Evidential note. The questions that I’m noting in this post are based on statistics of confirmed cases. The problem is that this makes any attempt at analysis, whether explicit analysis or tacit analysis presupposed by the questions, prone to a whole bunch of measurement artifacts and sampling bias. The availability and turnaround time of tests have been a substantial bottleneck on finding out anything about the disease, but the bottleneck itself has changed over time as testing procedures changed. Tests are not randomly distributed or performed. (Nor should they be, if their more acute clinical function of determining who to treat is currently more important than their epidemiological function of finding out about the spread of the disease. Which of course it is. But that’s another reason to note that it’s hard to find things out about the disease, and to exercise some caution about the possibility of being misled. In any case, this was written in the mid-morning on March 28, 2020; case numbers change rapidly, and at different rates in different places, so this very tentative set of questions may presuppose a number of things that have been true until recently, but no longer are.

It’s hard to know for sure, but such numbers as we have^[1] seem to indicate (1) that Covid-19 has spread everywhere throughout the United States, but (2) that it has spread much more in the northeast, and in New York and New Jersey, than everywhere else. There was a lot of reporting this past week when the U.S. surpassed 100,000 reported cases, and when it surpassed Italy^[2] and China^[3] There are a lot of cases everywhere, but in fact the magnitude of the U.S.’s rapidly jumping numbers of reported is currently explained almost entirely by the numbers from New York and New Jersey:

U.S. Total (March 28, AM):	102,636
#1. New York:	44,635
#2. New Jersey:	8,825
#3. California:	4,914

The estimated total population of the U.S. in 2018 was 327,167,434 people; New York (19,453,561) and New Jersey (8,882,190) have about 8.6% of the total population of the United States. But they account for well over half (just under 52.1%) of all the reported cases of Covid-19 in the U.S.; while reported Covid-19 cases have increased everywhere in the country, the huge increases in reported cases in New York and New Jersey over the last week explain nearly half (49.3%) of all the increased cases in the U.S. since March 21. So there’s some reason to worry that discussions heavily based on aggregated nationwide numbers are likely to be misleading about the actual patterns of the outbreak. The fact that just two contiguous states have more than half of the reported outbreaks, and account for just under half of the increase in reported outbreaks in the last week, also provides some reason to wonder, what’s going on with New York and New Jersey that have led to such a heavy regional concentration?

• How much is the population due to outbreaks in population centers? New York City is the megalopolis that connects New York State with New Jersey, and as of March 28, New York City alone accounts for 25,398 reported cases, over half the reported cases in all of New York State. Of course New York City is atypical compared to the rest of the United States.

• Could New York and New Jersey simply be in the later phases of an epidemic pathway that, without intervention, will also be experienced everywhere else as exponential growth continues? Well, maybe — New York has close connections and extreme, constant travel between both the Pacific Rim and Western Europe. Maybe the outbreak started earlier there and it has progressed longer, but if you give it time in other parts of the country, further out on the periphery of global social and economic graphs, you’ll get similar progress in infections. On the other hand, the cases in New York and New Jersey now dwarf those on the West Coast (California alone has more population than New York and New Jersey combined, but has less than a tenth of the reported cases), even though the West Coast contains the vast globally connected megalopolises of Los Angeles and the San Francisco Bay Area,^[4] and the earliest epidemic outbreaks in the United States were in Washington State. They plausibly ought to be at the same point or even a later point of an epidemic curve as New York and New Jersey, but they haven’t had anything like the huge spike in reporting of cases.

• Could New York and New Jersey be more severely affected than the rest of the U.S. because of population differences? Well, maybe. They’re certainly more densely populated than a lot of states; New York City has a vast population, and a number of the mid-size cities surrounding it, have a much greater population density than anywhere else in the U.S, including even other large, dense megalopolises like San Francisco or Chicago. On the other hand, it’s not ten times more dense than, say, San Francisco. You might want to look not only at densities but at other features of how those populations go about and live their lives; for example, New York is unusual within the United States not only in having a very dense population but also in having extremely high levels of transit and subway usage within the inner city, unusually low rates of car ownership per household and per capita, etc.

