Do Masks Help Part 4: Fluid Mechanics to the rescue

"Visualizing the effectiveness of face masks in obstructing respiratory jets" by Verna et al. reports on a simulation based on a manikin simulating coughs with a recreational smoke machine. Whether or not this should be considered an acceptable simulation of coughs is not something I can assess. The report looks at several scenarios: uncovered simulated coughs and simulated coughs through a variety of masks. While the results are quite striking, I believe they are answering the wrong question. Most of the arguments I've seen for wearing masks focus on reducing the spread from asymptomatic people,  especially in reducing the droplets spread through talking. I'd hope most people these days are considerate enough to cover their masks when the cough, so how well a mask stops the spread of droplets when the person coughs is not the question we should be seeking to answer.

Do Masks help Part 3: A study from PNAS

In "Identifying airborne transmission as the dominant route for the spread of COVID-19," Zhang et al. claim to demonstrate a big impact mask-wearing has on the spread of COVID. I'm skeptical about their results because the results are too tidy (direct link to image):

As I understand the spread of COVID, there is a delay between exposure and positive tests; an intervention that either increases or decreases the spread of COVID should take about 5 days to show up in the numbers infected. In this case, the response shows up immediately, which makes me think there is something else going on.

The Henry Ford study on Hydroxychloroquine

The Henry Ford Health System published a study claiming Hydroxychlorquine (HCQ) saves lives in COVID patients. I don't find the study convincing. The study was well-analyzed based on how I was trained such studies should be analyzed. However, the study is an observational study of the patients of one health system. Then need to be careful about who they measure when drawing larger conclusions. Moreover, the patients getting HCQ met certain clinical guidelines, and thus were not like the patients not getting HCQ, so they needed to have been more careful about what they measured.

A Portugese study claiming HCQ prevents COVID

A recent preprint, "Chronic treatment with hydroxychloroquine and SARS-CoV-2 infection" by Ferreira et al. reports on a study of data from Portugal. In Portugal, there is anonymized data available on who gets prescriptions, who is tested for COVID, and who tested positive. Ferreira et al. analyzed this data to see if the people taking hydroxychloroquine (HCQ) tested positive for COVID at a higher or lower rate than the general population. They found that the relative risk of testing positive for COVID among patients taking HCQ was about half that those that weren't.  Based on these findings, the authors suggest using HCQ as a preventative measure for preventing infections of COVID (with caveats about monitoring usage, and praising it as an inexpensive drug).

Based on the issue that we need to be careful who we measure that I wrote about before, I completely disagree with this assessment. People who were prescribed HCQ in Portugal are not representative of the general population. For starters, they have rheumatoid arthritis, lupus, or another autoimmune disease; those taking it for malaria had too small a dose to be included in the study. My assumption is that people with arthritis severe enough to require medication are less likely to be going out and about as much, and will mix less with other people. These less active people are less likely to be exposed to COVID, leading to the reduced infection rate. Am I right? I don't know. But my story is consistent with the data, and an assumption that HCQ does nothing for COVID. Hence the study doesn't show HCQ helps, and certainly doesn't justify pursuing any changes of policies or medical interventions.  

Checking the assumptions that led to headlines reporting 8.7 million infected with COVID

Media reports, based on a Penn State University press release, have discussed "a new study [that] estimates that the number of early COVID cases in the U.S. may have been more than 80 times greater and doubled nearly twice as fast as originally believed." This has led some to conclude that "By the time governors in the U.S. forced lockdowns, COVID-19 had already extended beyond a point in which lockdowns could be effective in slowing the spread" The key piece to this chain of reasoning is the "may have been," and I believe this is a great case study in the challenges of scientific communication.

The study attempts to answer the question of how many undiagnosed cases of COVID are in the US. The answer is based on analyzing an existing Center for Disease Control (CDC) database of Influenza-Like Illness (ILI), estimating how much ILI has increased over previous years, and attributing an appropriate amount of the excess ILI to COVID cases. In transforming excess ILI at participating hospitals to COVID cases in the general population, a number of assumptions are necessary; I think the answer is highly dependent on these assumptions. I am very pleased that the authors of the study properly caveat their conclusions, and call for the appropriate follow-on studies. They are also careful to summarize their major assumptions and highlight most of them. However, I believe a number of their assumptions are flawed, and as a result, they run the risk of greatly over-estimating the true number of total cases. The only way to tell the actual number of early COVID cases in the U.S. is through further testing, as suggested by the authors, because these numbers are, after all, extrapolated from multiple assumptions. 

What this study is not is definitive proof that the shutdowns were an example of proverbially locking the barn door after the horse left.