{WIP, DON'T SHARE YET THX!}
+"The only thing to fear is fear itself" was stupid advice.
-If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we use our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers.
+Sure, don't hoard toilet paper – but if policymakers fear fear itself, they'll downplay dangers to us to avoid "mass panic". Fear's not the problem, it's how we channel our fear. Fear gives us energy to deal with dangers now, and prepare for dangers later.
-Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel our worries into making these playable simulations, so that you can channel your worries into understanding:
+Honestly, we (Marcel, epidemiologist + Nicky, art/code) are worried. We bet you are, too! That's why we've channelled our fear into making these playable simulations, so that you can channel your fear into understanding:
This guide is meant to give you hope and fear. To beat this virus in a way that also protects our mental & financial health, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, “The optimist invents the airplane and the pessimist the parachute.”
+This guide (published April 30th, 20201) is meant to give you hope and fear. To beat COVID-19 in a way that also protects our mental & financial health, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, “The optimist invents the airplane and the pessimist the parachute.”
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:
+So, let's make a simple "epidemic flight simulator"! In this simulation,
// pic
+
At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.1 (On average. Remember, there's lots of variation.)
+It's estimated that, at the start of a COVID-19 outbreak, the virus jumps from an
Here's a simulation of a population with just 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" and nothing else, what happens?
+If we simulate "double every 4 days" and nothing else, on a population starting with just 0.001%
Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)
+Click "Start" to play the simulation! You can re-play it later with different settings: (technical caveats: 3)
This is the exponential growth curve. Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create mass graves in rich cities".
-// pic - exponential double rice
+
But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others already have the virus:
-// pic - 100% spread, 50% spread, 0% spread
+
The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the slower (s)s become (i)s.
+The more
Now, what happens if we simulate that?
+How's this change the growth of an epidemic? Let's find out:
-// 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.
+But, this simulation is still wrong. We're missing the fact that
For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – for now! – that they stay immune for life.
+For simplicity's sake, let's pretend that all
When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.2 (Again, on average.)
+With COVID-19, it's estimated you're
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?
+Now, what happens if you simulate S-shaped logistic growth with recovery?
-// pic
+
Let's find out. Here's a simulation of an epidemic with recovery:
+Let's find out:
-// sim
+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 Model5 –
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.
+NOTE: The simulations that inform policy are far more sophisticated than this! But the SIR Model can still explain the same findings, even if missing the nuances.
-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).
+Actually, let's add one more nuance: before an
(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?
+(This variant is called the SEIR Model6, where the "E" stands for
// sim
+For COVID-19, it's estimated that you're
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
+Not much, actually! How long you stay
Why's that? Because of the first-most important idea in Epidemiology 101:
-// pic - "R"
+
Which is short for "Reproduction Number". It's the average number of people an (i) infects before they recover (or die).
+Short for "Reproduction number". It's the average number of people an
// 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.
+R0 (pronounced R-nought) is what R is at the start of an outbreak, before immunity or interventions. R0 more closely reflects the power of the virus itself, but it still changes from place to place. For example, R0 is higher in dense cities than sparse rural areas.
-(A lot of news outlets – and even academic papers! – confuse R and R0. Again, science terminology is bad.)
+(Most news articles – and even some scientific 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
+The R0 for "the" seasonal flu is around 1.288. This means, at the start of a flu outbreak, each
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.
+The R0 for COVID-19 is estimated to be around 2.29, though a not-yet-finalized CDC study estimates it was 5.7(!) in Wuhan.10
-Play around with this R0 calculator, to see how R0 depends on recovery time & new-infection time:
+In our simulations – at the start & on average – an
// calc
+Play with this R0 calculator, to see how R0 depends on recovery time & new-infection time:
-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
+But remember, the fewer
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:
+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.
-// sim
+Now, let's play the SEIR Model again, but showing R0, R over time, and the herd immunity threshold:
-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
+Note: Total cases (gray curve) does not stop at herd immunity, but overshoots it! And it does this exactly when current cases (pink curve) peaks. (This happens no matter how you change the settings – try it for yourself!)
-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.
+This is because when there are more non-
If there's only one lesson you take away from this whole guide, here it is, in big shiny letters:
+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:
-
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!
+It's a paradox. COVID-19 is extremely contagious, yet to contain it, we "only" need to stop more than 60% of infections. 60%?! If that was a school grade, that's a D-. But if R0 = 2.5, cutting that by 61% gives us R = 0.975, which is R < 1, virus is contained!12
-(And even if, extreme-worst-case, R0 = 6, you still "only" need to stop 84% of transmissions. That's a B grade.)
+
// calculator - custom
+(If you think R0 or the other numbers in our simulations are too low/high, that's good you're challenging our assumptions! There'll be a "Sandbox Mode" at the end of this guide, where you can plug in your own numbers, and simulate what happens.)
-Every COVID-19 intervention you've heard of – handwashing, social distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're all doing the same thing:
+Every COVID-19 intervention you've heard of – handwashing, social/physical distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're all doing the same thing:
Getting R < 1.
-So now, let's use our "epidemic flight simulator" to figure out the next few months! How will we get R < 1 in a way that protects not just our physical health, but also our mental health, social health, and financial health?
+So now, let's use our "epidemic flight simulator" to figure this out: How can we get R < 1 in a way that also protects our mental health and financial health?
Brace yourselves for an emergency landing...
