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Archive of posts filed under the Public Health category. A COVID-19 collaboration platform.

Following up on today’s post on design of studies for coronavirus treatments and vaccines, Z points to this site, which states: In the U.S. only a few COVID-19 randomized clinical trials (RCTs) have been centrally organized, e.g. by NIAID, PCORI and individual PIs. Over 400 such trials have been registered on with dozens being […]

This one’s important: Designing clinical trials for coronavirus treatments and vaccines

I’ve had various thoughts regarding clinical trials for coronavirus treatments and vaccines, and then I came across thoughtful posts by Thomas Lumley and Joseph Delaney on vaccines. So let’s talk, first about treatments, then about vaccines. Clinical trials for treatments The first thing I want to say is that designing clinical trials is not just […]

Hey, I think something’s wrong with this graph! Free copy of Regression and Other Stories to the first commenter who comes up with a plausible innocent explanation of this one.

Paul Alper points us to this column by Dana Milbank discussing the above graph from Georgia’s Department of Public Health: Ok, the comb-style bar graph is, as always, a bad idea, as it multiplexes two dimensions (county and time) on a single x-axis. The graph should be a lineplot, with one line per county, and […]

What a difference a month makes (polynomial extrapolation edition)

Someone pointed me to this post from Cosma Shalizi conveniently using R to reproduce the famous graph endorsed by public policy professor and A/Chairman @WhiteHouseCEA. Here’s the original graph that caused all that annoyance: Here’s Cosma’s reproduction in R (retro-Cosma is using base graphics!), fitting a third-degree polynomial on the logarithms of the death counts: […]

If the outbreak ended, does that mean the interventions worked? (Jon Zelner talk tomorrow)

Jon Zelner speaks tomorrow (Thurs) at 1pm: PREDICTING COVID-19 TRANSMISSION In this talk Dr. Zelner will discuss some ongoing modeling work focused on understanding when we can and cannot infer that interventions meant to stop or slow infectious disease transmission have actually worked, and when observed outcomes cannot be distinguished from selection bias. Dude’s an […]

Years of Life Lost due to coronavirus

This post is by Phil Price, not Andrew. A few days ago I posted some thoughts about the coronavirus response, one of which was that I wanted to see ‘years of life lost’ in addition to (or even instead of) ‘deaths’. Mendel pointed me to a source of data for Florida cases and deaths, which […]

Update on OHDSI Covid19 Activities.

I have been providing some sense of the ongoing activities of the OHDSI group working on Covid19. In particular, this gives a quick sense of one of the newer activities: I believe there is a lot of studying to be done yet…  

Is JAMA potentially guilty of manslaughter?

No, of course not. I would never say such a thing. Sander Greenland, though, he’s a real bomb-thrower. He writes: JAMA doubles down on distortion – and potential manslaughter if on the basis of this article anyone prescribes HCQ in the belief it is unrelated to cardiac mortality: – “compared with patients receiving neither drug […]

“1919 vs. 2020”

We had this discussion the other day about a questionable claim regarding the effects of social distancing policies during the 1918/1919 flu epidemic, and then I ran across this post by Erik Loomis who compares the social impact of today’s epidemic to what happened 102 years ago: It’s really remarkable to me [Loomis] that the […]

Coronavirus Grab Bag: deaths vs qalys, safety vs safety theater, ‘all in this together’, and more.

This post is by Phil Price, not Andrew. This blog’s readership has a very nice wind-em-up-and-watch-them-go quality that I genuinely appreciate: a thought-provoking topic provokes some actual thoughts. So here are a few things I’ve been thinking about, without necessarily coming to firm conclusions. Help me think about some of these. This post is rather […]

They want open peer review for their paper, and they want it now. Any suggestions?

Someone writes: We’re in the middle of what feels like a drawn out process of revise and resubmit with one of the big journals (though by pre-pandemic standards everything has moved quite quickly), and what’s most frustrating is that the helpful criticisms and comments from the reviewers, plus our extensive responses and new sensitivity analyses […]

Standard deviation, standard error, whatever!

Ivan Oransky points us to this amusing retraction of a meta-analysis. The problem: “Standard errors were used instead of standard deviations when using data from one of the studies”! Actually, I saw something similar happen in a consulting case once. The other side had a report with estimates and standard errors . . . the […]

“Positive Claims get Publicity, Refutations do Not: Evidence from the 2020 Flu”

Part 1 Andrew Lilley, Gianluca Rinaldi, and Matthew Lilley write: You might be familiar with a recent paper by Correira, Luck, and Verner who argued that cities that enacted non-pharmaceutical interventions earlier / for longer during the Spanish Flu of 1918 had higher subsequent economic growth. The paper has had extensive media coverage – e.g. […]

University of Washington biostatistician unhappy with ever-changing University of Washington coronavirus projections

The University of Washington in Seattle is a big place. It includes the Institute for Health Metrics and Evaluation (IHME), which has produced a widely-circulated and widely-criticized coronavirus model. As we’ve discussed, the IHME model is essentially a curve-fitting exercise that makes projections using the second derivative of the time trend on the log scale. […]

Calibration and recalibration. And more recalibration. IHME forecasts by publication date

Carlos Ungil writes: The IHME released an update to their model yesterday. Using now a better model and taking into account the relaxation of mitigation measures their forecast for US deaths has almost doubled to 134k (95% uncertainty range 95k-243k). My [Ungil’s] charts of the evolution of forecasts across time can be found here. I […]

Imperial College report on Italy is now up

See here. Please share your reactions and suggestions in comments. I’ll be talking with Seth Flaxman tomorrow, and we’d appreciate all your criticisms and suggestions. All this is important not just for Italy but for making sensible models to inform policy all over the world, including here.

Bayesian analysis of Santa Clara study: Run it yourself in Google Collab, play around with the model, etc!

The other day we posted some Stan models of coronavirus infection rate from the Stanford study in Santa Clara county. The Bayesian setup worked well because it allowed us to directly incorporate uncertainty in the specificity, sensitivity, and underlying infection rate. Mitzi Morris put all this in a Google Collab notebook so you can run […]

NPR’s gonna NPR (special coronavirus junk science edition)

1. The news! Zad’s cat, pictured above, is not impressed by this bit of cargo-cult science that two people sent to me: No vaccine or effective treatment has yet been found for people suffering from COVID-19. Under the circumstances, a physician in Kansas City wonders whether prayer might make a difference, and he has launched […]

Simple Bayesian analysis inference of coronavirus infection rate from the Stanford study in Santa Clara county

tl;dr: Their 95% interval for the infection rate, given the data available, is [0.7%, 1.8%]. My Bayesian interval is [0.3%, 2.4%]. Most of what makes my interval wider is the possibility that the specificity and sensitivity of the tests can vary across labs. To get a narrower interval, you’d need additional assumptions regarding the specificity […]

Updated Santa Clara coronavirus report

Joseph Candelora in comments pointed to this updated report on the Santa Clara study we discussed last week. The new report is an improvement on the first version. Here’s what I noticed in a quick look: