USA Devastation: Taming the Curve!

[Asher Kelman]

New York Times hired epidemiologists to put all available COVID-19 data and build a prediction model in how a 2 month USA Federally mandates quarantine might effect the viral spread and disease explosion now devastating the country and overwhelming the health care system.

Already thousands of health care workers are infected and some ~60 doctors have died!

4tg year students are being sent to the wards to replace exhausted and stricken staff.

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“President Trump says he wants the United States “raring to go” in two and a half weeks, on Easter, with “packed churches all over our country.” He and many other political conservatives suggest that we are responding to something like the flu with remedies that may be more devastating than the disease.

We created this interactive model with epidemiologists to show why quickly returning to normal could be a historic mistake that would lead to an explosion of infections, hospitalizations and deaths.” source

 

[Michael_Stones]

There are genuine problems concerning outcomes of government actions based on epidemiological modelling of Covid-19 effects. I'm not going to comment on testing issues because differences across countries have been documented reasonably well. Mortality data, on the other hand, provides a seemingly less controversial data base. Not so, however. Comments from the UK and Italy suggest under-reporting of Covide-19 mortality because the deaths reported include only those that occur in hospitals. The UK is in the process of revising the database to include deaths outside of hospitals that include 'Covid-19' on the death certificate. Such inclusion might increase the number of reported deaths by as much as 25%.

Another concern is that the media creates public panic by emphasizing 'worst case' scenarios from epidemiological modelling. Examples include reactions to 'worst case' mortality predictions by Ferguson's Imperial College group. That panic resulted in changes to the government's intervention strategy, which was formerly based on epidemiological predictions of lesser mortality. The latter continues to provide a main rationale for the more passive intervention strategy deployed by the Swedish government.

There's an old saying often made about statistical modelling: "garbage in and garbage out". The data used as input to current epidemiological modelling is problematic for more reasons than failure to include deaths outside of hospitals. There is also the issue of causation. An association between diagnosis and mortality does not necessarily mean Covid-19 caused that death. There is a difference between deaths from Covid-19 and deaths with Covid-19. At this early stage of epidemiological study, that distinction is more a matter of guesswork than knowledge.

 

[Asher Kelman]

Michael,

You are correct about the difficulties in making models, especially here, where the Chinese data is admitted to be questionable.

In war, the best guesses are used for planning, but then a good commander doubles that!

Otherwise soldiers run out of water and tanks run out of gas!

But the fundamental impediment with pandemics is simply our leaders are environmental science-averse. Actually, they are at a level of primitive tribes in appreciation of “germ theory” or grasping “logarithmic growth”.

So they are cognitively crippled.

Then, even those who should know better, still believe prayer is more necessary than science!

Asher