diff --git a/index.html b/index.html index e431999..c3d60dd 100644 --- a/index.html +++ b/index.html @@ -22,25 +22,21 @@ -
"The only thing to fear is fear itself" is stupid.1
+"The only thing to fear is fear itself" was stupid advice.
-Sure, don't hoard toilet paper. But if someone's so scared to think about scary things, that they deny danger when it's already here, then they've got more problems2 than toilet paper.
+If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we use our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.
-The problem's not fear, but how we use our fear. Taiwan and South Korea bravely used their fear (from SARS) to invest in "pandemic insurance", and it paid off in controlling COVID-19! Fear gives you energy to deal with present dangers & plan for future dangers – if you know how to channel your fear.
- -So, we (Marcel & Nicky) have channeled our COVID-19 fears into making these playable simulations – so that you can channel your fear into gaining a deep, intuitive understanding of:
+Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel our worries into making these playable simulations, so that you can channel your worries into understanding:
Note: We're publishing this on April 30th, 2020. Still the early days. As humanity learns more about COVID-19, our plans will and should change – but we hope this post will address 90%+ of all future possibilities!
+This guide is meant to give you hope and fear. To beat this virus in a way that also protects our mental & financial health, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, “The optimist invents the airplane and the pessimist the parachute.”
-Honestly, some of the possibilities are scary. And some are hopeful! But preparing for the scary possibilites is what creates the hopeful possibilites. You don't get to save the prince/ss without facing the dragon.
- -Let's bravely use our fear, and face this dragon.
+So, buckle in: we're about to experience some turbulence.
...has been a real worldwide cram-school in Epidemiology 101.
+Pilots use flight simulators to learn how not to crash planes.
-Pilots use flight simulators to learn how not to crash planes. Epidemiologists use epidemic simulators to learn how not to crash humanity.
+Epidemiologists use epidemic simulators to learn how not to crash humanity.
-So, let's set up an epidemic "flight simulator"! First, we need some simulation rules.
- -Let's say you have some Infected (i) people and not-yet-infected Susceptible (s) people. One (i) infects a (s), those 2 (i) infect another 2 (s), those 4 (i) infect another 4 (s), and so on:
+So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s:
// pic
-On average, COVID-19 jumps from an (i) to a (s) every 4 days.1 The average # of days it takes for an (i) to infect an (s) is called the "generation time"2-note. (Click the gray circles for sources, and the blue squares for side-notes!)
+At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.1 (On average. Remember, there's lots of variation.)
-Rule #1: The more (i)s there are, the faster (s)s become (i)s.
+Here's a simulation of a population with just 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" and nothing else, what happens?
-// pic - rule
- -If we simulate just this rule and nothing else, here's what it looks like over 3 months, starting with 99.9% (s) and just 0.1% (i):
- -Click "Start" play the simulation! You can then change the "generation time", and see how that changes the simulation:
+Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)
Starts small ("it's just a flu"), then explodes ("oh right, flus don't break hospitals in rich countries"). This is the "J-shaped" exponential growth curve.
+This is the exponential growth curve. Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create mass graves in rich cities".
-But this simulation is wrong. There are things that prevent an (i) from infecting someone else – like if that other person is already an (i):
+// pic - exponential double rice
+ +But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others already have the virus:
// pic - 100% spread, 50% spread, 0% spread
-Rule #2: The fewer (s)s there are, the slower (s)s become (i)s.
+The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the slower (s)s become (i)s.
-// pic - rule
- -Now, what happens if we simulate both these rules?
- -Again, click Start to play the simulation!
+Now, what happens if we simulate that?
// sim
-Starts small, explodes, then slows down again. This is the "S-shaped" logistic growth curve.
+This is the "S-shaped" logistic growth curve. Starts small, explodes, then slows down again.
-Still, this simulation predicts 100% of people will get the virus, and even the most pessimistic COVID-19 simulations don't predict that.
+But, this simulation is still wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.
-What we're missing: You stop being infectious for COVID-19 when you recover... or die.
+For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – for now! – that they stay immune for life.
-For the sake not making these simulations too depressing, let's only simulate Infected (i) becoming (r) Recovered. (The math works out the same.) And let's assume (for now!!!) that (r)s can't get infected again. So, new rule:
+When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.2 (Again, on average.)
-Rule #3: (i)s eventually become (r)s.
- -// pic - rule
- -Let's have (i)s become (r)s after 14 days, on average.3-note This means some (i)s will recover before 14 days, and some recover after! This is closer to real life.
- -To show only Rule #3, here's a simulation starting with 100% (i):
+Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder of that remainder recover after another 12 days, etc:
// sim
-This is the "flipped-J-shaped" exponential decay curve.
+This is the opposite of exponential growth, the exponential decay curve.
-Now, what happens if you simulate all 3 rules at once? What happens when you combine an S-shaped logistic curve with a flipped-J exponential decay curve?
+Now, what happens if you combine this with the S-shaped logistic curve of infection?
// pic
-Let's find out:
+Let's find out. Here's a simulation of an epidemic with recovery:
// sim
@@ -120,70 +102,84 @@// pic: 3 rules
-This is the SIR Model, the second-most important idea in epidemiology.
+This is the the SIR Model, ((s) Susceptible → (i) Infectious → (r) Recovered) the second-most important idea in Epidemiology 101.
-NOTE: The simulations you've been hearing in the news are far more complex than the ones you're seeing here! But the sims you'll play with here reach the same general conclusions, even if missing the nuances.
+Note: The simulations that inform policy are far more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances.
-One nuance you could add is the SIRS Model, where the final "S" also stands for (s) Susceptible – this is when people recover, are immune for a bit, then lose that immunity and can be infected again. (We'll consider this in the Next Few Years section)
+Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infected but not yet infectious – they have the virus but can't pass it on (yet).
-Another nuanced version is the SEIR Model, where the "E" stands for (e) Exposed, a brief period of time after you've been infected, but before you can infect others. This is called the "latent period", and for COVID-19 it's around 3 days.4
+(This variant is called the SEIR Model, where "E" stands for (e) Exposed. Note this isn't the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.)
-Here's what happens if you simulate that:
+For COVID-19, it's estimated that you're in this "latent period" for around 3 days.3 What happens if we add that to the simulation?
// sim
-Doesn't change much, so let's stick to the vanilla SIR model. We brought (e)s up because the exact timing of contagiousness is important in "contact tracing", which we'll explain in the Next Few Months section.
+Not much, actually! The "latent period" only changes when the peak happens, but the height of the peak – and total people infected – remain the same:
-Oh! But almost forgot, the first-most important idea in epidemiology:
+// pics
-"R"
+Why's that? Because of the first-most important idea in Epidemiology 101:
-Which is short for "Reproduction Number". It's the average number of people an (i) will infect before they recover:
+// pic - "R"
-// pic - R>1 R=1 R<1
+Which is short for "Reproduction Number". It's the average number of people an (i) infects before they recover (or die).
-R0 (pronounced R-nought) is the Reproduction Number for a virus at the very beginning of an outbreak, before we have immunity or interventions. (Also called "Basic Reproduction Number")
+// R > 1, R = 1, R < 1 pic
-Rt (the 't' stands for time) is the Reproduction Number right now, after we have some immunity or interventions. (Also called "Re", e standing for "Effective Reproduction Number". Also called just "R", to... confuse people)
+R changes over the course of an outbreak, as we get more immunity & interventions.
-// pic of R0 and Rt over time for the Famous Curve – with peak for inflection!
+R0 (pronounced R-nought) is what R is at the start of an outbreak, before immunity or interventions. R0 is also called the "basic reproduction number". R0 more closely reflects the power of the virus itself, but it still changes from place to place. For example, because heat 'kills' coronaviruses, R0 for COVID-19 is lower in hot places than cold ones. Not low enough to contain it, though.
-(A lot of news outlets confuse these two Rs! They're different!)
+(A lot of news outlets – and even academic papers! – confuse R and R0. Again, science terminology is bad.)
-The R0 for the flu6 is around 1.3. The R0 for COVID-19 is somewhere between 2 and 5.7 The huge uncertainty is because R0 depends on exactly how quickly new people are infected ("generation time") vs how quickly people recover8:
+The R0 for the flu4 is around 1.3. The R0 estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.5
+ +In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, on average, an (i) infects 3 (s)s before they recover. So for our simulations, R0 is 3.
+ +Play around with this R0 calculator, to see how R0 depends on recovery time & new-infection time:
+ +// calc
+ +But remember, the fewer (s)s there are, the slower (s)s become (i)s. + R depends not just on R0, but also how many people are no longer Susceptible – due to, say, having recovered & gotten natural immunity.
+ +// calc 2
+ +When enough people have natural immunity, R < 1, and the virus is contained! This is called herd immunity, and while it's terrible policy, (we'll explain why later – it's not for the reason you may think!) it's essential to understanding Epidemiology 101.
+ +Now, let's play the last simulation again, but showing R0, R over time, and the herd immunity threshold:
// sim
-Rt for COVID-19 depends on the interventions we do (or don't) have, as well as how many people aren't (s) Susceptible. (because they're (r) Recovered, currently (i) Infected, or... dead.)
+Note: Total cases (the gray curve) does not stop at herd immunity, but overshoots it! And it does this exactly when current cases (the pink curve) peaks. This happens no matter how you change the settings:
-// sim
+// pic
-Note that when (s)% is low enough, you can get Rt<1 – containing the virus! This is called the "herd immunity" threshold. "Herd immunity" is a terrible policy (TODO: explain why), but it's important for understanding epidemiology.
+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.
-Now, let's run the same SIR model simulation again, but this time showing 1) Rt changing over time, and 2) the herd immunity threshold:
+If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:
-// sim
+Note how total cases ((i)+(r)) overshoots the herd immunity threshold! And the exact moment it does this is when infections peak and when Rt drops below 1!
+If there's only one lesson you take away today, here it is, in big shiny letters:
+This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!
-It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if R0 = 3, cutting that by 67% gives us R = 0.99, which is R < 1, which means the virus is contained!
-(And even if, extreme-worst-case, R0 = 6, you still "only" need to stop 84% of transmissions. That's a B grade.)
-NOTE: We do not need to catch all transmissions, or even nearly all transmissions, to stop COVID-19.
- -It's a paradox – COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 72% of infections. 72%?! That's, like, a C– grade. But if R0 = 3.5, then reducing that by 72% will make Rt < 1 = good.
- -(And even if worst-case, R0=5, you "only" need to stop 80%. That's a B–.)
+// calculator - custom
Every COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're all doing the same thing:
-Reducing Rt.
+Getting R < 1.
-Let's see how we can get Rt<1 – in a way that protects not just our physical health, but also our mental health, social health, and financial health!
+So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, but also our mental health, social health, and financial health?
+Brace yourselves for an emergency landing...
+ +"The only thing to fear is fear itself" was stupid advice.
+ +If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we use our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.
+ +Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel our worries into making these playable simulations, so that you can channel your worries into understanding:
+ +This guide is meant to give you hope and fear. To beat this virus in a way that also protects our mental & financial health, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, “The optimist invents the airplane and the pessimist the parachute.”
+ +So, buckle in: we're about to experience some turbulence.
+ +Pilots use flight simulators to learn how not to crash planes.
+ +Epidemiologists use epidemic simulators to learn how not to crash humanity.
+ +So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s:
+ +// pic
+ +At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.1 (On average. Remember, there's lots of variation.)
+ +Here's a simulation of a population with just 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" and nothing else, what happens?
+ +Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)
+ +// sim
+ +This is the exponential growth curve. Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create mass graves in rich cities".
+ +// pic - exponential double rice
+ +But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others already have the virus:
+ +// pic - 100% spread, 50% spread, 0% spread
+ +The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the slower (s)s become (i)s.
+ +Now, what happens if we simulate that?
+ +// sim
+ +This is the "S-shaped" logistic growth curve. Starts small, explodes, then slows down again.
+ +But, this simulation is still wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying.
+ +For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – for now! – that they stay immune for life.
+ +When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.2 (Again, on average.)
+ +Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder of that remainder recover after another 12 days, etc:
+ +// sim
+ +This is the opposite of exponential growth, the exponential decay curve.
+ +Now, what happens if you combine this with the S-shaped logistic curve of infection?
+ +// pic
+ +Let's find out. Here's a simulation of an epidemic with recovery:
+ +// sim
+ +And that's where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten.
+ +// pic: 3 rules
+ +This is the the SIR Model, ((s) Susceptible → (i) Infectious → (r) Recovered) the second-most important idea in Epidemiology 101.
+ +Note: The simulations that inform policy are far more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances.
+ +Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infected but not yet infectious – they have the virus but can't pass it on (yet).
+ +(This variant is called the SEIR Model, where "E" stands for (e) Exposed. Note this isn't the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.)
+ +For COVID-19, it's estimated that you're in this "latent period" for around 3 days.3 What happens if we add that to the simulation?
+ +// sim
+ +Not much, actually! The "latent period" only changes when the peak happens, but the height of the peak – and total people infected – remain the same:
+ +// pics
+ +Why's that? Because of the first-most important idea in Epidemiology 101:
+ +// pic - "R"
+ +Which is short for "Reproduction Number". It's the average number of people an (i) infects before they recover (or die).
+ +// R > 1, R = 1, R < 1 pic
+ +R changes over the course of an outbreak, as we get more immunity & interventions.
+ +R0 (pronounced R-nought) is what R is at the start of an outbreak, before immunity or interventions. R0 is also called the "basic reproduction number". R0 more closely reflects the power of the virus itself, but it still changes from place to place. For example, because heat 'kills' coronaviruses, R0 for COVID-19 is lower in hot places than cold ones. Not low enough to contain it, though.
+ +(A lot of news outlets – and even academic papers! – confuse R and R0. Again, science terminology is bad.)
+ +The R0 for the flu4 is around 1.3. The R0 estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.5
+ +In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, on average, an (i) infects 3 (s)s before they recover. So for our simulations, R0 is 3.
+ +Play around with this R0 calculator, to see how R0 depends on recovery time & new-infection time:
+ +// calc
+ +But remember, the fewer (s)s there are, the slower (s)s become (i)s. +R depends not just on R0, but also how many people are no longer Susceptible – due to, say, having recovered & gotten natural immunity.
+ +// calc 2
+ +When enough people have natural immunity, R < 1, and the virus is contained! This is called herd immunity, and while it's terrible policy, (we'll explain why later – it's not for the reason you may think!) it's essential to understanding Epidemiology 101.
+ +Now, let's play the last simulation again, but showing R0, R over time, and the herd immunity threshold:
+ +// sim
+ +Note: Total cases (the gray curve) does not stop at herd immunity, but overshoots it! And it does this exactly when current cases (the pink curve) peaks. This happens no matter how you change the settings:
+ +// pic
+ +This is because, by definition, when there are more non-(s)s than the herd immunity threshold, you get R < 1. And, by definition, R < 1 means new cases stop growing.
+ +If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:
+ +This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!
+ +It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if R0 = 3, cutting that by 67% gives us R = 0.99, which is R < 1, which means the virus is contained!
+ +(And even if, extreme-worst-case, R0 = 6, you still "only" need to stop 84% of transmissions. That's a B grade.)
+ +// calculator - custom
+ +Every COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're all doing the same thing:
+ +Getting R < 1.
+ +So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, but also our mental health, social health, and financial health?
+ +Brace yourselves for an emergency landing...
+ + + + + + + + + diff --git a/words_epi_101.md b/words/words_epi_101.md similarity index 100% rename from words_epi_101.md rename to words/words_epi_101.md