• Could New York and New Jersey be more severely affected because New York City is more severely affected, due to peculiar events and/or local political failures within New York? Derek Thompson at the Atlantic (warning: Twitter thread) thinks that at least some of it is down to fuck-ups and wavering by Bill de Blasio personally, or by the people around him. Maybe; although of course it will be pretty hard to measure how much regime uncertainty did or did not affect New Yorkers’ decisions; we’d have to have some other comparison point with other mayors and decision-makers elsewhere in the country; it’s easy to ridicule or to condemn politicians or policy fuck-ups, and often right to do so, but it may be hard for some time to come how much actual difference in infection rates could be attributed to any given erroneous, wrongheaded, ridiculous, or contemptible behavior. What might we do to gather more information on this? I suppose you could, for example, try to put together some timelines on a matrix of different decisions across several cities with notably different levels of reported cases (for example, when did the first reported cases show up, when were restaurants encouraged or forced to close, when were schools encouraged or forced to close, etc.; of course some questions — such as whether or not the city government decided to cancel a large public gathering like the St. Patrick’s Day Parade — depend on peculiar features that won’t be shared across all cities). Of course, differences might also have to do with differences not in local policies, but in state policies, since New York and New Jersey have state governments with very distinctive dynamics, and a large portion of the relevant decision making here is made by state departments of public health. Or it might have to do with relationships between state departments of public health and city or county authorities — in some places these relationships are fairly cooperative or plainly deferential, in other places fairly antagonistic or competitive and turf-protecting; has there been a difference in these dynamics between New York and/or New Jersey state-local politics, and state-local politics elsewhere in the country?

• Could New York and NEw Jersey be more severely affected because of distinctive environmental factors? They’re way up north, and weather in early March is relatively chilly. Nobody knows very well whether or not infections with Covid-19 will be limited seasonally by hotter weather, although there have been some pious hopes that they might (like some other airborne infections, especially seasonal influenza). If they are, this could be a relevant difference between the northeast and the rest of the country and explain part of the difference; if they aren’t, then I suppose it wouldn’t. Or of course there might be other environmental factors.

• Could New York and New Jersey have higher numbers of reported cases because there is something different about the testing or the reporting? Every state has had different access to test kits and different approaches to testing, in particular to the implementation of third-party laboratory testing. If so, the jump in reported cases might be partly accounted for by differences in the reporting, rather than differences in the disease or in the population. If so the question would not so much be, how did so many more people get infected? but rather, how did so many more people get tested? If this does turn out to be the true explanation, of course, it might be relatively better news for New York and New Jersey (since it would indicate that there isn’t something that they’re differentially doing wrong compared to the rest of the country, or a bad circumstance that they’re differentially stuck with); on the other hand, it would be relatively worse news for the rest of the country, since it would tend to suggest that the situations elsewhere might be worse than the reported numbers indicate.^[5]

Among people who are very worried about Covid-19, the effect of the outbreak on politics has often been to call in very stark terms for huge, drastic, nationally-uniform policy responses. (For example, on March 24, the New York Times Editorial Board argued that man in the White House ought to call for a two-week shelter-in-place order, now, as part of a coherent national strategy for the coronavirus.^[6] Maybe they are right about that — if New York and New Jersey are just an early vision of the future for other states within the United States, then that would be one reason to think that what’s helpful for them now, or what would have been helpful for them if enacted a couple of weeks ago, may be helpful soon, or helpful now, for the rest of the country. Or if they have the highest reported numbers because they have done more testing and reporting than other places, then that would be another reason to think that the situation is less regionally concentrated and more uniform than the numbers would indicate. On the other hand, if there are features distinctive of New York and New Jersey that help explain the regional concentration, that would also help provide some information about how to intelligently respond in different states where the situation might differ. The question should be, how much do we know or how much do we guess about these issues now, and how good is the evidential basis for what we think or guess about it? What evidence could we gather that would help clarify the situation? Is that evidence accessible now, or could it be reasonably approximated in time for it to be useful?

