When you ask a question of a computer model, it provides out to several decimal places answers that can be off by several orders of magnitude. Give me a clear, logical back-of-the-envelope calculation grounded in real-world data over a model simulation any day.
Several people have sent me links to papers that use computer models to purport to simulate the economic consequences of alternative strategies for dealing with the virus. I don’t bother reading them. When I see that Jeffrey Shaman’s pronouncements about the rate of asymptomatic spreading are based on a simulation model, I assign them low confidence.
Once you build a model that is so complex that it can only be solved by a computer, you lose control over the way that errors in the data can propagate through the model. For me, it is important to look at data from a perspective of “How much can I trust this? What could make it misleadingly high? What could make it misleadingly low?” before you incorporate that data into a complex model with a lot of parameters.
I read that in the U.S. we have done 250,000 tests for the virus, and yet we have only 35,000 positive cases. But before we jump to any conclusions based on this, we ought to get an idea of how many of these tests are re-tests. If the average person who is tested is tested three times, then almost half of the people being tested are positive. I have no idea what the average number of tests per person actually is–it probably isn’t as high as three, but it isn’t as low as one, either.
A lot of people are quoting lines from Gene Krantz in the movie Apollo 13. One of my favorites is when he warns against “guessin’.” Computer models are just “guessin'” in my view. Making decisions based on models is approximately as bad as making them based on blind panic.
I am constantly calling for taking a random sample of the population, say 5000 people, and testing them on a repeated bases. I am quite willing to take some testing resources away from being used for people walking in with symptoms. If people have symptoms and we don’t have resources to test them, then isolate them as if they were infected, in a non-hospital setting. You can base the decision about when to hospitalize the person on how their symptoms progress.
We don’t yet have a proven drug treatment, so you don’t really help an infected person by testing them. Testing helps reassure the uninfected people that they don’t need to be totally isolated. That is a benefit, but not enough to justify putting all our resources into people with symptoms, leaving no resources for random testing.
The OODA loop says, “Observe, Orient, Decide, Act.” Right now, our public policy seems like we’re in an ADOO loop–“act, decide, orient, observe.” I find it frustrating.
I’m repeating my comment to the “Addressing the issue of asymptomatic spreading” post because I think it is relevant.
The Jack Ma Foundation’s COVID-19 Chinese Consultation Center has published a pdf “Handbook of COVID-19 Prevention and Treatment”. The following section clarifies the confusion over asymptomatic spread [bold added]:
You have to test the crud coughed up from the lungs before you can be sure of a positive result.
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Since reading the Columbia Surgery memo that mentioned antibody testing I’d be more inclined to run Kling’s random study but I don’t think you would find anything that isn’t obvious from the “facts on the ground” Canadian data. Neither modelling nor the historical PCR testing scenarios should be relied upon. I’m positive this disease is spread by cough droplets in everyday settings (hospitals are different) and it takes Super-Coughers (rather than Super-Spreaders) to cause Korean Case 31 style clusters.
https://cleantechnica.com/2020/03/21/iceland-is-doing-science-50-of-people-with-covid-19-not-showing-symptoms-50-have-very-moderate-cold-symptoms/
That link says 0.86% infected. Random group of more than 5000 persons.
We don’t yet have a proven drug treatment
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Others partially contradict. Using some unknown large numbers of Chinese patients, doctors have narrowed down some treatments that cut symptoms nearly in half. Time of onset to time of remission down below a week, they say, on par with the flu:
https://www.webmd.com/lung/news/20200318/flu-drug-works-vs-coronavirus-chinese-study
Reinfection immunity must be about six weeks, we have had 12 weeks and a large number of remissions. I am not hearing alarming news about re-infection. If we observed high re-infection then our panic would be justified. We are short home triage, informing the households and firms to take temperatures, go home to bed if needed, call the doctor, do not visit. Add the last part and we are home free, this is just a very severe form of something flu-like.
And there is solid evidence that Hydroxychloroquine and azithromycin work:
https://www.americanthinker.com/articles/2020/03/fda_must_approve_hydroxychloroquine_now.html
Identifying people who have had the virus for certain is the most important use of the test. Knowing for certain who has recovered and has immunity, at least in the short term, is critical. They will be the most valuable people we have.
Thanks for explaining why model simulations have little bite.
Excellent post. I have found myself making this type of statement often lately as well.
Here’s my back-of-the envelope calculation of economic impacts:
The Leisure and Hospitality sector is essentially shut down. January employment in that sector was 16.9 million.
That’s almost 10% of payroll employment.
There were 5.8 million people unemployed in January. Add 15 million more just from leisure and hospitality and you get 21 million unemployed. Divide that by a labor force of 164 million (I realize that the numbers are from 2 separate surveys, but this is ballpark economics) and you have an unemployment rate of 12.8%. And that assumes no layoffs anywhere else in the economy. To be fair, it also assumes no gains in places like trucking, warehouses, or supermarkets. Some macro model forecasts I’ve seen have unemployment rising to 7% by summer.
Either my envelope is wrong or the models can’t correctly calibrate what happens when the government pushes the stop button on the economy.