Merge branch 'TJScalzo-patch-1'
This commit is contained in:
Nicky Case
commit
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README.md
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README.md
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# How To Translate
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**NOTE: I'm still editing some stuff! Words will be finalized-ish on May 4th.
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You can start on stuff below, then check for new commits on the 4th to see what else to
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translate. You're wonderful, thank you! 💖**
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**NOTE 2: Sorry these instructions are really sloppy. Am writing this at midnight, rushing
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to publish this.**
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**NOTE 3: Indie gamedev is horrible.**
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Step 1)
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Check to see if it's already been translated to your language!
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@ -29,7 +20,7 @@ Translate `words.md`, (6000 words) then export it to html
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– make sure your Markdown app supports footnotes –
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and copy-paste that into the `<article></article>` part of `index.html`.
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Translate the `<head>`, sidebars, & footer of `index.html` (200 words)
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Translate the `<head>`, sidebars, & footer of `index.html` (200 words) **Feel free to add yourself in the header/footer credits as a translator! :)**
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Translate the images in `/pics` (800 words)
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If you don't have image-editing software, ask for help on the Github Issue!
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@ -95,6 +95,10 @@ icon[r]{
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background-image: url(../icons/r.png);
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}
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.nowrap{
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white-space: nowrap;
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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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138
index.html
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index.html
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@ -37,8 +37,8 @@
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<!-- - - - - - - - - - - - - - - - - - - - - - - -->
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<div id="translations">
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Translations!
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<ul>
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<li><a href='https://ncase.me/covid-19/'>English</a></li>
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<li><a href='https://eed3si9n.github.io/covid-19/'>日本語</a></li>
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</ul>
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@ -49,7 +49,7 @@
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<div id="sharing">
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Help this guide
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get R > 1:
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get its R > 1:
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<br>
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<span id='share_title'>
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What Happens Next? COVID-19 Futures, Explained With Playable Simulations
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@ -123,7 +123,7 @@
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<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> every 4 days, <em>on average</em>.<sup id="fnref2"><a href="#fn2" rel="footnote">2</a></sup> (remember, there's a lot of variation)</p>
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<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>
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<p>If we simulate "double every 4 days" <em>and nothing else</em>, on a population starting with just 0.001% <span class="nowrap"><icon i></icon>,</span> what happens? </p>
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<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>
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@ -139,7 +139,7 @@
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<p><img src="pics/susceptibles.png" alt=""></p>
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<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>
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<p>The more <span class="nowrap"><icon i></icon>s</span> there are, the faster <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s,</span> <strong>but the fewer <span class="nowrap"><icon s></icon>s</span> there are, the <em>slower</em> <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s.</span></strong></p>
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<p>How's this change the growth of an epidemic? Let's find out:</p>
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@ -151,9 +151,9 @@
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<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>
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<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 are dead.) <icon r></icon>s can't be infected again, and let's pretend – <em>for now!</em> – that they stay immune for life.</p>
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<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 are dead.) <span class="nowrap"><icon r></icon>s</span> can't be infected again, and let's pretend – <em>for now!</em> – that they stay immune for life.</p>
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<p>With COVID-19, it's estimated you're <icon i></icon> Infectious for 10 days, <em>on average</em>.<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> That means some folks will recover before 10 days, some after. <strong>Here's what that looks like, with a simulation <em>starting</em> with 100% <icon i></icon>:</strong></p>
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<p>With COVID-19, it's estimated you're <icon i></icon> Infectious for 10 days, <em>on average</em>.<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> That means some folks will recover before 10 days, some after. <strong>Here's what that looks like, with a simulation <em>starting</em> with 100% <span class="nowrap"><icon i></icon>:</span></strong></p>
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<div class="sim">
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<iframe src="sim?stage=epi-3" width="800" height="540"></iframe>
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@ -167,9 +167,9 @@
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<p>Let's find out.</p>
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<p><b style='color:#ff4040'>Red curve</b> is <em>current</em> cases <icon i></icon>,<br>
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<b style='color:#999999'>Gray curve</b> is <em>total</em> cases (current + recovered <icon r></icon>),
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starts at just 0.001% <icon i></icon>:</p>
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<p><b style='color:#ff4040'>Red curve</b> is <em>current</em> cases <span class="nowrap"><icon i></icon>,</span><br>
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<b style='color:#999999'>Gray curve</b> is <em>total</em> cases (current + recovered <span class="nowrap"><icon r></icon>),</span>
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starts at just 0.001% <span class="nowrap"><icon i></icon>:</span></p>
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<div class="sim">
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<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
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@ -185,7 +185,7 @@
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<p><strong>NOTE: The simulations that inform policy are way, <em>way</em> more sophisticated than this!</strong> But the SIR Model can still explain the same general findings, even if missing the nuances.</p>
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<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>
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<p>Actually, let's add one more nuance: before an <icon s></icon> becomes an <span class="nowrap"><icon i></icon>,</span> 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>
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<p><img src="pics/seir.png" alt=""></p>
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@ -193,14 +193,14 @@
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<p>For COVID-19, it's estimated that you're <icon e></icon> infected-but-not-yet-infectious for 3 days, <em>on average</em>.<sup id="fnref7"><a href="#fn7" rel="footnote">7</a></sup> What happens if we add that to the simulation?</p>
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<p><b style='color:#ff4040'>Red <b style='color:#FF9393'>+ Pink</b> curve</b> is <em>current</em> cases (infectious <icon i></icon> + exposed <icon e></icon>),<br>
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<b style='color:#888'>Gray curve</b> is <em>total</em> cases (current + recovered <icon r></icon>):</p>
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||||
<p><b style='color:#ff4040'>Red <b style='color:#FF9393'>+ Pink</b> curve</b> is <em>current</em> cases (infectious <icon i></icon> + exposed <span class="nowrap"><icon e></icon>),</span><br>
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||||
<b style='color:#888'>Gray curve</b> is <em>total</em> cases (current + recovered <span class="nowrap"><icon r></icon>):</span></p>
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||||
<div class="sim">
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||||
<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
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||||
</div>
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||||
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||||
<p>Not much changes! How long you stay <icon e></icon> Exposed changes the ratio of <icon e></icon>-to-<icon i></icon>, and <em>when</em> current cases peak... but the <em>height</em> of that peak, and total cases in the end, stays the same.</p>
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<p>Not much changes! How long you stay <icon e></icon> Exposed changes the ratio of <span class="nowrap"><icon e></icon>-to-<icon i></icon>,</span> and <em>when</em> current cases peak... but the <em>height</em> of that peak, and total cases in the end, stays the same.</p>
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<p>Why's that? Because of the <em>first</em>-most important idea in Epidemiology 101:</p>
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<iframe src="sim?stage=epi-6a&format=calc" width="285" height="255"></iframe>
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</div>
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<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>
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<p>But remember, the fewer <span class="nowrap"><icon s></icon>s</span> there are, the <em>slower</em> <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s.</span> 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>
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<div class="sim">
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<iframe src="sim?stage=epi-6b&format=calc" width="285" height="390"></iframe>
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<p><strong>NOTE: Total cases <em>does not stop</em> at herd immunity, but overshoots it!</strong> And it crosses the threshold <em>exactly</em> when current cases peak. (This happens no matter how you change the settings – try it for yourself!)</p>