1. ^[1]Evidential note: I’m using the numbers from New York Times‘s frequently updated Coronavirus in the U.S.: Latest Map and Case Count page, which are based on a published dataset maintained by New York Times reporters compiled from state, federal, and territorial public health authorities, with some editorial intervention and normalization by the Times staff; they discuss their Methodology on the Github page’s README. I cross-checked those numbers and found that they show about the same results as taking the relevant aggregate numbers from CDC’s counts of confirmed or presumptive positive cases of Covid-19 in the United States, and the ECDC’s national-level data set on global cases.↩
2. ^[2]This was a totally meaningless and uninformative statistical milestone. The United States has more than 5 times as many people as Italy; as of March 28, 2020, the prevalence of reported Covid-19 cases per 100,000 population was about 450% higher in Italy than in the United States. (Evidential note: For numbers, I used the cases and popData2018 columns helpfully provided with the ECDC’s data set, and corroborated the population estimate numbers with a Google search sanity-check.) Of course, the U.S.’s reported cases are growing rapidly; so depending on how things go over the next several days, the U.S. may overtake Italy in reported cases in the near future.↩
3. ^[3]China has a much larger population than Italy, and a much larger population than the U.S., so this is somewhat more meaningful.↩
4. ^[4]Which of course have even more intense travel, economic and population connections with China and the Pacific Rim.↩
5. ^[5]Although that’s a complicated question, too. If there are lots and lots of undetected Covid-19 cases in the country that aren’t reflected in the confirmed case numbers, that would mean both (1) Covid-19 is potentially much more contagious than the reported cases alone indicate, maybe at the higher end of ranges of estimated reproduction numbers; and (2) Covid-19 is also much harder to track and contain, since lots of cases are passing uncounted. On the other hand, if true, that would also suggested (3) Covid-19 is significantly less lethal than the reported case numbers indicate; if the denominator in the ratio of deaths to infections is actually much higher than we could measure, and there are lots and lots of hidden cases, that would mean that the risk associated with getting an infection is correspondingly lower than it is in models based on officially reported case fatality rates.↩
6. ^[6]Their Editorial does later recognize the fact that the President of the United States actually has no legal authority to issue a nation-wide shelter-in-place order; but they wish that he would use his position to emphatically cajole state authorities into doing so in the several states.↩

One of the major bottlenecks in capacity for medical response to Covid-19 (and for any emergency healthcare, in general, during the outbreak) are critical shortages of medical supplies, especially masks, gloves, and protective gowns. The Flexport.org Fund at CAF America is raising money through GoFundMe to arrange and cover the logistical costs of transporting newly manufactured medical equipment to emergency healthcare providers.

GiveDirectly has set up an emergency relief project to directly assist low-income families impacted by Covid-19 in the United States. They are also preparing to develop an international response program. If you think that emergency relief to households struggling between the disease, quarantine conditions, job losses and insecurity, then here is a well-tested way that you (we) can make that happen, now, without petitions or politicians, without politicking, without parties, without delay and without hanging it on a Christmas-tree bill of trillion-dollar corporate bailouts.

We're delivering cash to families impacted by COVID-19 in the U.S.

We're responding to this crisis by doing what we've done for a decade: delivering cash. Each household will receive $1k, and we expect the main constraint on how many we can reach will be how much we can raise.

We also plan to respond internationally, and are finalizing those details. Will share shortly.

We're beginning by targeting vulnerable households enrolled in SNAP, living in the areas hardest hit by COVID-19.

In partnership with Propel, we're able to identify vulnerable households on SNAP, most of whom are single mothers. We'll begin by enrolling 200 households to receive the first payments and then expand, focusing on other populations in need who could be missed or underserved by other programs. We'll update payment size and structure as we learn more about the need.

We're doing this because we believe ...

1. Cash is the right instrument.

You won't be surprised to hear that from us, but to recap a decade of evidence and experience: it is fast, efficient, effective, and can have multiplier effects on the economy.

2. The government response will likely not be enough.

We're glad that the government is considering direct cash payments, but even if it does as we hope, we expect there will be delays and not enough money fast enough to the most vulnerable.

3. We can execute well.

We've been giving cash since 2008, and have delivered over $150M to poor people in situations as diverse as Liberia, DRC during Ebola, urban Nairobi, and Puerto Rico after Hurricane Maria. When we've responded selectively to US crises in the past, we've seen positive results both in terms of immediate impact and longer-term interest in giving internationally.

Send cash now

Send funds directly to those hardest hit by the economic effects of COVID-19.

Give now

— GiveDirectly, COVID-19 emergency relief
World Wide Web. Accessed March 25, 2020.

GiveDirectly’s Donate link is here. They can accept Credit/Debit Cards, PayPal payments, checks, wire transfer, stock transfer, or Bitcoin.

If you’re not familiar, here’s more information about GiveDirectly and an independent, measurable-output based evaluation of their programs.^[1] Frequently Asked Questions about Covid-19 donations, operations, and Emergency Cash Response are available on the GiveDirectly page.

1. ^[1]Evidential Note: From November 2018. Of course, they just began the Covid-19 relief program, so the evaluation does not speak to that new program. However, it does discuss the operations and effectiveness of several of their existing direct cash assistance programs, which focus on relief of extreme poverty in the developing world.↩

Evidential Notes: This post links to two related editorials by John P.A. Ioannidis, a long prominent figure in the field of medical statistics and in the evidence-based medicine movement. The earlier editorial, from STAT, is from March 17, about a week old at posting time. The later editorial is a fully peer-reviewed pre-print publication in the European Journal of Clinical Investigation, which was published on March 19, five days old at posting time. When the first article was written the ECDC reported about 4,600 cases confirmed in the U.S. Today, at posting time, they report about 46,000 cases confirmed.^[1]

Pandemic Covid-19 is a serious public health threat. Of course it is. One reason it’s serious is biological: it’s a potentially serious disease for anyone who catches it, and a very serious life-threatening disease for some of the people who catch it, and so far it seems to be a fairly contagious disease that’s hard to contain. Another reason it’s serious is institutional: the disease and reactions to the disease have caused overwhelming congestion, resource starvation, temporary breakdowns, and catastrophic failures in some countries’ healthcare systems, which have led to appalling conditions and to deaths, both from the disease itself and from other health emergencies that could not be adequately treated. You should take it seriously, and you absolutely should do what you can to keep yourself healthy and safe to those around you.

There’s also a lot that we do not yet know about Covid-19 as a disease, and a lot that we do not yet know about the effects of the drastic responses that people and institutions have already made in response to the Covid-19 pandemic, or the capabilities and knock-on effects of continuing and escalating measures that are being proposed. There has been an immense, often frenetic attempt to gather more information about the disease, about its spread, and about social and institutional responses to it — not just among experts or hobbyists, but by nearly everyone through news media, scientific journals, situation reports, social media, and every other outlet under the sun. That produces lots of information; it also produces lots and lots of low-quality information and specious info-garbage.

The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.

At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.

. . . The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don't know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.

This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.

Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

That huge range markedly affects how severe the pandemic is and what should be done. . . .

— John P.A. Ioannidis, A fiasco in the making?
STAT (March 17, 2020). Boldface emphasis added.

(Archival PDF of the paper as accessed from Wiley.com on March 24, 2020)

The evolving coronavirus disease 2019 (COVID-19) pandemic1is certainly cause forconcern. Proper communication and optimal decision-making is an ongoing challenge, as data evolve. The challenge is compounded, however, by exaggerated information. This can lead to inappropriate actions. It is important to differentiate promptly the true epidemic from an epidemic of false claims and potentially harmful actions. . . .

— John P.A. Ioannidis, Coronavirus disease 2019: the harms of exaggerated information and non-evidence-based measures
European Journal of Clinical Investigation, March 19, 2020. doi:10.1111/eci.13222

What are some likely error bars on early, widely disseminated and decontextualized estimates of the contagiousness of the virus and case fatality rate for the disease? They’re pretty wide.

Exaggerated pandemic estimates: An early speculation that 40-70% of the global population will be infected went viral.^[4] Early estimates of the basic reproduction number (how many people get infected by each infected person) have varied widely, from 1.3 to 6.5.^[5] These estimates translate into many-fold difference in the proportion of the population eventually infected and dramatically different expectations on what containment measures (or even any future vaccine) can achieve. The fact that containment measures do seem to work, means that the basic reproduction number is probably in the lower bound of the 1.3-6.5 range, and can decrease below 1 with proper measures. The originator of the 40-70% of the population estimate tweeted on March 3 a revised estimate of 20-60% of adults, but this is probably still substantially exaggerated. Even after the 40-70% quote was revised downward, it still remained quoted in viral interviews.^[6]

Exaggerated case fatality rate (CFR): Early reported CFR figures also seem exaggerated. The most widely quoted CFR has been 3.4%, reported by WHO dividing the number of deaths by documented cases in early March.^[7] This ignores undetected infections and the strong age-dependence of CFR. The most complete data come from Diamond Princess passengers, with CFR=1% observed in an elderly cohort; thus, CFR may be much lower than 1% in the general population; probably higher than seasonal flu (CFR=0.1%), but not much so. Observed crude CFR in South Korea and in Germany^[8], the countries with most extensive testing, is 0.9% and 0.2%, respectively as of March 14 and crude CFR in Scandinavian countries is about 0.1%. Some deaths of infected, seriously ill people will occur later, and these deaths have not been counted yet. However even in these countries many infections probably remain undiagnosed. Therefore, CFR may be even lower rather than higher than these crude estimates.

