The graph above is a snapshot from this site depicting the reproduction rate of SARS-CoV-2 by state as of today, frequently expressed as R(t). I cannot confirm its veracity but it’s interesting. You can click on the image for a larger version.

When the initial reproduction rate (R(0)) is 1 or above, that means the virus is spreading at an exponential rate; when below 1, the spread is decaying, declining. No one really knows what the R(0) for SARS-CoV-2 was. It may be anything from 2.2 to 6.5.

Here’s my question. Assuming the veracity of the information expressed here and the historic information presented, how to you explain Illinois’s policy? I don’t think you can.

BTW I’ve just received an email from my employer saying that someone with COVID-19 had been in the building yesterday on the floor above us and the building’s lobby, the 3rd floor (entrance from the building’s garage, and the floor in question had been disinfected).

You may not be familiar with the name of Donald Henderson. Dr. Henderson was an epidemiologist and is one of those credited with eradicating smallpox. I wanted to pass along a quote of his I stumbled across:

The interest in quarantine reflects the views and conditions prevalent more than 50 years ago, when much less was known about the epidemiology of infectious diseases and when there was far less international and domestic travel in a less densely populated world. It is difficult to identify circumstances in the past half-century when large-scale quarantine has been effectively used in the control of any disease. The negative consequences of large-scale quarantine are so extreme (forced confinement of sick people with the well; complete restriction of movement of large populations; difficulty in getting critical supplies, medicines, and food to people inside the quarantine zone) that this mitigation measure should be eliminated from serious consideration.

I do not pass this along as dispositive but as evidence that epidemiologists do not speak with one voice on this subject.

In his most recent New York Times column Nikolas Kristof encourages four bits of advice, taken from unnamed “public health experts”:

First, don’t swoon at every vaccine announcement.
[…]
Second, gather more data.
[…]
Third, cautiously open some schools.
[…]
Fourth, be relentlessly empirical.

I’ve already pointed out the issue with point 3. The highest hurdle in opening schools won’t be the kids or their parents. Indeed, for every parent who is nervous about sending his or her child to school due to the risk of contracting COVID-19, there are probably three eager for some respite. No, the biggest hurdle to overcome will be convincing the teachers and staff that it’s safe for them to go back to school.

#2 is good advice but not particularly helpful. We’re still gathering data on the Spanish Flu. And the Black Plague for that matter.

WWAREPD? What would a relentlessly empirical person do? Some of the things we are doing have little empirical support, e.g. closing parks and beaches. Wearing face masks outside, ditto.

What should you think about measures which don’t actually have much empirical evidence to support them but there is empirical evidence for measures that are tangentially related and with enough extrapolation, generalization, and umbrage can be claimed to be empirical? There’s a difference between scientific and “scientific”. When does post hoc propter hoc constitute empirical evidence? When you don’t know what else to do?

Let’s engage in a little exercise in map-reading. The graph above, taken from the site of the Illinois Department of Health, illustrates the number of newly-diagnosed cases of COVID-19 in Illinois by day since March 10, 2020 to date. As I’ve said before it’s noisy.

How would you interpret that graph? I don’t think it actually tells us anything. My eyeball-o-meter tells me that it’s more likely to reflect irregular reporting of data than anything else. When looked at on a weekly basis, the last couple of weeks have seen about the same number of new cases diagnosed on average each day. There’s a chart available graphing deaths due to COVID-19 per day. That’s even noisier if anything. Again, to my eye it’s more indicative of irregularities in reporting than anything else.

There’s one thing not reflected in the graph. More tests are being done week-on-week. In other words it’s a Red Queen’s Race. We’re running twice as fast to stay in the same place. Or, again said another way, the graph is misleading.

This article by Alexis C. Madrigal and Robinson Meyer at The Atlantic convinces me that the Centers for Disease Control needs attention:

The Centers for Disease Control and Prevention is conflating the results of two different types of coronavirus tests, distorting several important metrics and providing the country with an inaccurate picture of the state of the pandemic. We’ve learned that the CDC is making, at best, a debilitating mistake: combining test results that diagnose current coronavirus infections with test results that measure whether someone has ever had the virus. The upshot is that the government’s disease-fighting agency is overstating the country’s ability to test people who are sick with COVID-19. The agency confirmed to The Atlantic on Wednesday that it is mixing the results of viral and antibody tests, even though the two tests reveal different information and are used for different reasons.

This is not merely a technical error. States have set quantitative guidelines for reopening their economies based on these flawed data points.

The CDC has made enough serious errors over the last few months that it’s obvious that it needs some serious attention. The organization’s problems didn’t start with its mishandling of finding a good, reliable test for SARS-CoV-2 but that was indicative of the problems.

I am not much given to finding fault but I’ll engage in a little finger-pointing here. Whose fault is this mess? First of all, it’s President Trump’s fault in a “buck stops here” sense. Ultimately, the CDC reports to him. The voters who voted for him are at fault. Robert Redfield, the current director of the CDC, appointed by President Trump, is at fault. And most of all the professional staff at the CDC is at fault. I don’t believe that Dr. Redfield is a poor director of the CDC because he’s not an epidemiologist (he’s a virologist). I think he’s a poor director because he’s a lousy manager.

It is not merely a misconception but a lie that to be an effective manager of professional staff you’ve got to be a professional (lawyer, doctor, violinist, etc.) yourself. I don’t believe that in the history of the world any MD has gone to medical school to become a manager. In general I don’t think docs have the temperament, attitude, training, or skills to be good managers.

My experience has been that professional staff commonly chafe at being managed at all but particularly by non-professionals. The path of least resistance for a manager whose job it is to manage professionals is to give the job to a professional but that may not be the right decision. Most of all you need a good manager.

