From 187818f1397a32553a8d7bfccc400095b1582bb7 Mon Sep 17 00:00:00 2001 From: Jonathan S Date: Sat, 2 May 2020 14:52:59 -0500 Subject: [PATCH 01/10] Fix Celsius-Fahrenheit conversions --- index.html | 2 +- words/words.html | 2 +- words/words.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/index.html b/index.html index d420ccf..68b5639 100644 --- a/index.html +++ b/index.html @@ -485,7 +485,7 @@

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%.35 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

+

For COVID-19, every extra 1° Celsius (1.8° Fahrenheit) makes R drop by 1.2%.35 The summer-winter difference in New York City is 15°C (27°F), so summer will make R drop by 18%.

diff --git a/words/words.html b/words/words.html index 396aa37..807cdef 100644 --- a/words/words.html +++ b/words/words.html @@ -435,7 +435,7 @@ the second-most important idea in Epidemiology 101:

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%.35 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

+

For COVID-19, every extra 1° Celsius (1.8° Fahrenheit) makes R drop by 1.2%.35 The summer-winter difference in New York City is 15°C (27°F), so summer will make R drop by 18%.

diff --git a/words/words.md b/words/words.md index 691356b..8c86858 100644 --- a/words/words.md +++ b/words/words.md @@ -524,7 +524,7 @@ Masks *alone* won't get R < 1. But if handwashing & "Test, Trace, Isolate" only 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%.[^heat] The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%. +For COVID-19, every extra 1° Celsius (1.8° Fahrenheit) makes R drop by 1.2%.[^heat] The summer-winter difference in New York City is 15°C (27°F), so summer will make R drop by 18%. [^heat]: “One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. [Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng](https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767) From 3a4022bbad792d698ffa51de6d01061658832f90 Mon Sep 17 00:00:00 2001 From: Pietro Monticone <38562595+pitmonticone@users.noreply.github.com> Date: Sat, 2 May 2020 23:30:10 +0200 Subject: [PATCH 02/10] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 43b7810..927e37f 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ to publish this.** Step 1) Check to see if it's already been translated to your language! -Go to the main site, look at the left sidebar. +Go to the [main site](https://ncase.me/covid-19/), look at the left sidebar. Step 2) From c379c2b1dfb97e219e1d19e588c0bcb668d2172a Mon Sep 17 00:00:00 2001 From: Tim Scalzo Date: Sun, 3 May 2020 01:49:47 -0400 Subject: [PATCH 03/10] Add nowrap spans around icons and adjacent text --- css/index.css | 4 ++++ index.html | 38 +++++++++++++++++++------------------- words/words.html | 38 +++++++++++++++++++------------------- words/words.md | 38 +++++++++++++++++++------------------- 4 files changed, 61 insertions(+), 57 deletions(-) diff --git a/css/index.css b/css/index.css index 0bc03aa..bd80272 100644 --- a/css/index.css +++ b/css/index.css @@ -95,6 +95,10 @@ icon[r]{ background-image: url(../icons/r.png); } +.nowrap{ + white-space: nowrap; +} + p > img{ width: 100%; border: 1px solid #ddd; diff --git a/index.html b/index.html index d420ccf..689fce5 100644 --- a/index.html +++ b/index.html @@ -119,7 +119,7 @@

It's estimated that, at the start of a COVID-19 outbreak, the virus jumps from an to an every 4 days, on average.2 (remember, there's a lot of variation)

-

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

+

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)

@@ -135,7 +135,7 @@

-

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

+

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:

@@ -147,9 +147,9 @@

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 are dead.) 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 Infectious people become Recovered. (Just remember that in reality, some are dead.) 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 10 days, on average.4 That means some folks will recover before 10 days, some after. Here's what that looks like, with a simulation starting with 100% :

+

With COVID-19, it's estimated you're Infectious for 10 days, on average.4 That means some folks will recover before 10 days, some after. Here's what that looks like, with a simulation starting with 100% :

@@ -163,9 +163,9 @@

Let's find out.

-

Red curve is current cases ,
- Gray curve is total cases (current + recovered ), - starts at just 0.001% :

+

Red curve is current cases ,
+ Gray curve is total cases (current + recovered ), + starts at just 0.001% :

@@ -181,7 +181,7 @@

NOTE: The simulations that inform policy are way, way more sophisticated than this! But the SIR Model can still explain the same general findings, even if missing the nuances.

-

Actually, let's add one more nuance: before an 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.

+

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.

@@ -189,14 +189,14 @@

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

-

Red + Pink curve is current cases (infectious + exposed ),
- Gray curve is total cases (current + recovered ):

+

Red + Pink curve is current cases (infectious + exposed ),
+ Gray curve is total cases (current + recovered ):

-

Not much changes! How long you stay Exposed changes the ratio of -to-, and when current cases peak... but the height of that peak, and total cases in the end, stays the same.

+

Not much changes! How long you stay Exposed changes the ratio of -to-, and when current cases peak... but the height of that peak, and total cases in the end, stays the same.

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

@@ -224,7 +224,7 @@
-

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.)

+

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.)

@@ -240,7 +240,7 @@

NOTE: Total cases does not stop at herd immunity, but overshoots it! And it crosses the threshold exactly when current cases peak. (This happens no matter how you change the settings – try it for yourself!)

-

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

+

This is because when there are more 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:

@@ -300,7 +300,7 @@

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)

+

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)

@@ -336,7 +336,7 @@

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.

+

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.

@@ -388,7 +388,7 @@

This is called contact tracing. It's an old idea, was used at an unprecedented scale to contain Ebola23, and now it's core part of how Taiwan & South Korea are containing COVID-19!

-

(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

+

(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

Traditionally, contacts are found with in-person interviews, but those alone are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – NOT replaced by – contact tracing apps.

@@ -418,7 +418,7 @@

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

-

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

+

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

@@ -554,7 +554,7 @@

But for COVID-19 in humans, as of May 1st 2020, "how long" is the big unknown.

For these simulations, let's say it's 1 year. - Here's a simulation starting with 100% , exponentially decaying into susceptible, no-immunity s after 1 year, on average, with variation:

+ Here's a simulation starting with 100% , exponentially decaying into susceptible, no-immunity s after 1 year, on average, with variation:

@@ -584,7 +584,7 @@

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.

+

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:

diff --git a/words/words.html b/words/words.html index 396aa37..459f09e 100644 --- a/words/words.html +++ b/words/words.html @@ -69,7 +69,7 @@

It's estimated that, at the start of a COVID-19 outbreak, the virus jumps from an to an every 4 days, on average.2 (remember, there's a lot of variation)

-

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

+

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)

@@ -85,7 +85,7 @@

-

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

+

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:

@@ -97,9 +97,9 @@

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 are dead.) 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 Infectious people become Recovered. (Just remember that in reality, some are dead.) 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 10 days, on average.4 That means some folks will recover before 10 days, some after. Here's what that looks like, with a simulation starting with 100% :

+

With COVID-19, it's estimated you're Infectious for 10 days, on average.4 That means some folks will recover before 10 days, some after. Here's what that looks like, with a simulation starting with 100% :

@@ -113,9 +113,9 @@

Let's find out.

-

Red curve is current cases ,
-Gray curve is total cases (current + recovered ), -starts at just 0.001% :

+

Red curve is current cases ,
+Gray curve is total cases (current + recovered ), +starts at just 0.001% :

@@ -131,7 +131,7 @@ the second-most important idea in Epidemiology 101:

NOTE: The simulations that inform policy are way, way more sophisticated than this! But the SIR Model can still explain the same general findings, even if missing the nuances.

-

Actually, let's add one more nuance: before an 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.

+

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.

@@ -139,14 +139,14 @@ the second-most important idea in Epidemiology 101:

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

-

Red + Pink curve is current cases (infectious + exposed ),
-Gray curve is total cases (current + recovered ):

+

Red + Pink curve is current cases (infectious + exposed ),
+Gray curve is total cases (current + recovered ):

-

Not much changes! How long you stay Exposed changes the ratio of -to-, and when current cases peak... but the height of that peak, and total cases in the end, stays the same.

+

Not much changes! How long you stay Exposed changes the ratio of -to-, and when current cases peak... but the height of that peak, and total cases in the end, stays the same.

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

@@ -174,7 +174,7 @@ the second-most important idea in Epidemiology 101:

-

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.)

+

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.)

@@ -190,7 +190,7 @@ the second-most important idea in Epidemiology 101:

NOTE: Total cases does not stop at herd immunity, but overshoots it! And it crosses the threshold exactly when current cases peak. (This happens no matter how you change the settings – try it for yourself!)

-

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

+

This is because when there are more 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:

@@ -250,7 +250,7 @@ the second-most important idea in Epidemiology 101:

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)

+

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)

@@ -286,7 +286,7 @@ the second-most important idea in Epidemiology 101:

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.

+

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.

@@ -338,7 +338,7 @@ the second-most important idea in Epidemiology 101:

This is called contact tracing. It's an old idea, was used at an unprecedented scale to contain Ebola23, and now it's core part of how Taiwan & South Korea are containing COVID-19!

-

(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

+

(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

Traditionally, contacts are found with in-person interviews, but those alone are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – NOT replaced by – contact tracing apps.

@@ -368,7 +368,7 @@ the second-most important idea in Epidemiology 101:

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

-

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

+

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

@@ -504,7 +504,7 @@ the second-most important idea in Epidemiology 101:

But for COVID-19 in humans, as of May 1st 2020, "how long" is the big unknown.

