From 835ef308000cdaabdf31acfb79f0e2686bb25301 Mon Sep 17 00:00:00 2001 From: Nicky Case Date: Fri, 15 May 2020 17:54:55 -0400 Subject: [PATCH] italian translation and updated citations --- index.html | 169 +++++++++++++++++++++++++---------------------- words/words.html | 166 +++++++++++++++++++++++++--------------------- words/words.md | 20 ++++-- 3 files changed, 197 insertions(+), 158 deletions(-) diff --git a/index.html b/index.html index 41624e8..880a929 100644 --- a/index.html +++ b/index.html @@ -44,12 +44,12 @@
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    • Help make a translation? @@ -186,7 +186,7 @@

    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 Model,5
    +

    This is the SIR Model,5
    (Susceptible Infectious Recovered)
    the second-most important idea in Epidemiology 101:

    @@ -286,7 +286,7 @@

    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 bed per 3400 people.14 Therefore, the USA can handle 20 out of 3400 people being simultaneously infected – or, 0.6% of the population.

    +

    Roughly 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 bed 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:

    @@ -296,24 +296,24 @@

    Not good.

    -

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

    That's what the March 16 Imperial College report found: do nothing, and we run out of ICUs, with more than 80% of the population getting infected. (remember: total cases overshoots herd immunity)

    -

    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.

    +

    Even if only 0.5% of infected die15 – 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

    +

    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.16

    Both plans, though, had a literally fatal flaw.

    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%:

    +

    Increased handwashing cuts flus & colds in high-income countries by ~25%17, while the city-wide lockdown in London cut close contacts by ~70%18. 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.19)

    @@ -357,7 +357,7 @@

    Scenario 3: Intermittent Lockdown

    -

    This solution was first suggested by the March 16 Imperial College report, and later again by a Harvard paper.19

    +

    This solution was first suggested by the March 16 Imperial College report, and later again by a Harvard paper.20

    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)

    @@ -369,7 +369,7 @@

    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

    +

    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.21

    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 that makes life worth living. And besides, poverty itself has horrible impacts on mental and physical health.

    @@ -385,7 +385,7 @@

    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:

    +

    To understand how Taiwan & South Korea contained COVID-19, we need to understand the exact timeline of a typical COVID-19 infection22:

    @@ -393,13 +393,13 @@

    -

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

    +

    And in fact, 44% of all transmissions are like this: pre-symptomatic! 23

    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. 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!

    +

    This is called contact tracing. It's an old idea, was used at an unprecedented scale to contain Ebola24, 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.)

    @@ -415,15 +415,15 @@

    -

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

    +

    (Here's the full comic. Details about "pranking"/false positives/etc in footnote:25)

    -

    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!

    +

    Along with similar teams like TCN Protocol26 and MIT PACT27, they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.28 (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.

    -

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

    +

    (Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:29)

    -

    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:

    +

    Isolating symptomatic cases would reduce R by up to 40%, and quarantining their pre/a-symptomatic contacts would reduce R by up to 50%30:

    @@ -472,17 +472,17 @@

    "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 sick30... they stop you from getting others sick.

    +

    You're right. Masks don't stop you from getting sick31... they stop you from getting others sick.

    -

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

    +

    To put a number on it: surgical masks on the infectious person reduce cold & flu viruses in aerosols by 70%.32 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.32) 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.33) 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,33 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,34 we have to make cost/benefit analyses under uncertainty. Like so:

    -

    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.

    +

    Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks35), 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)

    @@ -490,7 +490,7 @@
    -

    (other arguments for/against masks:35)

    +

    (other arguments for/against masks:36)

    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!

    @@ -498,7 +498,7 @@

    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 (1.8° Fahrenheit) makes R drop by 1.2%.36 The summer-winter difference in New York City is 26°C (47°F),37 so summer will make R drop by ~31%.

    +

    For COVID-19, every extra 1° Celsius (1.8° Fahrenheit) makes R drop by 1.2%.37 The summer-winter difference in New York City is 26°C (47°F),38 so summer will make R drop by ~31%.

    @@ -558,10 +558,10 @@

    ...for how long?

      -
    • COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.38
      • -
    • The coronaviruses that cause "the" common cold give you 8 months of immunity.39
      • -
    • There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.40
      • -
    • One not-yet-peer-reviewed study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.41
      • +
    • COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.39
      • +
    • The coronaviruses that cause "the" common cold give you 8 months of immunity.40
      • +
    • There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.41
      • +
    • One not-yet-peer-reviewed study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.42

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

    @@ -613,7 +613,7 @@

    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?42 What if we rush it, and it's not safe?43

    +

    Still, infectious disease researchers have expressed worries: What if we can't make enough?43 What if we rush it, and it's not safe?44

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

    @@ -652,7 +652,7 @@
    -

    Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.44

    +

    Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.45

    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.

    @@ -680,6 +680,8 @@

    These footnotes will have sources, links, or bonus commentary. Like this commentary! ↩

    This guide was published on May 1st, 2020. Many details will become outdated, but we're confident this guide will cover 95% of possible futures, and that Epidemiology 101 will remain forever useful.

    + +

    (Update May 15: Added citations for "1 in 20 of infected are hospitalized" and "0.5% of infected die")

  • @@ -741,7 +743,15 @@
  • -

    "Percentage of COVID-19 cases in the United States from February 12 to March 16, 2020 that required intensive care unit (ICU) admission, by age group". Between 4.9% to 11.5% of all COVID-19 cases required ICU. Generously picking the lower range, that's 5% or 1 in 20. Note that this total is specific to the US's age structure, and will be higher in countries with older populations, lower in countries with younger populations. ↩

    +

    [UPDATED MAY 15] Many of you rightly pointed out that our previous citation for "1 in 20 need hospitalization" was based off old USA data on confirmed cases – which was way lower than the real number of cases, due to lack of tests. ↩

    + +

    So, let's look at the country with the most tests per capita: Iceland. As of May 15th, 2020, they had 115 hospitalized among 1802 confirmed cases ≈ 6.4% hospitalization rate, or 1 in 16.

    + +

    A more recent study of COVID-19 in France – using not just official confirmed cases but also antibody test data – found that “3.6% of infected individuals are hospitalized”. Or, 1 in 28.

    + +

    Overall, there's a lot of uncertainty, but "1 in 20" is roughly close. Besides, for the rest of these simulations, we triple hospital capacity – so, even if "1 in 20" is three times too high, the point still stands.

    + +

    Old citation: "Percentage of COVID-19 cases in the United States from February 12 to March 16, 2020 that required intensive care unit (ICU) admission, by age group". Between 4.9% to 11.5% of all COVID-19 cases required ICU. Generously picking the lower range, that's 5% or 1 in 20. Note that this total is specific to the US's age structure, and will be higher in countries with older populations, lower in countries with younger populations.

  • @@ -749,77 +759,81 @@
  • -

    “He says that the actual goal is the same as that of other countries: flatten the curve by staggering the onset of infections. As a consequence, the nation may achieve herd immunity; it’s a side effect, not an aim. [...] The government’s actual coronavirus action plan, available online, doesn’t mention herd immunity at all.” ↩

    +

    [UPDATED MAY 15] Researchers in Indiana, USA did a random-sample test of the population, and found an infection-fatality rate (IFR) of 0.58%. ↩

    +
    • + +
  • +

    “He says that the actual goal is the same as that of other countries: flatten the curve by staggering the onset of infections. As a consequence, the nation may achieve herd immunity; it’s a side effect, not an aim. [...] The government’s actual coronavirus action plan, available online, doesn’t mention herd immunity at all.” ↩

    From a The Atlantic article by Ed Yong

    • -
  • -

    “All eight eligible studies reported that handwashing lowered risks of respiratory infection, with risk reductions ranging from 6% to 44% [pooled value 24% (95% CI 6–40%)].” We rounded up the pooled value to 25% in these simulations for simplicity. Rabie, T. and Curtis, V. Note: as this meta-analysis points out, the quality of studies for handwashing (at least in high-income countries) are awful. ↩

    -
    • -
  • -

    “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 ↩

    +

    “All eight eligible studies reported that handwashing lowered risks of respiratory infection, with risk reductions ranging from 6% to 44% [pooled value 24% (95% CI 6–40%)].” We rounded up the pooled value to 25% in these simulations for simplicity. Rabie, T. and Curtis, V. Note: as this meta-analysis points out, the quality of studies for handwashing (at least in high-income countries) are awful. ↩

  • -

    This distortion would go away if we plotted R on a logarithmic scale... but then we'd have to explain logarithmic scales. ↩

    +

    “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 ↩

  • -

    “Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022.” Kissler and Tedijanto et al ↩

    +

    This distortion would go away if we plotted R on a logarithmic scale... but then we'd have to explain logarithmic scales. ↩

  • -

    See Figure 6 from Holt-Lunstad & Smith 2010. Of course, big disclaimer that they found a correlation. But unless you want to try randomly assigning people to be lonely for life, observational evidence is all you're gonna get. ↩

    +

    “Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022.” Kissler and Tedijanto et al ↩

  • -

    3 days on average to infectiousness: “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.  ↩

    +

    See Figure 6 from Holt-Lunstad & Smith 2010. Of course, big disclaimer that they found a correlation. But unless you want to try randomly assigning people to be lonely for life, observational evidence is all you're gonna get. ↩

    +
    • + +
  • +

    3 days on average to infectiousness: “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.  ↩

    4 days on average to infecting someone else: “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

    5 days on average to feeling symptoms: “The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days)” Lauer SA, Grantz KH, Bi Q, et al

    • -
  • -

    “We estimated that 44% (95% confidence interval, 25–69%) of secondary cases were infected during the index cases’ presymptomatic stage” He, X., Lau, E.H.Y., Wu, P. et al ↩

    -
    • -
  • -

    “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. ↩

    +

    “We estimated that 44% (95% confidence interval, 25–69%) of secondary cases were infected during the index cases’ presymptomatic stage” He, X., Lau, E.H.Y., Wu, P. et al ↩

  • -

    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. ↩

    +

    “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. ↩

    +
    • + +
  • +

    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!

    • -
  • -

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

    -
    • -
  • -

    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:  ↩