covid-19/words/words.md

{WIP, DON'T SHARE YET THX!}

"The only thing to fear is fear itself" was stupid advice.

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, we (Marcel, epidemiologist + Nicky, art/code) are worried. We bet you are, too! That's why we've channelled our fear into making these playable simulations, so that you can channel your fear into understanding:

• The Last Few Months (epidemiology 101, SEIR model, R & R₀)
• The Next Few Months (lockdowns, contact tracing, masks?)
• The Next Few Years (loss of immunity? no vaccine?)

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.

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 make a simple "epidemic flight simulator"! In this simulation, Infectious people can turn Susceptible people into more Infectious people:

It's estimated that, at the start of a COVID-19 outbreak, the virus jumps from an to an approximately every 4 days.²

If we simulate "double every 4 days" and nothing else, on a population starting with just 0.001% , what happens?

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".

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:

The more s there are, the faster s become s, but the fewer s there are, the slower s become s.

How's this change the growth of an epidemic? Let's find out:

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 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 Infectious people become Recovered. (Just remember that, in reality, some of them are dying.) s can't be infected again, and let's pretend – for now! – that they stay immune for life.

With COVID-19, it's estimated you're Infectious for approximately 10 days.⁴ Let's simulate a population starting at 100% , most of whom recover after 10 days, then most of the remainder recover after another 10 days, then most of that remainder recover after another 10 days, etc:

This is the opposite of exponential growth, the exponential decay curve.

Now, what happens if you simulate S-shaped logistic growth with recovery?

Let's find out:

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.

This is the the SIR Model⁵ – Susceptible Infectious 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 explain the same findings, even if missing the nuances.

Actually, let's add one more nuance: before an becomes an , they first become Exposed. This is when they have the virus but can't pass it on yet – infected but not yet infectious.

(This variant is called the SEIR Model⁶, where the "E" stands for "Exposed". Note this isn't the everyday meaning of "exposed", when you might or might not have the virus. In this technical definition, "Exposed" means you definitely have it. Science terminology is bad.)

For COVID-19, it's estimated that you're infected-but-not-yet-infectious for approximately 3 days.⁷ What happens if we add that to the simulation?

Not much, actually! How long you stay Exposed changes the ratio of -to-, and when the peak of current cases (+) happens... but the height of that peak, and the total % of people infected in the end, stays the same.

Why's that? Because of the first-most important idea in Epidemiology 101:

Short for "Reproduction number". It's the average number of people an infects before they recover (or die).

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 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.

(Most news articles – and even some scientific papers! – confuse R and R₀. Again, science terminology is bad)

The R₀ for "the" seasonal flu is around 1.28⁸. This means, at the start of a flu outbreak, each infects 1.28 others on average. (If it sounds weird that this isn't a whole number, remember that the "average" mom has 2.4 children. This doesn't mean there's half-children running about.)

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

In our simulations – at the start & on average – an infects someone every 4 days, over 10 days. "4 days" goes into "10 days" two-and-a-half times. This means – at the start & on average – each infects 2.5 others. Therefore, R₀ = 2.5. (caveats:¹¹)

Play with this R₀ calculator, to see how R₀ depends on recovery time & new-infection time:

But remember, the fewer s there are, the slower s become s. The current reproduction number (R) depends not just on the basic reproduction number (R₀), but also on how many people are no longer Susceptible. (For example, by recovering & getting natural immunity.)

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 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!)

This is because when there are more non-s than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.

If there's only one lesson you take away from this guide, here it is – it's an extremely complex diagram so please take time to fully absorb it:

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 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 contained¹²

(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:

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?

Brace yourselves for an emergency landing...

The Next Few Months

...could have been worse. Here's a parallel universe we avoided:

###Scenario 0: Do Absolutely 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.

Even if we more than tripled that capacity to 2%, here's what would've happened if we did absolutely nothing:

Not good.

That's what the March 16 Imperial College report found: do nothing, and we run out of ICUs with 80%+ of the population infected.

Even if only 0.5% of infected die – a generous assumption when there's no more ICUs – in a large country like the US, with 300 million people, 0.5% of 80% of 300 million = still 1.2 million dead... IF we did nothing.

(Lots of news & social media reported "80%+ will be infected" without "IF WE DO NOTHING". Fear was channelled into clicks, not understanding. Sigh.)

###Scenario 1: Flatten The Curve / Herd Immunity

The "Flatten The Curve" plan was touted by every public health organization, while the United Kingdom's original "herd immunity" plan was universally booed. They were the same plan. The UK just communicated theirs poorly.¹⁵

Both plans, though, are horribly flawed.

First, let's look at the two main ways to "flatten the curve": handwashing & physical distancing.

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%:

Play with this calculator to see how % of non-, handwashing, and distancing reduce R: (this calculator visualizes their relative effects, which is why increasing one looks like it decreases the effect of the others.¹⁸)

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:

Three notes:

1. This reduces total cases! Lots of folks think "Flatten The Curve" spread outs cases without reducing the total. This is impossible in any Epidemiology 101 model. But because the news reported "80%+ will be infected" as inevitable, folks thought total cases will be the same no matter what. Sigh.

2. Due to the extra interventions, current cases (pink curve) peaks before herd immunity is reached. And in fact, total cases doesn't overshoot, but goes to herd immunity – the UK's plan! At that point, R < 1, you can let go of all other interventions, and COVID-19 stays contained! Well, except for one problem...

3. You still run out of ICUs. For several months. (and remember, we already tripled ICUs for these simulations)

That was the other finding of the March 16 Imperial College report, which convinced the UK to abandon its original plan. Any attempt at mitigation (reduce R, but R > 1) will fail. The only way out is suppression (reduce R so that R < 1).

// pic: difference

That is, don't merely "flatten" the curve, crush the curve. For example, with a...

###Scenario 2: Months-Long Lockdown

Let's see what happens if we crush the curve with a 5-month lockdown, reduce to nearly nothing, then finally – finally – return to normal life:

Oh.

This is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover (or imported ) can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing.

A lockdown isn't a cure, it's just a restart.

So, what, do we just lockdown again & again?

###Scenario 3: Intermittent Lockdown

This solution was first suggested by the Imperial College report, and later again by a Harvard paper¹⁹.

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 cases below ICU capacity! We'd just need to... shut everything down for few months, open up for a few, shut down for a few, open up for a few... and repeat until a vaccine is available. (And if there's no vaccine, repeat until herd immunity is reached... in 2022.)

Look, it's nice to draw a line saying "ICU capacity", but there's lots of important things we can't simulate here. Like:

Mental Health: Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as associated with an early death as smoking 15 cigarettes a day.²⁰

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.

Not saying we shouldn't lock down again! We'll look at "circuit breaker" lockdowns later. Still, it's not ideal.

But wait... haven't Taiwan and South Korea already contained COVID-19? For 4 whole months, without long-term lockdowns?

How?

###Scenario 4: Test, Trace, Isolate

"Sure, we *could've* done what Taiwan & South Korea did at the start, but it's too late now. We missed the start."

But that's exactly it! “A lockdown isn't a cure, it's just a restart”... and a fresh start is what we need.

To understand how Taiwan & South Korea contained COVID-19, we need to understand the exact timeline of a typical COVID-19 infection²¹:

If cases only self-isolate when they know they're sick (that is, they feel symptoms), the virus can still spread:

And in fact, 44% of all transmissions are like this: pre-symptomatic! ²²

But, if we find and quarantine a symptomatic case's recent close contacts... we stop the spread, by staying one step ahead!

This is called contact tracing, and it's a core part of Taiwan & South Korea's successful strategies.

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.

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.

Here's how it works:

(Here's the full comic, and here's a video adaptation by 3Blue1Brown)

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!

But what about folks without smartphones? Or infections through doorknobs? Or "true" asymptomatic cases? Contact tracing apps can't catch all transmissions... and that's okay! We don't need to catch all transmissions, just 60%+ to get R < 1.

(rant about the confusion about pre-symptomatic vs. "true" asymptomatic:²³)

Anyway, isolating cases would reduce R by up to 40%, and quarantining their contacts would reduce R by up to 50%²⁴:

Thus, even without 100% contact quarantining, we can get R < 1 without a lockdown! Much better for our mental & financial health. (As for the cost to folks who have to self-isolate/quarantine, governments should support them – subsidized paid leave, job protection, etc. Still way cheaper than intermittent lockdown.)

We then keep R < 1 until we have a vaccine, which turns susceptible s into immune s. Herd immunity, the right way:

Okay, enough talk. Here's a simulation of:

1. A few-month lockdown, until we can...
2. Switch to "Test, Trace, Isolate" until we can...
3. Vaccinate enough people, which means...
4. We win.

So that's it! That's how we make an emergency landing on this plane.

That's how we beat COVID-19.

...

But what if things still go wrong? Things have gone horribly wrong already. That's fear, and that's good! Fear gives us energy to create backup plans.

The pessimist invents the parachute.

###Scenario 4+: Masks For All, Summer, Circuit Breakers

What if R₀ is way higher than we thought, and the above interventions, even with mild distancing, still aren't enough to get R < 1?

If so, here's a few supplements:

Masks For All:

"Wait," you might ask, "I thought face masks don't stop you from getting sick?"

You're right. Masks don't stop you from getting sick... they stop you from getting others sick.

(sources for the comic: ^{25 26 27 28})

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:

Cost: If homemade cloth masks, same as the cost of all that soap for handwashing. If surgical masks, more expensive but still pretty cheap.

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)

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)

[TODO: Actually allow toggling between cloth/surgical. Currently locked to cloth]

(other arguments for/against masks:³³)

Masks alone won't get R < 1. But if handwashing & "Test, Trace, Isolate" only gets us to R = 1.10, having just 2/3 of people wear cloth masks would tip that over to R < 1, virus contained!

Summer:

Okay, this isn't an "intervention" we 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 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%.

[TODO: Fix weird arrow glitch]

Summer alone won't make R < 1, but if we have limited resources, we can scale back some interventions in the summer – so we can scale them higher in the winter.

A "Circuit Breaker" Lockdown:

And if all that still isn't enough to get R < 1... we can do another lockdown.

But we wouldn't have to be 2-months-closed / 1-month-open over & over! Because R is reduced, we'd only need one or two more "circuit breaker" lockdowns before a vaccine is available. (Singapore had to do this recently, "despite" having controlled COVID-19 for 4 months. That's not failure: this is what success takes.)

Here's a simulation a "lazy case" scenario:

1. Lockdown, then
2. A moderate amount of hygiene + "Test, Trace, Isolate" + cloth "Masks For All", then...
3. One more "circuit breaker" lockdown before a vaccine's found.

[TODO: Other options like temperature testing at malls, quarantines for travellers, replacing handshaking, etc]

. . .

We hope these plans give you hope.

Even under a pessimistic scenario, it is possible to beat COVID-19, while protecting our mental and financial health. Use the lockdown as a restart, keep R < 1 with privacy-protecting contract tracing, supplemented with at least cloth masks... and life can get back to a normal-ish!

Sure, your hands may be dry. But you'll get to invite a date out to a comics bookstore! You'll get to go out with friends to watch the latest Hollywood cash-grab. You'll get to people-watch at a library, taking joy in people going about the simple business of being alive.

Even under the worst-case scenario... life perseveres.

So now, let's plan for some worse worst-case scenarios. Water landing, get your life jacket, and please follow the lights to the emergency exits:

The Next Few Years

You get COVID-19, and recover. Or you get the COVID-19 vaccine. Either way, you're now immune...

...for how long?

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.

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:

What if COVID-19 immunity doesn't last?

Here's a simulation starting with 100% , exponentially decaying into s after 10 days... but then back to susceptible, no-immunity s after 1 year:

Return of the exponential decay!

This is the SEIRS Model. The final "S" stands for Susceptible, again.

Now let's simulate a COVID-19 outbreak, over 10 years, with no interventions... if immunity only lasts a year:

Previously, we only had one ICU-overwhelming spike. Now, we have several, and cases come to a rest permanently at ICU capacity. (Which, remember, we tripled for these simulations)

R = 1, it's endemic.

Thankfully, because summer reduces R, it'll make the situation better:

Oh.

Counterintuitively, summer makes the spikes worse and regular! This is because summer reduces new s, but that in turn reduces new immune s. Which means immunity plummets in the summer, creating large regular spikes in the winter.

Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:

(After playing the recording, try simulating your own vaccination campaigns! Remember you can pause/continue the sim at any time)

But here's the scarier question:

What if there's no vaccine for years? Or ever?

To be clear: this is unlikely. Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment. Coronaviruses aren't any more complex than the viruses we already have vaccines for, so most infectious disease researchers expect a vaccine in 1 to 2 years.

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

Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:

1. Do intermittent or loose R < 1 interventions, to reach "natural herd immunity". (Warning: this will result in many deaths & damaged lungs. And won't work if immunity doesn't last.)

2. Do the R < 1 interventions forever. Contact tracing & wearing masks just becomes a new norm in the post-COVID-19 world, like how STI tests & wearing condoms became a new norm in the post-HIV world. (Nobody suggested "herd immunity" for HIV...)

3. Do the R < 1 interventions until we develop treatments that make COVID-19 way, way less likely to need critical care. (Which we should be doing anyway!) Reducing ICU use by 10x is the same as increasing our ICU capacity by 10x:

Here's a simulation of no lasting immunity, no vaccine, and not even any interventions – just slowly increasing capacity to survive the long-term spikes:

Even under the worst worst-case scenario... life perseveres.

. . .

Maybe you'd like to challenge our assumptions, and try different R₀'s or numbers. Or try simulating your own combination of intervention plans!

Here's an (optional) Sandbox Mode, with everything available. Simulate & play around to your heart's content:

[TODO: EMBED THIS IN A WAY THAT DOESN'T SUCK]

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...

The Now

Plane's in the ocean. We've scrambled onto the life rafts. It's time to find dry land.³⁹

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.

Here's the rough idea, with some (less-consensus) backup plans:

So what does this mean for YOU, right now?

For everyone: Respect the lockdown so we can get out of Phase I asap. Keep washing those hands. Make your own masks. Download a privacy-protecting contact tracing app when those are available next month. Stay healthy, physically & mentally! And write your local policymaker to get off their butt and...

For policymakers: Make laws to support folks who have to self-isolate/quarantine. Hire more manual contact tracers, supported by privacy-protecting contact tracing apps. Direct more funds into the stuff we should be building, like...

For builders: Build tests. Build ventilators. Build personal protective equipment for hospitals. Build 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.

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.

The only thing to fear is the idea that the only thing to fear is fear itself.

---

1. (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.) ↩︎

2. https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article ↩︎

3. source ↩︎

4. https://link.springer.com/article/10.1007/s11427-020-1661-4 ↩︎

5. source, and sidenote on 'infectious' ↩︎

6. source ↩︎

7. source ↩︎

8. https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-480 ↩︎

9. https://pubmed.ncbi.nlm.nih.gov/31995857/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/ ↩︎

10. https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article ↩︎

11. sas ↩︎

12. exact formula... ↩︎

13. https://www.statista.com/statistics/1105420/covid-icu-admission-rates-us-by-age-group/ Lower end, 5%. ↩︎

14. https://sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19 ↩︎

15. https://www.theatlantic.com/health/archive/2020/03/coronavirus-pandemic-herd-immunity-uk-boris-johnson/608065/ ↩︎

16. https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2006.01568.x ↩︎

17. https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html ↩︎

18. log scale ↩︎

19. https://science.sciencemag.org/content/early/2020/04/14/science.abb5793? ↩︎

20. https://journals.sagepub.com/doi/abs/10.1177/1745691614568352 ↩︎

21. sources plz, esp for incubation period 5 days ↩︎

22. https://www.nature.com/articles/s41591-020-0869-5 ↩︎

23. asds ↩︎

24. https://science.sciencemag.org/content/early/2020/04/09/science.abb6936 ↩︎

25. incoming ↩︎

26. outgoing_aerosols ↩︎

27. outgoing_droplets ↩︎

28. homemade ↩︎

29. ss ↩︎

30. That BMJ article ↩︎

31. s ↩︎

32. ss ↩︎

33. s ↩︎

34. 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 ↩︎

35. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/ ↩︎

36. https://pubmed.ncbi.nlm.nih.gov/2170159/ ↩︎

37. https://www.nature.com/articles/d41586-020-01063-8 ↩︎

38. https://www.nature.com/articles/d41586-020-00751-9 ↩︎

39. https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/ ↩︎