For these simulations, let's say it's 1 year. -Here's a simulation starting with 100% , exponentially decaying into susceptible, no-immunity s after 1 year, on average, with variation:

+Here's a simulation starting with 100% , exponentially decaying into susceptible, no-immunity s after 1 year, on average, with variation:

@@ -534,7 +534,7 @@ the second-most important idea in Epidemiology 101:

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.

+

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:

diff --git a/words/words.md b/words/words.md index 691356b..d5ba3aa 100644 --- a/words/words.md +++ b/words/words.md @@ -59,7 +59,7 @@ It's estimated that, *at the start* of a COVID-19 outbreak, the virus jumps from [^serial_interval]: “The mean [serial] interval was 3.96 days (95% CI 3.53–4.39 days)”. [Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Ancel Meyers L](https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article) (Disclaimer: Early release articles are not considered as final versions) -If we simulate "double every 4 days" *and nothing else*, on a population starting with just 0.001% , what happens? +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: [^caveats]) @@ -81,7 +81,7 @@ But, this simulation is wrong. Exponential growth, thankfully, can't go on forev ![](pics/susceptibles.png) -The more s there are, the faster s become s, **but the fewer s there are, the *slower* s become s.** +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: @@ -93,9 +93,9 @@ This is the "S-shaped" **logistic growth curve.** Starts small, explodes, then s But, this simulation is *still* wrong. We're missing the fact that 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 are dead.) 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 Infectious people become Recovered. (Just remember that in reality, some are dead.) 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 10 days, *on average*.[^infectiousness] That means some folks will recover before 10 days, some after. **Here's what that looks like, with a simulation *starting* with 100% :** +With COVID-19, it's estimated you're Infectious for 10 days, *on average*.[^infectiousness] That means some folks will recover before 10 days, some after. **Here's what that looks like, with a simulation *starting* with 100% :** [^infectiousness]: “The median communicable period \[...\] was 9.5 days.” [Hu, Z., Song, C., Xu, C. et al](https://link.springer.com/article/10.1007/s11427-020-1661-4) Yes, we know "median" is not the same as "average". For simplified educational purposes, close enough. @@ -111,9 +111,9 @@ Now, what happens if you simulate S-shaped logistic growth *with* recovery? Let's find out. -Red curve is *current* cases , -Gray curve is *total* cases (current + recovered ), -starts at just 0.001% : +Red curve is *current* cases , +Gray curve is *total* cases (current + recovered ), +starts at just 0.001% :
@@ -131,7 +131,7 @@ the *second*-most important idea in Epidemiology 101: **NOTE: The simulations that inform policy are way, *way* more sophisticated than this!** But the SIR Model can still explain the same general findings, even if missing the nuances. -Actually, let's add one more nuance: before an becomes an , they first become Exposed. This is when they have the virus but can't pass it on yet – infect*ed* but not yet infect*ious*. +Actually, let's add one more nuance: before an becomes an , they first become Exposed. This is when they have the virus but can't pass it on yet – infect*ed* but not yet infect*ious*. ![](pics/seir.png) @@ -143,14 +143,14 @@ For COVID-19, it's estimated that you're infected-but-not-yet-in [^latent]: “Assuming an incubation period distribution of mean 5.2 days from a separate study of early COVID-19 cases, we inferred that infectiousness started from 2.3 days (95% CI, 0.8–3.0 days) before symptom onset” (translation: Assuming symptoms start at 5 days, infectiousness starts 2 days before = Infectiousness starts at 3 days) [He, X., Lau, E.H.Y., Wu, P. et al.](https://www.nature.com/articles/s41591-020-0869-5) -Red + Pink curve is *current* cases (infectious + exposed ), -Gray curve is *total* cases (current + recovered ): +Red + Pink curve is *current* cases (infectious + exposed ), +Gray curve is *total* cases (current + recovered ):
-Not much changes! How long you stay Exposed changes the ratio of -to-, and *when* current cases peak... but the *height* of that peak, and total cases in the end, stays the same. +Not much changes! How long you stay Exposed changes the ratio of -to-, and *when* current cases peak... but the *height* of that peak, and total cases in the end, stays the same. Why's that? Because of the *first*-most important idea in Epidemiology 101: @@ -186,7 +186,7 @@ In our simulations – *at the start & on average* – an infect
-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.) +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.)
@@ -202,7 +202,7 @@ Now, let's play the SEIR Model again, but showing R0, R over time, an **NOTE: Total cases *does not stop* at herd immunity, but overshoots it!** And it crosses the threshold *exactly* when current cases peak. (This happens no matter how you change the settings – try it for yourself!) -This is because when there are more non-s than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak. +This is because when there are more 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: @@ -286,7 +286,7 @@ Increased handwashing cuts flus & colds in high-income countries by ~25%[^handwa [^london]: “We found a 73% reduction in the average daily number of contacts observed per participant. This would be sufficient to reduce R0 from a value from 2.6 before the lockdown to 0.62 (0.37 - 0.89) during the lockdown”. We rounded it down to 70% in these simulations for simplicity. [Jarvis and Zandvoort et al](https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html) -**Play with this calculator to see how % of non-, handwashing, and distancing reduce R:** (this calculator visualizes their *relative* effects, which is why increasing one *looks* like it decreases the effect of the others.