Ends.

Rather, the strongest predictor of whether a nation becomes democratic or not hinges on the values of its citizens in the preceding years. One cultural value is especially important in this transition: openness to diversity.

The paper explores how nations become democratic as opposed to, say, autocratic. Democracy is not an inevitable or per-ordained state of affairs. As recently as the 50s, just 20 countries were considered democratic.

In the wake of recent events, I keep thinking about this paper, published by @damianjruck et al. earlier this year. https://nature.com/articles/s41562-019-0769-1… short thread:

"Having once brought Boston-style medicine to Haiti, now PIH was trying to bring some elements of Haiti-style medicine to Boston. 'Reverse innovation.'" It's a call for us to spend more time listening to the collective wisdom of people who've done this before. 8/8

My editorial comment - I think the most valuable solutions are not going to come from scientists, but rather from people experienced in implementation and the "public health slog." We have a set of tools that we know can help. How do we make them work more effectively? 7/8

Importantly, the work is difficult because of the missing social safety net. People have "tested positive because their jobs required them to be caregivers... Now we’re requiring them, in terms of solidarity, to flatten the curve, to stay home. But they... can’t get food." 6/8

A tracer needs "a long list of agencies supplying various services, and volunteers willing to run errands for people who can’t. Much of his day is spent finding someone to pick up a particular bag of groceries from a particular food pantry, or a nebulizer from a pharmacy." 5/8

Category 3: Care-resource coordinators help people solve problems that might prevent them from being able to isolate themselves - how to get food or find a place to stay. "Without helping people to isolate, you would never persuade them to do so." 4/8

Category 2: Contact tracers call each of these contacts, ask them to isolate, and then follow up frequently to make sure they were doing so and to check for any symptoms. The essential part of their job is to persuade the contacts to isolate at home. "Voluminous coaxing." 3/8

Twenty-two thousand people have applied to work with PIH, some "comically overqualified" for the job. Hires are divided into three job categories. Category 1: Case investigators quickly call people who have tested positive and interview them extensively about their contacts. 2/8

Partners in Health @PIH is used to working in the poorest regions of the poorest countries. Now they are leading Massachusetts' contact tracing. Their experiences remind us of the importance of "support" in test, trace, isolate, support. (A thread 1/8)

Psychological problems associated with coping strategies: a web-based study in Peruvian population during the COVID-19 pandemic

31. Sort of. The problem is that IHME's model missed in the wrong direction for Handley's argument. It massively *underestimated* the number of US deaths, because it used a Farr's law approach to fitting the epidemic trajectory. (We'll come back to this.)

30. Handley then attacks the (original version of the) IHME model. He correctly notes that it performed very poorly, and questions its utility for prediction. I've said all of the same things at various times, so we should be in agreement on this much at least, right?

29. Handley goes on to drag Ferguson for other past predictions, without understanding how certainty ranges work. If Ferguson predicted 50 to 50,000 dead and in fact 117 died, you can complain that his range is broad but you can't complain that he was wrong.

28. But is this an overestimate? We've put controls into place, and we've had 117 thousand deaths in the US to date. At most about 5% of the population has been infected. Herd immunity will be at 50% or higher. Given that, Ferguson's estimate seems right on target.

27. Handley's attacks on Ferguson are the same old same old. Ferguson predicted 2.2M US deaths if the pandemic went through to herd immunity uncontrolled and 1.1M US deaths if it went through to herd immunity with controls in place. Handley sells this as an overestimate.

26. Fact 9 is misleading. It consists of an attack on Neil Ferguson's work at Imperial College, and on IHME's work at my own employer, the UW. I've been intensely critical of IHME's work myself, which adds an interesting twist here.

25. Handley's 8th fact is not wrong, to the best of my knowledge, but it may be a bit rhetorically misleading. We've reached other rather important milestones on the basis of "only theoretical modeling"—for example, detonating an atomic bomb, or landing humans on the moon.

24. Handley's 7th fact is perhaps not well phrased, but I largely concur. There's nothing magical about six feet of social distancing. It's a rough compromise between protection and feasibility. I'd rather see 12 or 18 feet when possible.

23. He cites the WHO: "Just today, the World Health Organization announced that masks should only be worn by healthy people if they are taking care of someone infected with COVID-19:" That recommendation is from MARCH. Here are the current WHO guidelines: https://who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/when-and-how-to-use-masks&publication=advice-on-the-use-of-masks-in-the-community-during-home-care-and-in-healthcare-settings-in-the-context-of-the-novel-coronavirus-(2019-ncov)-outbreak

22. To back up this claim, Handley cites a paper about influenza, a very different disease than COVID19. (He claims it is hosted on the CDC's own website which is at best misleading; it is a paper published in Emerging Infectious Diseases, a journal hosted by the CDC.)

21. Handley's sixth claim is just bizarre and contradicts a huge swath of evidence published over the past weeks. One of the big surprises that epidemiologists have encountered during the pandemic is just how valuable masks appear to be in stemming the spread of this disease.

20. Other studies have also shown outdoor transmission, e.g. https://medrxiv.org/content/10.1101/2020.02.28.20029272v2… ( Still, I'm somewhat sympathetic to Handley here. While his claim is wrong, I agree that outdoor transmission may be rare—and efforts to shut down outdoor recreation may be excessive.)

19. But this very study in fact shows the opposite of what he claims. From the abstract of Handley's own source:

18. Handley's 5th fact is FALSE as well. He claims that COVID-19 does not spread outdoors, and picks a single study that he claims supports his assertion.

17. Finally, Handley's sole source for Fact 4 does not inspire a lot of confidence with statements like this:

16. We also know enough about the germ theory of disease to understand that longer times lead to higher probabilities of transmission simply because there are more opportunities, not because there's some threshold amount of time in the same place necessary to transmit.

15. Science works by establishing *mechanisms*, and we know a lot about mechanisms of COVID transmission. We know that respiratory droplets are important, and that spread can readily occur that way.

14. Community spread while shopping etc is much harder to track down via contact tracing methodology than spread among co-workers, family members, etc., because there is no easy way to figure out who was in the store at the same time and connect spread that way.

13. Handley's fourth "fact" confuses the absence of evidence for the evidence of absence. This is not based on emerging science in the sense of actual research papers; it's based on a single interview in Business Insider.

12. Here's a thread on what happened with the WHO's misleading statement:

11. That's not how science works. You can't pick and choose like that. There is overwhelming evidence that people without symptoms—particular but not exclusively those who are presymptomatic—can transmit COVID. Here's one review paper:

10. Handley's third claim is FALSE, and outrageously irresponsible. He cherry picks two sources of evidence: 1) A single study based on a single patient 2) A confusing claim that Dr. Van Kerkove of the WHO made in a press conference, and subsequently retracted.

9. Handley's second claim is that older people have higher risk than the average IFR and younger people have lower risk than the IFR. This is TRUE. Risk increases with age, and that's how averages work.

8. Meanwhile flu is often cited as having an infection fatality rate of 0.1%. This is tricky to know because flu fatality rates vary so much from strain to strain and because so many cases go undiagnosed, but overall this seems at least two-fold too high.

7. Best estimates range from 0.5% to 1.5%. Even the CDC's lowball estimates, which caused a scandal when released, ranged from a best case of 0.1% to a best-guess of 0.4% to a worst case of 0.8%. That's far higher than Handley asserts.

6. We've been over the COVID19 IFR thing so many times. Here's one of many threads about the issue:

5. The first claim is that the infection fatality rate (IFR) for COVID19 is between 0.07 and 0.20, in line with seasonal flu. This is FALSE, on both counts. IFR for COVID-19 is higher. IFR for flu is lower.

4. I don't know if I have the energy to go through his whole #COVID19 blog post, but let's at least give it a go. The post is organized around a series of "facts" that are more like cherry-picked claims. I'll address them in order.

3. That's a gentle way of saying that Handley is committed to the false claim that vaccines cause autism. The three other posts on his blog revolve around this claim. The first of them, for example, makes the false claim that aluminum in vaccines causes autism.

2. This one is from J. B. Handley, who co-founded Generation Rescue, the organization that morphed into Jenny McCarthy's Autism Organization. Handley has some...heterodox...ideas.

1. Another day, another blog post of #COVID19 misinformation making the rounds.

Example of someone who knows what she is talking about (Dr. Grabowski), and someone who is (and has been for months) brand building off hyperbole and doesn’t know the science at all:

Also, anyone telling you that they ‘absolutely know the answer’ to COVID questions is a liar and you should run away.

The amount of experts who used to cry foul about people acting like experts in their field that have now chased the COVID story pretending to be virologists, ID epidemiologists, ID physicians, and/or infection control specialists to try and brand build is creepy and ghoulish.

The 3 W's drive down risk of Covid and must be common practice when we leave our home and are around others.

I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor @Kindskoepfe_Lab

behaves as one might expect, it is better to identify these glitches before a big winter wave hits. Here endeth ye tweetorial.

It's why preparation now is of the essence. In Ontario we have identified a lot of bugs in our public health and healthcare systems, particularly related to lab capacity, information systems, and communication. In a sense that's great, because if this disease...

which jibes with that idea. If that's true, that means we are likely to have a very challenging winter ahead of us: lots of susceptibility, weariness of distancing, and a seasonally juiced virus with lots of susceptible folks to infect.

Why is this important? Because the very waning of COVID-19 in the northern hemisphere right now, despite pretty crappy disease control efforts in many places, suggests it is indeed a very seasonal pattern. I've also noted the concave up patterns in S America right now

As in this excellent figure showing us the very irregular patterns of seasonal waves in influenza pandemics:

And ultimately will turn into predictable seasonal disease. But that initial crazy trajectory of a pandemic depends in part on the random element of when the disease emerges. Early waves can die but then come back with a vengeance due to seasonal boosting.

Here are the average trajectories across 20 batched runs. Starting to look a bit regular.

We can batch a few runs and see very different patterns. These are 5 runs and each color represents a different run. The patterns look very different, but it's the same (exactly the same) disease.

They look pretty different. It's the same model, same parameters. Just seeding it at different times means that the epidemics get boosted or suppressed by seasonal changes in R.

Because the fraction susceptible is around 1, we can get out of season epidemics with pandemic pathogens. These are called "herald waves". Let's add a single stochastic element to our model...I'll seed the model with a single case at a random time of year.

Here's another.

Here's a run of the same model:

We don't know when a novel pathogen is going to emerge. Perhaps it'll be at "peak season" (with respect to its R0) when it emerges, perhaps it'll be "off season". E.g., summertime emergence for a flu virus, wintertime emergence for (summertime seasonal) cholera.

Now let's throw in some randomness ("stochasticity")...because again, although these waves look irregular, this model produces exactly the same outputs every time I run it.

Here we go...I messed about by shortening duration of immunity and we now have a disease that explodes onto the scene as a pandemic but then becomes "seasonal flu" once there's some immunity in the population.

See here for genius work on this by @jd_mathbio

This isn't annual periodicity. We could muck about with the numbers and get this to have an "intrinsic" oscillatory frequency that's the same as the oscillatory frequency of R0, and then we could have seasonal epidemics as with flu.

So we've got some cool waves. That looks like what happens with pandemics. If I run this out over a long time (200 years, here) u can see that the combination of replenishment of susceptibility (births, deaths, viral drift) with some seasonal forcing gives us periodic epidemics

Epidemic waves in my deterministic model look like this, and are a function of the interplay between seasonality and replenishment of susceptibles over time.

I'm going to make an SEIRS model (susceptible-latent-infectious-removed-susceptible) model such as we might use for flu. People lose immunity over time...perhaps as a result of viral drift. This model is initially deterministic (get the same result every time).

Let's make a simple SIR model with a seasonally oscillatory R. My R looks, arbitrarily, like this... In winter it's COVID-y...up in the low 2's. In summer it drops to 1-point-something.

One reason may be a seasonally oscillatory R0, which we might expect to see with a coronavirus and which has been anticipated by investigators like @mlipsitch

That both reduced the R of H1N1, and also attenuated mortality, because those at greatest risk of death, conditional on infection, didn't get infected.

SARS-CoV-2 is different, because nobody, in any age group, has pre-existing immunity. Those who are predisposed to death, conditional on infection, are not protected against infection, as they were in 2009. Hence mortality patterns that look like this in Ontario (X-axis = age)

Also, because nobody has baseline immunity R ~ R0 so attack rates are predictably high. But wait: why doesn't this just rip through this susceptible population in a single wave? Why did we have an R ~ 3 in Ontario in March and now (despite weak distancing) do we have an R ~ 1?

Pandemics have initial R ~ R0. That's why the epidemics are so large. In the 2009 influenza pandemic, this wasn't true. Those born prior to 1957 had early life experience with a related H1N1 influenza A virus, and were protected against infection.

I think I've used this analogy before, but epidemics are like gardens: you need the seed (pathogen) and the soil (susceptible population and conditions that permit R0 > 1). As R ~ R0 x S (proportion of the population that's susceptible), and S ~ 1 at the beginning of a pandemic

I'm not sure if anyone's in the mood for this, given the state of N America right now, but I keep getting asked whether another wave of COVID-19 is "possible". I think that it is all but a certainty. Why are multiple waves a signature feature of pandemics?

I wrote about COVID-19 & racial disparities in April with @rapclassroom https://discoversociety.org/2020/04/15/covid-19-racism-and-health-outcomes/… /17

Here’s Macpherson’s definition of institutional racism in the Stephen Lawrence Inquiry, 1999 (first described by Carmichael & Hamilton in 1967) ./16

Finally I think it’s vital that we empower the affected communities to lead on research & interventions for their own communities. They are best placed to know what may work. Funding should reflect this. /15

Reasons for racial disparities health are complex. Here is Angela Saini on the topic in @theLancet https://tinyurl.com/y8bn5we3. I hope COVID-19 has highlighted the need to address racial inequalities in health, & will lead to long lasting changes in many areas. /14

UK born people from Black and Asian communities are more likely to be diagnosed with asthma. /13

The highest rates of hypertension (high blood pressure) are in Black groups. This is a risk factor for many health issues including stroke, chronic kidney disease, cardiovascular disease, retinopathy. /12

Rates of Type 2 diabetes are approximately three to five times higher than in BAME groups than the white British population. Diagnosis is more likely to occur at a younger age. /11

People from South Asian and Black backgrounds are three to five times more likely to start kidney dialysis than people from white backgrounds. /10 https://tinyurl.com/y6u8ec8z

Black people were more likely to have severe mental health symptoms, but were the least likely to receive treatment for mental illness https://tinyurl.com/ybxxxd9v. They are more likely to be detained in hospital https://tinyurl.com/y9dx2w8x /9

Black men are 2x as likely to be diagnosed with prostate cancer in the UK than white men and proportionately more Black men die of prostate cancer than other groups. /8

Jo’s Trust @JoTrust conducted a survey showing that women from BAME backgrounds are more likely to have never attended cervical screening. /7

The British National Survey of Sexual Attitudes and Lifestyles showed emergency contraception use was most commonly reported by Black Caribbean (30%) and mixed ethnicity women (28%) than White British women (23%) /6

74% heterosexual people receiving HIV care in the UK in 2018 were BAME of which 57% were of Black African ethnicity. The highest rate of late diagnosis (the most important predictor of HIV-related illness and death) was in heterosexual Black men (65%) /5 https://tinyurl.com/ybqmffsb

Rates of sexually transmitted infections are highest in Black communities especially Black Caribbean /4 https://tinyurl.com/y25z4v9w

Here we go. It's not a comfortable read (and neither should it be). Black women are 5x more likely to die in pregnancy than white women. /3 https://npeu.ox.ac.uk/downloads/file

This thread attempts to show racial health disparities in the UK are common & existed long before COVID-19. I have compiled a list of racial disparities in different areas of health in the UK, but this is not exhaustive and there are many that I haven't been able to include /2

live now! join us for an #IMPACTfulChats event with advisors @MS_Martinez_MD interviewing 7 y/o Olivia Tyler who is helping get masks out to the the homeless in Chicago and beyond! @IMPACT4HC

A 56-second overview of the first 5 months of 2020.

Thailand says it had no new virus infections or deaths over the past day - the first time in almost three weeks there were no new cases. It's also been 17 days since a local transmission was recorded. All other recent cases had been imported by people returning from abroad. The country's total stands at 3,125 confirmed infections, while 58 deaths have been linked to Covid-19.

1/4 Here are animated GIFs on #COVID cases per 100k in by county type. Although #urban has a larger share of cases nationally, when you look at individual states, trends change. #Thread

The last few months have convinced me of something: that MANY scientists are worryingly naive about the spread / consequences of their work. They misunderstand their own authority, and blunder into public life enormous and confused, like elephants who can't find their car keys.

From a behavioural angle, I would be asking how plausible is it for people to actually follow the rule even if we all want to. Psychophysics probably can shed some light on this—how good are people at perceiving how far things are away from themselves? (Research similar to this one may be relevant to understanding whether people over- or under-estimate how far they are keeping.)I also imagine that actually, people's perception of distance would be different depending on whether they are indoors or outdoors (and possibly more contextual factors—I think the gaze perception literature has much to offer here, especially those who may have tested this extensively for the purpose of virtual reality research!)

RT @MaartenvSmeden: Often more than 300 COVID-19 related scientific articles published *per day* Data from @evidencelive

RT @mrjamesob: An absolutely riveting report. Journalism of the very highest order. https://t.co/BxgW1tp1vq

"What are the behavioural implications of moving to a new, more shorter distance rule?" What impacts (positive or negative), concerns, and side effects do you foresee? Give your answers here: https://reddit.com/r/BehSciAsk/comments/h0zaco/scibehs_first_policy_problem_challenge_relaxing/… or in a reply to this tweet!

Introducing "Horizon Scanning" - a new http://scibeh.org activity designed to help identify policy relevant concerns and evidence that lie ahead for upcoming, future issues. One form this will take is as a recurring "Policy Problem Challenge". Our first one is this:

Much of the U.S. has opted for the worst of both worlds: 1) allow enough “reopening” to seed super-spreading 2) but full economic activity can’t resume because people don’t feel safe

Many of you are writing about race, racism and white supremacy right now. Please use this chart to correctly identify Black Americans. Accuracy in how we identify folks is paramount always but especially right now.

How can I promote my class without pestering my friends? This is not a small problem to me. #ethicalmarketing So, as my wise German friends say "Geklagtes Leid ist halbes Leid" (a problem shared is a problem halved) let me share a #sketchnote mapping where I am and what I have.

If anyone has any good training resources for sci comm, feel free to share them below. We could all benefit!

A general comment about science communications. Scientists are rarely trained to talk to the public. It’s hard to explain complicated concepts simply. It’s easier to retreat to our familiar technical language.

Almost all scientists briefing on background disagreed fundamentally with herd immunity & the lockdown delay; but almost none in a senior position would go on the record. Many who disagreed privately towed the line in public. Unravelling this dysfunction can’t happen quick enough https://twitter.com/devisridhar/status/1253973922053316610

RT @enenbee: There are now over 14,000 COVID-19 citations indexed by Pubmed. That's bananas. Use with caution...

social media play a central role in scientific responding to the pandemic (and increasingly in science communication more generally), but scientist's Twitter feeds include lots of information that we would never include in our scientific articles and...

But at the same time, the inevitability of value-ladenness or politicization doesn't mean that there are no meaningful degrees here: just like bias might be impossible to eliminate, we can all identify more and less egregious cases

and worldviews influences their science and has argued that a strict separation is not possible (a claim you might or might not buy). Likewise, scientific facts and opinions may have political consequences and end up feeding into increasingly polarised debate

this is an important discussion! What needs clarifying is what "objective" and "disinterested" can and cannot (and should and should not) mean in a science context A wealth of research in the philosophy of science and history of science has shown that scientists values and

Addendum: I just remembered, I mentioned the critiques of this literature to one of the authors of the preprint on twitter on April 2. Latest version of the preprint was posted April 9, but to be fair maybe the final draft was already finalized by Apr 2.

I'm less qualified to judge the rest, but it worries me that, for the lit I know best, they don't mention the very serious critiques, and they make very strong claims. That they do this while promising to highlight uncertainty/quality issues is maybe even more worrisome. /end

(I'd also argue that the implications of the claims in this paragraph are a bit outlandish and harmful as applied to COVID19 - it's not the type or amount of stress that matters, it's how you think about it? Really? Maybe for taking a math test, but for COVID19?) 7/n

"help reorganize our priorities, and can help lead to deeper relationships and a greater appreciation for life." At least some of this research is problematic, and the criticisms are known & published in visible outlets, e.g., https://journals.sagepub.com/doi/full/10.1177/0963721419827017… https://sciencedirect.com/science/article/pii/S0272735817302842… 6/n

I also found some problems. The literature I know best among the ones they review is the post traumatic growth literature. The authors write "the past twenty years of research on coping and stress suggest that it's not the type or amount of stress that determines its impact. 4/n

In this blog post, @StatModeling digs into a couple of their claims and finds that they are lacking in terms of portraying the uncertainty and the quality of the evidence. (Note: the post has attitude, but the points are good) https://statmodeling.stat.columbia.edu/2020/05/12/2-perspectives-on-the-relevance-of-social-science-to-our-current-predicament-1-social-scientists-should-back-off-or-2-social-science-has-a-lot-to-offer/… 3/n

Abstract: "we note the nature and quality of previous research, including uncertainty and unsettled issues" Intro: "we try to describe the quality of evidence to facilitate careful, critical engagement" Great goal. This encourages us to trust the authors. Do they deliver? 2/n

At the risk of piling on (tho the paper's been dowloaded > 8k times, so continued critical examination is called for, right?), here's one of the reasons I'm worried about "Using social & behavioural science to support COVID-19 pandemic response" (https://psyarxiv.com/y38m9) 1/n

Pleased to announce our newest Research Tool Report on First View: "http://PsychTable.org: A tool for biopolitical researchers, policymakers, & citizens" by Glass & Balachandran | @PsychTable @CUP_PoliSci @glenngeher @ml_fisher #evolution #research @tjw51

The pandemic has also wrecked the publicity campaigns of many great books that should find their audiences, but might not. I've mentioned Olga Khazan's WEIRD further up: (https://indiebound.org/book/9780316418485…) Let me recommend some others. (These aren't pandemic-related but they ARE great.)

Speaking of podcasts, you must listen to @roseveleth's Flash Forward. There's a pandemic episode, and I linked to the Y2K one in my recent piece. Her call to imagine better futures, and her commitment to inclusivity, are more important than ever.

.@maddie_sofia is the force of nature behind Short Wave, NPR’s wonderful daily science podcast. Loads of great pandemic coverage, interleaved with much-needed lighter palate-cleansers. Shout-out also to @emilykwong1234 & the whole Short Wave team.

And to add her to the main thread: someone asked me if I knew good economics writers who are covering the pandemic and, yes, I do! @AnnieLowrey is consistently excellent. I learn a ton from her work.

You might not think a space reporter would easily pivot to pandemic coverage but I will tell you a truth I’ve learned over the last years: @marinakoren can (and often will!) report on *anything* and it’ll be great.

Here's two great explainers on antibody testing--one by @apoorva_nyc and one by @CarolineYLChen. Both of them are consistently superb; read everything and anything they write.

.@rkhamsi has done some great work throughout the pandemic, inc. an early and very prescient piece on false comparisons to the flu. The one below was, I think, the first major piece to tackle the "airborne" question and holds up 2 months later.

.@amyyqin's early dispatches from Wuhan were my real "oh shit!" moments during the first months of the pandemic. Her work continues to illuminate.

This @stephaniemlee piece on the Santa Clara serology study is a textbook example of responsibly reporting on complicated and controversial new research. Stephanie is always great, as is the entire Buzzfeed science desk.

I could say the same of @olgakhazan, who's a reporting *machine*. So. Much. Good. Work. This grocery store piece has stuck with me. (Also check out her new book: https://hachettebooks.com/titles/olga-khazan/weird/9781549115127/…)

.@amandamull is so so good at unpacking the weird details of modern society, and zooming up and down from broad systems to everyday life. This piece on Georgia is awesome, but just always read her.

.@zeynep is a formidable thinker and has written so many pieces that have shaped how I've thought about the pandemic. This one on what models mean is great; so are all the rest.

@lizneeley's piece on how to talk about the coronavirus draws on her very deep knowledge of the science of science communication. It's a north star for all the difficult conversations ahead.

.@sarahzhang is one of the most formidable science writers working today. This story on the various options for creating an anti-covid drug is great, and just read all her stories on treatments and antibody testing.

.@maggiekb1 is the god of the "this ostensibly simple thing is way more complicated than you think, but let me walk you through it" explainer. Here she is on why it's so hard to build a good COVID-19 model.

Are any reporters on the infectious disease beat more respected than @helenbranswell? I doubt it. Her interview with CDC director Robert Redfield still lingers in the mind, but just read anything she writes.

There are few people I would've trusted to do a big piece on coronavirus and climate change. @meehancrist is on the top of a short list, and she utterly delivers here. Thoughtful, sweeping, forward-looking.

I keep thinking about this haunting first-person account from @DrHelenOuyang of how the pandemic swept through NY hospitals, juxtaposed against similar experiences in Lombardy. It's an *incredible* feat of writing.

Hello! A lot of you have started following me in the last couple of months, so let me introduce you to some people I respect, who've created some of the pandemic writing that's really stuck with me.

Ahem. HERE is @JenSeniorNY’s poignant, sensitive, and thoughtful piece about an NY doctor’s suicide and what that says about what we expect from the medical profession. (Reposting because I mangled the link upthread)

There's so much. I get asked to do a lot of interviews and talks and I try to punt opportunities out to colleagues when I can, but producers, you'd be so lucky to bag any of these amazing people (and the ones in the previous thread) for your shows, podcasts, & seminars. /fin

And of course the people I tagged in the first thread have continued producing incredible work. A sampling:

Prisons are perhaps the single most important hotspot of COVID-19 in the US and this @ethiopienne's piece is a thoughtful and invaluable look at what that means and how to think about it.

