-7&format=lines&height=620" width="800" height="620"></iframe>
|
||||
</div>
|
||||
|
||||
<p>. . .</p>
|
||||
|
||||
<p>We hope these plans give you hope. </p>
|
||||
|
||||
<p>It <em>is</em> possible to keep Rt<1, <em>without</em> locking down for most of 18 months. With plans like "Test, Trace, Isolate", supplemented with backup plans like "Masks For All", we can get back to a normal-ish life!</p>
|
||||
<p><strong>Even under a pessimistic scenario, it <em>is</em> possible to beat COVID-19, while protecting our mental and financial health.</strong> Use the lockdown as a restart, keep R < 1 with privacy-protecting contract tracing, supplemented with at <em>least</em> cloth masks... and life can get back to a normal-ish!</p>
|
||||
|
||||
<p>Sure, your hands may be dry. But you'll get to invite a date out to a comics bookstore! You'll get to watch the latest cash-grab Hollywood sequel with friends. You'll get to people-watch at a library, taking joy in people going about the simple business of <em>being alive.</em></p>
|
||||
<p>Sure, your hands may be dry. But you'll get to invite a date out to a comics bookstore! You'll get to go out with friends to watch the latest Hollywood cash-grab. You'll get to people-watch at a library, taking joy in people going about the simple business of <em>being alive.</em></p>
|
||||
|
||||
<p>Life will go on, even under the worst-case scenario.</p>
|
||||
<p>Even under the worst-case scenario... life perseveres.</p>
|
||||
|
||||
<p>So now, let's use our fear's energy, and plan for some <em>even worse</em> worst-case scenarios:</p>
|
||||
<p>So now, let's plan for some <em>worse</em> worst-case scenarios. Water landing, get your life jacket, and please follow the lights to the emergency exits:</p>
|
||||
|
||||
<hr>
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Next Few Years</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<h1 id="toc_12">The Next Few Years</h1>
|
||||
|
||||
<p>You get COVID-19, and recover. Or you get the COVID-19 vaccine! Either way, you're now immune...</p>
|
||||
<p>You get COVID-19, and recover. Or you get the COVID-19 vaccine. Either way, you're now immune...</p>
|
||||
|
||||
<p>...<em>for how long?</em></p>
|
||||
|
||||
<p>SARS, which was closely related (TODO: is it?) to this new coronavirus, gave its survivors around 2 years of immunity.<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/">12</a>. Some coronaviruses, like the ones that cause "the" common cold[13], give you just <a href="https://pubmed.ncbi.nlm.nih.gov/2170159/">1 year of immunity</a>. (TODO: MERS' immunity)</p>
|
||||
<p>There's been reports of folks who test positive again after recovering, but those were false positives. Still, the possibility of <strong>waning immunity</strong> is very real. Either a new mutant strain evolves, or your immune system just... forgets.</p>
|
||||
|
||||
<p>Let's think about the scariest scenario: immunity doesn't last.</p>
|
||||
<p>The coronavirus responsible for COVID-19 is most closely related to the coronavirus responsible for SARS. SARS (probably) gave its survivors around 2 years of immunity.<sup id="fnref35"><a href="#fn35" rel="footnote">35</a></sup> The coronaviruses that cause "the" common cold give you 1 year of immunity<sup id="fnref36"><a href="#fn36" rel="footnote">36</a></sup>. So:</p>
|
||||
|
||||
<p><em>Rule #4: (r)s eventually become (s)s</em></p>
|
||||
<p><em>What if COVID-19 immunity doesn't last?</em></p>
|
||||
|
||||
<p>// pic</p>
|
||||
<p>Here's a simulation starting with 100% <icon i></icon>, exponentially decaying into <icon r></icon>s after 10 days... but then back to susceptible, no-immunity <icon s></icon>s after 1 year:</p>
|
||||
|
||||
<p>The SIRS model: the (r) Recovered become (s) Susceptible again.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-1" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Let's simulate what that'll look like, with <em>no</em> interventions:</p>
|
||||
<p>Return of the exponential decay!</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>This is the <strong>SEIRS Model</strong>. The final "S" stands for <icon s></icon> Susceptible, again.</p>
|
||||
|
||||
<p>Previously, with no interventions, we only had <em>one</em> hospital-breaking spike. Now, we have several, <em>and</em> the simulation comes to a rest with % of (i) infected <em>permanently above</em> hospital capacity. </p>
|
||||
<p><img src="pics/seirs.png" alt=""></p>
|
||||
|
||||
<p>(If you replay the simulation above with immunity lasting 3 years, that wouldn't be so bad! The % of (i) would rest comfortably <em>below</em> capacity. There'd still be spikes, but you can deal with them using the same interventions listed in last section)</p>
|
||||
<p>Now let's simulate a COVID-19 outbreak, over 10 years, with no interventions... <em>if immunity only lasts a year:</em></p>
|
||||
|
||||
<p>It's like a pendulum: total (i)+(r) cases swings around the "herd immunity" threshold, before settling exactly at "herd immunity", where Rt=1. The virus no longer grows or shrinks. It's just with us forever: it's <strong>endemic.</strong></p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-2&format=lines&height=600" width="800" height="600"></iframe>
|
||||
</div>
|
||||
|
||||
<p>// pic?</p>
|
||||
<p>Previously, we only had <em>one</em> ICU-overwhelming spike. Now, we have several, <em>and</em> <icon i></icon> cases come to a rest <em>permanently at</em> ICU capacity. (Which, remember, we <em>tripled</em> for these simulations)</p>
|
||||
|
||||
<p>Thankfully, summer will make it better by reducing Rt:</p>
|
||||
<p>R = 1, it's <strong>endemic.</strong></p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>Thankfully, because summer reduces R, it'll make the situation better:</p>
|
||||
|
||||
<p>Oh wait no it doesn't. Summer <em>does</em> reduce new people becoming (i) infected, but that also reduces new people becoming (r) immune. Which means immunity in the population will drop <em>even further</em> with summer, allowing for <em>big regular spikes</em> in the winter.</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-3&format=lines&height=640" width="800" height="640"></iframe>
|
||||
</div>
|
||||
|
||||
<p>It's like a pendulum where you're moving the top back and forth: that just makes the cycles <em>worse</em>.</p>
|
||||
<p>Oh. Counterintuitively, summer makes the spikes <em>worse</em>, and regular! This is because summer reduces new <icon i></icon>s, but that in turn reduces new immune <icon r></icon>s. Which means immunity plummets in the summer, <em>creating</em> large regular spikes in the winter.</p>
|
||||
|
||||
<p>// pic?</p>
|
||||
<p>Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:</p>
|
||||
|
||||
<p>Finally, the <em>worst</em> worst-case:</p>
|
||||
<p><strong>(After playing the recording, try simulating your own vaccination campaigns! Remember you can pause/continue the sim at any time)</strong></p>
|
||||
|
||||
<p>What if, like HIV, there's just <em>never</em> a vaccine?</p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-4&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Our only option now is to increase our capacity for COVID-19 cases. You could do this directly, by creating more hospital beds and ventilators. Or you could do this indirectly, by creating treatments for COVID-19, so that if you <em>do</em> get it, you're less likely to need a hospital bed or ventilator.</p>
|
||||
<p>But here's the scarier question:</p>
|
||||
|
||||
<p>Here's the same simulation, but 1) starting with herd immunity (which wanes quickly), and 2) with adjustable hospital capacity:</p>
|
||||
<p>What if there's no vaccine for <em>years</em>? Or <em>ever?</em></p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p><strong>To be clear: this is unlikely.</strong> Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment. Coronaviruses aren't any more complex than the viruses we already have vaccines for, so most infectious disease researchers expect a vaccine in 1 to 2 years.</p>
|
||||
|
||||
<p>HIV/AIDS killed millions, mostly in marginalized communities. And yet, despite it being the worst-case pandemic scenario, and despite all the stigma against people who have it... HIV isn't a death sentence anymore.</p>
|
||||
<p>Still, they've expressed worries about a vaccine: What if we can't make enough?<sup id="fnref37"><a href="#fn37" rel="footnote">37</a></sup> What if we rush it, and it's not safe?<sup id="fnref38"><a href="#fn38" rel="footnote">38</a></sup></p>
|
||||
|
||||
<p>HIV has no vaccine. There's <em>definitely</em> no herd immunity. And yet, with treatments like antiretroviral therapy, people can and <em>are</em> living full lives with the virus. COVID-19 is devastating, but nowhere as much as HIV.</p>
|
||||
<p>Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:</p>
|
||||
|
||||
<p>Life will go on, even under the <em>worst</em> worst-case scenario.</p>
|
||||
<p>1) Do intermittent or loose R < 1 interventions, to reach "natural herd immunity". (Warning: this will result in many deaths & damaged lungs. <em>And</em> won't work if immunity doesn't last.)</p>
|
||||
|
||||
<p>...</p>
|
||||
<p>2) Do the R < 1 interventions forever. Contact tracing & wearing masks just becomes a new norm in the post-COVID-19 world, like how STI tests & wearing condoms became a new norm in the post-HIV world. (Nobody suggested "herd immunity" for HIV...)</p>
|
||||
|
||||
<p>That said, the virus behind COVID-19 is way simpler than HIV, so there'll almost definitely be a vaccine, even if it only grants immunity for a year. If so, we'll just have to do a vaccination campaign each autumn – and we can just do this alongside our regular flu shots:</p>
|
||||
<p>3) Do the R < 1 interventions until we develop treatments that make COVID-19 way, way less likely to need critical care. (Which we should be doing <em>anyway!</em>) Reducing ICU use by 10x is the same as increasing our ICU capacity by 10x:</p>
|
||||
|
||||
<p>// sim</p>
|
||||
<p><strong>Here's a simulation of <em>no</em> lasting immunity, <em>no</em> vaccine, and not even any interventions – just increasing ICU capacity to survive the long-term spikes:</strong></p>
|
||||
|
||||
<p><strong>Finally, here's a Simulation Sandbox, with <em>every</em> option available. You can now also share your <em>own</em> simulations!</strong></p>
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-5&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>// sim</p>
|
||||
<p>Even under the <em>worst</em> worst-case scenario... life perseveres.</p>
|
||||
|
||||
<p>Play around to intuitively understand the core rules of epidemiology. </p>
|
||||
<p>. . .</p>
|
||||
|
||||
<p>Try simulating different COVID-19 scenarios, plans, and backup plans. </p>
|
||||
<p>Maybe you'd like to challenge our assumptions, and try different R<sub>0</sub>'s or numbers. Or try simulating your <em>own</em> combination of intervention plans!</p>
|
||||
|
||||
<p>Ask questions, try to find an answer with the sim, and share your sim with others.</p>
|
||||
<p><strong>Here's an (optional) Sandbox Mode, with <em>everything</em> available. Simulate & play around to your heart's content:</strong></p>
|
||||
|
||||
<p>This (again, very <em>VERY</em> basic!) simulation has let us answer so many questions about the past few months, next few months, and next few years.</p>
|
||||
<p>[TODO TODO TODO!]</p>
|
||||
|
||||
<p>So now, let's return to...</p>
|
||||
<p>This basic "epidemic flight simulator" has taught us so much. It's let us answer questions about the past few months, next few months, and next few years.</p>
|
||||
|
||||
<hr>
|
||||
<p>So finally, let's return to...</p>
|
||||
|
||||
<h1 id="toc_13">The Now</h1>
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Now</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<p>In summary, here's how we bravely use our fear, slay the dragon, and save the lives of millions of princes(ses):</p>
|
||||
<p>Plane's in the ocean. We've scrambled onto the life rafts. It's time to find dry land.<sup id="fnref39"><a href="#fn39" rel="footnote">39</a></sup></p>
|
||||
|
||||
<p><strong>PHASE 1) Lockdown to get a fresh start.</strong></p>
|
||||
<p>Teams of epidemiologists and policymakers (<a href="https://www.americanprogress.org/issues/healthcare/news/2020/04/03/482613/national-state-plan-end-coronavirus-crisis/">left</a>, <a href="https://www.aei.org/research-products/report/national-coronavirus-response-a-road-map-to-reopening/">right</a>, and <a href="https://ethics.harvard.edu/covid-roadmap">multi-partisan</a>) have come to a consensus on how to beat COVID-19, while protecting our lives <em>and</em> liberties.</p>
|
||||
|
||||
<p>Get current (i)s low, while building capability to do...</p>
|
||||
<p>Here's the rough idea, with some (less-consensus) backup plans:</p>
|
||||
|
||||
<p><strong>PHASE 2) "Test, Trace, Isolate"</strong></p>
|
||||
<p><img src="pics/plan.png" alt=""></p>
|
||||
|
||||
<p>We replace lockdown with other ways to get Rt<1. Life gets back to normal-ish! 🎉</p>
|
||||
|
||||
<p>More testing so we can <em>actually</em> tell what Rt currently is.</p>
|
||||
|
||||
<p>Create policies to get cases to isolate/quarantine. Paid leave & bonus financial incentives if they do, <em>maybe</em> fines if they don't. </p>
|
||||
|
||||
<p>Use <em>privacy-protecting</em> contact tracing apps to find contacts. Remember, not everybody has to have the app to get Rt<1.</p>
|
||||
|
||||
<p>If Rt still not below 1: "Masks For All". Get most people to wear at <em>least</em> cloth face masks.</p>
|
||||
|
||||
<p>If Rt <em>still</em> not below 1: Deep clean public spaces often. Mild social distancing. Maybe one or two more "circuit breaker" lockdowns. (but still avoiding "lockdown for most of 18 months"!)</p>
|
||||
|
||||
<p>This will buy us time to finally do...</p>
|
||||
|
||||
<p><strong>PHASE 3) Vaccinate!</strong></p>
|
||||
|
||||
<p>If immunity doesn't last long: Vaccination campaign every autumn, like we already do for flu shots. </p>
|
||||
|
||||
<p>If vaccine is <em>never</em> available: Raise our capacity for COVID-19 cases by creating more hospital beds & ventilators, and developing antivirals & treatments. (which we should be doing <em>anyway!</em>)</p>
|
||||
|
||||
<p><strong>What's this mean for YOU, <em>right now?</em></strong></p>
|
||||
<p>So what does this mean for YOU, right now?</p>
|
||||
|
||||
<p><strong>For everyone:</strong> Respect the lockdown so we can get out of Phase I asap. Keep washing those hands. Make your own masks. Download a <em>privacy-protecting</em> contact tracing app when those are available next month. Stay healthy, physically & mentally! And write your local policymaker to get off their butt and...</p>
|
||||
|
||||
<p><strong>For policymakers:</strong> Create policies that compensate (or reward!) folks who have to self-isolate/quarantine. Direct funds into all the stuff we should be building, like...</p>
|
||||
<p><strong>For policymakers:</strong> Make laws to support folks who have to self-isolate/quarantine. Hire more manual contact tracers, <em>supported</em> by privacy-protecting contact tracing apps. Direct more funds into the stuff we should be building, like...</p>
|
||||
|
||||
<p><strong>For builders:</strong> Build tests. Build ventilators. Build masks – cloth, surgical and N95. Build apps. Build antivirals and other treatments. Build vaccines. Build science. </p>
|
||||
<p><strong>For builders:</strong> Build tests. Build ventilators. Build personal protective equipment for hospitals. Build tests. Build masks. Build apps. Build antivirals, prophylactics, and other treatments that aren't vaccines. Build vaccines. Build tests. Build tests. Build tests. Build hope. </p>
|
||||
|
||||
<p>Will we <em>need</em> all that? "Probably" not, the same way you "probably" won't need safety belts, fire insurance, or parachutes on planes. It's like doing a cost/benefit analysis of Russian Roulette: the chance of disaster is small, but the <em>cost</em> of disaster is far, far bigger.</p>
|
||||
<p>Don't downplay fear to build up hope. Our fear should <em>team up</em> with our hope, like the inventors of airplanes & parachutes. Preparing for horrible futures is how we <em>create</em> a hopeful future.</p>
|
||||
|
||||
<p>In situations like this, it pays to listen <em>honestly</em> to your fears. Don't deny or downplay them, just face them, and prepare for them.</p>
|
||||
|
||||
<p>The only thing to fear is people who think the only thing to fear is fear itself.</p>
|
||||
|
||||
<hr>
|
||||
|
||||
<p>(TODO: US vs Korea/Taiwain resources)</p>
|
||||
<p>The only thing to fear is the idea that the only thing to fear is fear itself.</p>
|
||||
|
||||
<div class="footnotes">
|
||||
<hr>
|
||||
<ol>
|
||||
|
||||
<li id="fn1">
|
||||
<p>hello! <a href="#fnref1" rev="footnote">↩</a></p>
|
||||
<p>(NOTE: This guide was published on April 30th, 2020. Many details will become outdated, but Epidemiology 101 will remain true, and we're confident this guide will cover 95% of possible futures.) <a href="#fnref1" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn2">
|
||||
<p>but a snitch ain't one <a href="#fnref2" rev="footnote">↩</a></p>
|
||||
<p>https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article <a href="#fnref2" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn3">
|
||||
<p>source <a href="#fnref3" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn4">
|
||||
<p>https://link.springer.com/article/10.1007/s11427-020-1661-4 <a href="#fnref4" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn5">
|
||||
<p>source, and sidenote on 'infectious' <a href="#fnref5" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn6">
|
||||
<p>source <a href="#fnref6" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn7">
|
||||
<p>source <a href="#fnref7" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn8">
|
||||
<p>https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-480 <a href="#fnref8" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn9">
|
||||
<p>https://pubmed.ncbi.nlm.nih.gov/31995857/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/ <a href="#fnref9" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn10">
|
||||
<p>https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article <a href="#fnref10" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn11">
|
||||
<p>sas <a href="#fnref11" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn12">
|
||||
<p>exact formula... <a href="#fnref12" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn13">
|
||||
<p>https://www.statista.com/statistics/1105420/covid-icu-admission-rates-us-by-age-group/ Lower end, 5%. <a href="#fnref13" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn14">
|
||||
<p>https://sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19 <a href="#fnref14" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn15">
|
||||
<p>https://www.theatlantic.com/health/archive/2020/03/coronavirus-pandemic-herd-immunity-uk-boris-johnson/608065/ <a href="#fnref15" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn16">
|
||||
<p>https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2006.01568.x <a href="#fnref16" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn17">
|
||||
<p>https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html <a href="#fnref17" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn18">
|
||||
<p>log scale <a href="#fnref18" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn19">
|
||||
<p>https://science.sciencemag.org/content/early/2020/04/14/science.abb5793? <a href="#fnref19" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn20">
|
||||
<p>https://journals.sagepub.com/doi/abs/10.1177/1745691614568352 <a href="#fnref20" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn21">
|
||||
<p>sources plz, esp for incubation period 5 days <a href="#fnref21" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn22">
|
||||
<p>https://www.nature.com/articles/s41591-020-0869-5 <a href="#fnref22" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn23">
|
||||
<p>asds <a href="#fnref23" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn24">
|
||||
<p>https://science.sciencemag.org/content/early/2020/04/09/science.abb6936 <a href="#fnref24" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn25">
|
||||
<p>incoming <a href="#fnref25" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn26">
|
||||
<p>outgoing_aerosols <a href="#fnref26" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn27">
|
||||
<p>outgoing_droplets <a href="#fnref27" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn28">
|
||||
<p>homemade <a href="#fnref28" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn29">
|
||||
<p>ss <a href="#fnref29" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn30">
|
||||
<p>That BMJ article <a href="#fnref30" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn31">
|
||||
<p>s <a href="#fnref31" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn32">
|
||||
<p>ss <a href="#fnref32" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn33">
|
||||
<p>s <a href="#fnref33" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn34">
|
||||
<p>https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767 The average R-value of these 100 cities is 1.83 , One-degree Celsius increase in temperature and one percent increase in relative humidity lower R by 0.0225 <a href="#fnref34" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn35">
|
||||
<p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/ <a href="#fnref35" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn36">
|
||||
<p>https://pubmed.ncbi.nlm.nih.gov/2170159/ <a href="#fnref36" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn37">
|
||||
<p>https://www.nature.com/articles/d41586-020-01063-8 <a href="#fnref37" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn38">
|
||||
<p>https://www.nature.com/articles/d41586-020-00751-9 <a href="#fnref38" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn39">
|
||||
<p>https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/ <a href="#fnref39" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
</ol>
|
||||
|
|
|
|||
542
words/words.md
542
words/words.md
|
|
@ -1,234 +1,278 @@
|
|||
# What Happens Next?
|
||||
|
||||
## COVID-19 Futures, Explained With Playable Simulations
|
||||
**{WIP, DON'T SHARE YET THX!}**
|
||||
|
||||
"The only thing to fear is fear itself" was stupid advice.
|
||||
|
||||
If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we *use* our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.
|
||||
Sure, don't hoard toilet paper – but if policymakers fear fear itself, they'll downplay dangers to us to avoid "mass panic". Fear's not the problem, it's how we *channel* our fear. Fear gives us energy to deal with dangers now, and prepare for dangers later.
|
||||
|
||||
Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we've channeled *our* worries into making these **playable simulations**, so that you can channel *your* worries into understanding:
|
||||
Honestly, we (Marcel, epidemiologist + Nicky, art/code) are worried. We bet you are, too! That's why we've channelled our fear into making these **playable simulations**, so that *you* can channel your fear into understanding:
|
||||
|
||||
* **The Last Few Months** (epidemiology 101, SEIR model, R & R<sub>0</sub>)
|
||||
* **The Next Few Months** (lockdowns, contact tracing, masks)
|
||||
* **The Next Few Years** (loss of immunity? no safe vaccine?)
|
||||
* **The Next Few Months** (lockdowns, contact tracing, masks?)
|
||||
* **The Next Few Years** (loss of immunity? no vaccine?)
|
||||
|
||||
This guide is meant to give you hope *and* fear. To beat this virus **in a way that also protects our mental & financial health**, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, *“The optimist invents the airplane and the pessimist the parachute.”*
|
||||
This guide (published April 30th, 2020[^timestamp]) is meant to give you hope *and* fear. To beat COVID-19 **in a way that also protects our mental & financial health**, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, *“The optimist invents the airplane and the pessimist the parachute.”*
|
||||
|
||||
[^timestamp]: (NOTE: This guide was published on April 30th, 2020. Many details will become outdated, but Epidemiology 101 will remain true, and we're confident this guide will cover 95% of possible futures.)