— John P.A. Ioannidis, Coronavirus disease 2019: the harms of exaggerated information and non-evidence-based measures
European Journal of Clinical Investigation, March 19, 2020. doi:10.1111/eci.13222

How much of the divergence in estimates is due to errors of analysis? How much to artifacts of measurement (for example, the wildly different availability of testing and different methods of assigning tests in different countries)? How much of it is due to real biological or institutional differences in the different countries involved (differences in health care systems and their capabilities, differences in the age and health and customs of different populations)? In particular, I’m writing from the southeastern U.S., so a lot of the people that I know are wondering whether the situation where they are, or in the U.S. as a whole, is going to be more like the situation in Hubei Province, China, or more like the situation in Korea, or more like the situation in northern Italy. That’s a complicated question, but a lot of people are treating it as if it were a simple one. It’s not immediately obvious. It’s also not at all obvious how the actions being taken in response to the pandemic will affect the biological risk-factors, or how they will affect the institutional risk-factors, from the epidemic in the U.S. How effective are they at their intended goal? Do they have foreseeable side-effects or negative unintended consequences?

Extreme measures: Under alarming circumstances, extreme measures of unknown effectiveness are adopted. . . . Evidence is lacking for the most aggressive measures. A systematic review on measures to prevent the spread of respiratory viruses found insufficient evidence for entry port screening and social distancing in reducing epidemic spreading.^[10] Plain hygienic measures have the strongest evidence.^[10][11] Frequent hand washing and staying at home and avoiding contacts when sick are probably very useful. Their routine endorsement may save many lives. Most lives saved may actually be due to reduced transmission of influenza rather than coronavirus.

Most evidence on protective measures comes from non-randomized studies prone to bias. A systematic review of personal protective measures in reducing pandemic influenza risk found only two randomized trials, one on hand sanitizer and another on facemasks and hand hygiene in household members of people infected with influenza.^[11]

Harms from non-evidence based measures: Given the uncertainties, one may opt for abundant caution and implement the most severe containment measures. By this perspective, no opportunity should be missed to gain any benefit, even in absence of evidence or even with mostly negative evidence.

This reasoning ignores possible harms. Impulsive actions can indeed cause major harm. One clear example is the panic shopping which depleted supplies of face masks, escalation of prices and a shortage for medical personnel. Masks, gloves, and gowns are clearly needed for medical personnel; their lack poses health care workers' lives at risk. Conversely, they are meaningless for the uninfected general population. However, a prominent virologist's comment^[12] that people should stock surgical masks and wear them around the clock to avoid touching their nose went viral.

Misallocation of resources: Policy-makers feel pressure from opponents who lambast inaction. Also adoption of measures in one institution, jurisdiction, or country creates pressure for taking similar measures elsewhere under fear of being accused of negligence. Moreover, many countries pass legislation that allocates major resources and funding to the coronavirus response. This is justified, but the exact allocation priorities can become irrational. . . .

. . . [E]nhanced detection of infections and lower hospitalization thresholds may increase demands for hospital beds. For patients without severe symptoms, hospitalizations offer no benefit and may only infect health workers causing shortage of much-needed personnel. Even for severe cases, effectiveness of intensive supportive care is unknown. Excess admissions may strain health care systems and increase mortality from other serious diseases where hospital care is clearly effective. . . .

Economic and social disruption: The potential consequences onthe global economy are already tangible. February 22-28 was the worst week for global markets since 2008 and the worse may lie ahead. Moreover, some political decisions may be confounded with alternative motives. Lockdowns weaponized by suppressive regimes can create a precedent for easy adoption in the future. Closure of borders may serve policies focused on limiting immigration. Regardless, even in the strongest economies, disruption of social life, travel, work, and school education may have major adverse consequences. The eventual cost of such disruption is notoriously difficult to project. . . .

Learning from COVID-19: . . . If COVID-19 is indeed the pandemic of the century, we need the most accurate evidence to handle it. Open data sharing of scientific information is a minimum requirement. This should include data on the number and demographics of tested individuals per day in each country. Proper prevalence studies and trials are also indispensable.

If COVID-19 is not as grave as it is depicted, high evidence standards are equally relevant. Exaggeration and over-reaction may seriously damage the reputation of science, public health, media, and policy makers.It may foster disbelief that will jeopardize the prospects of an appropriately strong response if and when a more major pandemic strikes in the future.

— John P.A. Ioannidis, Coronavirus disease 2019: the harms of exaggerated information and non-evidence-based measures
European Journal of Clinical Investigation, March 19, 2020. doi:10.1111/eci.13222

Dissenting Views: Ioannidis’s articles have been controversial. For some direct responses, see the comments on the first article, and see also Marc Lipsitch, We know enough to act decisively against Covid-19.

See also.

• GT 2020-03-23: The Infovore’s Dilemma

1. ^[1]Source: ECDC Data on Geographic Distribution of COVID-19 cases worldwide, accessed 24-Mar-2020. Exact totals from the figures in the data table are 4,661 and 46,442, respectively. I’m saying about and sticking to two significant digits here, because different agencies’ reports gather numbers from different sources at different times and people following the data have found their aggregate numbers to be consistently very close to each other, but with persistent gaps due to differences in methodology.↩
2. ^[4]McGinty JC. How many people might one person with coronavirus infect? Wall Street Journal, February 14, 2020, accessed February 27, 2020 at https://www.wsj.com/articles/how-many-people-might-one-person-with-coronavirus-infect-11581676200↩
3. ^[5]Tang B, Bragazzi NL, Li Q, Tang S, Xiao Y, Wu J. An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infect Dis Model. 2020;5:248-255.↩
4. ^[6]Axelrod J, CBS News, March 2, 2020: Coronavirus may infect up to 70% of world’s population, expert warns. Accessed in https://www.cbsnews.com/news/coronavirus-infection-outbreak-worldwide-virus-expert-warning-today-2020-03-02/ on March 3, 2020.↩
5. ^[7]https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19—3-march-2020↩
6. ^[8]Frank Jordans. Experts: Rapid testing helps explain few German virus deaths. Associated press, https://apnews.com/ad9a6af47c3b55fd83080c9168afaaf4, accessed March 10, 2020.↩
7. ^[10]Jefferson T, Del Mar CB, Dooley L, Ferroni E, Al-Ansary LA, Bawazeer GA, et al. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database Syst Rev. 2011;(7):CD006207.↩
8. ^[11]Saunders-Hastings P, Crispo JAG, Sikora L, Krewski D. Effectiveness of personal protective measures in reducing pandemic influenza transmission: A systematic review and meta-analysis. Epidemics. 2017;20:1-20.↩
9. ^[12]https://www.thetomahawk.com/featured-news/what-i-am-doing-to-minimize-corona-virus-infection-from-james-robb-m-d/, accessed March 5, 20202.↩

The basic predicament for intelligent action in a crisis is that information is laborious to sort, measurements are costly to get and costly to vet, and analysis takes time. Peer review and consideration takes longer. People act under uncertainty; when they are urged to act rapidly and drastically, they necessarily act — at best — on the best information they have at hand, and they are going to seek out and to produce lots more information — tentative results, estimates, conjectures and seemingly reasonable assumptions — that are very rough, because they also happen to be ready. The more rapidly, the more urgently, and the more drastically decision-makers start to act, or try to react, the sharper the predicament becomes: the supply of information available becomes necessarily more rushed and more tentative at the same time that the demand for certainty and unanimity becomes higher; the range of possible effects grows wider and deeper; the decisions and reactions themselves change the very regularities in other people’s choices, other people’s knowledge, other people’s circumstances, and the natural world that you are trying to observe or to assume. The more drastically and rapidly you act, the more they change, both in ways that you may be able to foresee and in ways that you cannot foresee, do not intend, and cannot control.

The result is that crises often produce a vast glut of information; but a lot of that information, and often the information most critical to making urgent decisions or taking drastic measures, is relatively low-quality information at best, information which has been produced rapidly, not vetted carefully, made on multiple simplifying assumptions, with huge error bars and wild, systematic skews that may be understandable in the pragmatic context of making a decision. (The extreme worse-case scenario might be highly salient even if it’s unlikely; data sets that aren’t entirely comparable may be the best you have for two things that you really need to compare; you might need to piously hope that some things go as planned even if you can’t be sure.) The more or less necessary predicament — you need time and effort to understand intelligently, but you need speed and freedom to take action — is often made worse by a number of extremely tempting, but extremely misleading, errors. A real need bold conjectures and decisive action is often conflated with unrealistic demands for dogmatic certainty; the real benefits of coordinated action are often conflated with a punitive demand for unanimity in belief and deference or conformity to appointed authorities. The deep epistemic problem with understanding the situation intelligently becomes not only the fact that high-quality information becomes so hard to find, but that low-quality information, or misinformation, crowds around all the watering holes in the cognitive ecosystem. Anecdotes are presented as data, toy models are presented as charts, tentative results are presented as What We Know Now, large scale syntheses of poorly comparable data from disparate sources are put forward as observed facts, third-hand sloganeering reports of experts’ tentative conclusions are put forward as conclusive arguments, simplifying assumptions are put forward as obvious and incontestable dogmatic principles. Actively seeking out information and absorbing it doesn’t necessarily serve to better inform or to improve your cognitive position; it often ends up being an exhausting means to skew your own judgment towards the prevailing trends and groupthink of the info-garbage that is most readily available to you.

None of this is any reason not to rely on imperfect information if you have to make a decision — what else are you going to rely on? It is a reason to act with the awareness that you’re taking a certain number of shots in the dark. It is a reason to prominently state simplifying assumptions used in arguments or models, and to acknowledge them as assumptions, not as oracular revelations, wherever possible. It is a reason to actively seek out, and publicize, the parts of what you’re saying that you’re least certain of, or that you know will be most contested by others, and to acknowledge what would follow or what might follow if those underlying premises turned out to be false. It’s a reason to be ready for and to do whatever you can to hedge against the risks of unintended consequences. It’s a reason to state numbers with error bars and to try to figure out lowball and highball constraints on what the real figure might be, if you’re wrong.

In circumstances that lead to a high risk of groupthink and overreach, it’s a reason to explicitly employ evidential markers when reporting claims; it’s a reason to cite and link to specific sources for specific claims rather than simply repeating them or presenting them as What Experts Are Saying, and it’s a reason for readers to spend some time following links and footnotes where they have been made available, or to significantly discount stories that don’t bother to provide them. It’s also a reason to actively seek out and cultivate second guesses, minority reports and dissenting opinions, rather than ignoring, scolding or punishing them.

In a high info-garbage environment, it is often worthwhile to deliberately limit, compartmentalize or substitute the consumption of certain kinds of low-quality or risky information. In particular, to restrict your intake of information where the persuasive power of the presentation is especially likely to outrun its real evidential import. You may be better off glancing at boring charts a few times over a few days than you are looking at infographics in a newspaper article; you are almost certainly better off reading the abstract and a paragraph or two of one scientific paper than you are reading through an explainer article attempting to gloss the conclusion of that paper while weaving it together narratively with interviews from two or three other pronouncements by experts in the field. Commentary is prone to be less valuable than reporting, and reporting less valuable than sources or data. In a high info-garbage environment it’s also especially important to be sensitive to the likelihood of mistakes, to record claims in a testable and falsifiable form and to go back and check on them over time, to prepare for imperfect or piecemeal implementation of plans, and actively try to gather information on potential or actual unintended consequences and perverse incentives.

The problem here is not that people will draw conclusions that are wrong, or to make decisions that turn out to be mistakes. Of course they will. If that wasn’t a real danger, then it wouldn’t be a crisis in the first place. The problem here is that if you want to draw conclusions that are less wrong, more often, — if you want to do less damage and realize more quickly when you make the wrong decision, — if you want to lower the chance of being misled — then that may mean being more selective rather than more completist in the sources of information that you pursue. And the sources to be most selective about will often be the ones that seem the most appealing from the standpoint of your own social and ideological starting-points. Consume thoughtful discussion and information, not too much, mostly data.