A lot of people in the US are wondering if they got the coronavirus in January. @rachgutman answers that question in a thoroughly reported piece that instead of shooting for easy answers, does the much harder task of delineating uncertainty.

I spoke to @ashleyshoo about how the disability community is experiencing the pandemic. Her own op/ed is essential. There is a lot of wisdom here.

.@JenSeniorNY's piece on a NY doctor's suicide, and what it says about what the medical profession goes through, is astonishing. This is SUCH a hard subject to write about well, and Jen utterly pulls it off. I hope this piece saves lives.

.@MarionRenault's piece on ICU delirium was an eye-opener for me. It's fascinating and important. This is what journalism should do: clearly and empathetically illuminate a problem that deserves more attention.

Speaking of which, @tarahaelle's piece on how to understand and respond to the Pl*ndemic video is spot-on, informed by the evidence base on communication.

.@ellencushing's extraordinary essay about her past as a teenage conspiracy theorist is not specifically about the pandemic but is HIGHLY relevant, and a crucial insight into the mindset that will likely become increasingly common.

@AshleyFetters's piece on how the pandemic is shaking up the wedding industry is perfectly observed, as are all of Ashley's pieces on how relationships and families are being affected. (Editors! Hire or commission Ashley! https://twitter.com/AshleyFetters/status/1263511204368130048…)

Of the many pieces written about how COVID-19 is exacerbating health inequalities, I found @juliacraven's to be exceptional, combining the stories of two women against a sweeping look at centuries-old problems.

As we begin to re-emerge into an uncertain world, I found @JuliaLMarcus's piece on quarantine fatigue to be a compassionate, sensible, and much-needed guide to navigating risk.

This @katchow piece about the loss of her uncle is eulogy and memoir, beautiful and heartbreaking, and a necessary reminder that, as Kat writes, "Time is of the essence."

Hello! More of you have started following me in the last weeks since I last did this, so let me introduce you to YET MORE people I respect, who've created some pandemic writing that's really stuck with me. (And do check out the original thread below.)

Despite anticipating an immediate and substantial drop in clinic visits during the lockdown period, the authors report there was no overall reduction in the number of adult visits per clinic per day during Level 5 of lockdown.

In uMkhanyakude, visits for perinatal care & family planning remained consistent after lockdown & HIV care visits briefly increased just after lockdown. Vaccination & growth monitoring visits for children dropped by 50% just after lockdown, with slow return to near normal.

New work from AHRI about the effect of the Level 5 #Covid19SA lockdown on access to healthcare has found a largely resilient primary healthcare system in rural KZN among adults, but some early warning signs for child health.

@PsyArXivBot just got a sibling I just created a twitter bot for @metaarxiv for all the people interested in following meta-research preprints.

"The government should've brought in [universities and researchers] from the outset" @GregClarkMP, chair of the @CommonsSTC, talks to #5LiveBreakfast about some of the lessons to learn about how the UK handled #COVID19.

These maps come from the brilliant Wellcome Collection: https://wellcomecollection.org/works/ And I wrote a similar threat with full sources back in 2018 http://spatial.ly/2019/03/mapping-and-visualising-cholera-data/… /ends.

...and also looking to the geology of London in later years.

Of course, they were an essential rebuke to the miasma theory and enabled the attention to turn to London's water supply. It turned out that certain suppliers were responsible for higher deaths than others...

So Snow had his work cut out...he pounded the streets getting detailed on the ground data - not just headline figures for cities. The black bars on his map show a death from cholera. I think it has a humanity that Farr's visualisations lack...

And others made scary maps showing the "cholera mist" as it spread across London...

Farr's charts make a particularly beautiful case for the miasma theory...

People wanted answers so William Farr stepped up. He was a rigorous statistician & excellent data visualiser. His (and the prevailing) theory was that cholera was spread by bad smells "miasma". London was a smelly place and outbreaks peaked with hot weather when it was smelliest.

Still no sign of Snow's map at this point...but it was getting serious in London...

As it arrived in Britain towns and cities were hardest hit...here's the extent of the epidemic in 1849.

Of course, cholera remains a global issue killing tens of thousands a year. Here's an early map showing its spread from India to Britain...we've seen so many recent versions of this with COVID-19.

John Snow's map of cholera looked as dull as (cholera filled) dishwater compared to his competitors... His brilliance was a solid data collection & then a simple map presenting what he knew. Each death marked in black and white. Here's a lesson for COVID-19 dataviz...