|
||||
|
||||
So, buckle in: we're about to experience some turbulence.
|
||||
|
||||
---
|
||||
|
||||
# The Last Few Months
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Last Few Months</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Pilots use flight simulators to learn how not to crash planes.
|
||||
|
||||
**Epidemiologists use epidemic simulators to learn how not to crash humanity.**
|
||||
|
||||
So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s:
|
||||
So, let's make a simple "epidemic flight simulator"! In this simulation, <icon i></icon> Infectious people can turn <icon s></icon> Susceptible people into more <icon i></icon> Infectious people:
|
||||
|
||||
// pic
|
||||
|
||||
|
||||
At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.[^1] (*On average.* Remember, there's lots of variation.)
|
||||
It's estimated that, *at the start* of a COVID-19 outbreak, the virus jumps from an <icon i></icon> to an <icon s></icon> *approximately* every 4 days.[^serial_interval]
|
||||
|
||||
[^1]: source
|
||||
[^serial_interval]: https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article
|
||||
|
||||
Here's a simulation of a population with *just* 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" *and nothing else*, what happens?
|
||||
If we simulate "double every 4 days" *and nothing else*, on a population starting with just 0.001% <icon i></icon>, what happens?
|
||||
|
||||
**Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)**
|
||||
**Click "Start" to play the simulation! You can re-play it later with different settings:** (technical caveats: [^caveats])
|
||||
|
||||
// sim
|
||||
[^caveats]: source
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-1" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
This is the **exponential growth curve.** Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create *mass graves in rich cities*".
|
||||
|
||||
// pic - exponential double rice
|
||||
|
||||
|
||||
But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others *already* have the virus:
|
||||
|
||||
// pic - 100% spread, 50% spread, 0% spread
|
||||
|
||||
|
||||
**The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the *slower* (s)s become (i)s.**
|
||||
The more <icon i></icon>s there are, the faster <icon s></icon>s become <icon i></icon>s, **but the fewer <icon s></icon>s there are, the *slower* <icon s></icon>s become <icon i></icon>s.**
|
||||
|
||||
Now, what happens if we simulate that?
|
||||
How's this change the growth of an epidemic? Let's find out:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-2" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
This is the "S-shaped" **logistic growth curve.** Starts small, explodes, then slows down again.
|
||||
|
||||
But, this simulation is *still* wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.
|
||||
But, this simulation is *still* wrong. We're missing the fact that <icon i></icon> Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.
|
||||
|
||||
For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – *for now!* – that they stay immune for life.
|
||||
For simplicity's sake, let's pretend that all <icon i></icon> Infectious people become <icon r></icon> Recovered. (Just remember that, in reality, some of them are dying.) <icon r></icon>s can't be infected again, and let's pretend – *for now!* – that they stay immune for life.
|
||||
|
||||
When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.[^2] (Again, *on average.*)
|
||||
With COVID-19, it's estimated you're <icon i></icon> Infectious for *approximately* 10 days.[^infectiousness] Let's simulate a population starting at 100% <icon i></icon>, most of whom recover after 10 days, then most of the remainder recover after another 10 days, then most of *that* remainder recover after another 10 days, etc:
|
||||
|
||||
[^2]: source
|
||||
[^infectiousness]: https://link.springer.com/article/10.1007/s11427-020-1661-4
|
||||
|
||||
Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder *of that remainder* recover after another 12 days, etc:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-3" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
This is the opposite of exponential growth, the **exponential decay curve**.
|
||||
|
||||
Now, what happens if you combine this with the S-shaped logistic curve of infection?
|
||||
Now, what happens if you simulate S-shaped logistic growth *with* recovery?
|
||||
|
||||
// pic
|
||||
|
||||
|
||||
Let's find out. Here's a simulation of an epidemic *with* recovery:
|
||||
Let's find out:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
And *that's* where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten.
|
||||
|
||||
// pic: 3 rules
|
||||
This is the the **SIR Model**[^sir] – <icon s></icon>**S**usceptible <icon s></icon>**I**nfectious <icon s></icon>**R**ecovered – the second-most important idea in Epidemiology 101:
|
||||
|
||||
This is the the **SIR Model**, ((s) **S**usceptible → (i) **I**nfectious → (r) **R**ecovered) the second-most important idea in Epidemiology 101.
|
||||
[^sir]: source, and sidenote on 'infectious'
|
||||
|
||||
Note: The simulations that inform policy are *far* more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances.
|
||||
|
||||
|
||||
Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infect*ed* but not yet infect*ious* – they have the virus but can't pass it on (yet).
|
||||
NOTE: The simulations that inform policy are *far* more sophisticated than this! But the SIR Model can still explain the same findings, even if missing the nuances.
|
||||
|
||||
(This variant is called the **SEIR Model**, where "E" stands for (e) Exposed. Note this *isn't* the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.)
|
||||
Actually, let's add one more nuance: before an <icon s></icon> becomes an <icon i></icon>, they first become <icon e></icon> Exposed. This is when they have the virus but can't pass it on yet – infect*ed* but not yet infect*ious*.
|
||||
|
||||
For COVID-19, it's estimated that you're in this "latent period" for around 3 days.[^3] What happens if we add that to the simulation?
|
||||
|
||||
|
||||
[^3]: source
|
||||
(This variant is called the **SEIR Model**[^seir], where the "E" stands for <icon e></icon> "Exposed". Note this *isn't* the everyday meaning of "exposed", when you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Science terminology is bad.)
|
||||
|
||||
// sim
|
||||
[^seir]: source
|
||||
|
||||
Not much, actually! The "latent period" only changes *when* the peak happens, but the *height* of the peak – and total people infected – remain the same:
|
||||
For COVID-19, it's estimated that you're <icon e></icon> infected-but-not-yet-infectious for *approximately* 3 days.[^latent] What happens if we add that to the simulation?
|
||||
|
||||
// pics
|
||||
[^latent]: source
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Not much, actually! How long you stay <icon e></icon> Exposed changes the ratio of <icon e></icon>-to-<icon i></icon>, and *when* the peak of current cases (<icon e></icon>+<icon i></icon>) happens... but the *height* of that peak, and the total % of people infected in the end, stays the same.
|
||||
|
||||
Why's that? Because of the *first*-most important idea in Epidemiology 101:
|
||||
|
||||
// pic - **"R"**
|
||||
|
||||
|
||||
Which is short for "Reproduction Number". It's the *average* number of people an (i) infects *before* they recover (or die).
|
||||
Short for "Reproduction number". It's the *average* number of people an <icon i></icon> infects *before* they recover (or die).
|
||||
|
||||
// R > 1, R = 1, R < 1 pic
|
||||
|
||||
|
||||
**R** changes over the course of an outbreak, as we get more immunity & interventions.
|
||||
|
||||
**R<sub>0</sub>** (pronounced R-nought) is what R is *at the start of an outbreak, before immunity or interventions*. R<sub>0</sub> is also called the "basic reproduction number". <!--R<sub>0</sub> more closely reflects the power of the virus itself, but it still changes from place to place. For example, because heat 'kills' coronaviruses, R<sub>0</sub> for COVID-19 is lower in hot places than cold ones. Not low enough to contain it, though. [source!]-->
|
||||
**R<sub>0</sub>** (pronounced R-nought) is what R is *at the start of an outbreak, before immunity or interventions*. R<sub>0</sub> more closely reflects the power of the virus itself, but it still changes from place to place. For example, R<sub>0</sub> is higher in dense cities than sparse rural areas.
|
||||
|
||||
(A lot of news outlets – and even academic papers! – confuse R and R<sub>0</sub>. Again, science terminology is bad.)
|
||||
(Most news articles – and even some scientific papers! – confuse R and R<sub>0</sub>. Again, science terminology is bad)
|
||||
|
||||
The R<sub>0</sub> for the flu[^r0_flu] is around 1.3. The R<sub>0</sub> estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.[^r0_covid]
|
||||
The R<sub>0</sub> for "the" seasonal flu is around 1.28[^r0_flu]. This means, at the *start* of a flu outbreak, each <icon i></icon> infects 1.28 others *on average.* (If it sounds weird that this isn't a whole number, remember that the "average" mom has 2.4 children. This doesn't mean there's half-children running about.)
|
||||
|
||||
[^r0_flu]: source
|
||||
[^r0_flu]: https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-480
|
||||
|
||||
[^r0_covid]: source
|
||||
The R<sub>0</sub> for COVID-19 is estimated to be around 2.2[^r0_covid], though a not-yet-finalized CDC study estimates it was 5.7(!) in Wuhan.[^r0_wuhan]
|
||||
|
||||
In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, *on average*, an (i) infects 3 (s)s before they recover. So for our simulations, R<sub>0</sub> is 3.
|
||||
[^r0_covid]: https://pubmed.ncbi.nlm.nih.gov/31995857/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/
|
||||
|
||||
**Play around with this R<sub>0</sub> calculator, to see how R<sub>0</sub> depends on recovery time & new-infection time:**
|
||||
[^r0_wuhan]: https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article
|
||||
|
||||
// calc
|
||||
In our simulations – *at the start & on average* – an <icon i></icon> infects someone every 4 days, over 10 days. "4 days" goes into "10 days" two-and-a-half times. This means – *at the start & on average* – each <icon i></icon> infects 2.5 others. Therefore, R<sub>0</sub> = 2.5. (caveats:[^r0_caveats_sim])
|
||||
|
||||
But remember, the fewer (s)s there are, the *slower* (s)s become (i)s.
|
||||
R depends not just on R<sub>0</sub>, but also how many people are no longer Susceptible – due to, say, having recovered & gotten natural immunity.
|
||||
[^r0_caveats_sim]: sas
|
||||
|
||||
// calc 2
|
||||
**Play with this R<sub>0</sub> calculator, to see how R<sub>0</sub> depends on recovery time & new-infection time:**
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-6a&format=calc" width="285" height="255"></iframe>
|
||||
</div>
|
||||
|
||||
But remember, the fewer <icon s></icon>s there are, the *slower* <icon s></icon>s become <icon i></icon>s. The *current* reproduction number (R) depends not just on the *basic* reproduction number (R<sub>0</sub>), but *also* on how many people are no longer <icon s></icon> Susceptible. (For example, by recovering & getting natural immunity.)
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-6b&format=calc" width="285" height="390"></iframe>
|
||||
</div>
|
||||
|
||||
When enough people have natural immunity, R < 1, and the virus is contained! This is called **herd immunity**, and while it's *terrible* policy (we'll explain why later – it's not for the reason you may think!), it's essential to understanding Epidemiology 101.
|
||||
|
||||
Now, let's play the last simulation again, but showing R<sub>0</sub>, R over time, and the herd immunity threshold:
|
||||
Now, let's play the SEIR Model again, but showing R<sub>0</sub>, R over time, and the herd immunity threshold:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-7" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Note: Total cases (the gray curve) does not stop at herd immunity, but *overshoots* it! And it does this *exactly when* current cases (the pink curve) peaks. This happens no matter how you change the settings:
|
||||
Note: Total cases (gray curve) does not stop at herd immunity, but *overshoots* it! And it does this *exactly when* current cases (pink curve) peaks. (This happens no matter how you change the settings – try it for yourself!)
|
||||
|
||||
// pic
|
||||
This is because when there are more non-<icon s></icon>s than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.
|
||||
|
||||
This is because, by definition, when there are more non-(s)s than the herd immunity threshold, you get R < 1. And, by definition, R < 1 means new cases stop growing.
|
||||
**If there's only one lesson you take away from this guide, here it is** – it's an extremely complex diagram so please take time to fully absorb it:
|
||||
|
||||
If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:
|
||||
|
||||
# R > 1 = bad
|
||||
# R < 1 = good (R=1, meh)
|
||||
|
||||
|
||||
**This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!**
|
||||
|
||||
It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if R<sub>0</sub> = 3, cutting that by 67% gives us R = 0.99, which is R < 1, which means the virus is contained!
|
||||
It's a paradox. COVID-19 is extremely contagious, yet to contain it, we "only" need to stop more than 60% of infections. 60%?! If that was a school grade, that's a D-. But if R<sub>0</sub> = 2.5, cutting that by 61% gives us R = 0.975, which is R < 1, virus is contained![^exact_formula]
|
||||
|
||||
// pic calc
|
||||
[^exact_formula]: exact formula...
|
||||
|
||||
*Every* COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're *all* doing the same thing:
|
||||
|
||||
|
||||
(If you think R<sub>0</sub> or the other numbers in our simulations are too low/high, that's good you're challenging our assumptions! There'll be a "Sandbox Mode" at the end of this guide, where you can plug in your *own* numbers, and simulate what happens.)
|
||||
|
||||
*Every* COVID-19 intervention you've heard of – handwashing, social/physical distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're *all* doing the same thing:
|
||||
|
||||
Getting R < 1.
|
||||
|
||||
So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, **but also our mental health, social health, *and* financial health?**
|
||||
So now, let's use our "epidemic flight simulator" to figure this out: How can we get R < 1 in a way **that also protects our mental health *and* financial health?**
|
||||
|
||||
Brace yourselves for an emergency landing...
|
||||
|
||||
---
|
||||
|
||||
# The Next Few Months
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Next Few Months</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
...could have been worse. Here's a parallel universe we avoided:
|
||||
|
||||
###Scenario 0: Do Absolutely Nothing
|
||||
|
||||
Around 1 in 20 people (i) infected with COVID-19 need an ICU (Intensive Care Unit).[^ic] In a rich country like the US, there's around 1 ICU per 3000 people.[^icus] Therefore, the US can handle 20 out of 3000 people being simultaneously (i) infected with COVID-19, or, 0.67% of the population.
|
||||
Around 1 in 20 people infected with COVID-19 need to go to an ICU (Intensive Care Unit).[^icu_covid] In a rich country like the USA, there's 1 ICU per 3400 people.[^icu_us] Therefore, the USA can handle 20 out of 3400 people being *simultaneously* infected – or, 0.6% of the population.
|
||||
|
||||
Even if we *tripled* that capacity, to handle 2% of the population simultaneously (i) infected, here's what would happen *if we had done absolutely nothing:*
|
||||
[^icu_covid]: https://www.statista.com/statistics/1105420/covid-icu-admission-rates-us-by-age-group/ Lower end, 5%.
|
||||
|
||||
// sim
|
||||
[^icu_us]: https://sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19
|
||||
|
||||
It's not good.
|
||||
Even if we *more than tripled* that capacity to 2%, here's what would've happened *if we did absolutely nothing:*
|
||||
|
||||
That's what the March 16 Imperial College report found: if we did absolutely nothing, we run out of ICUs & 80%+ of the population gets infected.
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-1&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Even if only 0.5% of (i)s die – a generous assumption when there's no more ICUs – in a large country like the US, with 300 million people, 0.5% of 80% of 300 million = still 1.2 million dead, *IF WE DID NOTHING.*
|
||||
Not good.
|
||||
|
||||
(A lot of news outlets reported the scary bit, *without* "IF WE DO NOTHING". One can channel fear into good use, but here, fear was channeled into clicks. *Sigh.*)
|
||||
That's what [the March 16 Imperial College report](http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/) found: do nothing, and we run out of ICUs with 80%+ of the population infected.
|
||||
|
||||
Even if only 0.5% of infected die – a generous assumption when there's no more ICUs – in a large country like the US, with 300 million people, 0.5% of 80% of 300 million = still 1.2 million dead... *IF we did nothing.*
|
||||
|
||||
(Lots of news & social media reported "80%+ will be infected" *without* "IF WE DO NOTHING". Fear was channelled into clicks, not understanding. *Sigh.*)
|
||||
|
||||
###Scenario 1: Flatten The Curve / Herd Immunity
|
||||
|
||||
The "Flatten The Curve" plan was touted by every public health organization, while the United Kingdom's original "herd immunity" plan was universally booed. They were *the same plan.*[^yong] The UK just communicated their plan terribly.
|
||||
The "Flatten The Curve" plan was touted by every public health organization, while the United Kingdom's original "herd immunity" plan was universally booed. They were *the same plan.* The UK just communicated theirs poorly.[^yong]
|
||||
|
||||
[^yong]: s
|
||||
[^yong]: https://www.theatlantic.com/health/archive/2020/03/coronavirus-pandemic-herd-immunity-uk-boris-johnson/608065/
|
||||
|
||||
Both plans, though, are horribly flawed.
|
||||
|
||||
First, the plans' specific interventions: mainly, handwashing & social distancing.
|
||||
First, let's look at the two main ways to "flatten the curve": handwashing & physical distancing.
|
||||
|
||||
Increased handwashing cuts flus & colds in "developed nations" by 25%[^handwashing], while a city-wide lockdown cuts close person-to-person proximity by 70%[^london]. So, let's assume handwashing can reduce R by *up to* 25%, and distancing can reduce R by *up to* 70%:
|
||||
Increased handwashing cuts flus & colds in high-income countries by ~25%[^handwashing], while the city-wide lockdown in London cut close contacts by ~70%[^london]. So, let's assume handwashing can reduce R by *up to* 25%, and distancing can reduce R by *up to* 70%:
|
||||
|
||||
[^handwashing]: s
|
||||
[^handwashing]: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2006.01568.x
|
||||
|
||||
[^london]: s
|
||||
[^london]: https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html
|
||||
|
||||
// calc
|
||||
**Play with this calculator to see how % of non-<icon s></icon>, handwashing, and distancing reduce R:** (this calculator visualizes their *relative* effects, which is why increasing one *looks* like it decreases the effect of the others.[^log_caveat])
|
||||
|
||||
Now, let's simulate what happens if we apply fervent handwashing and *mild* social distancing – so that R is lower, but still above 1:
|
||||
[^log_caveat]: log scale
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-2a&format=calc" width="285" height="260"></iframe>
|
||||
</div>
|
||||
|
||||
Now, let's simulate what happens to a COVID-19 epidemic if, starting March 2020, we had increased handwashing but only *mild* physical distancing – so that R is lower, but still above 1:
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-2&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Three notes:
|
||||
|
||||
1. This *reduces* total cases! Lots of folks think "Flattening The Curve" *spread outs* cases without reducing the total. This is impossible in *any* Epidemiology 101 model. But because the news reported "80%+ of world will be infected" as inevitable – not *IF WE DO NOTHING* – folks thought total cases will be the same no matter what. *Sigh.*
|
||||
1. This *reduces* total cases! Lots of folks think "Flatten The Curve" spread outs cases without reducing the total. This is impossible in *any* Epidemiology 101 model. But because the news reported "80%+ will be infected" as inevitable, folks thought total cases will be the same no matter what. *Sigh.*
|
||||
|
||||
2. Due to the extra interventions, current cases ((e)+(i)) peaks *before* herd immunity is reached. And in fact, total cases doesn't overshoot, but *goes to* herd immunity – the UK's plan! At that point, R < 1, you can let go of all other interventions, and COVID-19 stays contained! Well, except for one problem...
|
||||
2. Due to the extra interventions, current cases (pink curve) peaks *before* herd immunity is reached. And in fact, total cases doesn't overshoot, but *goes to* herd immunity – the UK's plan! At that point, R < 1, you can let go of all other interventions, and COVID-19 stays contained! Well, except for one problem...
|
||||
|
||||
3. You still run out of ICUs. Several times over. For months.
|
||||
3. You still run out of ICUs. For several months. (and remember, we *already* tripled ICUs for these simulations)
|
||||
|
||||
That was the other finding of the March 16 Imperial College report, which convinced the UK to abandon its original plan. Any attempt at **mitigation** (reduce R, but R > 1) will fail. The only way out is **suppression** (reduce R so that R < 1).
|
||||
|
||||
// pic: difference
|
||||
// pic: difference
|
||||
|
||||
That is, don't merely "flatten" the curve, *crush* the curve. For example, with a...
|
||||
|
||||
###Scenario 2: Months-Long Lockdown (we are here)
|
||||
###Scenario 2: Months-Long Lockdown
|
||||
|
||||
Let's see what happens if we *crush* the curve with a lockdown for 5 months. Get R < 1, and smack (i)s to the bottom.
|
||||
Let's see what happens if we *crush* the curve with a 5-month lockdown, reduce <icon i></icon> to nearly nothing, then finally – *finally* – return to normal life:
|
||||
|
||||
Then finally, *finally*, we can return to normal life:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-3&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Oh.
|
||||
|
||||
Right, this is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover (i) (or a single imported (i)) can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing.
|
||||
This is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover <icon i></icon> (or imported <icon i></icon>) can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing.
|
||||
|
||||
**A lockdown isn't a cure, it's just a restart.**
|
||||
|
||||
|
|
@ -236,263 +280,341 @@ So, what, do we just lockdown again & again?
|
|||
|
||||
###Scenario 3: Intermittent Lockdown
|
||||
|
||||
This solution was first suggested by the Imperial College, and later again by Harvard[^lockdown_harvard]:
|
||||
This solution was first suggested by the Imperial College report, and later again by a Harvard paper[^lockdown_harvard].
|
||||
|
||||
[^lockdown_harvard]: https://science.sciencemag.org/content/early/2020/04/14/science.abb5793?
|
||||
|
||||
// sim
|
||||
**Here's a simulation:** (After playing the "recorded scenario", you can try simulating your *own* lockdown schedule, by changing the sliders *while* the simulation is running! Remember you can pause & continue the sim, and change the simulation speed)
|
||||
|
||||
This *would* in fact keep cases below ICU capacity! We'd just need to... shut everything down for 2 months, open up for 1 month, then repeat until a vaccine is available in 18 months. That's a year in total.