I am more interested in solving problems than admiring them. At this point I’m not convinced that the CDC should continue to exist.

I honestly don’t know what to make of this metastudy by John P.A. Ioannidis from medRxiv (PDF):

While COVID-19 is a formidable threat, the fact that its IFR is much lower than originally feared, is a welcome piece of evidence. The fact that its IFR can vary substantially also based on case-mix and settings involved also creates additional ground for evidence-based, more precise management strategies. Decision-makers can use measures that will try to avert having the virus infect people and settings who are at high risk of severe outcomes. These measures may be possible to be far more precise and tailored to specific high-risk individuals and settings than blind lockdown of the entire society.

I pass it along not as advocacy but as an additional data point.

Elaine He’s piece at Bloomberg on the effectiveness of European countries’ lockdowns strikes me as quite important. I’ll try to summarize it for you. In terms of cubic inches much of the piece consists of three infographics: a heat chart indicating the stringency and onset of lockdown measures for 17 European countries, graphs illustrating excess mortality over time for 12 of them, and, finally, a scattergram illustrating the change in industrial production and, presumably, “economic pain” from the lockdowns for 13 of them.

Here are some telling passages from the piece:

While not a gauge of whether the decisions taken were the right ones, nor of how strictly they were followed, the analysis gives a clear sense of each government’s strategy for containing the virus. Some — above all Italy and Spain — enforced prolonged and strict lockdowns after infections took off. Others — especially Sweden — preferred a much more relaxed approach. Portugal and Greece chose to close down while cases were relatively low. France and the U.K. took longer before deciding to impose the most restrictive measures.

But, as our next chart shows, there’s little correlation between the severity of a nation’s restrictions and whether it managed to curb excess fatalities — a measure that looks at the overall number of deaths compared with normal trends.

On excess mortality:

In Europe, roughly three groups of countries emerge in terms of fatalities. One group, including the U.K., the Netherlands and Spain, experienced extremely high excess mortality. Another, encompassing Sweden and Switzerland, suffered many more deaths than usual, but significantly less than the first group. Finally, there were countries where deaths remained within a normal range such as Greece and Germany.

Yet the data show that the relative strictness of a country’s containment measures had little bearing on its membership in any of the three groups above. While Germany had milder restrictions than Italy, it has been much more successful in containing the virus.

The overall impression is that while restrictions on movement were seen as a necessary tool to halt the spread of the virus, when and how they were wielded was more important than their severity. Early preparation, and plentiful health-care resources, were enough for several countries to avoid draconian lockdowns. Germany, with better testing and contact tracing and more intensive care units than its neighbors, could afford to keep the economy a bit more open. Greece, by acting quickly and surely, appears to have avoided the worst, so far.

and here’s her conclusion:

The Covid-19 experience has taught us that it’s far better to respond quickly and smartly, with the right technology and mass testing and tracing, rather than only relying on the crudest of shutdowns. If there are second waves of the virus, we shouldn’t repeat the mistakes of the first.

I’m not sure I’d draw those conclusions from the data she’s presented. I think I’d be more likely to conclude:

1. Lockdowns as such are not an effective method of reducing mortality but they are an effective method of reducing economic activity. It doesn’t matter much how early they were imposed or how strict they were. Lockdown measures as such do not reduce excess mortality. Despite imposing lockdown measures as early as February, Italy still suffered considerable excess mortality.
2. Confounding factors are probably more significant in reducing excess mortality than testing or contact tracing. Confounding factors include demographics, the level of health of the populace, the robustness of the health care system, who knows how many other factors, and, probably dumb luck.

I continue to believe that we should be looking more closely at Portugal’s example. They imposed their lockdown measures later than most and, initially, rather lightly, they’re opening up earlier than many, and their excess mortality has been relatively low. Maybe Portugal’s success is just a matter of being small, comparatively isolated, and having high social cohesion.

Bear with me on this. It has been discovered that antibodies to SARS-CoV provide some level of resistance to SARS-CoV-2 today, cf. this report from Live Science:

A person who had severe acute respiratory syndrome (SARS) 17 years ago could help scientists in the search for therapies to fight the new coronavirus, SARS-CoV-2, according to a new study from a biotech company.

The study researchers found that blood samples from this patient, who had SARS in 2003, contained an antibody that also appears to inhibit SARS-CoV-2.

Antibodies form part of the body’s immune response to pathogens. This particular antibody, which the researchers call S309, showed a strong ability to bind to and disable the “spike protein” on SARS-CoV-2 that allows the virus to enter cells, according to a statement from the University of Washington School of Medicine, which was involved in the research. Multiple authors on the study work for Vir Biotechnology, and the company is developing a therapeutic based on the study findings.

It has also been noted that there was asymptomatic spread of SARS 17 years ago as NCBI points out:

We conducted a study among healthcare workers (HCWs) exposed to patients with severe acute respiratory syndrome (SARS) before infection control measures were instituted. Of all exposed HCWs, 7.5% had asymptomatic SARS-positive cases. Asymptomatic SARS was associated with lower SARS antibody titers and higher use of masks when compared to pneumonic SARS.

We have a pretty good idea of which countries saw the greatest prevalence of SARS cases (China, Hong Kong, Taiwan, South Korea, et al.)

I would like to suggest that we should be prepared for the possibility that the reduction of severity in the outbreaks of COVID-19 in Hong Kong, Taiwan, and South Korea may be a result of more widespread distribution of SARS antibodies in those place than has been commonly recognized as well as the actions those places have taken in response to SARS-CoV-2. I don’t even know how you’d go about disaggregating those two factors.

We and the Europeans haven’t been willing to implement the measures that the countries mentioned above have taken to combat the virus but it’s not beyond the realm of possibility that even were we to do so we still wouldn’t get the same results.