The Supplementary Figures can be found here as a public link.

Whether this immunity is relevant in influencing clinical outcomes is unknown, but it is tempting to speculate that the crossreactive CD4+ T cells may be of value in protective immunity, based on SARS and flu data.

Crossreactive T cells are also relevant for vaccine development, as cross-reactive immunity could influence responsiveness to candidate vaccines

Detecting SARS2-reactive T cells in ~50% of unexposed people suggests cross-reactive T cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2. This might influence susceptibility to COVID-19 disease.

Additionally, any potential for crossreactive immunity from other coronaviruses has been predicted by epidemiologists to have significant implications for the pandemic going forwards. We detected SARS-CoV-2-reactive CD4+ T cells in ~50% of unexposed individuals.

We specifically chose to study people who had an average COVID19 disease course—non-hospitalized—to provide a solid benchmark for what a normal immune response to SARS2 looks like.

CD4+ T cell responses to spike, the main target of most vaccine efforts, were robust and correlated with the magnitude of the anti-SARS-CoV-2 IgG and IgA titers. Again, good news.

In our study, 100% of COVID-19 cases made antibodies. 100% of COVID-19 cases made CD4 T cells. 70% of COVID-19 cases made measurable CD8 T cells. We believe these findings are good news, and consistent with normal antiviral immunity.

There has been a huge amount of uncertainty about immunity to SARS2—both in the context of COVID19 disease pathogenesis and in the context of how to develop a good vaccine.

This is good news in multiple ways, for coronavirus vaccine development, understanding disease, and even modeling the future course of the pandemic.

Our new paper rapidly studied T cell + antibody immune responses in average COVID-19 cases. This is good news in multiple ways, including coronavirus vaccines.

For those pointing to countries like S.Korea, Singapore, China, Taiwan, Australia, Germany, Denmark to say there will be no second wave in the UK or US- take a deeper look at their incredible public health response & infrastructure. None sat back & said ‘Let’s see what happens.’

Most of the research thus far on treatment in #COVID19 seem to be targeting the wrong timeframe of disease...the point is to start treatment prior to hyperinflammation...would like to see more studies target earlier timeframe of disease #COVID19FOAM

PS. People interested in learning more about overconfidence among scientist-experts might want to check this piece out:

Anyway, excellent journalism by @MariaGuntherA and @MarrisW of @dagensnyheter / @dn_grafik /fin

Scientist overconfidence is a massive problem. In the short run, it undercuts efforts to use science to inform policy; in the long run, it reduces trust in science in general. We can and should do better /10

The people involved in these forecasts expressed themselves with *way* more confidence than what was justified at the time. This was an unforced error on their behalf /9

We should be less tolerant of overconfidence in particular and a lack of epistemic humility in general. A true expert would have known ahead of time just how much uncertainty was involved in their forecasts and expressed themselves accordingly /8

We should be tolerant of mistaken projections. These are incredibly difficult prediction tasks. The modellers here were trying to be useful, and they were working under great time pressure /7

From a sociology of science perspective, we should expect few modellers to admit having made mistakes: based on @PTetlock's research we should expect claims to the effect that they were "almost right." So far I haven't seen one saying "we were wrong." (But I could be wrong!) /6

In addition, some of these models apparently contain over 100 parameters, and would be difficult to calibrate under any conditions /5

From a philosophy of science perspective, this should not be surprising. Models work well when the underlying data-generating process is known and stable and when there has been ample time to calibrate the model. These conditions do not obtain here. /4

Around the same time, if I read their data file correctly, the IHME projected a demand of 4400, with a 95% uncertainty interval of 1400–11000. The real number is therefore way outside the interval /3

tl;dr Model-based projections drastically exaggerated the actual demand – sometimes by more than an order of magnitude. Today the number of patients in intensive care is about 450; it never exceeded 600 /2

Terrific assessment of projections of demand for Swedish ICU beds. The first two panels are model-based projections by academics; the third is a simple extrapolation by the public-health authority; the fourth is the actual outcome /1

The vast majority of manufacturers continued to operate through the COVID-19 crisis (67.1%) or temporarily halted only part of their operations (31.6%). Among large manufacturers, 50.8% are completely operational, while roughly 73% of small and medium-sized firms state the same.

Weak domestic demand was the top primary business challenge for manufacturers in the second quarter (83.1%), supplanting the inability to attract and retain talent (41%), which had been the top concern for 10 consecutive quarters.