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
(And if there's no vaccine, repeat until herd immunity is reached... in 2022.)
|
||||
This *would* keep cases below ICU capacity! We'd just need to... shut everything down for few months, open up for a few, shut down for a few, open up for a few... and repeat until a vaccine is available. (And if there's no vaccine, repeat until herd immunity is reached... in 2022.)
|
||||
|
||||
Look, it's nice to draw a line saying "healthcare capacity", but there's lots of important things we *can't* simulate here. Like:
|
||||
Look, it's nice to draw a line saying "ICU capacity", but there's lots of important things we *can't* simulate here. Like:
|
||||
|
||||
**Mental Health:** Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as negatively associated with an early death as smoking 15 cigarettes a day.
|
||||
**Mental Health:** Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as associated with an early death as smoking 15 cigarettes a day.[^loneliness]
|
||||
|
||||
[^loneliness]: https://journals.sagepub.com/doi/abs/10.1177/1745691614568352
|
||||
|
||||
**Financial Health:** "What about the economy" sounds like you care more about dollars than lives, but "the economy" isn't just stocks: it's people's ability to provide food & shelter for their loved ones, to invest in their kids' futures, and enjoy arts, foods, videogames – the stuff makes life worth living. And besides, poverty *itself* has horrible impacts on mental and physical health.
|
||||
|
||||
But wait... haven't Taiwan, South Korea, and other countries in East Asia *already* contained COVID-19? For 4 whole months? Without a single country-wide lockdown?
|
||||
Not saying we *shouldn't* lock down again! We'll look at "circuit breaker" lockdowns later. Still, it's not ideal.
|
||||
|
||||
But wait... haven't Taiwan and South Korea *already* contained COVID-19? For 4 whole months, *without* long-term lockdowns?
|
||||
|
||||
How?
|
||||
|
||||
###Scenario 4: Test, Trace, Isolate
|
||||
|
||||
You may be thinking:
|
||||
|
||||
*Sure, we \*could've\* done what Taiwan + South Korea did at the start, but it's too late now. We missed the start.*
|
||||
*"Sure, we \*could've\* done what Taiwan & South Korea did at the start, but it's too late now. We missed the start."*
|
||||
|
||||
But that's exactly it! “A lockdown isn't a cure, it's just a restart”... **and a fresh start is what we need.**
|
||||
|
||||
To understand how Taiwan & South Korea have contained COVID-19, we need to understand the exact timeline of the virus:
|
||||
To understand how Taiwan & South Korea contained COVID-19, we need to understand the exact timeline of a typical COVID-19 infection[^timeline]:
|
||||
|
||||
// timeline
|
||||
[^timeline]: sources plz, esp for incubation period 5 days
|
||||
|
||||
The problem is, if cases self-isolate *only* when they know they're infected (showing symptoms), the virus can still spread:
|
||||
|
||||
|
||||
// timeline
|
||||
If cases only self-isolate when they know they're sick (that is, they feel symptoms), the virus can still spread:
|
||||
|
||||
But if you can find who the case had recent close contact to... (e.g. was within 6 feet of someone for 30+ minutes in the last 14 days) ...and quarantine them as well, you stop the spread, by staying one step ahead!
|
||||
|
||||
|
||||
// timeline
|
||||
And in fact, 44% of all transmissions are like this: *pre*-symptomatic! [^pre_symp]
|
||||
|
||||
This is called **contact tracing**. It's a core part of Taiwan & South Korea's strategies, and several teams of Western epidemiologists + economists + policymakers, from across the political spectrum, have converged on the same answer: *this is what we need to control COVID-19, while protecting our mental & financial health.*
|
||||
[^pre_symp]: https://www.nature.com/articles/s41591-020-0869-5
|
||||
|
||||
Which is why, in the coming months, you may be asked to install a "contact tracing app" on your phone. Which sounds like a privacy nightmare, but teams of epidemiologists and cryptographers **have already created *anonymous, decentralized* contact tracing apps.**
|
||||
But, if we find *and quarantine* a symptomatic case's recent close contacts... we stop the spread, by staying one step ahead!
|
||||
|
||||
(Here's a comic we made about how!)
|
||||
|
||||
|
||||
But, critics say, contact tracing apps won't catch *all* transmissions. Some people don't have smartphones. Some transmissions are through surfaces like doorknobs.
|
||||
This is called **contact tracing**, and it's a core part of Taiwan & South Korea's successful strategies.
|
||||
|
||||
True, *and it doesn't matter*. We don't *need* to catch all or even nearly all transmissions! Just enough to get R < 1.
|
||||
Traditionally, contact tracing is done with in-person interviews, but that's too slow for COVID-19's ~48 hour window. That's why on March 31st, [an Oxford study](https://science.sciencemag.org/content/early/2020/04/09/science.abb6936) recommended helping contact tracers with *contact tracing apps*.
|
||||
|
||||
Specifically, a University of Oxford study estimates that isolating symptomatic cases can reduce R by up to 40%, and that quarantining their pre/a-symptomatic contacts can reduce R by up to 50%:
|
||||
Does that mean giving up privacy, giving in to Big Brother? Heck no! [DP-3T](https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing), a team of epidemiologists & cryptographers (including one of us, Marcel Salathé) is *already* making a contact tracing app that reveals **no info about your identity, location, who your contacts are, or even *how many contacts* you've had.**
|
||||
|
||||
(p.s: a rant about "pre" vs "a"-symptomatic)
|
||||
Here's how it works:
|
||||
|
||||
// calc
|
||||
|
||||
|
||||
So even if you don't isolate *all* cases or quarantine *all* contacts, you can still get R below 1, *with no social distancing!*
|
||||
([Here's the full comic](https://ncase.me/contact-tracing/), and [here's a video adaptation by 3Blue1Brown]())
|
||||
|
||||
Along with similar teams like [TCN Protocol](https://github.com/TCNCoalition/TCN#tcn-protocol) and [MIT PACT](https://pact.mit.edu/), they've inspired Apple & Google to bake privacy-first contact tracing [directly into Android/iOS](https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/). Next month, your local public health agency may ask you to download an app. If it's privacy-first & open-source, please do!
|
||||
|
||||
But what about folks without smartphones? Or infections through doorknobs? Or "true" asymptomatic cases? Contact tracing apps can't catch all transmissions... *and that's okay!* We don't need to catch *all* transmissions, just 60%+ to get R < 1.
|
||||
|
||||
(rant about the confusion about pre-symptomatic vs. "true" asymptomatic:[^rant])
|
||||
|
||||
[^rant]: asds
|
||||
|
||||
Anyway, isolating cases would reduce R by up to 40%, and quarantining their contacts would reduce R by up to 50%[^oxford]:
|
||||
|
||||
[^oxford]: https://science.sciencemag.org/content/early/2020/04/09/science.abb6936
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4a&format=calc" width="285" height="340"></iframe>
|
||||
</div>
|
||||
|
||||
Thus, even without 100% contact quarantining, we can get R < 1 *without a lockdown!* Much better for our mental & financial health. (As for the cost to folks who have to self-isolate/quarantine, *governments should support them* – subsidized paid leave, job protection, etc. Still way cheaper than intermittent lockdown.)
|
||||
|
||||
We then keep R < 1 until we have a vaccine, which turns susceptible <icon s></icon>s into immune <icon r></icon>s. Herd immunity, the *right* way:
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4b&format=calc" width="285" height="230"></iframe>
|
||||
</div>
|
||||
|
||||
Okay, enough talk. Here's a simulation of:
|
||||
|
||||
1. A few-month lockdown, followed by...
|
||||
2. A switch to "Test, Trace, Isolate", for 18 months until...
|
||||
3. We can vaccinate folks, turning (s)s to (r)s directly, which gets us "herd immunity" the *right* way, which means R < 1, which means...
|
||||
1. A few-month lockdown, until we can...
|
||||
2. Switch to "Test, Trace, Isolate" until we can...
|
||||
3. Vaccinate enough people, which means...
|
||||
4. We win.
|
||||
|
||||
// sim
|
||||
|
||||
Note: "Test, Trace, Isolate" is only possible when we have low enough *current* cases (which a lockdown will do) and high enough test kits (which a lockdown will buy us time to do). We also recommend that policymakers create policies giving people paid [see Vi's thing]
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-5&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
So that's it! That's how we make an emergency landing on this plane.
|
||||
|
||||
That's how we win.
|
||||
That's how we beat COVID-19.
|
||||
|
||||
...
|
||||
|
||||
But... you may be feeling a knot in your stomach. Things have *already* gone wrong, what if this plan goes wrong too? That's good – that's fear, which gives us energy to come up with *backup plans*.
|
||||
But what if things *still* go wrong? Things have gone horribly wrong already. That's fear, and that's good! Fear gives us energy to create *backup plans*.
|
||||
|
||||
The pessimist invents the parachute.
|
||||
|
||||
###Scenario 4+: Masks For All, Summer, Circuit Breakers
|
||||
|
||||
What if R<sub>0</sub> is way higher than we thought, and the above interventions *still* aren't enough to get R < 1?
|
||||
What if R<sub>0</sub> is way higher than we thought, and the above interventions, even with mild distancing, *still* aren't enough to get R < 1?
|
||||
|
||||
If so, here's a few supplements:
|
||||
|
||||
**Masks For All:**
|
||||
|
||||
If there's a surgical mask shortage in your country, obviously don't hoard surgical masks. But should we all wear *homemade* cloth masks? Or make enough surgical masks so *everyone* can wear one?
|
||||
*"Wait,"* you might ask, *"I thought face masks don't stop you from getting sick?"*
|
||||
|
||||
"Wait", you might ask, "I thought scientists showed masks don't stop you from getting sick?"
|
||||
You're right. Masks don't stop you from getting sick... they stop you from getting *others* sick.
|
||||
|
||||
You're right. Masks don't stop you from getting sick – they stop you from getting *others* sick. For colds/flus, surgical masks don't block incoming aerosols[^incoming]... but they block of 70% of *outgoing* aerosols[^outgoing aerosols], and almost 100% of *outgoing* droplets[^outgoing droplets]. (And home-made cloth masks are around half as efficient as surgical masks)
|
||||
[^incoming]: incoming
|
||||
|
||||
[^incoming]:
|
||||
[^outgoing_aerosols]: outgoing_aerosols
|
||||
|
||||
[^outgoing aerosols]:
|
||||
[^outgoing_droplets]: outgoing_droplets
|
||||
|
||||
[^outgoing droplets]:
|
||||
[^homemade]: homemade
|
||||
|
||||
Since pre-symptomatic folks account for almost *half* of transmissions, that's a big deal!
|
||||
|
||||
|
||||
// pic: why masks don't protect you, but protect others
|
||||
(sources for the comic: [^incoming] [^outgoing_aerosols] [^outgoing_droplets] [^homemade])
|
||||
|
||||
Still, there aren't (yet) any experiments of masks for the public on COVID-19 *specifically*. However:
|
||||
Still, in science, one should only publish a finding if you're 95% sure of it. (...*should.*[^replication]) Admittedly, the current evidence for face masks on COVID-19 *specifically*, rather than "just" colds and flus, is less than "95% sure".
|
||||
|
||||
1) There also aren't any experiments where we push people out of planes, and half of them get placebo parachutes. Sometimes, "solid" evidence isn't possible to get, so we have to settle for "circumstantial" evidence. (In this case: X, Y, Z) (sources)
|
||||
[^replication]: ss
|
||||
|
||||
2) Pandemics are like poker. Act only when you "have solid evidence", and you'll lose everything at stake. This isn't theory, it's practice – we *have* to make cost/benefit analyses under uncertainty. The cost of masks for the public (especially cloth masks) is low. The benefit is at worst nothing, at best a massive reduction in R.
|
||||
But, pandemics are like poker. **Make bets only when you're 95% sure, and you'll lose everything at stake.** We *have* to make cost/benefit analyses under uncertainty.[^precautionary] Like so:
|
||||
|
||||
How much, exactly? The "blocks 70% of areosols" study was for surgical masks, with proper fit. Assuming imperfect use by the public, let's guess surgical masks "only" reduce R by up to 50%. And since cloth masks are half as effective, let's guess cloth masks reduce R by up to 25%:
|
||||
[^precautionary]: That BMJ article
|
||||
|
||||
(If you think our guesses are too high – and that's good to challenge our assumptions! – just scale the sliders down)
|
||||
Cost: If homemade cloth masks, same as the cost of all that soap for handwashing. If surgical masks, more expensive but still pretty cheap.
|
||||
|
||||
// calc
|
||||
Benefit: Even if it's a 50–50 chance of surgical masks reducing transmission by 0% or 70%[^70_mask], the average "expected value" is still 35%, same as a half-lockdown! So let's guess-timate that surgical masks reduce R by up to 35%. (Again, you can challenge our assumptions by turning the sliders up/down)
|
||||
|
||||
Masks *alone* won't get R < 1. But if handwashing + "Test, Trace, Isolate" only gets us to R = 1.2, having just 2/3 of people wear *cloth* masks would tip that over to R < 1, virus contained!
|
||||
**Here's a calculator of how masks reduce R! You can switch between cloth & surgical:** (assumes cloth masks are half as effective as surgical masks[^half_surgical])
|
||||
|
||||
[TODO: Actually allow toggling between cloth/surgical. Currently locked to cloth]
|
||||
|
||||
[^half_surgical]: ss
|
||||
|
||||
[^70_mask]: s
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-6a&format=calc" width="285" height="380"></iframe>
|
||||
</div>
|
||||
|
||||
(other arguments for/against masks:[^mask_args])
|
||||
|
||||
[^mask_args]: s
|
||||
|
||||
Masks *alone* won't get R < 1. But if handwashing & "Test, Trace, Isolate" only gets us to R = 1.10, having just 2/3 of people wear *cloth* masks would tip that over to R < 1, virus contained!
|
||||
|
||||
**Summer:**
|
||||
|
||||
Okay, this isn't an "intervention" we have control of, but it will help! Some news outlets have reported that summer won't do anything to COVID-19. They're half right: summer won't get R < 1, but it *will* reduce R.
|
||||
Okay, this isn't an "intervention" we can control, but it will help! Some news outlets report that summer won't do anything to COVID-19. They're half right: summer won't get R < 1, but it *will* reduce R.
|
||||
|
||||
For every extra 1° Celsius (2.2° Fahrenheit), the R for COVID-19 drops by X%. The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by X%.
|
||||
For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.[^heat] The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.
|
||||
|
||||
// calc, over time - in your country
|
||||
[^heat]: https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767 The average R-value of these 100 cities is 1.83 , One-degree Celsius increase in temperature and one percent increase in relative humidity lower R by 0.0225
|
||||
|
||||
It's not R < 1, but if we have limited resources, we can scale back some interventions in the summer – so we can scale them *higher* in the winter.
|
||||
[TODO: Fix weird arrow glitch]
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-6b&format=calc" width="285" height="220"></iframe>
|
||||
</div>
|
||||
|
||||
Summer alone won't make R < 1, but if we have limited resources, we can scale back some interventions in the summer – so we can scale them *higher* in the winter.
|
||||
|
||||
**A "Circuit Breaker" Lockdown:**
|
||||
|
||||
And if all that *still* isn't enough to get R < 1... we can do another lockdown.
|
||||
|
||||
But we wouldn't have to be 2-months-closed/1-month-open over & over! Because R is significantly reduced, we'd only need one or two more "circuit breaker" lockdowns before a vaccine is available. (Singapore had to do this recently, "despite" having controlled COVID-19 for 4 months. That's not failure: this *is* what success takes.)
|
||||
But we wouldn't have to be 2-months-closed / 1-month-open over & over! Because R is reduced, we'd only need one or two more "circuit breaker" lockdowns before a vaccine is available. (Singapore had to do this recently, "despite" having controlled COVID-19 for 4 months. That's not failure: this *is* what success takes.)
|
||||
|
||||
Here's a simulation a "lazy case" scenario: we lockdown hard now, then do a moderate amount of "Test, Trace, Isolate" + a moderate amount of *cloth* "Masks For All", and do one circuit breaker lockdown before a vaccine is available:
|
||||
Here's a simulation a "lazy case" scenario:
|
||||
|
||||
// sim
|
||||
1. Lockdown, then
|
||||
2. A moderate amount of hygiene + "Test, Trace, Isolate" + *cloth* "Masks For All", then...
|
||||
3. One more "circuit breaker" lockdown before a vaccine's found.
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-7&format=lines&height=620" width="800" height="620"></iframe>
|
||||
</div>
|
||||
|
||||
[TODO: Other options like temperature testing at malls, quarantines for travellers, replacing handshaking, etc]
|
||||
|
||||
. . .
|
||||
|
||||
We hope these plans give you hope.
|
||||
|
||||
It *is* possible to keep R < 1, *while protecting our mental, social, and financial health*. Use the lockdown as a restart, keep R < 1 with privacy-protecting contract tracing apps, supplemented with cloth masks for all... and life can get back to a normal-ish!
|
||||
**Even under a pessimistic scenario, it *is* possible to beat COVID-19, while protecting our mental and financial health.** Use the lockdown as a restart, keep R < 1 with privacy-protecting contract tracing, supplemented with at *least* cloth masks... and life can get back to a normal-ish!
|
||||
|
||||
Sure, your hands may be dry. But you'll get to invite a date out to a comics bookstore! You'll get to go out with friends to watch the latest Hollywood cash-grab. You'll get to people-watch at a library, taking joy in people going about the simple business of *being alive.*
|
||||
|
||||
Even under the worst-case scenario... life perseveres.
|
||||
|
||||
So now, let's plan for some *even worse* worst-case scenarios.
|
||||
So now, let's plan for some *worse* worst-case scenarios. Water landing, get your life jacket, and please follow the lights to the emergency exits:
|
||||
|
||||
Water landing, get your life jacket, and please follow the lights to the emergency exits:
|
||||
|
||||
---
|
||||
|
||||
# The Next Few Years
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Next Few Years</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
You get COVID-19, and recover. Or you get the COVID-19 vaccine. Either way, you're now immune...
|
||||
|
||||
...*for how long?*
|
||||
|
||||
The news reports folks who test positive again after testing negative, but those were most likely false positives/negatives. Still, the possibility of **waning immunity** is very real.
|
||||
There's been reports of folks who test positive again after recovering, but those were false positives. Still, the possibility of **waning immunity** is very real. Either a new mutant strain evolves, or your immune system just... forgets.
|
||||
|
||||
"Coronavirus" is a category of viruses.[^nitpick_1] The coronavirus responsible for COVID-19 is most closely related to the coronavirus responsible for SARS. SARS probably gave its survivors around 2 years of immunity.[^SARS immunity] The coronaviruses that cause "the"[^nitpick_2] common cold give you 1 year of immunity[^cold immunity]. So here's the scary question:
|
||||
|
||||
[^nitpick_1]: sas
|
||||
|
||||
[^nitpick_2]: adsa
|
||||
The coronavirus responsible for COVID-19 is most closely related to the coronavirus responsible for SARS. SARS (probably) gave its survivors around 2 years of immunity.[^SARS immunity] The coronaviruses that cause "the" common cold give you 1 year of immunity[^cold immunity]. So:
|
||||
|
||||
[^SARS immunity]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/
|
||||
|
||||
[^cold immunity]: https://pubmed.ncbi.nlm.nih.gov/2170159/
|
||||
|
||||
What if COVID-19 immunity doesn't last?
|
||||
*What if COVID-19 immunity doesn't last?*
|
||||
|
||||
Here's a simulation starting with 100% (i), exponentially decaying into (r)s after 12 days... but then back to susceptible, no-immunity (s)s after 1 year:
|
||||
Here's a simulation starting with 100% <icon i></icon>, exponentially decaying into <icon r></icon>s after 10 days... but then back to susceptible, no-immunity <icon s></icon>s after 1 year:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-1" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Return of the exponential decay!
|
||||
|
||||
This is the **SEIRS Model**. The final "S" stands for <icon s></icon> Susceptible, again.
|
||||
|
||||
|
||||
|
||||
Now let's simulate a COVID-19 outbreak, over 10 years, with no interventions... *if immunity only lasts a year:*
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-2&format=lines&height=600" width="800" height="600"></iframe>
|
||||
</div>
|
||||
|
||||
This is the **SEIRS Model**. The final "S" stands for (s) Susceptible, again.
|
||||
|
||||
Previously, we only had *one* hospital-breaking spike. Now, we have several, *and* (i) cases come to a rest *permanently above* hospital capacity.
|
||||
Previously, we only had *one* ICU-overwhelming spike. Now, we have several, *and* <icon i></icon> cases come to a rest *permanently at* ICU capacity. (Which, remember, we *tripled* for these simulations)
|
||||
|
||||
R = 1, it's **endemic.**
|
||||
|
||||
Thankfully, because summer reduces R, it'll make the situation better:
|
||||
|
||||
// sim
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-3&format=lines&height=640" width="800" height="640"></iframe>
|
||||
</div>
|
||||
|
||||
Counterintuitively, summer makes spikes *worse*, and repeated!
|
||||
Oh.
|
||||
|
||||
Summer reduces new (i)s, but that in turn reduces new (r)s. Which means immunity plummets in the summer, *creating* large spikes in the winter. And the spikes overwhelm ICUs over and over, *even if we double hospital capacity*.
|
||||
Counterintuitively, summer makes the spikes worse *and* regular! This is because summer reduces new <icon i></icon>s, but that in turn reduces new immune <icon r></icon>s. Which means immunity plummets in the summer, *creating* large regular spikes in the winter.
|
||||
|
||||
Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:
|
||||
|
||||
// sim
|
||||
**(After playing the recording, try simulating your own vaccination campaigns! Remember you can pause/continue the sim at any time)**
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-4&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
But here's the scarier question:
|
||||
|
||||
What if there's just *never* a vaccine?
|
||||
What if there's no vaccine for *years*? Or *ever?*
|
||||
|
||||
**To be clear: this is unlikely.** Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment. Coronaviruses aren't any more complex than the viruses we already have vaccines for, so most public health scientists expect a vaccine *eventually*, even if years from now.
|
||||
**To be clear: this is unlikely.** Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment. Coronaviruses aren't any more complex than the viruses we already have vaccines for, so most infectious disease researchers expect a vaccine in 1 to 2 years.
|
||||
|
||||
Still, there are valid worries: when we find a COVID-19 vaccine, what if we can't make enough? What if it's not *safe* enough?[^nature]
|
||||
Still, they've expressed worries about a vaccine: What if we can't make enough?[^vax_enough] What if we rush it, and it's not safe?[^vax_safe]
|
||||
|
||||
[^nature]: link
|
||||
[^vax_enough]: https://www.nature.com/articles/d41586-020-01063-8
|
||||
|
||||
Even in this nightmare scenario, we still have 3 ways out. From most to least terrible:
|
||||
[^vax_safe]: https://www.nature.com/articles/d41586-020-00751-9
|
||||
|
||||
1) Do the R < 1 interventions, but *looser*, so that we can get a steady supply of (i) to reach "natural herd immunity".
|
||||
Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:
|
||||
|
||||
(Warning: this will result in many deaths & damaged lungs. *And* it won't work if immunity doesn't last.)
|
||||
|
||||
// sim
|
||||
1) Do intermittent or loose R < 1 interventions, to reach "natural herd immunity". (Warning: this will result in many deaths & damaged lungs. *And* won't work if immunity doesn't last.)
|
||||
|
||||
2) Do the R < 1 interventions forever. Contact tracing & wearing masks just becomes a new norm in the post-COVID-19 world, like how STI tests & wearing condoms became a new norm in the post-HIV world. (Nobody suggested "herd immunity" for HIV...)
|
||||
|
||||
3) Do the R < 1 interventions until we develop treatments that make COVID-19 way, way less likely to need hospitalization. (Which we should be doing *anyway!*) This is effectively the same as increasing our hospital capacity:
|
||||
3) Do the R < 1 interventions until we develop treatments that make COVID-19 way, way less likely to need critical care. (Which we should be doing *anyway!*) Reducing ICU use by 10x is the same as increasing our ICU capacity by 10x:
|
||||
|
||||
// sim
|
||||
**Here's a simulation of *no* lasting immunity, *no* vaccine, and not even any interventions – just slowly increasing capacity to survive the long-term spikes:**
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-5&format=lines" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Even under the *worst* worst-case scenario... life perseveres.
|
||||
|
||||
But, you may have more questions, more intervention plans & scenarios you'd like to try. Great! **Here's a Simulation Sandbox Mode, with *every* option available! Simulate & play around to your heart's content:**
|
||||
. . .
|
||||
|
||||
// sim
|
||||
Maybe you'd like to challenge our assumptions, and try different R<sub>0</sub>'s or numbers. Or try simulating your *own* combination of intervention plans!
|
||||
|
||||
Even just Epidemiology 101 + a basic "epidemic flight simulator" can teach us so much. It's let us answer questions about the past few months, next few months, and next few years.
|
||||
**Here's an (optional) Sandbox Mode, with *everything* available. Simulate & play around to your heart's content:**
|
||||
|
||||
So now, let's return to...
|
||||
[TODO: EMBED THIS IN A WAY THAT DOESN'T SUCK]
|
||||
|
||||
---
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=SB&format=sb&height=1000" width="800" height="1000"></iframe>
|
||||
</div>
|
||||
|
||||
# The Now
|
||||
This basic "epidemic flight simulator" has taught us so much. It's let us answer questions about the past few months, next few months, and next few years.
|
||||
|
||||
We've scrambled into the life rafts. It's time to find dry land.
|
||||
So finally, let's return to...
|
||||
|
||||
Here's the growing consensus COVID-19 plan, with some less-consensus backup plans:
|
||||
<div class="section">
|
||||
<div>
|
||||
<h1>The Now</h1>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
// pic
|
||||
Plane's in the ocean. We've scrambled onto the life rafts. It's time to find dry land.[^dry_land]
|
||||
|
||||
[^dry_land]: https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/
|
||||
|
||||
Teams of epidemiologists and policymakers ([left](https://www.americanprogress.org/issues/healthcare/news/2020/04/03/482613/national-state-plan-end-coronavirus-crisis/), [right](https://www.aei.org/research-products/report/national-coronavirus-response-a-road-map-to-reopening/ ), and [multi-partisan](https://ethics.harvard.edu/covid-roadmap)) have come to a consensus on how to beat COVID-19, while protecting our lives *and* liberties.
|
||||
|
||||
Here's the rough idea, with some (less-consensus) backup plans:
|
||||
|
||||
|
||||
|
||||
So what does this mean for YOU, right now?
|
||||
|
||||
**For everyone:** Respect the lockdown so we can get out of Phase I asap. Keep washing those hands. Make your own masks. Download a *privacy-protecting* contact tracing app when those are available next month. Stay healthy, physically & mentally! And write your local policymaker to get off their butt and...
|
||||
|
||||
**For policymakers:** Create policies that support folks who have to self-isolate/quarantine. Maybe make mask laws, if enough evidence accrues. Direct more funds into the stuff we should be building, like...
|
||||
**For policymakers:** Make laws to support folks who have to self-isolate/quarantine. Hire more manual contact tracers, *supported* by privacy-protecting contact tracing apps. Direct more funds into the stuff we should be building, like...
|
||||
|
||||
**For builders:** Build tests. Build ventilators. Build personal protective equipment for hospitals. Build masks. Build apps. Build antivirals, prophylactics, and other treatments that aren't vaccines. Build vaccines. Build better COVID-19 monitoring, so we can plan better. Build science. Build morale. Build hope.
|
||||
**For builders:** Build tests. Build ventilators. Build personal protective equipment for hospitals. Build tests. Build masks. Build apps. Build antivirals, prophylactics, and other treatments that aren't vaccines. Build vaccines. Build tests. Build tests. Build tests. Build hope.
|
||||
|
||||
Will we *need* all that? "Probably" not, the same way you "probably" won't need parachutes, life jackets, or inflatable rafts on an airplane. It's like a cost/benefit analysis of Russian Roulette: the chance of disaster may be small, but the *cost* of disaster is far, far worse.
|
||||
Don't downplay fear to build up hope. Our fear should *team up* with our hope, like the inventors of airplanes & parachutes. Preparing for horrible futures is how we *create* a hopeful future.
|
||||
|
||||
So, don't deny or downplay fear to try to build hope. Our fear should *team up* with our hope, like the inventors of airplanes & parachutes. Preparing for horrible futures is how we *create* hopeful futures.
|
||||
|
||||
The only thing to fear is people who think the only thing to fear is fear itself.
|
||||
|
||||
// summary slides
|
||||
|
||||
// footnotes
|
||||
The only thing to fear is the idea that the only thing to fear is fear itself.
|
||||
|
|
@ -1,204 +0,0 @@
|
|||
<!DOCTYPE html>
|
||||
<html>
|
||||
|
||||
<head>
|
||||
|
||||
<meta charset="utf-8">
|
||||
<title>words_epi_101</title>
|
||||
|
||||
|
||||
|
||||
</head>
|
||||
|
||||
<body>
|
||||
|
||||
<h1 id="toc_0">What Happens Next?</h1>
|
||||
|
||||
<h2 id="toc_1">COVID-19 Futures, Explained With Playable Simulations</h2>
|
||||
|
||||
<p>"The only thing to fear is fear itself" was stupid advice.</p>
|
||||
|
||||
<p>If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we <em>use</em> our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.</p>
|
||||
|
||||
<p>Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel <em>our</em> worries into making these <strong>playable simulations</strong>, so that you can channel <em>your</em> worries into understanding:</p>
|
||||
|
||||
<ul>
|
||||
<li><strong>The Last Few Months</strong> (epidemiology 101, SEIR model, R & R<sub>0</sub>)</li>
|
||||
<li><strong>The Next Few Months</strong> (lockdowns, contact tracing, masks)</li>
|
||||
<li><strong>The Next Few Years</strong> (vaccines, loss of immunity?)</li>
|
||||
</ul>
|
||||
|
||||
<p>This guide is meant to give you hope <em>and</em> fear. To beat this virus <strong>in a way that also protects our mental & financial health</strong>, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, <em>“The optimist invents the airplane and the pessimist the parachute.”</em></p>
|
||||
|
||||
<p>So, buckle in: we're about to experience some turbulence.</p>
|
||||
|
||||
<hr>
|
||||
|
||||
<h1 id="toc_2">The Last Few Months</h1>
|
||||
|
||||
<p>Pilots use flight simulators to learn how not to crash planes.</p>
|
||||
|
||||
<p><strong>Epidemiologists use epidemic simulators to learn how not to crash humanity.</strong></p>
|
||||
|
||||
<p>So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s:</p>
|
||||
|
||||
<p>// pic</p>
|
||||
|
||||
<p>At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.<sup id="fnref1"><a href="#fn1" rel="footnote">1</a></sup> (<em>On average.</em> Remember, there's lots of variation.)</p>
|
||||
|
||||
<p>Here's a simulation of a population with <em>just</em> 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" <em>and nothing else</em>, what happens?</p>
|
||||
|
||||
<p><strong>Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)</strong></p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>This is the <strong>exponential growth curve.</strong> Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create <em>mass graves in rich cities</em>". </p>
|
||||
|
||||
<p>// pic - exponential double rice</p>
|
||||
|
||||
<p>But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others <em>already</em> have the virus:</p>
|
||||
|
||||
<p>// pic - 100% spread, 50% spread, 0% spread</p>
|
||||
|
||||
<p><strong>The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the <em>slower</em> (s)s become (i)s.</strong></p>
|
||||
|
||||
<p>Now, what happens if we simulate that?</p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>This is the "S-shaped" <strong>logistic growth curve.</strong> Starts small, explodes, then slows down again.</p>
|
||||
|
||||
<p>But, this simulation is <em>still</em> wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.</p>
|
||||
|
||||
<p>For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – <em>for now!</em> – that they stay immune for life.</p>
|
||||
|
||||
<p>When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.<sup id="fnref2"><a href="#fn2" rel="footnote">2</a></sup> (Again, <em>on average.</em>)</p>
|
||||
|
||||
<p>Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder <em>of that remainder</em> recover after another 12 days, etc:</p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>This is the opposite of exponential growth, the <strong>exponential decay curve</strong>.</p>
|
||||
|
||||
<p>Now, what happens if you combine this with the S-shaped logistic curve of infection?</p>
|
||||
|
||||
<p>// pic</p>
|
||||
|
||||
<p>Let's find out. Here's a simulation of an epidemic <em>with</em> recovery:</p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>And <em>that's</em> where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten.</p>
|
||||
|
||||
<p>// pic: 3 rules</p>
|
||||
|
||||
<p>This is the the <strong>SIR Model</strong>, ((s) <strong>S</strong>usceptible → (i) <strong>I</strong>nfectious → (r) <strong>R</strong>ecovered) the second-most important idea in Epidemiology 101.</p>
|
||||
|
||||
<p>Note: The simulations that inform policy are <em>far</em> more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances.</p>
|
||||
|
||||
<p>Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infect<em>ed</em> but not yet infect<em>ious</em> – they have the virus but can't pass it on (yet).</p>
|
||||
|
||||
<p>(This variant is called the <strong>SEIR Model</strong>, where "E" stands for (e) Exposed. Note this <em>isn't</em> the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.)</p>
|
||||
|
||||
<p>For COVID-19, it's estimated that you're in this "latent period" for around 3 days.<sup id="fnref3"><a href="#fn3" rel="footnote">3</a></sup> What happens if we add that to the simulation?</p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>Not much, actually! The "latent period" only changes <em>when</em> the peak happens, but the <em>height</em> of the peak – and total people infected – remain the same:</p>
|
||||
|
||||
<p>// pics</p>
|
||||
|
||||
<p>Why's that? Because of the <em>first</em>-most important idea in Epidemiology 101:</p>
|
||||
|
||||
<p>// pic - <strong>"R"</strong></p>
|
||||
|
||||
<p>Which is short for "Reproduction Number". It's the <em>average</em> number of people an (i) infects <em>before</em> they recover (or die).</p>
|
||||
|
||||
<p>// R > 1, R = 1, R < 1 pic</p>
|
||||
|
||||
<p><strong>R</strong> changes over the course of an outbreak, as we get more immunity & interventions.</p>
|
||||
|
||||
<p><strong>R<sub>0</sub></strong> (pronounced R-nought) is what R is <em>at the start of an outbreak, before immunity or interventions</em>. R<sub>0</sub> is also called the "basic reproduction number". R<sub>0</sub> more closely reflects the power of the virus itself, but it still changes from place to place. For example, because heat 'kills' coronaviruses, R<sub>0</sub> for COVID-19 is lower in hot places than cold ones. Not low enough to contain it, though.</p>
|
||||
|
||||
<p>(A lot of news outlets – and even academic papers! – confuse R and R<sub>0</sub>. Again, science terminology is bad.)</p>
|
||||
|
||||
<p>The R<sub>0</sub> for the flu<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> is around 1.3. The R<sub>0</sub> estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.<sup id="fnref5"><a href="#fn5" rel="footnote">5</a></sup></p>
|
||||
|
||||
<p>In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, <em>on average</em>, an (i) infects 3 (s)s before they recover. So for our simulations, R<sub>0</sub> is 3.</p>
|
||||
|
||||
<p><strong>Play around with this R<sub>0</sub> calculator, to see how R<sub>0</sub> depends on recovery time & new-infection time:</strong></p>
|
||||
|
||||
<p>// calc</p>
|
||||
|
||||
<p>But remember, the fewer (s)s there are, the <em>slower</em> (s)s become (i)s.
|
||||
R depends not just on R<sub>0</sub>, but also how many people are no longer Susceptible – due to, say, having recovered & gotten natural immunity.</p>
|
||||
|
||||
<p>// calc 2</p>
|
||||
|
||||
<p>When enough people have natural immunity, R < 1, and the virus is contained! This is called <strong>herd immunity</strong>, and while it's <em>terrible</em> policy, (we'll explain why later – it's not for the reason you may think!) it's essential to understanding Epidemiology 101.</p>
|
||||
|
||||
<p>Now, let's play the last simulation again, but showing R<sub>0</sub>, R over time, and the herd immunity threshold:</p>
|
||||
|
||||
<p>// sim</p>
|
||||
|
||||
<p>Note: Total cases (the gray curve) does not stop at herd immunity, but <em>overshoots</em> it! And it does this <em>exactly when</em> current cases (the pink curve) peaks. This happens no matter how you change the settings:</p>
|
||||
|
||||
<p>// pic</p>
|
||||
|
||||
<p>This is because, by definition, when there are more non-(s)s than the herd immunity threshold, you get R < 1. And, by definition, R < 1 means new cases stop growing.</p>
|
||||
|
||||
<p>If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:</p>
|
||||
|
||||
<h1 id="toc_3">R > 1 = bad</h1>
|
||||
|
||||
<h1 id="toc_4">R < 1 = good (R=1, meh)</h1>
|
||||
|
||||
<p><strong>This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!</strong></p>
|
||||
|
||||
<p>It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if R<sub>0</sub> = 3, cutting that by 67% gives us R = 0.99, which is R < 1, which means the virus is contained!</p>
|
||||
|
||||
<p>(And even if, extreme-worst-case, R<sub>0</sub> = <em>6</em>, you still "only" need to stop 84% of transmissions. That's a B grade.)</p>
|
||||
|
||||
<p>// calculator - custom</p>
|
||||
|
||||
<p><em>Every</em> COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're <em>all</em> doing the same thing:</p>
|
||||
|
||||
<p>Getting R < 1.</p>
|
||||
|
||||
<p>So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, <strong>but also our mental health, social health, <em>and</em> financial health?</strong></p>
|
||||
|
||||
<p>Brace yourselves for an emergency landing...</p>
|
||||
|
||||
<div class="footnotes">
|
||||
<hr>
|
||||
<ol>
|
||||
|
||||
<li id="fn1">
|
||||
<p>source <a href="#fnref1" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn2">
|
||||
<p>source <a href="#fnref2" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn3">
|
||||
<p>source <a href="#fnref3" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn4">
|
||||
<p>source <a href="#fnref4" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn5">
|
||||
<p>source <a href="#fnref5" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
</ol>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
|
||||
</html>
|
||||
|
|
@ -1,163 +0,0 @@
|
|||
# What Happens Next?
|
||||
|
||||
## COVID-19 Futures, Explained With Playable Simulations
|
||||
|
||||
"The only thing to fear is fear itself" was stupid advice.
|
||||
|
||||
If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we *use* our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.
|
||||
|
||||
Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel *our* worries into making these **playable simulations**, so that you can channel *your* worries into understanding:
|
||||
|
||||
* **The Last Few Months** (epidemiology 101, SEIR model, R & R<sub>0</sub>)
|
||||
* **The Next Few Months** (lockdowns, contact tracing, masks)
|
||||
* **The Next Few Years** (vaccines, loss of immunity?)
|
||||
|
||||
This guide is meant to give you hope *and* fear. To beat this virus **in a way that also protects our mental & financial health**, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, *“The optimist invents the airplane and the pessimist the parachute.”*
|
||||
|
||||
So, buckle in: we're about to experience some turbulence.
|
||||
|
||||
---
|
||||
|
||||
# The Last Few Months
|
||||
|
||||
Pilots use flight simulators to learn how not to crash planes.
|
||||
|
||||
**Epidemiologists use epidemic simulators to learn how not to crash humanity.**
|
||||
|
||||
So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s:
|
||||
|
||||
// pic
|
||||
|
||||
At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.[^1] (*On average.* Remember, there's lots of variation.)
|
||||
|
||||
[^1]: source
|
||||
|
||||
Here's a simulation of a population with *just* 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" *and nothing else*, what happens?
|
||||
|
||||
**Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)**
|
||||
|
||||
// sim
|
||||
|
||||
This is the **exponential growth curve.** Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create *mass graves in rich cities*".
|
||||
|
||||
// pic - exponential double rice
|
||||
|
||||
But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others *already* have the virus:
|
||||
|
||||
// pic - 100% spread, 50% spread, 0% spread
|
||||
|
||||
**The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the *slower* (s)s become (i)s.**
|
||||
|
||||
Now, what happens if we simulate that?
|
||||
|
||||
// sim
|
||||
|
||||
This is the "S-shaped" **logistic growth curve.** Starts small, explodes, then slows down again.
|
||||
|
||||
But, this simulation is *still* wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.
|
||||
|
||||
For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – *for now!* – that they stay immune for life.
|
||||
|
||||
When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.[^2] (Again, *on average.*)
|
||||
|
||||
[^2]: source
|
||||
|
||||
Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder *of that remainder* recover after another 12 days, etc:
|
||||
|
||||
// sim
|
||||
|
||||
This is the opposite of exponential growth, the **exponential decay curve**.
|
||||
|
||||
Now, what happens if you combine this with the S-shaped logistic curve of infection?
|
||||
|
||||
// pic
|
||||
|
||||
Let's find out. Here's a simulation of an epidemic *with* recovery:
|
||||
|
||||
// sim
|
||||
|
||||
And *that's* where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten.
|
||||
|
||||
// pic: 3 rules
|
||||
|
||||
This is the the **SIR Model**, ((s) **S**usceptible → (i) **I**nfectious → (r) **R**ecovered) the second-most important idea in Epidemiology 101.
|
||||
|
||||
Note: The simulations that inform policy are *far* more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances.
|
||||
|
||||
Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infect*ed* but not yet infect*ious* – they have the virus but can't pass it on (yet).
|
||||
|
||||
(This variant is called the **SEIR Model**, where "E" stands for (e) Exposed. Note this *isn't* the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.)
|
||||
|
||||
For COVID-19, it's estimated that you're in this "latent period" for around 3 days.[^3] What happens if we add that to the simulation?
|
||||
|
||||
[^3]: source
|
||||
|
||||
// sim
|
||||
|
||||
Not much, actually! The "latent period" only changes *when* the peak happens, but the *height* of the peak – and total people infected – remain the same:
|
||||
|
||||
// pics
|
||||
|
||||
Why's that? Because of the *first*-most important idea in Epidemiology 101:
|
||||
|
||||
// pic - **"R"**
|
||||
|
||||
Which is short for "Reproduction Number". It's the *average* number of people an (i) infects *before* they recover (or die).
|
||||
|
||||
// R > 1, R = 1, R < 1 pic
|
||||
|
||||
**R** changes over the course of an outbreak, as we get more immunity & interventions.
|
||||
|
||||
**R<sub>0</sub>** (pronounced R-nought) is what R is *at the start of an outbreak, before immunity or interventions*. R<sub>0</sub> is also called the "basic reproduction number". R<sub>0</sub> more closely reflects the power of the virus itself, but it still changes from place to place. For example, because heat 'kills' coronaviruses, R<sub>0</sub> for COVID-19 is lower in hot places than cold ones. Not low enough to contain it, though.
|
||||
|
||||
(A lot of news outlets – and even academic papers! – confuse R and R<sub>0</sub>. Again, science terminology is bad.)
|
||||
|
||||
The R<sub>0</sub> for the flu[^r0_flu] is around 1.3. The R<sub>0</sub> estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.[^r0_covid]
|
||||
|
||||
[^r0_flu]: source
|
||||
|
||||
[^r0_covid]: source
|
||||
|
||||
In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, *on average*, an (i) infects 3 (s)s before they recover. So for our simulations, R<sub>0</sub> is 3.
|
||||
|
||||
**Play around with this R<sub>0</sub> calculator, to see how R<sub>0</sub> depends on recovery time & new-infection time:**
|
||||
|
||||
// calc
|
||||
|
||||
But remember, the fewer (s)s there are, the *slower* (s)s become (i)s.
|
||||
R depends not just on R<sub>0</sub>, but also how many people are no longer Susceptible – due to, say, having recovered & gotten natural immunity.
|
||||
|
||||
// calc 2
|
||||
|
||||
When enough people have natural immunity, R < 1, and the virus is contained! This is called **herd immunity**, and while it's *terrible* policy, (we'll explain why later – it's not for the reason you may think!) it's essential to understanding Epidemiology 101.
|
||||
|
||||
Now, let's play the last simulation again, but showing R<sub>0</sub>, R over time, and the herd immunity threshold:
|
||||
|
||||
// sim
|
||||
|
||||
Note: Total cases (the gray curve) does not stop at herd immunity, but *overshoots* it! And it does this *exactly when* current cases (the pink curve) peaks. This happens no matter how you change the settings:
|
||||
|
||||
// pic
|
||||
|
||||
This is because, by definition, when there are more non-(s)s than the herd immunity threshold, you get R < 1. And, by definition, R < 1 means new cases stop growing.
|
||||
|
||||
If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:
|
||||
|
||||
# R > 1 = bad
|
||||
# R < 1 = good (R=1, meh)
|
||||
|
||||
**This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!**
|
||||
|
||||
It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if R<sub>0</sub> = 3, cutting that by 67% gives us R = 0.99, which is R < 1, which means the virus is contained!
|
||||
|
||||
(And even if, extreme-worst-case, R<sub>0</sub> = *6*, you still "only" need to stop 84% of transmissions. That's a B grade.)
|
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|
||||
// calculator - custom
|
||||
|
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*Every* COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're *all* doing the same thing:
|
||||
|
||||
Getting R < 1.
|
||||
|
||||
So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, **but also our mental health, social health, *and* financial health?**
|
||||
|
||||
Brace yourselves for an emergency landing...
|
||||
Loading…
Reference in New Issue