[^log_caveat]) +**Play with this calculator to see how % of 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.[^log_caveat]) [^log_caveat]: This distortion would go away if we plotted R on a logarithmic scale... but then we'd have to explain *logarithmic scales.* @@ -324,7 +324,7 @@ Let's see what happens if we *crush* the curve with a 5-month lockdown, reduce < Oh. -This is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover (or imported ) can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing. +This is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover (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.** @@ -390,7 +390,7 @@ This is called **contact tracing**. It's an old idea, was used at an unprecedent [^ebola]: “Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history.” [Swanson KC, Altare C, Wesseh CS, et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152989/) -(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.) +(It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.) Traditionally, contacts are found with in-person interviews, but those *alone* are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – *NOT* replaced by – contact tracing apps. @@ -441,7 +441,7 @@ Isolating *symptomatic* cases would reduce R by up to 40%, and quarantining thei Thus, even without 100% contact quarantining, we can get R < 1 *without a lockdown!* Much better for our mental & financial health. (As for the cost to folks who have to self-isolate/quarantine, *governments should support them* – pay for the tests, job protection, subsidized paid leave, etc. Still way cheaper than intermittent lockdown.) -We then keep R < 1 until we have a vaccine, which turns susceptible s into immune s. Herd immunity, the *right* way: +We then keep R < 1 until we have a vaccine, which turns susceptible s into immune s. Herd immunity, the *right* way:
@@ -597,7 +597,7 @@ But for COVID-19 *in humans*, as of May 1st 2020, "how long" is the big unknown. [^monkeys]: From [Bao et al.](https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract) *Disclaimer: This article is a preprint and has not been certified by peer review (yet).* Also, to emphasize: they only tested re-infection 28 days later. For these simulations, let's say it's 1 year. -**Here's a simulation starting with 100% **, exponentially decaying into susceptible, no-immunity s after 1 year, on *average*, with variation: +**Here's a simulation starting with 100% **, exponentially decaying into susceptible, no-immunity s after 1 year, on *average*, with variation:
@@ -627,7 +627,7 @@ Thankfully, because summer reduces R, it'll make the situation better: Oh. -Counterintuitively, summer makes the spikes worse *and* regular! This is because summer reduces new s, but that in turn reduces new immune s. Which means immunity plummets in the summer, *creating* large regular spikes in the winter. +Counterintuitively, summer makes the spikes worse *and* regular! This is because summer reduces new 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: From 3872843e64216e0d542fdb6621918fc5e0c68134 Mon Sep 17 00:00:00 2001 From: Asher Barak Date: Sun, 3 May 2020 20:56:20 +0100 Subject: [PATCH 04/10] Explain how to publish the site using Github Pages Save translators a few moments looking this up --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 43b7810..1bbc991 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ Translate the thumbnail `sharing/thumbnail.png` Step 4) -Use Github Pages to put your forked translation live on the interweb! +Use Github Pages to put your forked translation live on the interweb (Go to 'Settings' of your repository page and choose your master branch as your source)! Step 5) From 3aa14c0acd86434f0e54ecc10942f3884725cfc0 Mon Sep 17 00:00:00 2001 From: Eugene Yokota Date: Sun, 3 May 2020 20:46:10 -0400 Subject: [PATCH 05/10] Link to Japanese translation --- index.html | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index d420ccf..61f1f41 100644 --- a/index.html +++ b/index.html @@ -37,9 +37,13 @@ From 0fef2f40db343e4d9e3ffe5ae0ed2bc113349050 Mon Sep 17 00:00:00 2001 From: Nicky Case Date: Mon, 4 May 2020 11:59:56 -0400 Subject: [PATCH 06/10] tiny fixes --- README.md | 11 +---- index.html | 102 +++++++++++++++++++++++++---------------------- sim/index.html | 2 +- words/words.html | 98 ++++++++++++++++++++++++--------------------- words/words.md | 10 ++++- 5 files changed, 118 insertions(+), 105 deletions(-) diff --git a/README.md b/README.md index 8c5cc28..da792e1 100644 --- a/README.md +++ b/README.md @@ -2,15 +2,6 @@ # How To Translate -**NOTE: I'm still editing some stuff! Words will be finalized-ish on May 4th. -You can start on stuff below, then check for new commits on the 4th to see what else to -translate. You're wonderful, thank you! 💖** - -**NOTE 2: Sorry these instructions are really sloppy. Am writing this at midnight, rushing -to publish this.** - -**NOTE 3: Indie gamedev is horrible.** - Step 1) Check to see if it's already been translated to your language! @@ -29,7 +20,7 @@ Translate `words.md`, (6000 words) then export it to html – make sure your Markdown app supports footnotes – and copy-paste that into the `
` part of `index.html`. -Translate the ``, sidebars, & footer of `index.html` (200 words) +Translate the ``, sidebars, & footer of `index.html` (200 words) **Feel free to add yourself in the header/footer credits as a translator! :)** Translate the images in `/pics` (800 words) If you don't have image-editing software, ask for help on the Github Issue! diff --git a/index.html b/index.html index 601e65a..06ec070 100644 --- a/index.html +++ b/index.html @@ -37,8 +37,8 @@
+ Translations! @@ -49,7 +49,7 @@
Help this guide - get R > 1: + get its R > 1:
What Happens Next? COVID-19 Futures, Explained With Playable Simulations @@ -228,7 +228,7 @@
-

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.)

+

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.)

@@ -406,15 +406,15 @@

-

(& here's the full comic)

+

(Here's the full comic. Details about "pranking"/false positives/etc in footnote:24)

-

Along with similar teams like TCN Protocol24 and MIT PACT25, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.26 (Don't trust Google/Apple? Good! The beauty of this system is it doesn't need trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!

+

Along with similar teams like TCN Protocol25 and MIT PACT26, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.27 (Don't trust Google/Apple? Good! The beauty of this system is it doesn't need trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!

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

-

(Rant about the confusion about pre-symptomatic vs "true" asymptomatic. "True" asymptomatics are rare:27)

+

(Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:28)

-

Isolating symptomatic cases would reduce R by up to 40%, and quarantining their pre/a-symptomatic contacts would reduce R by up to 50%28:

+

Isolating symptomatic cases would reduce R by up to 40%, and quarantining their pre/a-symptomatic contacts would reduce R by up to 50%29:

@@ -463,17 +463,17 @@

"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 sick29... they stop you from getting others sick.

+

You're right. Masks don't stop you from getting sick30... they stop you from getting others sick.

-

To put a number on it: surgical masks on the sick person reduce cold & flu viruses in aerosols by 70%.30 Reducing transmissions by 70% would be as large an impact as a lockdown!

+

To put a number on it: surgical masks on the sick person reduce cold & flu viruses in aerosols by 70%.31 Reducing transmissions by 70% would be as large an impact as a lockdown!

-

However, we don't know for sure the impact of masks on COVID-19 specifically. In science, one should only publish a finding if you're 95% sure of it. (...should.31) Masks, as of May 1st 2020, are less than "95% sure".

+

However, we don't know for sure the impact of masks on COVID-19 specifically. In science, one should only publish a finding if you're 95% sure of it. (...should.32) Masks, as of May 1st 2020, are less than "95% sure".

-

However, pandemics are like poker. Make bets only when you're 95% sure, and you'll lose everything at stake. As a recent article on masks in the British Medical Journal notes,32 we have to make cost/benefit analyses under uncertainty. Like so:

+

However, pandemics are like poker. Make bets only when you're 95% sure, and you'll lose everything at stake. As a recent article on masks in the British Medical Journal notes,33 we have to make cost/benefit analyses under uncertainty. Like so:

-

Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks33), super cheap. If surgical masks, more expensive but still pretty cheap.

+

Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks34), super cheap. 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%, the average "expected value" is still 35%, same as a half-lockdown! So let's guess-timate that surgical masks reduce R by up to 35%, discounted for our uncertainty. (Again, you can challenge our assumptions by turning the sliders up/down)

@@ -481,7 +481,7 @@
-

(other arguments for/against masks:34)

+

(other arguments for/against masks:35)

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

@@ -489,7 +489,7 @@

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%.35 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

+

For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.36 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

@@ -549,10 +549,10 @@

...for how long?

    -
  • COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.36
    • -
  • The coronaviruses that cause "the" common cold give you 8 months of immunity.37
    • -
  • There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.38
    • -
  • One not-yet-peer-reviewed study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.39
    • +
  • COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.37
    • +
  • The coronaviruses that cause "the" common cold give you 8 months of immunity.38
    • +
  • There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.39
    • +
  • One not-yet-peer-reviewed study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.40

But for COVID-19 in humans, as of May 1st 2020, "how long" is the big unknown.

@@ -604,7 +604,7 @@

To be clear: this is unlikely. Most epidemiologists expect a vaccine in 1 to 2 years. Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment.

-

Still, infectious disease researchers have expressed worries: What if we can't make enough?40 What if we rush it, and it's not safe?41

+

Still, infectious disease researchers have expressed worries: What if we can't make enough?41 What if we rush it, and it's not safe?42

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

@@ -643,7 +643,7 @@
-

Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.42

+

Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.43

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.

@@ -782,27 +782,35 @@
  • -

    Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol ↩

    +

    To prevent "pranking" (people falsely claiming to be infected), the DP-3T Protocol requires that the hospital first give you a One-Time Passcode that lets you upload your messages. ↩

    + +

    False positives are a problem in both manual & digital contact tracing. Still, we can reduce false positives in 2 ways: 1) By notifying Bobs only if they heard, say, 30+ min worth of messages, not just one message in passing. And 2) If the app does think Bob's been exposed, it can refer Bob to a manual contact tracer, for an in-depth follow-up interview.

    + +

    For other issues like data bandwidth, source integrity, and other security issues, check out the open-source DP-3T whitepapers!

  • -

    PACT: Private Automated Contact Tracing ↩

    +

    Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol ↩

  • -

    Apple and Google partner on COVID-19 contact tracing technology . Note they're not making the apps themselves, just creating the systems that will support those apps. ↩

    +

    PACT: Private Automated Contact Tracing ↩

  • -

    Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms ever" (true asymptomatic). The only way you could tell the difference is by following up with cases later. ↩

    +

    Apple and Google partner on COVID-19 contact tracing technology . Note they're not making the apps themselves, just creating the systems that will support those apps. ↩

    +
    • + +
  • +

    Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms ever" (true asymptomatic). The only way you could tell the difference is by following up with cases later. ↩

    Which is what this study did. (Disclaimer: "Early release articles are not considered as final versions.") In a call center in South Korea that had a COVID-19 outbreak, "only 4 (1.9%) remained asymptomatic within 14 days of quarantine, and none of their household contacts acquired secondary infections."

    So that means "true asymptomatics" are rare, and catching the disease from a true asymptomatic may be even rarer!

    • -
  • -

    From the same Oxford study that first recommended apps to fight COVID-19: Luca Ferretti & Chris Wymant et al See Figure 2. Assuming R0 = 2.0, they found that:  ↩

    +
  • +

    From the same Oxford study that first recommended apps to fight COVID-19: Luca Ferretti & Chris Wymant et al See Figure 2. Assuming R0 = 2.0, they found that:  ↩

    • Symptomatics contribute R = 0.8 (40%)
      • @@ -814,64 +822,64 @@

      And add up the pre- & a-symptomatic contacts (45% + 5%) and you get 50% of R!

      -
    • -

      “None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” Tara Oberg & Lisa M. Brosseau ↩

      -
      • -
    • -

      “The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ ↩

      +

      “None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” Tara Oberg & Lisa M. Brosseau ↩

    • -

      Any actual scientist who read that last sentence is probably laugh-crying right now. See: p-hacking, the replication crisis) ↩

      +

      “The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ ↩

    • -

      “It is time to apply the precautionary principle” Trisha Greenhalgh et al [PDF] ↩

      +

      Any actual scientist who read that last sentence is probably laugh-crying right now. See: p-hacking, the replication crisis) ↩

    • -

      Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested. ↩

      +

      “It is time to apply the precautionary principle” Trisha Greenhalgh et al [PDF] ↩

    • -

      "We need to save supplies for hospitals." Absolutely agreed. But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks. ↩

      +

      Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested. ↩

      +
      • + +
    • +

      "We need to save supplies for hospitals." Absolutely agreed. But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks. ↩

      "They're hard to wear correctly." It's also hard to wash your hands according to the WHO Guidelines – seriously, "Step 3) right palm over left dorsum"?! – but we still recommend handwashing, because imperfect is still better than nothing.

      "It'll make people more reckless with handwashing & social distancing." Sure, and safety belts make people ignore stop signs, and flossing makes people eat rocks. But seriously, we'd argue the opposite: masks are a constant physical reminder to be careful – and in East Asia, masks are also a symbol of solidarity!

      • -
    • -

      “One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng ↩

      -
      • -
    • -

      “SARS-specific antibodies were maintained for an average of 2 years [...] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” Wu LP, Wang NC, Chang YH, et al. "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly. ↩

      +

      “One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng ↩

    • -

      “We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” Marta Galanti & Jeffrey Shaman (PDF) ↩

      +

      “SARS-specific antibodies were maintained for an average of 2 years [...] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” Wu LP, Wang NC, Chang YH, et al. "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly. ↩

    • -

      “Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” from STAT News by Andrew Joseph ↩

      +

      “We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” Marta Galanti & Jeffrey Shaman (PDF) ↩

    • -

      From Bao et al. Disclaimer: This article is a preprint and has not been certified by peer review (yet). Also, to emphasize: they only tested re-infection 28 days later.  ↩

      +

      “Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” from STAT News by Andrew Joseph ↩