- +The only thing to fear is the idea that the only thing to fear is fear itself.
hello! ↩
+(NOTE: This guide was published on April 30th, 2020. Many details will become outdated, but Epidemiology 101 will remain true, and we're confident this guide will cover 95% of possible futures.) ↩
but a snitch ain't one ↩
+https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article ↩
+source ↩
+https://link.springer.com/article/10.1007/s11427-020-1661-4 ↩
+source, and sidenote on 'infectious' ↩
+source ↩
+source ↩
+https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-480 ↩
+https://pubmed.ncbi.nlm.nih.gov/31995857/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/ ↩
+https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article ↩
+sas ↩
+exact formula... ↩
+https://www.statista.com/statistics/1105420/covid-icu-admission-rates-us-by-age-group/ Lower end, 5%. ↩
+https://sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19 ↩
+https://www.theatlantic.com/health/archive/2020/03/coronavirus-pandemic-herd-immunity-uk-boris-johnson/608065/ ↩
+https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2006.01568.x ↩
+https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html ↩
+log scale ↩
+https://science.sciencemag.org/content/early/2020/04/14/science.abb5793? ↩
+https://journals.sagepub.com/doi/abs/10.1177/1745691614568352 ↩
+sources plz, esp for incubation period 5 days ↩
+https://www.nature.com/articles/s41591-020-0869-5 ↩
+asds ↩
+https://science.sciencemag.org/content/early/2020/04/09/science.abb6936 ↩
+incoming ↩
+outgoing_aerosols ↩
+outgoing_droplets ↩
+homemade ↩
+ss ↩
+That BMJ article ↩
+s ↩
+ss ↩
+s ↩
+https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767 The average R-value of these 100 cities is 1.83 , One-degree Celsius increase in temperature and one percent increase in relative humidity lower R by 0.0225 ↩
+https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/ ↩
+https://pubmed.ncbi.nlm.nih.gov/2170159/ ↩
+https://www.nature.com/articles/d41586-020-01063-8 ↩
+https://www.nature.com/articles/d41586-020-00751-9 ↩
+https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/ ↩
{WIP, DON'T SHARE YET THX!}
-"The only thing to fear is fear itself" was stupid advice.
-"The only thing to fear is fear itself" is stupid.1
+Sure, don't hoard toilet paper – but if policymakers fear fear itself, they'll downplay dangers to us to avoid "mass panic". Fear's not the problem, it's how we channel our fear. Fear gives us energy to deal with dangers now, and prepare for dangers later.
-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.
- -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, we (Marcel, epidemiologist + Nicky, art/code) are worried. We bet you are, too! That's why we've channelled our fear into making these playable simulations, so that you can channel your fear into understanding:
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 (published April 30th, 20201) is meant to give you hope and fear. To beat COVID-19 in a way that also protects our mental & financial health, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, “The optimist invents the airplane and the pessimist the parachute.”
-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.
+So, buckle in: we're about to experience some turbulence.
-Let's bravely use our fear, and face this dragon.
+Pilots use flight simulators to learn how not to crash planes.
-Epidemiologists use epidemic simulators to learn how not to crash humanity.
-...has been a real worldwide cram-school in Epidemiology 101.
+So, let's make a simple "epidemic flight simulator"! In this simulation,
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 set up an epidemic "flight simulator"! First, we need some simulation rules.
+It's estimated that, at the start of a COVID-19 outbreak, the virus jumps from an
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:
+If we simulate "double every 4 days" and nothing else, on a population starting with just 0.001%
// pic
+Click "Start" to play the simulation! You can re-play it later with different settings: (technical caveats: 3)
-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!)
+Rule #1: The more (i)s there are, the faster (s)s become (i)s.
+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 - 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):
+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:
-Click "Start" play the simulation! You can then change the "generation time", and see how that changes the simulation:
+
// sim
+The more
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.
+How's this change the growth of an epidemic? Let's find out:
-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 - 100% spread, 50% spread, 0% spread
+This is the "S-shaped" logistic growth curve. Starts small, explodes, then slows down again.
-Rule #2: The fewer (s)s there are, the slower (s)s become (i)s.
+But, this simulation is still wrong. We're missing the fact that
// pic - rule
+For simplicity's sake, let's pretend that all
Now, what happens if we simulate both these rules?
+With COVID-19, it's estimated you're
Again, click Start to play the simulation!
+// sim
+This is the opposite of exponential growth, the exponential decay curve.
-Starts small, explodes, then slows down again. This is the "S-shaped" logistic growth curve.
+Now, what happens if you simulate S-shaped logistic growth with recovery?
-Still, this simulation predicts 100% of people will get the virus, and even the most pessimistic COVID-19 simulations don't predict that.
- -What we're missing: You stop being infectious for COVID-19 when you recover... or die.
- -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:
- -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):
- -// sim
- -This is the "flipped-J-shaped" 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?
- -// pic
+
Let's find out:
-// 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 Model5 –
This is the SIR Model, the second-most important idea in epidemiology.
+
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 explain the same findings, 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
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
+
Here's what happens if you simulate that:
+(This variant is called the SEIR Model6, where the "E" stands for
// sim
+For COVID-19, it's estimated that you're
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.
+Oh! But almost forgot, the first-most important idea in epidemiology:
+Not much, actually! How long you stay
"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>1 R=1 R<1
+Short for "Reproduction number". It's the average number of people an
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")
+
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 more closely reflects the power of the virus itself, but it still changes from place to place. For example, R0 is higher in dense cities than sparse rural areas.
-(A lot of news outlets confuse these two Rs! They're different!)
+(Most news articles – and even some scientific 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" seasonal flu is around 1.288. This means, at the start of a flu outbreak, each
// sim
+The R0 for COVID-19 is estimated to be around 2.29, though a not-yet-finalized CDC study estimates it was 5.7(!) in Wuhan.10
-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.)
+In our simulations – at the start & on average – an
// sim
+Play with this R0 calculator, to see how R0 depends on recovery time & new-infection time:
-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.
+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:
+But remember, the fewer
// 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!
+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.
-If there's only one lesson you take away today, here it is, in big shiny letters:
+Now, let's play the SEIR Model again, but showing R0, R over time, and the herd immunity threshold:
-Note: Total cases (gray curve) does not stop at herd immunity, but overshoots it! And it does this exactly when current cases (pink curve) peaks. (This happens no matter how you change the settings – try it for yourself!)
-NOTE: We do not need to catch all transmissions, or even nearly all transmissions, to stop COVID-19.
+This is because when there are more non-
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.
+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:
-(And even if worst-case, R0=5, you "only" need to stop 80%. That's a B–.)
+
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:
+This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!
-Reducing Rt.
+It's a paradox. COVID-19 is extremely contagious, yet to contain it, we "only" need to stop more than 60% of infections. 60%?! If that was a school grade, that's a D-. But if R0 = 2.5, cutting that by 61% gives us R = 0.975, which is R < 1, virus is contained!12
-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!
+
(If you think R0 or the other numbers in our simulations are too low/high, that's good you're challenging our assumptions! There'll be a "Sandbox Mode" at the end of this guide, where you can plug in your own numbers, and simulate what happens.)
-Every COVID-19 intervention you've heard of – handwashing, social/physical distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're all doing the same thing:
-...could have been worse.
+Getting R < 1.
-So now, let's use our "epidemic flight simulator" to figure this out: How can we get R < 1 in a way that also protects our mental health and financial health?
-For COVID-19, 1 in 20 (i)s need to be hospitalized. In rich countries like the US and UK, there's 1 hospital bed for every 1000 people. Therefore: a rich country can handle a maximum of 20 (i)s per 1000 people – or, a maximum of 2% of the population being simultaneously sick.
+Brace yourselves for an emergency landing...
-Here's the same simulation from before, but with the "2%" threshold drawn:
+// sim
+...could have been worse. Here's a parallel universe we avoided:
-It's not good.
+That's the same thing the March 16th Imperial College report found: if we do nothing, hospitals break. Almost everyone gets infected. Even with a low 0.5% infection fatality ratio, 80% of people infected in a large country like the US still means over a million dead... IF we did nothing.
+Around 1 in 20 people infected with COVID-19 need to go to an ICU (Intensive Care Unit).13 In a rich country like the USA, there's 1 ICU per 3400 people.14 Therefore, the USA can handle 20 out of 3400 people being simultaneously infected – or, 0.6% of the population.
-(A lot of news & social media chose to report the scary bit, without "IF WE DO NOTHING". Fear was channeled into clicks, not understanding. Sigh.)
+Even if we more than tripled that capacity to 2%, here's what would've happened if we did absolutely nothing:
-Handwashing was discovered in ____ by the doctor _______, when he realized that by getting his staff to wash their hands, child deaths in his hospital were cut by 90%!
+Not good.
-Doctors around the world immediately hailed his life-saving discovery, and ha ha just kidding they committed him to an asylum where he was beat to death by guards.
+That's what the March 16 Imperial College report found: do nothing, and we run out of ICUs with 80%+ of the population infected.
-In any case, frequent handwashing reduces your chances of catching influenza by 50%[9]() And if we combine this with other hygiene tips – cough into your elbow, don't touch your face – let's guess-timate that 100% compliance (which we will NOT get) will result in a 60% reduction in new infections, in Rt:
+Even if only 0.5% of infected die – a generous assumption when there's no more ICUs – in a large country like the US, with 300 million people, 0.5% of 80% of 300 million = still 1.2 million dead... IF we did nothing.
-// controls
+(Lots of news & social media reported "80%+ will be infected" without "IF WE DO NOTHING". Fear was channelled into clicks, not understanding. Sigh.)
-It can't get Rt<1, but it does reduce it! How does that affect the epidemic?
+// sim
+The "Flatten The Curve" plan was touted by every public health organization, while the United Kingdom's original "herd immunity" plan was universally booed. They were the same plan. The UK just communicated theirs poorly.15
-That's a... better catastrophe.
+Both plans, though, are horribly flawed.
-Contrary to many news & social media posts, "flattening the curve" does also reduce total cases. But as long as Rt is still above 1, our hospitals will still most likely shatter.
+First, let's look at the two main ways to "flatten the curve": handwashing & physical distancing.
-That's what the Imperial College report also found: any attempt at mere "mitigation" (Reduce Rt, but still Rt>1 = bad) will fail, and the only way out is "suppression". (Reduce Rt, so that Rt<1 = good!)
+Increased handwashing cuts flus & colds in high-income countries by ~25%16, while the city-wide lockdown in London cut close contacts by ~70%17. So, let's assume handwashing can reduce R by up to 25%, and distancing can reduce R by up to 70%:
-Crush the curve, not just flatten it. For example, by doing a...
+Play with this calculator to see how % of non-
There's different degrees of "physical distancing". (previously called "social distancing") At the mildest, avoiding crowds. At the strongest, a full city-wide lockdown.
+Now, let's simulate what happens to a COVID-19 epidemic if, starting March 2020, we had increased handwashing but only mild physical distancing – so that R is lower, but still above 1:
-London's full lockdown reduced Rt by 70%.11 So, let's guess-timate that as the maximum effect for distancing.
+Here's how hygiene & distancing together change Rt:
+Three notes:
-// calc
+This reduces total cases! Lots of folks think "Flatten The Curve" spread outs cases without reducing the total. This is impossible in any Epidemiology 101 model. But because the news reported "80%+ will be infected" as inevitable, folks thought total cases will be the same no matter what. Sigh.
Due to the extra interventions, current cases (pink curve) peaks before herd immunity is reached. And in fact, total cases doesn't overshoot, but goes to herd immunity – the UK's plan! At that point, R < 1, you can let go of all other interventions, and COVID-19 stays contained! Well, except for one problem...
You still run out of ICUs. For several months. (and remember, we already tripled ICUs for these simulations)
That's Rt<1 = good!
+That was the other finding of the March 16 Imperial College report, which convinced the UK to abandon its original plan. Any attempt at mitigation (reduce R, but R > 1) will fail. The only way out is suppression (reduce R so that R < 1).
-Let's see what happens if we crush the curve with a lockdown for 3 months, then finally, finally return to normal life:
+// pic: difference
-Remember, you can re-play the simulation, and change the sliders WHILE it's running, to simulate your own COVID-19 strategy! You can also pause & slow down the simulation:
+That is, don't merely "flatten" the curve, crush the curve. For example, with a...
-// sim
+Let's see what happens if we crush the curve with a 5-month lockdown, reduce
Oh.
-Right, as soon as you remove the lockdown, Rt>1 again, and so you get a spike in cases that's almost as bad as if you'd done nothing at all.
+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
A lockdown isn't a cure, it's just a restart.
So, what, do we just lockdown again & again?
-// sim
+This solution was first suggested by the Imperial College report, and later again by a Harvard paper19.
-This was one solution suggested by the March 16 Imperial College report, and analyzed again by Marc Lipsitch ______ etc. [https://science.sciencemag.org/content/early/2020/04/14/science.abb5793?]
+Here's a simulation: (After playing the "recorded scenario", you can try simulating your own lockdown schedule, by changing the sliders while the simulation is running! Remember you can pause & continue the sim, and change the simulation speed)
-This would keep hospitals below capacity! You just have to... shut everything down for 2 months, every 3 months, until a vaccine is available in 18 months. That's... one year total out of 18 months.
+Look, it's all well & good to draw a line on a graph saying "healthcare capacity", but there's lots of important things we can't simulate here. Like:
+This would keep cases below ICU capacity! We'd just need to... shut everything down for few months, open up for a few, shut down for a few, open up for a few... and repeat until a vaccine is available. (And if there's no vaccine, repeat until herd immunity is reached... in 2022.)
-Mental Health) Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as negatively associated with an early death as smoking 15 cigarettes a day.
+Look, it's nice to draw a line saying "ICU capacity", but there's lots of important things we can't simulate here. Like:
-Financial Health) "What about the economy" sounds like you care more about dollars than lives, but "the economy" isn't just stocks: it's people's ability to provide food & shelter for their loved ones, to invest in their kids' futures, and enjoy arts, foods, videogames – the stuff makes life worth living. And besides, poverty itself has horrible impacts on mental and physical health.
+Mental Health: Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as associated with an early death as smoking 15 cigarettes a day.20
-Not saying we should rule out intermittent lockdowns! But it's not ideal.
+Financial Health: "What about the economy" sounds like you care more about dollars than lives, but "the economy" isn't just stocks: it's people's ability to provide food & shelter for their loved ones, to invest in their kids' futures, and enjoy arts, foods, videogames – the stuff makes life worth living. And besides, poverty itself has horrible impacts on mental and physical health.
-Wait, didn't we say Taiwan & South Korea "bravely used their fear" to control COVID-19? For 4 whole months? How?
+Not saying we shouldn't lock down again! We'll look at "circuit breaker" lockdowns later. Still, it's not ideal.
-But wait... haven't Taiwan and South Korea already contained COVID-19? For 4 whole months, without long-term lockdowns?
-You may be thinking:
+How?
-Sure, we *could* have done what Taiwan + South Korea did at the start, but it's too late now. We missed the start.
+But that's exactly it! A lockdown isn't a cure, it's just a restart – and a fresh start is what we need. (TODO: Actually, South Korea started late!)
+"Sure, we *could've* done what Taiwan & South Korea did at the start, but it's too late now. We missed the start."
-The lockdown will let us reduce (i) cases, and buy time to copy what Taiwan & South Korea are already successfully doing: isolating COVID-19 cases, and finding out who've they been in extended close contact with ("contact tracing") and quarantining them too.
+But that's exactly it! “A lockdown isn't a cure, it's just a restart”... and a fresh start is what we need.
-(Pedantic note: "isolate" is for infected cases, "quarantine" is for contacts)
+To understand how Taiwan & South Korea contained COVID-19, we need to understand the exact timeline of a typical COVID-19 infection21:
-Why do we need to quarantine the contacts? Because they could have been (e) Exposed & caught the virus, but not know it yet:
+
// timeline
+If cases only self-isolate when they know they're sick (that is, they feel symptoms), the virus can still spread:
-If you only isolate the cases, the virus can still spread:
+
// timeline
+And in fact, 44% of all transmissions are like this: pre-symptomatic! 22
-But if you also quarantine the contacts, you stop the spread, by staying one step ahead!
+But, if we find and quarantine a symptomatic case's recent close contacts... we stop the spread, by staying one step ahead!
-// timeline
+
(TODO: 30 min+ exposure)
+This is called contact tracing, and it's a core part of Taiwan & South Korea's successful strategies.
-Contact tracing was how they contained Ebola in (where?) Africa! And that was just good ol' fashioned "ask people who they met" contact tracing.
+Traditionally, contact tracing is done with in-person interviews, but that's too slow for COVID-19's ~48 hour window. That's why on March 31st, an Oxford study recommended helping contact tracers with contact tracing apps.
-...which, unfortunately, will not work for COVID-19. Interviews are too slow and human memory is too unreliable. [MARCEL'S SOURCE] There's only 3 days between being exposed to the virus (e) and being able to infect others (i).
+Does that mean giving up privacy, giving in to Big Brother? Heck no! DP-3T, a team of epidemiologists & cryptographers (including one of us, Marcel Salathé) is already making a contact tracing app that reveals no info about your identity, location, who your contacts are, or even how many contacts you've had.
-So, regrettably, some countries have resorted to privacy-invasive techniques, like grabbing loads of citizens' phone location data. But does protecting human lives mean surrendering to Big Brother?
+Here's how it works:
-HECK NO
+
Here's a short comic we made, explaining how you can do digital contact tracing in a privacy-protecting way. And when we say "privacy-protecting", we mean that even if the central server was hacked and all its data stolen, the hacker would learn nothing about people's identities, locations, or who met who.
+(Here's the full comic, and here's a video adaptation by 3Blue1Brown)
-(And here's a 3Blue1Brown video adaptation of our comic! Thanks Grant!)
+Along with similar teams like TCN Protocol and MIT PACT, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS. Next month, your local public health agency may ask you to download an app. If it's privacy-first & open-source, please do!
-And this isn't just "in theory". Several apps are already being developed for this. The European council vote (FILL IN). And Google/Apple's new announcement specifically supports the privacy-protecting protocol as described in our comic above. (Don't trust Google/Apple? Neither do we! The beauty of the protocol is that it doesn't rely on trust.)
+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.
-Okay okay, enough tooting our own horn. How does isolating cases & quarantining contacts reduce Rt?
+(rant about the confusion about pre-symptomatic vs. "true" asymptomatic:23)
-University of Oxford study estimates that:
+Anyway, isolating cases would reduce R by up to 40%, and quarantining their contacts would reduce R by up to 50%24:
-Loong note about "pre" vs "a" symptomatic & how the media screwed it up AGAIN
+Thus, we can get R < 1 without a lockdown! Much better for our mental & financial health. (As for the cost to folks who have to self-isolate/quarantine, governments should support them – subsidized paid leave, job protection, etc. Still way cheaper than intermittent lockdown.)
-So, combined, isolating cases & quarantining contacts can get Rt comfortably below 1, even with NO physical distancing!
+We then keep R < 1 until we have a vaccine, which turns susceptible
// calc
+Remember: we do not need to catch all transmissions, or even nearly all transmissions, to stop COVID-19. So the fact that not everybody is able (or willing) to download a privacy-protecting contact tracing app isn't a dealbreaker.
+Okay, enough talk. Here's a simulation of:
-We don't need to catch all contacts, isolate all cases, or even wash all the hands. Just enough to get that C– grade of 72%, to get Rt<1 = good.
+(do wash your hands, though)
+Alright, enough chat. Here's a simulation of using a lockdown as reset, then switching to "Test, Trace, Isolate":
+So that's it! That's how we make an emergency landing on this plane.
-// sim
+That's how we beat COVID-19.
-And here it is again, with a vaccine at 18 months, which converts (s) into an immune (r), without having to become a (i). This gives us "herd immunity" the right way, and we can finally stop all other interventions.
+...
-(actually, keep washing your hands. come on, a doctor was beaten to death in an asylum.)
+But what if things still go wrong? Things have gone horribly wrong already. That's fear, and that's good! Fear gives us energy to create backup plans.
-// sim
+The pessimist invents the parachute.
-So that's it!
+That's currently the best working plan, recommended by several independent teams of epidemiologists & policymakers from across the political spectrum. (LINKS) Lockdown to get a fresh start, switch to Taiwan & South Korea's strategy later.
+What if R0 is way higher than we thought, and the above interventions, even with mild distancing, still aren't enough to get R < 1?
-But...
- -...you may be feeling a knot in your stomach. Things have already gone horribly wrong, more stuff could still go horribly wrong with this plan, right?
- -You're dang right it could. Let's channel that fear... into making some backup plans:
- -If handwashing + case isolation + contact quarantining still isn't enough to get Rt<1... we can supplement it with three things:
- -Deep Cleaning:
- -Remember we said "stuff like doorknobs" accounts for 10% of new infections? The technical jargon for things that can pass a virus from one human to another is a "fomite".
- -10% means frequent deep cleanings of public spaces – subways, libraries, and malls – can reduce Rt by up to 10%. Which sounds useless, but if it reduces Rt from 1.05 to 0.95... that's Rt<1 = lives saved.
- -// calc?
+If so, here's a few supplements:
Masks For All:
-[small brain] Correlation implies causation!
+"Wait," you might ask, "I thought face masks don't stop you from getting sick?"
-[normal brain] Correlation doesn't imply causation, you need Randomized Controlled Trials (RCTs) to prove things.
+You're right. Masks don't stop you from getting sick... they stop you from getting others sick.
-[large brain] Actually, under Bayes' Theorem, all correlations are evidence for causation, because the likelihood of {seeing a correlation, given causation} is greater than the likelihood of {seeing a correlation, given no causation}. It's just not 100% proof, because nothing in science is 100% proof, not even RCTs (hence the replication crisis). Evidence isn't 0% or 100%, they have a full range of "weights". And though correlational evidence has a lower "weight" than an RCT, it is still evidence. (See this 3Blue1Brown video for a visual explanation of Bayes' Theorem)
+
What we're trying to say is:
+(sources for the comic: 25 26 27 28)
-There aren't any RCTs (yet) testing "Cloth masks prevent COVID-19 spread" specifically. But there's lots of suggestive evidence, if of lower "weight":
+Still, in science, one should only publish a finding if you're 95% sure of it. (...should.29) Admittedly, the current evidence for face masks on COVID-19 specifically, rather than "just" colds and flus, is less than "95% sure".
-But, pandemics are like poker. Make bets only when you're 95% sure, and you'll lose everything at stake. We have to make cost/benefit analyses under uncertainty.30 Like so:
-Pandemics are like poker. Act only when you "have enough info", and you'll lose everything at stake. You'll never have enough info, just cost/benefit analyses under uncertainty. Like so:
+Cost: If homemade cloth masks, same as the cost of all that soap for handwashing. If surgical masks, more expensive but still pretty cheap.
-Cost of cloth masks (certain): Small. Same as handwashing.
+Benefit: Even if it's a 50–50 chance of surgical masks reducing transmission by 0% or 70%31, the average "expected value" is still 35%, same as a half-lockdown! So let's guess-timate that surgical masks reduce R by up to 35%. (Again, you can challenge our assumptions by turning the sliders up/down)
-Benefit of cloth masks (uncertain): They probably don't stop the wearer from getting COVID-19, but they probably stop a pre-symptomatic wearer from spreading COVID-19. Let's guess masks reduce Rt by 0% to 20%. Even though "0%" is still likely, the average "expected value" is halfway between 0% and 20% – that is, 10%, same as deep cleaning, but at minuscule cost.
+Here's a calculator of how masks reduce R! You can switch between cloth & surgical: (assumes cloth masks are half as effective as surgical masks32)
-Analysis: If someone offered you a coin flip, where tails = nothing happens, and heads = 1000s of lives saved... and the price for playing this game is a rag and two rubber bands... even though "nothing" is as likely as "lives saved", you should do it.
+Cloth masks for all: do it!
+(other arguments for/against masks:33)
-// calc?
+Masks alone won't get R < 1. But if handwashing & "Test, Trace, Isolate" only gets us to R = 1.10, having just 2/3 of people wear cloth masks would tip that over to R < 1, virus contained!
Summer:
-Okay, this is not an "intervention" we have control of, but it does help reduce Rt!
+Okay, this isn't an "intervention" we can control, but it will help! Some news outlets report that summer won't do anything to COVID-19. They're half right: summer won't get R < 1, but it will reduce R.
-For every extra 1° Celsius (2.2° Fahrenheit), Rt drops by ___%. The average difference between winter & summer in New York is 15°C (60°F), so summer will make Rt drop by _%.
+For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.34 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.
-Many news sites (wrongly) report summer won't slow COVID-19. They're probably trying not to get your hopes up: with R0=3.5, a _% reduction is Rt=_, still above 1.
+But still, it's something. If we have limited resources, we can scale back some interventions in the summer – so we can scale them higher in the winter.
- -// calc? over time
+Summer alone won't make R < 1, but if we have limited resources, we can scale back some interventions in the summer – so we can scale them higher in the winter.
A "Circuit Breaker" Lockdown:
-And if all that still isn't enough to get Rt<1... we can do another lockdown.
+And if all that still isn't enough to get R < 1... we can do another lockdown.
-But because Rt was reduced dramatically, we wouldn't have to do a 2-month-lockdown-every-3-months! Probably just one more 1-month lockdown, between now and when we have a vaccine.
+But we wouldn't have to be 2-months-closed / 1-month-open over & over! Because R is reduced, we'd only need one or two more "circuit breaker" lockdowns before a vaccine is available. (Singapore had to do this recently, "despite" having controlled COVID-19 for 4 months. That's not failure: this is what success takes.)
-Here's a simulation of that (with sliders for ALL the interventions):
+Here's a simulation a "lazy case" scenario:
-// sim
+. . .
We hope these plans give you hope.
-It is possible to keep Rt<1, without locking down for most of 18 months. With plans like "Test, Trace, Isolate", supplemented with backup plans like "Masks For All", we can get back to a normal-ish life!
+Even under a pessimistic scenario, it is possible to beat COVID-19, while protecting our mental and financial health. Use the lockdown as a restart, keep R < 1 with privacy-protecting contract tracing, supplemented with at least cloth masks... and life can get back to a normal-ish!
-Sure, your hands may be dry. But you'll get to invite a date out to a comics bookstore! You'll get to watch the latest cash-grab Hollywood sequel with friends. You'll get to people-watch at a library, taking joy in people going about the simple business of being alive.
+Sure, your hands may be dry. But you'll get to invite a date out to a comics bookstore! You'll get to go out with friends to watch the latest Hollywood cash-grab. You'll get to people-watch at a library, taking joy in people going about the simple business of being alive.
-Life will go on, even under the worst-case scenario.
+Even under the worst-case scenario... life perseveres.
-So now, let's use our fear's energy, and plan for some even worse worst-case scenarios:
+So now, let's plan for some worse worst-case scenarios. Water landing, get your life jacket, and please follow the lights to the emergency exits:
-You get COVID-19, and recover. Or you get the COVID-19 vaccine! Either way, you're now immune...
+You get COVID-19, and recover. Or you get the COVID-19 vaccine. Either way, you're now immune...
...for how long?
-SARS, which was closely related (TODO: is it?) to this new coronavirus, gave its survivors around 2 years of immunity.12. Some coronaviruses, like the ones that cause "the" common cold[13], give you just 1 year of immunity. (TODO: MERS' immunity)
+There's been reports of folks who test positive again after recovering, but those were false positives. Still, the possibility of waning immunity is very real. Either a new mutant strain evolves, or your immune system just... forgets.
-Let's think about the scariest scenario: immunity doesn't last.
+The coronavirus responsible for COVID-19 is most closely related to the coronavirus responsible for SARS. SARS (probably) gave its survivors around 2 years of immunity.35 The coronaviruses that cause "the" common cold give you 1 year of immunity36. So:
-Rule #4: (r)s eventually become (s)s
+What if COVID-19 immunity doesn't last?
-// pic
+Here's a simulation starting with 100%
The SIRS model: the (r) Recovered become (s) Susceptible again.
+Let's simulate what that'll look like, with no interventions:
+Return of the exponential decay!
-// sim
+This is the SEIRS Model. The final "S" stands for
Previously, with no interventions, we only had one hospital-breaking spike. Now, we have several, and the simulation comes to a rest with % of (i) infected permanently above hospital capacity.
+
(If you replay the simulation above with immunity lasting 3 years, that wouldn't be so bad! The % of (i) would rest comfortably below capacity. There'd still be spikes, but you can deal with them using the same interventions listed in last section)
+Now let's simulate a COVID-19 outbreak, over 10 years, with no interventions... if immunity only lasts a year:
-It's like a pendulum: total (i)+(r) cases swings around the "herd immunity" threshold, before settling exactly at "herd immunity", where Rt=1. The virus no longer grows or shrinks. It's just with us forever: it's endemic.
+// pic?
+Previously, we only had one ICU-overwhelming spike. Now, we have several, and
Thankfully, summer will make it better by reducing Rt:
+R = 1, it's endemic.
-// sim
+Thankfully, because summer reduces R, it'll make the situation better:
-Oh wait no it doesn't. Summer does reduce new people becoming (i) infected, but that also reduces new people becoming (r) immune. Which means immunity in the population will drop even further with summer, allowing for big regular spikes in the winter.
+It's like a pendulum where you're moving the top back and forth: that just makes the cycles worse.
+Oh. Counterintuitively, summer makes the spikes worse, and regular! This is because summer reduces new
// pic?
+Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:
-Finally, the worst worst-case:
+(After playing the recording, try simulating your own vaccination campaigns! Remember you can pause/continue the sim at any time)
-What if, like HIV, there's just never a vaccine?
+Our only option now is to increase our capacity for COVID-19 cases. You could do this directly, by creating more hospital beds and ventilators. Or you could do this indirectly, by creating treatments for COVID-19, so that if you do get it, you're less likely to need a hospital bed or ventilator.
+But here's the scarier question:
-Here's the same simulation, but 1) starting with herd immunity (which wanes quickly), and 2) with adjustable hospital capacity:
+What if there's no vaccine for years? Or ever?
-// sim
+To be clear: this is unlikely. Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment. Coronaviruses aren't any more complex than the viruses we already have vaccines for, so most infectious disease researchers expect a vaccine in 1 to 2 years.
-HIV/AIDS killed millions, mostly in marginalized communities. And yet, despite it being the worst-case pandemic scenario, and despite all the stigma against people who have it... HIV isn't a death sentence anymore.
+Still, they've expressed worries about a vaccine: What if we can't make enough?37 What if we rush it, and it's not safe?38
-HIV has no vaccine. There's definitely no herd immunity. And yet, with treatments like antiretroviral therapy, people can and are living full lives with the virus. COVID-19 is devastating, but nowhere as much as HIV.
+Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:
-Life will go on, even under the worst worst-case scenario.
+1) Do intermittent or loose R < 1 interventions, to reach "natural herd immunity". (Warning: this will result in many deaths & damaged lungs. And won't work if immunity doesn't last.)
-...
+2) Do the R < 1 interventions forever. Contact tracing & wearing masks just becomes a new norm in the post-COVID-19 world, like how STI tests & wearing condoms became a new norm in the post-HIV world. (Nobody suggested "herd immunity" for HIV...)
-That said, the virus behind COVID-19 is way simpler than HIV, so there'll almost definitely be a vaccine, even if it only grants immunity for a year. If so, we'll just have to do a vaccination campaign each autumn – and we can just do this alongside our regular flu shots:
+3) Do the R < 1 interventions until we develop treatments that make COVID-19 way, way less likely to need critical care. (Which we should be doing anyway!) Reducing ICU use by 10x is the same as increasing our ICU capacity by 10x:
-// sim
+Here's a simulation of no lasting immunity, no vaccine, and not even any interventions – just increasing ICU capacity to survive the long-term spikes:
-Finally, here's a Simulation Sandbox, with every option available. You can now also share your own simulations!
+// sim
+Even under the worst worst-case scenario... life perseveres.
-Play around to intuitively understand the core rules of epidemiology.
+. . .
-Try simulating different COVID-19 scenarios, plans, and backup plans.
+Maybe you'd like to challenge our assumptions, and try different R0's or numbers. Or try simulating your own combination of intervention plans!
-Ask questions, try to find an answer with the sim, and share your sim with others.
+Here's an (optional) Sandbox Mode, with everything available. Simulate & play around to your heart's content:
-This (again, very VERY basic!) simulation has let us answer so many questions about the past few months, next few months, and next few years.
+[TODO TODO TODO!]
-So now, let's return to...
+This basic "epidemic flight simulator" has taught us so much. It's let us answer questions about the past few months, next few months, and next few years.
-So finally, let's return to...
-In summary, here's how we bravely use our fear, slay the dragon, and save the lives of millions of princes(ses):
+Plane's in the ocean. We've scrambled onto the life rafts. It's time to find dry land.39
-PHASE 1) Lockdown to get a fresh start.
+Teams of epidemiologists and policymakers (left, right, and multi-partisan) have come to a consensus on how to beat COVID-19, while protecting our lives and liberties.
-Get current (i)s low, while building capability to do...
+Here's the rough idea, with some (less-consensus) backup plans:
-PHASE 2) "Test, Trace, Isolate"
+
We replace lockdown with other ways to get Rt<1. Life gets back to normal-ish! 🎉
- -More testing so we can actually tell what Rt currently is.
- -Create policies to get cases to isolate/quarantine. Paid leave & bonus financial incentives if they do, maybe fines if they don't.
- -Use privacy-protecting contact tracing apps to find contacts. Remember, not everybody has to have the app to get Rt<1.
- -If Rt still not below 1: "Masks For All". Get most people to wear at least cloth face masks.
- -If Rt still not below 1: Deep clean public spaces often. Mild social distancing. Maybe one or two more "circuit breaker" lockdowns. (but still avoiding "lockdown for most of 18 months"!)
- -This will buy us time to finally do...
- -PHASE 3) Vaccinate!
- -If immunity doesn't last long: Vaccination campaign every autumn, like we already do for flu shots.
- -If vaccine is never available: Raise our capacity for COVID-19 cases by creating more hospital beds & ventilators, and developing antivirals & treatments. (which we should be doing anyway!)
- -What's this mean for YOU, right now?
+So what does this mean for YOU, right now?
For everyone: Respect the lockdown so we can get out of Phase I asap. Keep washing those hands. Make your own masks. Download a privacy-protecting contact tracing app when those are available next month. Stay healthy, physically & mentally! And write your local policymaker to get off their butt and...
-For policymakers: Create policies that compensate (or reward!) folks who have to self-isolate/quarantine. Direct funds into all the stuff we should be building, like...
+For policymakers: Make laws to support folks who have to self-isolate/quarantine. Hire more manual contact tracers, supported by privacy-protecting contact tracing apps. Direct more funds into the stuff we should be building, like...
-For builders: Build tests. Build ventilators. Build masks – cloth, surgical and N95. Build apps. Build antivirals and other treatments. Build vaccines. Build science.
+For builders: Build tests. Build ventilators. Build personal protective equipment for hospitals. Build tests. Build masks. Build apps. Build antivirals, prophylactics, and other treatments that aren't vaccines. Build vaccines. Build tests. Build tests. Build tests. Build hope.
-Will we need all that? "Probably" not, the same way you "probably" won't need safety belts, fire insurance, or parachutes on planes. It's like doing a cost/benefit analysis of Russian Roulette: the chance of disaster is small, but the cost of disaster is far, far bigger.
+Don't downplay fear to build up hope. Our fear should team up with our hope, like the inventors of airplanes & parachutes. Preparing for horrible futures is how we create a hopeful future.
-In situations like this, it pays to listen honestly to your fears. Don't deny or downplay them, just face them, and prepare for them.
- -The only thing to fear is people who think the only thing to fear is fear itself.
- -(TODO: US vs Korea/Taiwain resources)
+The only thing to fear is the idea that the only thing to fear is fear itself.
hello! ↩
+(NOTE: This guide was published on April 30th, 2020. Many details will become outdated, but Epidemiology 101 will remain true, and we're confident this guide will cover 95% of possible futures.) ↩
but a snitch ain't one ↩
+https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article ↩
+source ↩
+https://link.springer.com/article/10.1007/s11427-020-1661-4 ↩
+source, and sidenote on 'infectious' ↩
+source ↩
+source ↩
+https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-480 ↩
+https://pubmed.ncbi.nlm.nih.gov/31995857/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/ ↩
+https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article ↩
+sas ↩
+exact formula... ↩
+https://www.statista.com/statistics/1105420/covid-icu-admission-rates-us-by-age-group/ Lower end, 5%. ↩
+https://sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19 ↩
+https://www.theatlantic.com/health/archive/2020/03/coronavirus-pandemic-herd-immunity-uk-boris-johnson/608065/ ↩
+https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2006.01568.x ↩
+https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html ↩
+log scale ↩
+https://science.sciencemag.org/content/early/2020/04/14/science.abb5793? ↩
+https://journals.sagepub.com/doi/abs/10.1177/1745691614568352 ↩
+sources plz, esp for incubation period 5 days ↩
+https://www.nature.com/articles/s41591-020-0869-5 ↩
+asds ↩
+https://science.sciencemag.org/content/early/2020/04/09/science.abb6936 ↩
+incoming ↩
+outgoing_aerosols ↩
+outgoing_droplets ↩
+homemade ↩
+ss ↩
+That BMJ article ↩
+s ↩
+ss ↩
+s ↩
+https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767 The average R-value of these 100 cities is 1.83 , One-degree Celsius increase in temperature and one percent increase in relative humidity lower R by 0.0225 ↩
+https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/ ↩
+https://pubmed.ncbi.nlm.nih.gov/2170159/ ↩
+https://www.nature.com/articles/d41586-020-01063-8 ↩
+https://www.nature.com/articles/d41586-020-00751-9 ↩
+https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/ ↩
"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/words_epi_101.md b/words/words_epi_101.md deleted file mode 100644 index 810c97f..0000000 --- a/words/words_epi_101.md +++ /dev/null @@ -1,163 +0,0 @@ -# What Happens Next? - -## COVID-19 Futures, Explained With Playable Simulations - -"The only thing to fear is fear itself" was stupid advice. - -If people fear fear itself, they'll deny danger because they don't want to create "mass panic". The problem's not fear, but how we *use* our fear. Fear, used well, gives you energy to deal with current dangers, and prepare for future dangers. - -Honestly, the two of us (Marcel, epidemiologist + Nicky, artist/coder) are worried about the future. We bet you are, too. That's why we want to channel *our* worries into making these **playable simulations**, so that you can channel *your* worries into understanding: - -* **The Last Few Months** (epidemiology 101, SEIR model, R & R0) -* **The Next Few Months** (lockdowns, contact tracing, masks) -* **The Next Few Years** (vaccines, loss of immunity?) - -This guide is meant to give you hope *and* fear. To beat this virus **in a way that also protects our mental & financial health**, we need optimism to create plans, and pessimism to create backup plans. As Gladys Bronwyn Stern once said, *“The optimist invents the airplane and the pessimist the parachute.”* - -So, buckle in: we're about to experience some turbulence. - ---- - -# The Last Few Months - -Pilots use flight simulators to learn how not to crash planes. - -**Epidemiologists use epidemic simulators to learn how not to crash humanity.** - -So, let's create a very simple "epidemic flight simulator"! Here, we have some (i) Infectious people & some not-yet-infected (s) Susceptible people. (i)s turn (s)s into more (i)s: - -// pic - -At the start of a COVID-19 outbreak, it's estimated that the virus jumps from an (i) to an (s) every 4 days.[^1] (*On average.* Remember, there's lots of variation.) - -[^1]: source - -Here's a simulation of a population with *just* 0.001% (i) and 99.999% (s), over 6 months. If we simulate "double every 4 days" *and nothing else*, what happens? - -**Click "Start" to play the simulation! (Afterwards, you can re-play the simulation with different settings)** - -// sim - -This is the **exponential growth curve.** Starts small, then explodes. "Oh it's just a flu" to "Oh right, flus don't create *mass graves in rich cities*". - -// pic - exponential double rice - -But, this simulation is wrong. Exponential growth, thankfully, can't go on forever. One thing that stops a virus from spreading is if others *already* have the virus: - -// pic - 100% spread, 50% spread, 0% spread - -**The more (i)s there are, the faster (s)s become (i)s, but the fewer (s)s there are, the *slower* (s)s become (i)s.** - -Now, what happens if we simulate that? - -// sim - -This is the "S-shaped" **logistic growth curve.** Starts small, explodes, then slows down again. - -But, this simulation is *still* wrong. We're missing the fact that (i) Infectious people eventually stop being infectious, either by 1) recovering, 2) "recovering" with lung damage, or 3) dying. - -For simplicity's sake, let's pretend that all (i) Infectious people become (r) Recovered. (r)s can't be infected again, and let's pretend – *for now!* – that they stay immune for life. - -When you're infected with COVID-19, it's estimated you stay (i) infectious for 12 days.[^2] (Again, *on average.*) - -[^2]: source - -Here's a simulation that starts with 100% (i). Most people recover after 12 days, then most of the remainder recover after another 12 days, then most of the remainder *of that remainder* recover after another 12 days, etc: - -// sim - -This is the opposite of exponential growth, the **exponential decay curve**. - -Now, what happens if you combine this with the S-shaped logistic curve of infection? - -// pic - -Let's find out. Here's a simulation of an epidemic *with* recovery: - -// sim - -And *that's* where that famous curve comes from! It's not a bell curve, it's not even a "log-normal" curve. It has no name. But you've seen it a zillion times, and beseeched to flatten. - -// pic: 3 rules - -This is the the **SIR Model**, ((s) **S**usceptible → (i) **I**nfectious → (r) **R**ecovered) the second-most important idea in Epidemiology 101. - -Note: The simulations that inform policy are *far* more sophisticated than this! But the SIR model can still help us understand a lot about COVID-19, even if missing the nuances. - -Actually, let's add one more nuance: before an (s) becomes an (i), they first become an (e) Exposed person, when they're infect*ed* but not yet infect*ious* – they have the virus but can't pass it on (yet). - -(This variant is called the **SEIR Model**, where "E" stands for (e) Exposed. Note this *isn't* the everyday meaning of "exposed", where you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Yeah, science terminology is bad.) - -For COVID-19, it's estimated that you're in this "latent period" for around 3 days.[^3] What happens if we add that to the simulation? - -[^3]: source - -// sim - -Not much, actually! The "latent period" only changes *when* the peak happens, but the *height* of the peak – and total people infected – remain the same: - -// pics - -Why's that? Because of the *first*-most important idea in Epidemiology 101: - -// pic - **"R"** - -Which is short for "Reproduction Number". It's the *average* number of people an (i) infects *before* they recover (or die). - -// R > 1, R = 1, R < 1 pic - -**R** changes over the course of an outbreak, as we get more immunity & interventions. - -**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 flu[^r0_flu] is around 1.3. The R0 estimates for COVID-19 are usually between 2 and 3, maybe as high as 6.[^r0_covid] - -[^r0_flu]: source - -[^r0_covid]: source - -In our simulations, an (i) recovers in 12 days, but infects one new (s) every 4 days. That means, *on average*, an (i) infects 3 (s)s before they recover. So for our simulations, 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: - -# R > 1 = bad -# R < 1 = good (R=1, meh) - -**This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!** - -It's a paradox. COVID-19 is incredibly contagious, yet to contain it, we "only" need to stop 67% of infections. 67%?! If that was a school grade, that's a D+. But if 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... \ No newline at end of file