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<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>
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<p>This is because when there are more <span class="nowrap">non-<icon s></icon>s</span> than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.</p>
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<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>
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<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>
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<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>
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<p><strong>Play with this calculator to see how % of <span class="nowrap">non-<icon s></icon>,</span> 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>
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<div class="sim">
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<iframe src="sim?stage=int-2a&format=calc" width="285" height="260"></iframe>
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<p>Oh.</p>
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<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>
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<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 <span class="nowrap"><icon i></icon>)</span> can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing.</p>
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<p><strong>A lockdown isn't a cure, it's just a restart.</strong></p>
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<p>This is called <strong>contact tracing</strong>. It's an old idea, was used at an unprecedented scale to contain Ebola<sup id="fnref23"><a href="#fn23" rel="footnote">23</a></sup>, and now it's core part of how Taiwan & South Korea are containing COVID-19!</p>
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<p>(It also lets us use our limited tests more efficiently, to find pre-symptomatic <icon i></icon>s without needing to test almost everyone.)</p>
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<p>(It also lets us use our limited tests more efficiently, to find pre-symptomatic <span class="nowrap"><icon i></icon>s</span> without needing to test almost everyone.)</p>
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<p>Traditionally, contacts are found with in-person interviews, but those <em>alone</em> are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – <em>NOT</em> replaced by – contact tracing apps.</p>
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<p><img src="pics/dp3t.png" alt=""></p>
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<p>(& <a href="https://ncase.me/contact-tracing/">here's the full comic</a>)</p>
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<p>(<a href="https://ncase.me/contact-tracing/">Here's the full comic</a>. Details about "pranking"/false positives/etc in footnote:<sup id="fnref24"><a href="#fn24" rel="footnote">24</a></sup>)</p>
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<p>Along with similar teams like TCN Protocol<sup id="fnref24"><a href="#fn24" rel="footnote">24</a></sup> and MIT PACT<sup id="fnref25"><a href="#fn25" rel="footnote">25</a></sup>, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.<sup id="fnref26"><a href="#fn26" rel="footnote">26</a></sup> (Don't trust Google/Apple? Good! The beauty of this system is it doesn't <em>need</em> trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!</p>
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<p>Along with similar teams like TCN Protocol<sup id="fnref25"><a href="#fn25" rel="footnote">25</a></sup> and MIT PACT<sup id="fnref26"><a href="#fn26" rel="footnote">26</a></sup>, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.<sup id="fnref27"><a href="#fn27" rel="footnote">27</a></sup> (Don't trust Google/Apple? Good! The beauty of this system is it doesn't <em>need</em> trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!</p>
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<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>
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<p>(Rant about the confusion about pre-symptomatic vs "true" asymptomatic. "True" asymptomatics are rare:<sup id="fnref27"><a href="#fn27" rel="footnote">27</a></sup>)</p>
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<p>(Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:<sup id="fnref28"><a href="#fn28" rel="footnote">28</a></sup>)</p>
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<p>Isolating <em>symptomatic</em> cases would reduce R by up to 40%, and quarantining their <em>pre/a-symptomatic</em> contacts would reduce R by up to 50%<sup id="fnref28"><a href="#fn28" rel="footnote">28</a></sup>:</p>
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<p>Isolating <em>symptomatic</em> cases would reduce R by up to 40%, and quarantining their <em>pre/a-symptomatic</em> contacts would reduce R by up to 50%<sup id="fnref29"><a href="#fn29" rel="footnote">29</a></sup>:</p>
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<div class="sim">
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<iframe src="sim?stage=int-4a&format=calc" width="285" height="340"></iframe>
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<p>Thus, even without 100% contact quarantining, 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> – pay for the tests, job protection, subsidized paid leave, etc. Still way cheaper than intermittent lockdown.)</p>
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<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>
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<p>We then keep R < 1 until we have a vaccine, which turns susceptible <span class="nowrap"><icon s></icon>s</span> into immune <span class="nowrap"><icon r></icon>s.</span> Herd immunity, the <em>right</em> way:</p>
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<div class="sim">
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<iframe src="sim?stage=int-4b&format=calc" width="285" height="230"></iframe>
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@ -463,17 +463,17 @@
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<p><em>"Wait,"</em> you might ask, <em>"I thought face masks don't stop you from getting sick?"</em></p>
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<p>You're right. Masks don't stop you from getting sick<sup id="fnref29"><a href="#fn29" rel="footnote">29</a></sup>... they stop you from getting <em>others</em> sick.</p>
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<p>You're right. Masks don't stop you from getting sick<sup id="fnref30"><a href="#fn30" rel="footnote">30</a></sup>... they stop you from getting <em>others</em> sick.</p>
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||||
<p><img src="pics/masks.png" alt=""></p>
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<p>To put a number on it: surgical masks <em>on the sick person</em> reduce cold & flu viruses in aerosols by 70%.<sup id="fnref30"><a href="#fn30" rel="footnote">30</a></sup> Reducing transmissions by 70% would be as large an impact as a lockdown!</p>
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<p>To put a number on it: surgical masks <em>on the sick person</em> reduce cold & flu viruses in aerosols by 70%.<sup id="fnref31"><a href="#fn31" rel="footnote">31</a></sup> Reducing transmissions by 70% would be as large an impact as a lockdown!</p>
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<p>However, we don't know for sure the impact of masks on COVID-19 <em>specifically</em>. In science, one should only publish a finding if you're 95% sure of it. (...should.<sup id="fnref31"><a href="#fn31" rel="footnote">31</a></sup>) Masks, as of May 1st 2020, are less than "95% sure".</p>
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<p>However, we don't know for sure the impact of masks on COVID-19 <em>specifically</em>. In science, one should only publish a finding if you're 95% sure of it. (...should.<sup id="fnref32"><a href="#fn32" rel="footnote">32</a></sup>) Masks, as of May 1st 2020, are less than "95% sure".</p>
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<p>However, pandemics are like poker. <strong>Make bets only when you're 95% sure, and you'll lose everything at stake.</strong> As a recent article on masks in the British Medical Journal notes,<sup id="fnref32"><a href="#fn32" rel="footnote">32</a></sup> we <em>have</em> to make cost/benefit analyses under uncertainty. Like so:</p>
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<p>However, pandemics are like poker. <strong>Make bets only when you're 95% sure, and you'll lose everything at stake.</strong> As a recent article on masks in the British Medical Journal notes,<sup id="fnref33"><a href="#fn33" rel="footnote">33</a></sup> we <em>have</em> to make cost/benefit analyses under uncertainty. Like so:</p>
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|
||||
<p>Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks<sup id="fnref33"><a href="#fn33" rel="footnote">33</a></sup>), super cheap. If surgical masks, more expensive but still pretty cheap.</p>
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<p>Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks<sup id="fnref34"><a href="#fn34" rel="footnote">34</a></sup>), super cheap. If surgical masks, more expensive but still pretty cheap.</p>
|
||||
|
||||
<p>Benefit: Even if it's a 50–50 chance of surgical masks reducing transmission by 0% or 70%, 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%, discounted for our uncertainty. (Again, you can challenge our assumptions by turning the sliders up/down)</p>
|
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@ -481,7 +481,7 @@
|
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<iframe src="sim?stage=int-6a&format=calc" width="285" height="380"></iframe>
|
||||
</div>
|
||||
|
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<p>(other arguments for/against masks:<sup id="fnref34"><a href="#fn34" rel="footnote">34</a></sup>)</p>
|
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<p>(other arguments for/against masks:<sup id="fnref35"><a href="#fn35" rel="footnote">35</a></sup>)</p>
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|
||||
<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 1/3 of people wear masks would tip that over to R < 1, virus contained!</p>
|
||||
|
||||
|
|
@ -489,7 +489,7 @@
|
|||
|
||||
<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 COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.<sup id="fnref35"><a href="#fn35" rel="footnote">35</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>For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.<sup id="fnref36"><a href="#fn36" rel="footnote">36</a></sup> The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-6b&format=calc" width="285" height="220"></iframe>
|
||||
|
|
@ -549,16 +549,16 @@
|
|||
<p>...<em>for how long?</em></p>
|
||||
|
||||
<ul>
|
||||
<li>COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.<sup id="fnref36"><a href="#fn36" rel="footnote">36</a></sup></li>
|
||||
<li>The coronaviruses that cause "the" common cold give you 8 months of immunity.<sup id="fnref37"><a href="#fn37" rel="footnote">37</a></sup></li>
|
||||
<li>There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.<sup id="fnref38"><a href="#fn38" rel="footnote">38</a></sup></li>
|
||||
<li>One <em>not-yet-peer-reviewed</em> study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.<sup id="fnref39"><a href="#fn39" rel="footnote">39</a></sup></li>
|
||||
<li>COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.<sup id="fnref37"><a href="#fn37" rel="footnote">37</a></sup></li>
|
||||
<li>The coronaviruses that cause "the" common cold give you 8 months of immunity.<sup id="fnref38"><a href="#fn38" rel="footnote">38</a></sup></li>
|
||||
<li>There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.<sup id="fnref39"><a href="#fn39" rel="footnote">39</a></sup></li>
|
||||
<li>One <em>not-yet-peer-reviewed</em> study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.<sup id="fnref40"><a href="#fn40" rel="footnote">40</a></sup></li>
|
||||
</ul>
|
||||
|
||||
<p>But for COVID-19 <em>in humans</em>, as of May 1st 2020, "how long" is the big unknown.</p>
|
||||
|
||||
<p>For these simulations, let's say it's 1 year.
|
||||
<strong>Here's a simulation starting with 100% <icon r></icon></strong>, exponentially decaying into susceptible, no-immunity <icon s></icon>s after 1 year, on <em>average</em>, with variation:</p>
|
||||
<strong>Here's a simulation starting with 100% <span class="nowrap"><icon r></icon></strong>,</span> exponentially decaying into susceptible, no-immunity <span class="nowrap"><icon s></icon>s</span> after 1 year, on <em>average</em>, with variation:</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-1&format=lines&height=600" width="800" height="600"></iframe>
|
||||
|
|
@ -588,7 +588,7 @@
|
|||
|
||||
<p>Oh.</p>
|
||||
|
||||
<p>Counterintuitively, summer makes the spikes worse <em>and</em> 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>Counterintuitively, summer makes the spikes worse <em>and</em> regular! This is because summer reduces new <span class="nowrap"><icon i></icon>s,</span> but that in turn reduces new immune <span class="nowrap"><icon r></icon>s.</span> Which means immunity plummets in the summer, <em>creating</em> large regular spikes in the winter.</p>
|
||||
|
||||
<p>Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:</p>
|
||||
|
||||
|
|
@ -604,7 +604,7 @@
|
|||
|
||||
<p><strong>To be clear: this is unlikely.</strong> Most epidemiologists expect a vaccine in 1 to 2 years. 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. </p>
|
||||
|
||||
<p>Still, infectious disease researchers have expressed worries: What if we can't make enough?<sup id="fnref40"><a href="#fn40" rel="footnote">40</a></sup> What if we rush it, and it's not safe?<sup id="fnref41"><a href="#fn41" rel="footnote">41</a></sup></p>
|
||||
<p>Still, infectious disease researchers have expressed worries: What if we can't make enough?<sup id="fnref41"><a href="#fn41" rel="footnote">41</a></sup> What if we rush it, and it's not safe?<sup id="fnref42"><a href="#fn42" rel="footnote">42</a></sup></p>
|
||||
|
||||
<p>Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:</p>
|
||||
|
||||
|
|
@ -643,7 +643,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<p>Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.<sup id="fnref42"><a href="#fn42" rel="footnote">42</a></sup></p>
|
||||
<p>Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.<sup id="fnref43"><a href="#fn43" rel="footnote">43</a></sup></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>
|
||||
|
||||
|
|
@ -782,27 +782,35 @@
|
|||
</li>
|
||||
|
||||
<li id="fn24">
|
||||
<p><a href="https://github.com/TCNCoalition/TCN#tcn-protocol">Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol</a> <a href="#fnref24" rev="footnote">↩</a></p>
|
||||
<p>To prevent "pranking" (people falsely claiming to be infected), the DP-3T Protocol requires that the hospital first give you a One-Time Passcode that lets you upload your messages. <a href="#fnref24" rev="footnote">↩</a></p>
|
||||
|
||||
<p>False positives are a problem in both manual & digital contact tracing. Still, we can reduce false positives in 2 ways: 1) By notifying Bobs only if they heard, say, 30+ min worth of messages, not just one message in passing. And 2) If the app <em>does</em> think Bob's been exposed, it can refer Bob to a <em>manual</em> contact tracer, for an in-depth follow-up interview.</p>
|
||||
|
||||
<p>For other issues like data bandwidth, source integrity, and other security issues, check out <a href="https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing">the open-source DP-3T whitepapers!</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn25">
|
||||
<p><a href="https://pact.mit.edu/">PACT: Private Automated Contact Tracing</a> <a href="#fnref25" rev="footnote">↩</a></p>
|
||||
<p><a href="https://github.com/TCNCoalition/TCN#tcn-protocol">Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol</a> <a href="#fnref25" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn26">
|
||||
<p><a href="https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/">Apple and Google partner on COVID-19 contact tracing technology </a>. Note they're not making the apps <em>themselves</em>, just creating the systems that will <em>support</em> those apps. <a href="#fnref26" rev="footnote">↩</a></p>
|
||||
<p><a href="https://pact.mit.edu/">PACT: Private Automated Contact Tracing</a> <a href="#fnref26" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn27">
|
||||
<p>Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms <em>ever</em>" (true asymptomatic). The only way you could tell the difference is by following up with cases later. <a href="#fnref27" rev="footnote">↩</a></p>
|
||||
<p><a href="https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/">Apple and Google partner on COVID-19 contact tracing technology </a>. Note they're not making the apps <em>themselves</em>, just creating the systems that will <em>support</em> those apps. <a href="#fnref27" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn28">
|
||||
<p>Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms <em>ever</em>" (true asymptomatic). The only way you could tell the difference is by following up with cases later. <a href="#fnref28" rev="footnote">↩</a></p>
|
||||
|
||||
<p>Which is what <a href="https://wwwnc.cdc.gov/eid/article/26/8/20-1274_article">this study</a> did. (Disclaimer: "Early release articles are not considered as final versions.") In a call center in South Korea that had a COVID-19 outbreak, "only 4 (1.9%) remained asymptomatic within 14 days of quarantine, and none of their household contacts acquired secondary infections."</p>
|
||||
|
||||
<p>So that means "true asymptomatics" are rare, and catching the disease from a true asymptomatic may be even rarer!</p>
|
||||
</li>
|
||||
|
||||
<li id="fn28">
|
||||
<p>From the same Oxford study that first recommended apps to fight COVID-19: <a href="https://science.sciencemag.org/content/early/2020/04/09/science.abb6936/tab-figures-data">Luca Ferretti & Chris Wymant et al</a> See Figure 2. Assuming R<sub>0</sub> = 2.0, they found that: <a href="#fnref28" rev="footnote">↩</a></p>
|
||||
<li id="fn29">
|
||||
<p>From the same Oxford study that first recommended apps to fight COVID-19: <a href="https://science.sciencemag.org/content/early/2020/04/09/science.abb6936/tab-figures-data">Luca Ferretti & Chris Wymant et al</a> See Figure 2. Assuming R<sub>0</sub> = 2.0, they found that: <a href="#fnref29" rev="footnote">↩</a></p>
|
||||
|
||||
<ul>
|
||||
<li>Symptomatics contribute R = 0.8 (40%)</li>
|
||||
|
|
@ -814,64 +822,64 @@
|
|||
<p>And add up the pre- & a-symptomatic contacts (45% + 5%) and you get 50% of R!</p>
|
||||
</li>
|
||||
|
||||
<li id="fn29">
|
||||
<p>“None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” <a href="https://www.sciencedirect.com/science/article/pii/S0196655307007742">Tara Oberg & Lisa M. Brosseau</a> <a href="#fnref29" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn30">
|
||||
<p>“The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/">Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ</a> <a href="#fnref30" rev="footnote">↩</a></p>
|
||||
<p>“None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” <a href="https://www.sciencedirect.com/science/article/pii/S0196655307007742">Tara Oberg & Lisa M. Brosseau</a> <a href="#fnref30" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn31">
|
||||
<p>Any actual scientist who read that last sentence is probably laugh-crying right now. See: <a href="https://en.wikipedia.org/wiki/Data_dredging">p-hacking</a>, <a href="https://en.wikipedia.org/wiki/Replication_crisis">the replication crisis</a>) <a href="#fnref31" rev="footnote">↩</a></p>
|
||||
<p>“The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/">Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ</a> <a href="#fnref31" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn32">
|
||||
<p>“It is time to apply the precautionary principle” <a href="https://www.bmj.com/content/bmj/369/bmj.m1435.full.pdf">Trisha Greenhalgh et al [PDF]</a> <a href="#fnref32" rev="footnote">↩</a></p>
|
||||
<p>Any actual scientist who read that last sentence is probably laugh-crying right now. See: <a href="https://en.wikipedia.org/wiki/Data_dredging">p-hacking</a>, <a href="https://en.wikipedia.org/wiki/Replication_crisis">the replication crisis</a>) <a href="#fnref32" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn33">
|
||||
<p><a href="https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/testing-the-efficacy-of-homemade-masks-would-they-protect-in-an-influenza-pandemic/0921A05A69A9419C862FA2F35F819D55">Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A</a> See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested. <a href="#fnref33" rev="footnote">↩</a></p>
|
||||
<p>“It is time to apply the precautionary principle” <a href="https://www.bmj.com/content/bmj/369/bmj.m1435.full.pdf">Trisha Greenhalgh et al [PDF]</a> <a href="#fnref33" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn34">
|
||||
<p><strong>"We need to save supplies for hospitals."</strong> <em>Absolutely agreed.</em> But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks. <a href="#fnref34" rev="footnote">↩</a></p>
|
||||
<p><a href="https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/testing-the-efficacy-of-homemade-masks-would-they-protect-in-an-influenza-pandemic/0921A05A69A9419C862FA2F35F819D55">Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A</a> See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested. <a href="#fnref34" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn35">
|
||||
<p><strong>"We need to save supplies for hospitals."</strong> <em>Absolutely agreed.</em> But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks. <a href="#fnref35" rev="footnote">↩</a></p>
|
||||
|
||||
<p><strong>"They're hard to wear correctly."</strong> It's also hard to wash your hands according to the WHO Guidelines – seriously, "Step 3) right palm over left dorsum"?! – but we still recommend handwashing, because imperfect is still better than nothing.</p>
|
||||
|
||||
<p><strong>"It'll make people more reckless with handwashing & social distancing."</strong> Sure, and safety belts make people ignore stop signs, and flossing makes people eat rocks. But seriously, we'd argue the opposite: masks are a <em>constant physical reminder</em> to be careful – and in East Asia, masks are also a symbol of solidarity!</p>
|
||||
</li>
|
||||
|
||||
<li id="fn35">
|
||||
<p>“One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. <a href="https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767">Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng</a> <a href="#fnref35" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn36">
|
||||
<p>“SARS-specific antibodies were maintained for an average of 2 years [...] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/">Wu LP, Wang NC, Chang YH, et al.</a> "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly. <a href="#fnref36" rev="footnote">↩</a></p>
|
||||
<p>“One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. <a href="https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767">Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng</a> <a href="#fnref36" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn37">
|
||||
<p>“We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” <a href="http://www.columbia.edu/%7Ejls106/galanti_shaman_ms_supp.pdf">Marta Galanti & Jeffrey Shaman (PDF)</a> <a href="#fnref37" rev="footnote">↩</a></p>
|
||||
<p>“SARS-specific antibodies were maintained for an average of 2 years [...] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/">Wu LP, Wang NC, Chang YH, et al.</a> "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly. <a href="#fnref37" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn38">
|
||||
<p>“Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” <a href="https://www.statnews.com/2020/04/20/everything-we-know-about-coronavirus-immunity-and-antibodies-and-plenty-we-still-dont/">from STAT News by Andrew Joseph</a> <a href="#fnref38" rev="footnote">↩</a></p>
|
||||
<p>“We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” <a href="http://www.columbia.edu/%7Ejls106/galanti_shaman_ms_supp.pdf">Marta Galanti & Jeffrey Shaman (PDF)</a> <a href="#fnref38" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn39">
|
||||
<p>From <a href="https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract">Bao et al.</a> <em>Disclaimer: This article is a preprint and has not been certified by peer review (yet).</em> Also, to emphasize: they only tested re-infection 28 days later. <a href="#fnref39" rev="footnote">↩</a></p>
|
||||
<p>“Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” <a href="https://www.statnews.com/2020/04/20/everything-we-know-about-coronavirus-immunity-and-antibodies-and-plenty-we-still-dont/">from STAT News by Andrew Joseph</a> <a href="#fnref39" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn40">
|
||||
<p>“If a coronavirus vaccine arrives, can the world make enough?” <a href="https://www.nature.com/articles/d41586-020-01063-8">by Roxanne Khamsi, on Nature</a> <a href="#fnref40" rev="footnote">↩</a></p>
|
||||
<p>From <a href="https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract">Bao et al.</a> <em>Disclaimer: This article is a preprint and has not been certified by peer review (yet).</em> Also, to emphasize: they only tested re-infection 28 days later. <a href="#fnref40" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn41">
|
||||
<p>“Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” <a href="https://www.nature.com/articles/d41586-020-00751-9">by Shibo Jiang, on Nature</a> <a href="#fnref41" rev="footnote">↩</a></p>
|
||||
<p>“If a coronavirus vaccine arrives, can the world make enough?” <a href="https://www.nature.com/articles/d41586-020-01063-8">by Roxanne Khamsi, on Nature</a> <a href="#fnref41" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn42">
|
||||
<p>Dry land metaphor <a href="https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/">from Marc Lipsitch & Yonatan Grad, on STAT News</a> <a href="#fnref42" rev="footnote">↩</a></p>
|
||||
<p>“Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” <a href="https://www.nature.com/articles/d41586-020-00751-9">by Shibo Jiang, on Nature</a> <a href="#fnref42" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn43">
|
||||
<p>Dry land metaphor <a href="https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/">from Marc Lipsitch & Yonatan Grad, on STAT News</a> <a href="#fnref43" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
</ol>
|
||||
|
|
@ -887,7 +895,7 @@
|
|||
|
||||
<div>
|
||||
|
||||
Help this post get R > 1: <span class='shareables in_footer' style='position: relative; top: 17px; right: -6px;'></span>
|
||||
Help this post get its R > 1: <span class='shareables in_footer' style='position: relative; top: 17px; right: -6px;'></span>
|
||||
<br><br>
|
||||
This guide is
|
||||
<br>
|
||||
|
|
|
|||
|
|
@ -96,7 +96,7 @@
|
|||
<br>
|
||||
</span>
|
||||
<span id="int_block_4">
|
||||
Summer
|
||||
Strength of Summer
|
||||
<br>
|
||||
<input class="sim_input recordable" type="range" id="p_summer" min="0" max="1" step="0.001" value="0">
|
||||
<br>
|
||||
|
|
|
|||
132
words/words.html
132
words/words.html
|
|
@ -69,7 +69,7 @@
|
|||
|
||||
<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> every 4 days, <em>on average</em>.<sup id="fnref2"><a href="#fn2" rel="footnote">2</a></sup> (remember, there's a lot of variation)</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>If we simulate "double every 4 days" <em>and nothing else</em>, on a population starting with just 0.001% <span class="nowrap"><icon i></icon>,</span> what happens? </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>
|
||||
|
||||
|
|
@ -85,7 +85,7 @@
|
|||
|
||||
<p><img src="pics/susceptibles.png" alt=""></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>The more <span class="nowrap"><icon i></icon>s</span> there are, the faster <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s,</span> <strong>but the fewer <span class="nowrap"><icon s></icon>s</span> there are, the <em>slower</em> <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s.</span></strong></p>
|
||||
|
||||
<p>How's this change the growth of an epidemic? Let's find out:</p>
|
||||
|
||||
|
|
@ -97,9 +97,9 @@
|
|||
|
||||
<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>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 are dead.) <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>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 are dead.) <span class="nowrap"><icon r></icon>s</span> can't be infected again, and let's pretend – <em>for now!</em> – that they stay immune for life.</p>
|
||||
|
||||
<p>With COVID-19, it's estimated you're <icon i></icon> Infectious for 10 days, <em>on average</em>.<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> That means some folks will recover before 10 days, some after. <strong>Here's what that looks like, with a simulation <em>starting</em> with 100% <icon i></icon>:</strong></p>
|
||||
<p>With COVID-19, it's estimated you're <icon i></icon> Infectious for 10 days, <em>on average</em>.<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> That means some folks will recover before 10 days, some after. <strong>Here's what that looks like, with a simulation <em>starting</em> with 100% <span class="nowrap"><icon i></icon>:</span></strong></p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-3" width="800" height="540"></iframe>
|
||||
|
|
@ -113,9 +113,9 @@
|
|||
|
||||
<p>Let's find out.</p>
|
||||
|
||||
<p><b style='color:#ff4040'>Red curve</b> is <em>current</em> cases <icon i></icon>,<br>
|
||||
<b style='color:#999999'>Gray curve</b> is <em>total</em> cases (current + recovered <icon r></icon>),
|
||||
starts at just 0.001% <icon i></icon>:</p>
|
||||
<p><b style='color:#ff4040'>Red curve</b> is <em>current</em> cases <span class="nowrap"><icon i></icon>,</span><br>
|
||||
<b style='color:#999999'>Gray curve</b> is <em>total</em> cases (current + recovered <span class="nowrap"><icon r></icon>),</span>
|
||||
starts at just 0.001% <span class="nowrap"><icon i></icon>:</span></p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
|
||||
|
|
@ -131,7 +131,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p><strong>NOTE: The simulations that inform policy are way, <em>way</em> more sophisticated than this!</strong> But the SIR Model can still explain the same general findings, even if missing the nuances.</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>Actually, let's add one more nuance: before an <icon s></icon> becomes an <span class="nowrap"><icon i></icon>,</span> 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><img src="pics/seir.png" alt=""></p>
|
||||
|
||||
|
|
@ -139,14 +139,14 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p>For COVID-19, it's estimated that you're <icon e></icon> infected-but-not-yet-infectious for 3 days, <em>on average</em>.<sup id="fnref7"><a href="#fn7" rel="footnote">7</a></sup> What happens if we add that to the simulation?</p>
|
||||
|
||||
<p><b style='color:#ff4040'>Red <b style='color:#FF9393'>+ Pink</b> curve</b> is <em>current</em> cases (infectious <icon i></icon> + exposed <icon e></icon>),<br>
|
||||
<b style='color:#888'>Gray curve</b> is <em>total</em> cases (current + recovered <icon r></icon>):</p>
|
||||
<p><b style='color:#ff4040'>Red <b style='color:#FF9393'>+ Pink</b> curve</b> is <em>current</em> cases (infectious <icon i></icon> + exposed <span class="nowrap"><icon e></icon>),</span><br>
|
||||
<b style='color:#888'>Gray curve</b> is <em>total</em> cases (current + recovered <span class="nowrap"><icon r></icon>):</span></p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
<p>Not much changes! How long you stay <icon e></icon> Exposed changes the ratio of <icon e></icon>-to-<icon i></icon>, and <em>when</em> current cases peak... but the <em>height</em> of that peak, and total cases in the end, stays the same.</p>
|
||||
<p>Not much changes! How long you stay <icon e></icon> Exposed changes the ratio of <span class="nowrap"><icon e></icon>-to-<icon i></icon>,</span> and <em>when</em> current cases peak... but the <em>height</em> of that peak, and total cases in the end, stays the same.</p>
|
||||
|
||||
<p>Why's that? Because of the <em>first</em>-most important idea in Epidemiology 101:</p>
|
||||
|
||||
|
|
@ -174,7 +174,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
<iframe src="sim?stage=epi-6a&format=calc" width="285" height="255"></iframe>
|
||||
</div>
|
||||
|
||||
<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>But remember, the fewer <span class="nowrap"><icon s></icon>s</span> there are, the <em>slower</em> <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s.</span> 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>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-6b&format=calc" width="285" height="390"></iframe>
|
||||
|
|
@ -190,7 +190,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p><strong>NOTE: Total cases <em>does not stop</em> at herd immunity, but overshoots it!</strong> And it crosses the threshold <em>exactly</em> when current cases peak. (This happens no matter how you change the settings – try it for yourself!)</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>This is because when there are more <span class="nowrap">non-<icon s></icon>s</span> than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.</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>
|
||||
|
||||
|
|
@ -250,7 +250,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</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><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>
|
||||
<p><strong>Play with this calculator to see how % of <span class="nowrap">non-<icon s></icon>,</span> 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>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-2a&format=calc" width="285" height="260"></iframe>
|
||||
|
|
@ -286,7 +286,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p>Oh.</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>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 <span class="nowrap"><icon i></icon>)</span> 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>
|
||||
|
||||
|
|
@ -338,7 +338,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p>This is called <strong>contact tracing</strong>. It's an old idea, was used at an unprecedented scale to contain Ebola<sup id="fnref23"><a href="#fn23" rel="footnote">23</a></sup>, and now it's core part of how Taiwan & South Korea are containing COVID-19!</p>
|
||||
|
||||
<p>(It also lets us use our limited tests more efficiently, to find pre-symptomatic <icon i></icon>s without needing to test almost everyone.)</p>
|
||||
<p>(It also lets us use our limited tests more efficiently, to find pre-symptomatic <span class="nowrap"><icon i></icon>s</span> without needing to test almost everyone.)</p>
|
||||
|
||||
<p>Traditionally, contacts are found with in-person interviews, but those <em>alone</em> are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – <em>NOT</em> replaced by – contact tracing apps.</p>
|
||||
|
||||
|
|
@ -352,15 +352,15 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p><img src="pics/dp3t.png" alt=""></p>
|
||||
|
||||
<p>(& <a href="https://ncase.me/contact-tracing/">here's the full comic</a>)</p>
|
||||
<p>(<a href="https://ncase.me/contact-tracing/">Here's the full comic</a>. Details about "pranking"/false positives/etc in footnote:<sup id="fnref24"><a href="#fn24" rel="footnote">24</a></sup>)</p>
|
||||
|
||||
<p>Along with similar teams like TCN Protocol<sup id="fnref24"><a href="#fn24" rel="footnote">24</a></sup> and MIT PACT<sup id="fnref25"><a href="#fn25" rel="footnote">25</a></sup>, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.<sup id="fnref26"><a href="#fn26" rel="footnote">26</a></sup> (Don't trust Google/Apple? Good! The beauty of this system is it doesn't <em>need</em> trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!</p>
|
||||
<p>Along with similar teams like TCN Protocol<sup id="fnref25"><a href="#fn25" rel="footnote">25</a></sup> and MIT PACT<sup id="fnref26"><a href="#fn26" rel="footnote">26</a></sup>, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.<sup id="fnref27"><a href="#fn27" rel="footnote">27</a></sup> (Don't trust Google/Apple? Good! The beauty of this system is it doesn't <em>need</em> trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!</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>(Rant about the confusion about pre-symptomatic vs "true" asymptomatic. "True" asymptomatics are rare:<sup id="fnref27"><a href="#fn27" rel="footnote">27</a></sup>)</p>
|
||||
<p>(Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:<sup id="fnref28"><a href="#fn28" rel="footnote">28</a></sup>)</p>
|
||||
|
||||
<p>Isolating <em>symptomatic</em> cases would reduce R by up to 40%, and quarantining their <em>pre/a-symptomatic</em> contacts would reduce R by up to 50%<sup id="fnref28"><a href="#fn28" rel="footnote">28</a></sup>:</p>
|
||||
<p>Isolating <em>symptomatic</em> cases would reduce R by up to 40%, and quarantining their <em>pre/a-symptomatic</em> contacts would reduce R by up to 50%<sup id="fnref29"><a href="#fn29" rel="footnote">29</a></sup>:</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4a&format=calc" width="285" height="340"></iframe>
|
||||
|
|
@ -368,7 +368,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p>Thus, even without 100% contact quarantining, 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> – pay for the tests, job protection, subsidized paid leave, etc. Still way cheaper than intermittent lockdown.)</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>We then keep R < 1 until we have a vaccine, which turns susceptible <span class="nowrap"><icon s></icon>s</span> into immune <span class="nowrap"><icon r></icon>s.</span> Herd immunity, the <em>right</em> way:</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4b&format=calc" width="285" height="230"></iframe>
|
||||
|
|
@ -409,17 +409,17 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p><em>"Wait,"</em> you might ask, <em>"I thought face masks don't stop you from getting sick?"</em></p>
|
||||
|
||||
<p>You're right. Masks don't stop you from getting sick<sup id="fnref29"><a href="#fn29" rel="footnote">29</a></sup>... they stop you from getting <em>others</em> sick.</p>
|
||||
<p>You're right. Masks don't stop you from getting sick<sup id="fnref30"><a href="#fn30" rel="footnote">30</a></sup>... they stop you from getting <em>others</em> sick.</p>
|
||||
|
||||
<p><img src="pics/masks.png" alt=""></p>
|
||||
|
||||
<p>To put a number on it: surgical masks <em>on the sick person</em> reduce cold & flu viruses in aerosols by 70%.<sup id="fnref30"><a href="#fn30" rel="footnote">30</a></sup> Reducing transmissions by 70% would be as large an impact as a lockdown!</p>
|
||||
<p>To put a number on it: surgical masks <em>on the sick person</em> reduce cold & flu viruses in aerosols by 70%.<sup id="fnref31"><a href="#fn31" rel="footnote">31</a></sup> Reducing transmissions by 70% would be as large an impact as a lockdown!</p>
|
||||
|
||||
<p>However, we don't know for sure the impact of masks on COVID-19 <em>specifically</em>. In science, one should only publish a finding if you're 95% sure of it. (...should.<sup id="fnref31"><a href="#fn31" rel="footnote">31</a></sup>) Masks, as of May 1st 2020, are less than "95% sure".</p>
|
||||
<p>However, we don't know for sure the impact of masks on COVID-19 <em>specifically</em>. In science, one should only publish a finding if you're 95% sure of it. (...should.<sup id="fnref32"><a href="#fn32" rel="footnote">32</a></sup>) Masks, as of May 1st 2020, are less than "95% sure".</p>
|
||||
|
||||
<p>However, pandemics are like poker. <strong>Make bets only when you're 95% sure, and you'll lose everything at stake.</strong> As a recent article on masks in the British Medical Journal notes,<sup id="fnref32"><a href="#fn32" rel="footnote">32</a></sup> we <em>have</em> to make cost/benefit analyses under uncertainty. Like so:</p>
|
||||
<p>However, pandemics are like poker. <strong>Make bets only when you're 95% sure, and you'll lose everything at stake.</strong> As a recent article on masks in the British Medical Journal notes,<sup id="fnref33"><a href="#fn33" rel="footnote">33</a></sup> we <em>have</em> to make cost/benefit analyses under uncertainty. Like so:</p>
|
||||
|
||||
<p>Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks<sup id="fnref33"><a href="#fn33" rel="footnote">33</a></sup>), super cheap. If surgical masks, more expensive but still pretty cheap.</p>
|
||||
<p>Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks<sup id="fnref34"><a href="#fn34" rel="footnote">34</a></sup>), super cheap. If surgical masks, more expensive but still pretty cheap.</p>
|
||||
|
||||
<p>Benefit: Even if it's a 50–50 chance of surgical masks reducing transmission by 0% or 70%, 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%, discounted for our uncertainty. (Again, you can challenge our assumptions by turning the sliders up/down)</p>
|
||||
|
||||
|
|
@ -427,7 +427,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
<iframe src="sim?stage=int-6a&format=calc" width="285" height="380"></iframe>
|
||||
</div>
|
||||
|
||||
<p>(other arguments for/against masks:<sup id="fnref34"><a href="#fn34" rel="footnote">34</a></sup>)</p>
|
||||
<p>(other arguments for/against masks:<sup id="fnref35"><a href="#fn35" rel="footnote">35</a></sup>)</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 1/3 of people wear masks would tip that over to R < 1, virus contained!</p>
|
||||
|
||||
|
|
@ -435,7 +435,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</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 COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.<sup id="fnref35"><a href="#fn35" rel="footnote">35</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>For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.<sup id="fnref36"><a href="#fn36" rel="footnote">36</a></sup> The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-6b&format=calc" width="285" height="220"></iframe>
|
||||
|
|
@ -495,16 +495,16 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
<p>...<em>for how long?</em></p>
|
||||
|
||||
<ul>
|
||||
<li>COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.<sup id="fnref36"><a href="#fn36" rel="footnote">36</a></sup></li>
|
||||
<li>The coronaviruses that cause "the" common cold give you 8 months of immunity.<sup id="fnref37"><a href="#fn37" rel="footnote">37</a></sup></li>
|
||||
<li>There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.<sup id="fnref38"><a href="#fn38" rel="footnote">38</a></sup></li>
|
||||
<li>One <em>not-yet-peer-reviewed</em> study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.<sup id="fnref39"><a href="#fn39" rel="footnote">39</a></sup></li>
|
||||
<li>COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.<sup id="fnref37"><a href="#fn37" rel="footnote">37</a></sup></li>
|
||||
<li>The coronaviruses that cause "the" common cold give you 8 months of immunity.<sup id="fnref38"><a href="#fn38" rel="footnote">38</a></sup></li>
|
||||
<li>There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.<sup id="fnref39"><a href="#fn39" rel="footnote">39</a></sup></li>
|
||||
<li>One <em>not-yet-peer-reviewed</em> study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.<sup id="fnref40"><a href="#fn40" rel="footnote">40</a></sup></li>
|
||||
</ul>
|
||||
|
||||
<p>But for COVID-19 <em>in humans</em>, as of May 1st 2020, "how long" is the big unknown.</p>
|
||||
|
||||
<p>For these simulations, let's say it's 1 year.
|
||||
<strong>Here's a simulation starting with 100% <icon r></icon></strong>, exponentially decaying into susceptible, no-immunity <icon s></icon>s after 1 year, on <em>average</em>, with variation:</p>
|
||||
<strong>Here's a simulation starting with 100% <span class="nowrap"><icon r></icon></strong>,</span> exponentially decaying into susceptible, no-immunity <span class="nowrap"><icon s></icon>s</span> after 1 year, on <em>average</em>, with variation:</p>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-1&format=lines&height=600" width="800" height="600"></iframe>
|
||||
|
|
@ -534,7 +534,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p>Oh.</p>
|
||||
|
||||
<p>Counterintuitively, summer makes the spikes worse <em>and</em> 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>Counterintuitively, summer makes the spikes worse <em>and</em> regular! This is because summer reduces new <span class="nowrap"><icon i></icon>s,</span> but that in turn reduces new immune <span class="nowrap"><icon r></icon>s.</span> Which means immunity plummets in the summer, <em>creating</em> large regular spikes in the winter.</p>
|
||||
|
||||
<p>Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:</p>
|
||||
|
||||
|
|
@ -550,7 +550,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
|
||||
<p><strong>To be clear: this is unlikely.</strong> Most epidemiologists expect a vaccine in 1 to 2 years. 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. </p>
|
||||
|
||||
<p>Still, infectious disease researchers have expressed worries: What if we can't make enough?<sup id="fnref40"><a href="#fn40" rel="footnote">40</a></sup> What if we rush it, and it's not safe?<sup id="fnref41"><a href="#fn41" rel="footnote">41</a></sup></p>
|
||||
<p>Still, infectious disease researchers have expressed worries: What if we can't make enough?<sup id="fnref41"><a href="#fn41" rel="footnote">41</a></sup> What if we rush it, and it's not safe?<sup id="fnref42"><a href="#fn42" rel="footnote">42</a></sup></p>
|
||||
|
||||
<p>Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:</p>
|
||||
|
||||
|
|
@ -589,7 +589,7 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<p>Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.<sup id="fnref42"><a href="#fn42" rel="footnote">42</a></sup></p>
|
||||
<p>Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.<sup id="fnref43"><a href="#fn43" rel="footnote">43</a></sup></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>
|
||||
|
||||
|
|
@ -728,27 +728,35 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
</li>
|
||||
|
||||
<li id="fn24">
|
||||
<p><a href="https://github.com/TCNCoalition/TCN#tcn-protocol">Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol</a> <a href="#fnref24" rev="footnote">↩</a></p>
|
||||
<p>To prevent "pranking" (people falsely claiming to be infected), the DP-3T Protocol requires that the hospital first give you a One-Time Passcode that lets you upload your messages. <a href="#fnref24" rev="footnote">↩</a></p>
|
||||
|
||||
<p>False positives are a problem in both manual & digital contact tracing. Still, we can reduce false positives in 2 ways: 1) By notifying Bobs only if they heard, say, 30+ min worth of messages, not just one message in passing. And 2) If the app <em>does</em> think Bob's been exposed, it can refer Bob to a <em>manual</em> contact tracer, for an in-depth follow-up interview.</p>
|
||||
|
||||
<p>For other issues like data bandwidth, source integrity, and other security issues, check out <a href="https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing">the open-source DP-3T whitepapers!</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn25">
|
||||
<p><a href="https://pact.mit.edu/">PACT: Private Automated Contact Tracing</a> <a href="#fnref25" rev="footnote">↩</a></p>
|
||||
<p><a href="https://github.com/TCNCoalition/TCN#tcn-protocol">Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol</a> <a href="#fnref25" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn26">
|
||||
<p><a href="https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/">Apple and Google partner on COVID-19 contact tracing technology </a>. Note they're not making the apps <em>themselves</em>, just creating the systems that will <em>support</em> those apps. <a href="#fnref26" rev="footnote">↩</a></p>
|
||||
<p><a href="https://pact.mit.edu/">PACT: Private Automated Contact Tracing</a> <a href="#fnref26" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn27">
|
||||
<p>Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms <em>ever</em>" (true asymptomatic). The only way you could tell the difference is by following up with cases later. <a href="#fnref27" rev="footnote">↩</a></p>
|
||||
<p><a href="https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/">Apple and Google partner on COVID-19 contact tracing technology </a>. Note they're not making the apps <em>themselves</em>, just creating the systems that will <em>support</em> those apps. <a href="#fnref27" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn28">
|
||||
<p>Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms <em>ever</em>" (true asymptomatic). The only way you could tell the difference is by following up with cases later. <a href="#fnref28" rev="footnote">↩</a></p>
|
||||
|
||||
<p>Which is what <a href="https://wwwnc.cdc.gov/eid/article/26/8/20-1274_article">this study</a> did. (Disclaimer: "Early release articles are not considered as final versions.") In a call center in South Korea that had a COVID-19 outbreak, "only 4 (1.9%) remained asymptomatic within 14 days of quarantine, and none of their household contacts acquired secondary infections."</p>
|
||||
|
||||
<p>So that means "true asymptomatics" are rare, and catching the disease from a true asymptomatic may be even rarer!</p>
|
||||
</li>
|
||||
|
||||
<li id="fn28">
|
||||
<p>From the same Oxford study that first recommended apps to fight COVID-19: <a href="https://science.sciencemag.org/content/early/2020/04/09/science.abb6936/tab-figures-data">Luca Ferretti & Chris Wymant et al</a> See Figure 2. Assuming R<sub>0</sub> = 2.0, they found that: <a href="#fnref28" rev="footnote">↩</a></p>
|
||||
<li id="fn29">
|
||||
<p>From the same Oxford study that first recommended apps to fight COVID-19: <a href="https://science.sciencemag.org/content/early/2020/04/09/science.abb6936/tab-figures-data">Luca Ferretti & Chris Wymant et al</a> See Figure 2. Assuming R<sub>0</sub> = 2.0, they found that: <a href="#fnref29" rev="footnote">↩</a></p>
|
||||
|
||||
<ul>
|
||||
<li>Symptomatics contribute R = 0.8 (40%)</li>
|
||||
|
|
@ -760,64 +768,64 @@ the <em>second</em>-most important idea in Epidemiology 101:</p>
|
|||
<p>And add up the pre- & a-symptomatic contacts (45% + 5%) and you get 50% of R!</p>
|
||||
</li>
|
||||
|
||||
<li id="fn29">
|
||||
<p>“None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” <a href="https://www.sciencedirect.com/science/article/pii/S0196655307007742">Tara Oberg & Lisa M. Brosseau</a> <a href="#fnref29" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn30">
|
||||
<p>“The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/">Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ</a> <a href="#fnref30" rev="footnote">↩</a></p>
|
||||
<p>“None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” <a href="https://www.sciencedirect.com/science/article/pii/S0196655307007742">Tara Oberg & Lisa M. Brosseau</a> <a href="#fnref30" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn31">
|
||||
<p>Any actual scientist who read that last sentence is probably laugh-crying right now. See: <a href="https://en.wikipedia.org/wiki/Data_dredging">p-hacking</a>, <a href="https://en.wikipedia.org/wiki/Replication_crisis">the replication crisis</a>) <a href="#fnref31" rev="footnote">↩</a></p>
|
||||
<p>“The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/">Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ</a> <a href="#fnref31" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn32">
|
||||
<p>“It is time to apply the precautionary principle” <a href="https://www.bmj.com/content/bmj/369/bmj.m1435.full.pdf">Trisha Greenhalgh et al [PDF]</a> <a href="#fnref32" rev="footnote">↩</a></p>
|
||||
<p>Any actual scientist who read that last sentence is probably laugh-crying right now. See: <a href="https://en.wikipedia.org/wiki/Data_dredging">p-hacking</a>, <a href="https://en.wikipedia.org/wiki/Replication_crisis">the replication crisis</a>) <a href="#fnref32" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn33">
|
||||
<p><a href="https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/testing-the-efficacy-of-homemade-masks-would-they-protect-in-an-influenza-pandemic/0921A05A69A9419C862FA2F35F819D55">Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A</a> See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested. <a href="#fnref33" rev="footnote">↩</a></p>
|
||||
<p>“It is time to apply the precautionary principle” <a href="https://www.bmj.com/content/bmj/369/bmj.m1435.full.pdf">Trisha Greenhalgh et al [PDF]</a> <a href="#fnref33" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn34">
|
||||
<p><strong>"We need to save supplies for hospitals."</strong> <em>Absolutely agreed.</em> But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks. <a href="#fnref34" rev="footnote">↩</a></p>
|
||||
<p><a href="https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/testing-the-efficacy-of-homemade-masks-would-they-protect-in-an-influenza-pandemic/0921A05A69A9419C862FA2F35F819D55">Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A</a> See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested. <a href="#fnref34" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn35">
|
||||
<p><strong>"We need to save supplies for hospitals."</strong> <em>Absolutely agreed.</em> But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks. <a href="#fnref35" rev="footnote">↩</a></p>
|
||||
|
||||
<p><strong>"They're hard to wear correctly."</strong> It's also hard to wash your hands according to the WHO Guidelines – seriously, "Step 3) right palm over left dorsum"?! – but we still recommend handwashing, because imperfect is still better than nothing.</p>
|
||||
|
||||
<p><strong>"It'll make people more reckless with handwashing & social distancing."</strong> Sure, and safety belts make people ignore stop signs, and flossing makes people eat rocks. But seriously, we'd argue the opposite: masks are a <em>constant physical reminder</em> to be careful – and in East Asia, masks are also a symbol of solidarity!</p>
|
||||
</li>
|
||||
|
||||
<li id="fn35">
|
||||
<p>“One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. <a href="https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767">Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng</a> <a href="#fnref35" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn36">
|
||||
<p>“SARS-specific antibodies were maintained for an average of 2 years [...] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/">Wu LP, Wang NC, Chang YH, et al.</a> "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly. <a href="#fnref36" rev="footnote">↩</a></p>
|
||||
<p>“One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. <a href="https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767">Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng</a> <a href="#fnref36" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn37">
|
||||
<p>“We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” <a href="http://www.columbia.edu/%7Ejls106/galanti_shaman_ms_supp.pdf">Marta Galanti & Jeffrey Shaman (PDF)</a> <a href="#fnref37" rev="footnote">↩</a></p>
|
||||
<p>“SARS-specific antibodies were maintained for an average of 2 years [...] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/">Wu LP, Wang NC, Chang YH, et al.</a> "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly. <a href="#fnref37" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn38">
|
||||
<p>“Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” <a href="https://www.statnews.com/2020/04/20/everything-we-know-about-coronavirus-immunity-and-antibodies-and-plenty-we-still-dont/">from STAT News by Andrew Joseph</a> <a href="#fnref38" rev="footnote">↩</a></p>
|
||||
<p>“We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” <a href="http://www.columbia.edu/%7Ejls106/galanti_shaman_ms_supp.pdf">Marta Galanti & Jeffrey Shaman (PDF)</a> <a href="#fnref38" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn39">
|
||||
<p>From <a href="https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract">Bao et al.</a> <em>Disclaimer: This article is a preprint and has not been certified by peer review (yet).</em> Also, to emphasize: they only tested re-infection 28 days later. <a href="#fnref39" rev="footnote">↩</a></p>
|
||||
<p>“Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” <a href="https://www.statnews.com/2020/04/20/everything-we-know-about-coronavirus-immunity-and-antibodies-and-plenty-we-still-dont/">from STAT News by Andrew Joseph</a> <a href="#fnref39" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn40">
|
||||
<p>“If a coronavirus vaccine arrives, can the world make enough?” <a href="https://www.nature.com/articles/d41586-020-01063-8">by Roxanne Khamsi, on Nature</a> <a href="#fnref40" rev="footnote">↩</a></p>
|
||||
<p>From <a href="https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract">Bao et al.</a> <em>Disclaimer: This article is a preprint and has not been certified by peer review (yet).</em> Also, to emphasize: they only tested re-infection 28 days later. <a href="#fnref40" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn41">
|
||||
<p>“Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” <a href="https://www.nature.com/articles/d41586-020-00751-9">by Shibo Jiang, on Nature</a> <a href="#fnref41" rev="footnote">↩</a></p>
|
||||
<p>“If a coronavirus vaccine arrives, can the world make enough?” <a href="https://www.nature.com/articles/d41586-020-01063-8">by Roxanne Khamsi, on Nature</a> <a href="#fnref41" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn42">
|
||||
<p>Dry land metaphor <a href="https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/">from Marc Lipsitch & Yonatan Grad, on STAT News</a> <a href="#fnref42" rev="footnote">↩</a></p>
|
||||
<p>“Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” <a href="https://www.nature.com/articles/d41586-020-00751-9">by Shibo Jiang, on Nature</a> <a href="#fnref42" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
<li id="fn43">
|
||||
<p>Dry land metaphor <a href="https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/">from Marc Lipsitch & Yonatan Grad, on STAT News</a> <a href="#fnref43" rev="footnote">↩</a></p>
|
||||
</li>
|
||||
|
||||
</ol>
|
||||
|
|
|
|||
|
|
@ -59,7 +59,7 @@ It's estimated that, *at the start* of a COVID-19 outbreak, the virus jumps from
|
|||
|
||||
[^serial_interval]: “The mean [serial] interval was 3.96 days (95% CI 3.53–4.39 days)”. [Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Ancel Meyers L](https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article) (Disclaimer: Early release articles are not considered as final versions)
|
||||
|
||||
If we simulate "double every 4 days" *and nothing else*, on a population starting with just 0.001% <icon i></icon>, what happens?
|
||||
If we simulate "double every 4 days" *and nothing else*, on a population starting with just 0.001% <span class="nowrap"><icon i></icon>,</span> what happens?
|
||||
|
||||
**Click "Start" to play the simulation! You can re-play it later with different settings:** (technical caveats: [^caveats])
|
||||
|
||||
|
|
@ -81,7 +81,7 @@ But, this simulation is wrong. Exponential growth, thankfully, can't go on forev
|
|||
|
||||
|
||||
|
||||
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.**
|
||||
The more <span class="nowrap"><icon i></icon>s</span> there are, the faster <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s,</span> **but the fewer <span class="nowrap"><icon s></icon>s</span> there are, the *slower* <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s.</span>**
|
||||
|
||||
How's this change the growth of an epidemic? Let's find out:
|
||||
|
||||
|
|
@ -93,9 +93,9 @@ This is the "S-shaped" **logistic growth curve.** Starts small, explodes, then s
|
|||
|
||||
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 <icon i></icon> Infectious people become <icon r></icon> Recovered. (Just remember that in reality, some are dead.) <icon r></icon>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 are dead.) <span class="nowrap"><icon r></icon>s</span> can't be infected again, and let's pretend – *for now!* – that they stay immune for life.
|
||||
|
||||
With COVID-19, it's estimated you're <icon i></icon> Infectious for 10 days, *on average*.[^infectiousness] That means some folks will recover before 10 days, some after. **Here's what that looks like, with a simulation *starting* with 100% <icon i></icon>:**
|
||||
With COVID-19, it's estimated you're <icon i></icon> Infectious for 10 days, *on average*.[^infectiousness] That means some folks will recover before 10 days, some after. **Here's what that looks like, with a simulation *starting* with 100% <span class="nowrap"><icon i></icon>:</span>**
|
||||
|
||||
[^infectiousness]: “The median communicable period \[...\] was 9.5 days.” [Hu, Z., Song, C., Xu, C. et al](https://link.springer.com/article/10.1007/s11427-020-1661-4) Yes, we know "median" is not the same as "average". For simplified educational purposes, close enough.
|
||||
|
||||
|
|
@ -111,9 +111,9 @@ Now, what happens if you simulate S-shaped logistic growth *with* recovery?
|
|||
|
||||
Let's find out.
|
||||
|
||||
<b style='color:#ff4040'>Red curve</b> is *current* cases <icon i></icon>,
|
||||
<b style='color:#999999'>Gray curve</b> is *total* cases (current + recovered <icon r></icon>),
|
||||
starts at just 0.001% <icon i></icon>:
|
||||
<b style='color:#ff4040'>Red curve</b> is *current* cases <span class="nowrap"><icon i></icon>,</span>
|
||||
<b style='color:#999999'>Gray curve</b> is *total* cases (current + recovered <span class="nowrap"><icon r></icon>),</span>
|
||||
starts at just 0.001% <span class="nowrap"><icon i></icon>:</span>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
|
||||
|
|
@ -131,7 +131,7 @@ the *second*-most important idea in Epidemiology 101:
|
|||
|
||||
**NOTE: The simulations that inform policy are way, *way* more sophisticated than this!** But the SIR Model can still explain the same general findings, even if missing the nuances.
|
||||
|
||||
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*.
|
||||
Actually, let's add one more nuance: before an <icon s></icon> becomes an <span class="nowrap"><icon i></icon>,</span> 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*.
|
||||
|
||||
|
||||
|
||||
|
|
@ -143,14 +143,14 @@ For COVID-19, it's estimated that you're <icon e></icon> infected-but-not-yet-in
|
|||
|
||||
[^latent]: “Assuming an incubation period distribution of mean 5.2 days from a separate study of early COVID-19 cases, we inferred that infectiousness started from 2.3 days (95% CI, 0.8–3.0 days) before symptom onset” (translation: Assuming symptoms start at 5 days, infectiousness starts 2 days before = Infectiousness starts at 3 days) [He, X., Lau, E.H.Y., Wu, P. et al.](https://www.nature.com/articles/s41591-020-0869-5)
|
||||
|
||||
<b style='color:#ff4040'>Red <b style='color:#FF9393'>+ Pink</b> curve</b> is *current* cases (infectious <icon i></icon> + exposed <icon e></icon>),
|
||||
<b style='color:#888'>Gray curve</b> is *total* cases (current + recovered <icon r></icon>):
|
||||
<b style='color:#ff4040'>Red <b style='color:#FF9393'>+ Pink</b> curve</b> is *current* cases (infectious <icon i></icon> + exposed <span class="nowrap"><icon e></icon>),</span>
|
||||
<b style='color:#888'>Gray curve</b> is *total* cases (current + recovered <span class="nowrap"><icon r></icon>):</span>
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
|
||||
</div>
|
||||
|
||||
Not much changes! How long you stay <icon e></icon> Exposed changes the ratio of <icon e></icon>-to-<icon i></icon>, and *when* current cases peak... but the *height* of that peak, and total cases in the end, stays the same.
|
||||
Not much changes! How long you stay <icon e></icon> Exposed changes the ratio of <span class="nowrap"><icon e></icon>-to-<icon i></icon>,</span> and *when* current cases peak... but the *height* of that peak, and total cases in the end, stays the same.
|
||||
|
||||
Why's that? Because of the *first*-most important idea in Epidemiology 101:
|
||||
|
||||
|
|
@ -186,7 +186,7 @@ In our simulations – *at the start & on average* – an <icon i></icon> infect
|
|||
<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.)
|
||||
But remember, the fewer <span class="nowrap"><icon s></icon>s</span> there are, the *slower* <span class="nowrap"><icon s></icon>s</span> become <span class="nowrap"><icon i></icon>s.</span> 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>
|
||||
|
|
@ -202,7 +202,7 @@ Now, let's play the SEIR Model again, but showing R<sub>0</sub>, R over time, an
|
|||
|
||||
**NOTE: Total cases *does not stop* at herd immunity, but overshoots it!** And it crosses the threshold *exactly* when current cases peak. (This happens no matter how you change the settings – try it for yourself!)
|
||||
|
||||
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 when there are more <span class="nowrap">non-<icon s></icon>s</span> than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.
|
||||
|
||||
**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:
|
||||
|
||||
|
|
@ -286,7 +286,7 @@ Increased handwashing cuts flus & colds in high-income countries by ~25%[^handwa
|
|||
|
||||
[^london]: “We found a 73% reduction in the average daily number of contacts observed per participant. This would be sufficient to reduce R0 from a value from 2.6 before the lockdown to 0.62 (0.37 - 0.89) during the lockdown”. We rounded it down to 70% in these simulations for simplicity. [Jarvis and Zandvoort et al](https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html)
|
||||
|
||||
**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])
|
||||
**Play with this calculator to see how % of <span class="nowrap">non-<icon s></icon>,</span> 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])
|
||||
|
||||
[^log_caveat]: This distortion would go away if we plotted R on a logarithmic scale... but then we'd have to explain *logarithmic scales.*
|
||||
|
||||
|
|
@ -324,7 +324,7 @@ Let's see what happens if we *crush* the curve with a 5-month lockdown, reduce <
|
|||
|
||||
Oh.
|
||||
|
||||
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.
|
||||
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 <span class="nowrap"><icon i></icon>)</span> 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.**
|
||||
|
||||
|
|
@ -390,7 +390,7 @@ This is called **contact tracing**. It's an old idea, was used at an unprecedent
|
|||
|
||||
[^ebola]: “Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history.” [Swanson KC, Altare C, Wesseh CS, et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152989/)
|
||||
|
||||
(It also lets us use our limited tests more efficiently, to find pre-symptomatic <icon i></icon>s without needing to test almost everyone.)
|
||||
(It also lets us use our limited tests more efficiently, to find pre-symptomatic <span class="nowrap"><icon i></icon>s</span> without needing to test almost everyone.)
|
||||
|
||||
Traditionally, contacts are found with in-person interviews, but those *alone* are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – *NOT* replaced by – contact tracing apps.
|
||||
|
||||
|
|
@ -404,7 +404,13 @@ Here's how it works:
|
|||
|
||||
|
||||
|
||||
(& [here's the full comic](https://ncase.me/contact-tracing/))
|
||||
([Here's the full comic](https://ncase.me/contact-tracing/). Details about "pranking"/false positives/etc in footnote:[^dp3t_details])
|
||||
|
||||
[^dp3t_details]: To prevent "pranking" (people falsely claiming to be infected), the DP-3T Protocol requires that the hospital first give you a One-Time Passcode that lets you upload your messages.
|
||||
|
||||
False positives are a problem in both manual & digital contact tracing. Still, we can reduce false positives in 2 ways: 1) By notifying Bobs only if they heard, say, 30+ min worth of messages, not just one message in passing. And 2) If the app *does* think Bob's been exposed, it can refer Bob to a *manual* contact tracer, for an in-depth follow-up interview.
|
||||
|
||||
For other issues like data bandwidth, source integrity, and other security issues, check out [the open-source DP-3T whitepapers!](https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing)
|
||||
|
||||
Along with similar teams like TCN Protocol[^tcn] and MIT PACT[^pact], they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.[^gapple] (Don't trust Google/Apple? Good! The beauty of this system is it doesn't *need* trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!
|
||||
|
||||
|
|
@ -416,7 +422,7 @@ Along with similar teams like TCN Protocol[^tcn] and MIT PACT[^pact], they've in
|
|||
|
||||
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. "True" asymptomatics are rare:[^rant])
|
||||
(Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:[^rant])
|
||||
|
||||
[^rant]: Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms *ever*" (true asymptomatic). The only way you could tell the difference is by following up with cases later.
|
||||
|
||||
|
|
@ -441,7 +447,7 @@ Isolating *symptomatic* cases would reduce R by up to 40%, and quarantining thei
|
|||
|
||||
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* – pay for the tests, job protection, subsidized paid leave, 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:
|
||||
We then keep R < 1 until we have a vaccine, which turns susceptible <span class="nowrap"><icon s></icon>s</span> into immune <span class="nowrap"><icon r></icon>s.</span> Herd immunity, the *right* way:
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=int-4b&format=calc" width="285" height="230"></iframe>
|
||||
|
|
@ -597,7 +603,7 @@ But for COVID-19 *in humans*, as of May 1st 2020, "how long" is the big unknown.
|
|||
[^monkeys]: From [Bao et al.](https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract) *Disclaimer: This article is a preprint and has not been certified by peer review (yet).* Also, to emphasize: they only tested re-infection 28 days later.
|
||||
|
||||
For these simulations, let's say it's 1 year.
|
||||
**Here's a simulation starting with 100% <icon r></icon>**, exponentially decaying into susceptible, no-immunity <icon s></icon>s after 1 year, on *average*, with variation:
|
||||
**Here's a simulation starting with 100% <span class="nowrap"><icon r></icon>**,</span> exponentially decaying into susceptible, no-immunity <span class="nowrap"><icon s></icon>s</span> after 1 year, on *average*, with variation:
|
||||
|
||||
<div class="sim">
|
||||
<iframe src="sim?stage=yrs-1&format=lines&height=600" width="800" height="600"></iframe>
|
||||
|
|
@ -627,7 +633,7 @@ Thankfully, because summer reduces R, it'll make the situation better:
|
|||
|
||||
Oh.
|
||||
|
||||
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.
|
||||
Counterintuitively, summer makes the spikes worse *and* regular! This is because summer reduces new <span class="nowrap"><icon i></icon>s,</span> but that in turn reduces new immune <span class="nowrap"><icon r></icon>s.</span> 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:
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue