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HTML
205 lines
11 KiB
HTML
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<meta charset="utf-8">
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<title>words_epi_101</title>
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<h1 id="toc_0">What Happens Next?</h1>
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<h2 id="toc_1">COVID-19 Futures, Explained With Playable Simulations</h2>
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<p>"The only thing to fear is fear itself" was stupid advice.</p>
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<p>If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we <em>use</em> our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.</p>
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<p>Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel <em>our</em> worries into making these <strong>playable simulations</strong>, so that you can channel <em>your</em> worries into understanding:</p>
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<ul>
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<li><strong>The Last Few Months</strong> (epidemiology 101, SEIR model, R & R<sub>0</sub>)</li>
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<li><strong>The Next Few Months</strong> (lockdowns, contact tracing, masks)</li>
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<li><strong>The Next Few Years</strong> (vaccines, loss of immunity?)</li>
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</ul>
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<p>This guide is meant to give you hope <em>and</em> fear. To beat this virus <strong>in a way that also protects our mental & financial health</strong>, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, <em>“The optimist invents the airplane and the pessimist the parachute.”</em></p>
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<p>So, buckle in: we're about to experience some turbulence.</p>
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<hr>
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<h1 id="toc_2">The Last Few Months</h1>
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<p>Pilots use flight simulators to learn how not to crash planes.</p>
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<p><strong>Epidemiologists use epidemic simulators to learn how not to crash humanity.</strong></p>
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<p>So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s:</p>
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<p>// pic</p>
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<p>At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.<sup id="fnref1"><a href="#fn1" rel="footnote">1</a></sup> (<em>On average.</em> Remember, there's lots of variation.)</p>
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<p>Here's a simulation of a population with <em>just</em> 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" <em>and nothing else</em>, what happens?</p>
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<p><strong>Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)</strong></p>
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<p>// sim</p>
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<p>This is the <strong>exponential growth curve.</strong> Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create <em>mass graves in rich cities</em>". </p>
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<p>// pic - exponential double rice</p>
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<p>But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others <em>already</em> have the virus:</p>
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<p>// pic - 100% spread, 50% spread, 0% spread</p>
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<p><strong>The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the <em>slower</em> (s)s become (i)s.</strong></p>
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<p>Now, what happens if we simulate that?</p>
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<p>// sim</p>
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<p>This is the "S-shaped" <strong>logistic growth curve.</strong> Starts small, explodes, then slows down again.</p>
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<p>But, this simulation is <em>still</em> wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.</p>
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<p>For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – <em>for now!</em> – that they stay immune for life.</p>
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<p>When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.<sup id="fnref2"><a href="#fn2" rel="footnote">2</a></sup> (Again, <em>on average.</em>)</p>
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<p>Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder <em>of that remainder</em> recover after another 12 days, etc:</p>
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<p>// sim</p>
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<p>This is the opposite of exponential growth, the <strong>exponential decay curve</strong>.</p>
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<p>Now, what happens if you combine this with the S-shaped logistic curve of infection?</p>
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<p>// pic</p>
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<p>Let's find out. Here's a simulation of an epidemic <em>with</em> recovery:</p>
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<p>// sim</p>
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<p>And <em>that's</em> where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten.</p>
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<p>// pic: 3 rules</p>
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<p>This is the the <strong>SIR Model</strong>, ((s) <strong>S</strong>usceptible → (i) <strong>I</strong>nfectious → (r) <strong>R</strong>ecovered) the second-most important idea in Epidemiology 101.</p>
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<p>Note: The simulations that inform policy are <em>far</em> more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances.</p>
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<p>Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infect<em>ed</em> but not yet infect<em>ious</em> – they have the virus but can't pass it on (yet).</p>
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<p>(This variant is called the <strong>SEIR Model</strong>, where "E" stands for (e) Exposed. Note this <em>isn't</em> the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.)</p>
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<p>For COVID-19, it's estimated that you're in this "latent period" for around 3 days.<sup id="fnref3"><a href="#fn3" rel="footnote">3</a></sup> What happens if we add that to the simulation?</p>
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<p>// sim</p>
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<p>Not much, actually! The "latent period" only changes <em>when</em> the peak happens, but the <em>height</em> of the peak – and total people infected – remain the same:</p>
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<p>// pics</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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<p>// pic - <strong>"R"</strong></p>
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<p>Which is short for "Reproduction Number". It's the <em>average</em> number of people an (i) infects <em>before</em> they recover (or die).</p>
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<p>// R > 1, R = 1, R < 1 pic</p>
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<p><strong>R</strong> changes over the course of an outbreak, as we get more immunity & interventions.</p>
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<p><strong>R<sub>0</sub></strong> (pronounced R-nought) is what R is <em>at the start of an outbreak, before immunity or interventions</em>. R<sub>0</sub> is also called the "basic reproduction number". R<sub>0</sub> more closely reflects the power of the virus itself, but it still changes from place to place. For example, because heat 'kills' coronaviruses, R<sub>0</sub> for COVID-19 is lower in hot places than cold ones. Not low enough to contain it, though.</p>
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<p>(A lot of news outlets – and even academic papers! – confuse R and R<sub>0</sub>. Again, science terminology is bad.)</p>
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<p>The R<sub>0</sub> for the flu<sup id="fnref4"><a href="#fn4" rel="footnote">4</a></sup> is around 1.3. The R<sub>0</sub> estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.<sup id="fnref5"><a href="#fn5" rel="footnote">5</a></sup></p>
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<p>In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, <em>on average</em>, an (i) infects 3 (s)s before they recover. So for our simulations, R<sub>0</sub> is 3.</p>
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<p><strong>Play around with this R<sub>0</sub> calculator, to see how R<sub>0</sub> depends on recovery time & new-infection time:</strong></p>
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<p>// calc</p>
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<p>But remember, the fewer (s)s there are, the <em>slower</em> (s)s become (i)s.
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R depends not just on R<sub>0</sub>, but also how many people are no longer Susceptible – due to, say, having recovered & gotten natural immunity.</p>
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<p>// calc 2</p>
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<p>When enough people have natural immunity, R < 1, and the virus is contained! This is called <strong>herd immunity</strong>, and while it's <em>terrible</em> policy, (we'll explain why later – it's not for the reason you may think!) it's essential to understanding Epidemiology 101.</p>
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<p>Now, let's play the last simulation again, but showing R<sub>0</sub>, R over time, and the herd immunity threshold:</p>
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<p>// sim</p>
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<p>Note: Total cases (the gray curve) does not stop at herd immunity, but <em>overshoots</em> it! And it does this <em>exactly when</em> current cases (the pink curve) peaks. This happens no matter how you change the settings:</p>
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<p>// pic</p>
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<p>This is because, by definition, when there are more non-(s)s than the herd immunity threshold, you get R < 1. And, by definition, R < 1 means new cases stop growing.</p>
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<p>If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:</p>
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<h1 id="toc_3">R > 1 = bad</h1>
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<h1 id="toc_4">R < 1 = good (R=1, meh)</h1>
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<p><strong>This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!</strong></p>
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<p>It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if R<sub>0</sub> = 3, cutting that by 67% gives us R = 0.99, which is R < 1, which means the virus is contained!</p>
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<p>(And even if, extreme-worst-case, R<sub>0</sub> = <em>6</em>, you still "only" need to stop 84% of transmissions. That's a B grade.)</p>
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<p>// calculator - custom</p>
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<p><em>Every</em> COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're <em>all</em> doing the same thing:</p>
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<p>Getting R < 1.</p>
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<p>So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, <strong>but also our mental health, social health, <em>and</em> financial health?</strong></p>
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<p>Brace yourselves for an emergency landing...</p>
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<div class="footnotes">
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<hr>
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<ol>
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<li id="fn1">
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<p>source <a href="#fnref1" rev="footnote">↩</a></p>
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</li>
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<li id="fn2">
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<p>source <a href="#fnref2" rev="footnote">↩</a></p>
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</li>
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<li id="fn3">
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<p>source <a href="#fnref3" rev="footnote">↩</a></p>
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</li>
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<li id="fn4">
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<p>source <a href="#fnref4" rev="footnote">↩</a></p>
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</li>
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<li id="fn5">
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<p>source <a href="#fnref5" rev="footnote">↩</a></p>
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</li>
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</ol>
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</div>
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</body>
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