    • -

      “If a coronavirus vaccine arrives, can the world make enough?” by Roxanne Khamsi, on Nature ↩

      +

      From Bao et al. Disclaimer: This article is a preprint and has not been certified by peer review (yet). Also, to emphasize: they only tested re-infection 28 days later.  ↩

    • -

      “Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” by Shibo Jiang, on Nature ↩

      +

      “If a coronavirus vaccine arrives, can the world make enough?” by Roxanne Khamsi, on Nature ↩

    • -

      Dry land metaphor from Marc Lipsitch & Yonatan Grad, on STAT News ↩

      +

      “Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” by Shibo Jiang, on Nature ↩

      +
      • + +
    • +

      Dry land metaphor from Marc Lipsitch & Yonatan Grad, on STAT News ↩

      • @@ -887,7 +895,7 @@
      - Help this post get R > 1: + Help this post get its R > 1:

      This guide is
      diff --git a/sim/index.html b/sim/index.html index fa66bde..7895983 100644 --- a/sim/index.html +++ b/sim/index.html @@ -96,7 +96,7 @@
      - Summer + Strength of Summer

      diff --git a/words/words.html b/words/words.html index 459f09e..c07cfb3 100644 --- a/words/words.html +++ b/words/words.html @@ -174,7 +174,7 @@ the second-most important idea in Epidemiology 101:

      -

      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.)

      +

      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.)

      @@ -338,7 +338,7 @@ the second-most important idea in Epidemiology 101:

      This is called contact tracing. It's an old idea, was used at an unprecedented scale to contain Ebola23, and now it's core part of how Taiwan & South Korea are containing COVID-19!

      -

      (It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

      (It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

      +

      (It also lets us use our limited tests more efficiently, to find pre-symptomatic s without needing to test almost everyone.)

      Traditionally, contacts are found with in-person interviews, but those alone are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – NOT replaced by – contact tracing apps.

      @@ -352,15 +352,15 @@ the second-most important idea in Epidemiology 101:

      -

      (& here's the full comic)

      +

      (Here's the full comic. Details about "pranking"/false positives/etc in footnote:24)

      -

      Along with similar teams like TCN Protocol24 and MIT PACT25, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.26 (Don't trust Google/Apple? Good! The beauty of this system is it doesn't need trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!

      +

      Along with similar teams like TCN Protocol25 and MIT PACT26, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.27 (Don't trust Google/Apple? Good! The beauty of this system is it doesn't need trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!

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

      -

      (Rant about the confusion about pre-symptomatic vs "true" asymptomatic. "True" asymptomatics are rare:27)

      +

      (Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:28)

      -

      Isolating symptomatic cases would reduce R by up to 40%, and quarantining their pre/a-symptomatic contacts would reduce R by up to 50%28:

      +

      Isolating symptomatic cases would reduce R by up to 40%, and quarantining their pre/a-symptomatic contacts would reduce R by up to 50%29:

      @@ -409,17 +409,17 @@ the second-most important idea in Epidemiology 101:

      "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 sick29... they stop you from getting others sick.

      +

      You're right. Masks don't stop you from getting sick30... they stop you from getting others sick.

      -

      To put a number on it: surgical masks on the sick person reduce cold & flu viruses in aerosols by 70%.30 Reducing transmissions by 70% would be as large an impact as a lockdown!

      +

      To put a number on it: surgical masks on the sick person reduce cold & flu viruses in aerosols by 70%.31 Reducing transmissions by 70% would be as large an impact as a lockdown!

      -

      However, we don't know for sure the impact of masks on COVID-19 specifically. In science, one should only publish a finding if you're 95% sure of it. (...should.31) Masks, as of May 1st 2020, are less than "95% sure".

      +

      However, we don't know for sure the impact of masks on COVID-19 specifically. In science, one should only publish a finding if you're 95% sure of it. (...should.32) Masks, as of May 1st 2020, are less than "95% sure".

      -

      However, pandemics are like poker. Make bets only when you're 95% sure, and you'll lose everything at stake. As a recent article on masks in the British Medical Journal notes,32 we have to make cost/benefit analyses under uncertainty. Like so:

      +

      However, pandemics are like poker. Make bets only when you're 95% sure, and you'll lose everything at stake. As a recent article on masks in the British Medical Journal notes,33 we have to make cost/benefit analyses under uncertainty. Like so:

      -

      Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks33), super cheap. If surgical masks, more expensive but still pretty cheap.

      +

      Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks34), super cheap. 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%, the average "expected value" is still 35%, same as a half-lockdown! So let's guess-timate that surgical masks reduce R by up to 35%, discounted for our uncertainty. (Again, you can challenge our assumptions by turning the sliders up/down)

      @@ -427,7 +427,7 @@ the second-most important idea in Epidemiology 101:

      -

      (other arguments for/against masks:34)

      +

      (other arguments for/against masks:35)

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

      @@ -435,7 +435,7 @@ the second-most important idea in Epidemiology 101:

      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%.35 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

      +

      For COVID-19, every extra 1° Celsius (2.2° Fahrenheit) makes R drop by 1.2%.36 The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

      @@ -495,10 +495,10 @@ the second-most important idea in Epidemiology 101:

      ...for how long?

        -
      • COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.36
        • -
      • The coronaviruses that cause "the" common cold give you 8 months of immunity.37
        • -
      • There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.38
        • -
      • One not-yet-peer-reviewed study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.39
        • +
      • COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.37
        • +
      • The coronaviruses that cause "the" common cold give you 8 months of immunity.38
        • +
      • There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.39
        • +
      • One not-yet-peer-reviewed study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.40

      But for COVID-19 in humans, as of May 1st 2020, "how long" is the big unknown.

      @@ -550,7 +550,7 @@ the second-most important idea in Epidemiology 101:

      To be clear: this is unlikely. Most epidemiologists expect a vaccine in 1 to 2 years. Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment.

      -

      Still, infectious disease researchers have expressed worries: What if we can't make enough?40 What if we rush it, and it's not safe?41

      +

      Still, infectious disease researchers have expressed worries: What if we can't make enough?41 What if we rush it, and it's not safe?42

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

      @@ -589,7 +589,7 @@ the second-most important idea in Epidemiology 101:

      -

      Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.42

      +

      Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.43

      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.

      @@ -728,27 +728,35 @@ the second-most important idea in Epidemiology 101:

    • -

      Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol ↩

      +

      To prevent "pranking" (people falsely claiming to be infected), the DP-3T Protocol requires that the hospital first give you a One-Time Passcode that lets you upload your messages. ↩

      + +

      False positives are a problem in both manual & digital contact tracing. Still, we can reduce false positives in 2 ways: 1) By notifying Bobs only if they heard, say, 30+ min worth of messages, not just one message in passing. And 2) If the app does think Bob's been exposed, it can refer Bob to a manual contact tracer, for an in-depth follow-up interview.

      + +

      For other issues like data bandwidth, source integrity, and other security issues, check out the open-source DP-3T whitepapers!

    • -

      PACT: Private Automated Contact Tracing ↩

      +

      Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol ↩

    • -

      Apple and Google partner on COVID-19 contact tracing technology . Note they're not making the apps themselves, just creating the systems that will support those apps. ↩

      +

      PACT: Private Automated Contact Tracing ↩

    • -

      Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms ever" (true asymptomatic). The only way you could tell the difference is by following up with cases later. ↩

      +

      Apple and Google partner on COVID-19 contact tracing technology . Note they're not making the apps themselves, just creating the systems that will support those apps. ↩

      +
      • + +
    • +

      Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms ever" (true asymptomatic). The only way you could tell the difference is by following up with cases later. ↩

      Which is what this study did. (Disclaimer: "Early release articles are not considered as final versions.") In a call center in South Korea that had a COVID-19 outbreak, "only 4 (1.9%) remained asymptomatic within 14 days of quarantine, and none of their household contacts acquired secondary infections."

      So that means "true asymptomatics" are rare, and catching the disease from a true asymptomatic may be even rarer!

      • -
    • -

      From the same Oxford study that first recommended apps to fight COVID-19: Luca Ferretti & Chris Wymant et al See Figure 2. Assuming R0 = 2.0, they found that:  ↩

      +
    • +

      From the same Oxford study that first recommended apps to fight COVID-19: Luca Ferretti & Chris Wymant et al See Figure 2. Assuming R0 = 2.0, they found that:  ↩

    • - diff --git a/words/words.html b/words/words.html index 196620e..755c833 100644 --- a/words/words.html +++ b/words/words.html @@ -449,7 +449,7 @@ the second-most important idea in Epidemiology 101:

    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:

    +

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

    1. Lockdown, then
      2. diff --git a/words/words.md b/words/words.md index 4d996b1..1f12a49 100644 --- a/words/words.md +++ b/words/words.md @@ -548,7 +548,7 @@ 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: +Here's a simulation of a "lazy case" scenario: 1. Lockdown, then 2. A moderate amount of hygiene & "Test, Trace, Isolate", with a mild amount of "Masks For All", then... From a0ad59f23929f0d779a1188732f0118f26ed77e2 Mon Sep 17 00:00:00 2001 From: Nicky Case Date: Tue, 5 May 2020 11:49:58 -0400 Subject: [PATCH 10/10] urgh --- index.html | 1 + 1 file changed, 1 insertion(+) diff --git a/index.html b/index.html index d218aec..aa162e0 100644 --- a/index.html +++ b/index.html @@ -37,6 +37,7 @@
      + Translations: