More Articles and Studies

By June 24, 2020 Commentary

Here is a very interesting study using the influenza surveillance network data from the CDC to examine when coronavirus was likely circulating and what its effect was.  (Science Article)   I have mentioned this network and the preliminary work behind this study before.  CDC keeps track of how severe a flu season is by having a number of provider offices around the country regularly report to them on symptoms of flu.  Since coronavirus symptoms are similar to those of flu, it stands to reason that before there was widespread testing for coronavirus, their may have been infections that were treated as flu, and if so, the numbers reported to the CDC could have shown a notable increase.  It is widely believed that the official case numbers may be a substantial undercount, due to testing strategy, asymptomatic or mild illness and false negatives.  Not that many “flu” cases are actually tested, and there are a number of cases treated as non-influenza but influenza-like illness.  The authors constructed a ten-year baseline.  They found a surge of non-influenza ILI in March, correlated with known patterns of spread of coronavirus.  The surge appeared to peek in the middle of march, except in New York and New Jersey.  They considered and measured overall changes in care-seeking behaviors which might have affected their results.  They concluded that there were likely 8.7 million coronavirus cases between March 8 and March 28, with most of them being missed by infection tests.  They concluded that early in the epidemic cases were likely doubling in four days or less.  They also concluded that only 1% of cases were being captured by tests in early March but by the end of March that had risen to 12.5%.  The authors believe that it is likely that 80% of infections were not being detected by infection tests.

These researchers looked at immune system responses to the common coronavirus strains.  (Medrxiv Paper)   They compared responses in adults and children.  The samples were collected prior to the epidemic.  Out of 1399 samples, reactive memory to some portion of the typical coronavirus strains was common, including reaction to some previously unidentified protein segments.  Antibody reaction to the common viruses ranged from 4% of samples for one to 27% to another.  Children more frequently had reactive antibodies and those antibodies tended to be directed against multiple regions of the virus, including the spike protein.  There did appear to be the potential for cross-reactivity to the current strain of coronavirus.

I have made it clear how I feel about schools being open.  I feel the same way about day care facilities.  And I am so impressed with how some people on their own are doing a great job of gathering and reporting on useful data.  This person and group has crowdsourced data on cases in day care facilities.  (Day Care Data)   You can see the full spreadsheet but the summary is that 854 day care facilities caring for 20,436 children reported only 35 cases while 7244 workers reported 78 cases.  That is obviously a very low number.  Just self-reported data, but I am guessing it is fairly accurate.

And what are the risks to children in general?  (UK Article)  In the UK, as in the United States, the risk is far lower than likelihood of death from many other causes.  Only two deaths among 7 million school children aged 5 to 14.

A couple of recent papers have ripped the study from the Imperial College team, which followed up on their disastrous model at the start of the epidemic by now publishing a paper claiming that the lockdowns saved millions of lives.  These are the second and third papers I have seen tearing into that work, so some scientists must be pretty incensed about it.  Here is one, by Nicholas Lewis.  (JC Paper)   Mr. Lewis points out that the way the model was set up the only conclusion it could come to was that the lockdowns saved lives.  When he substitutes more realistic assumptions and parameters, the effect disappears.  He makes a number of other technical critiques of the approach.  And here is the other one.   (IC Paper)   In this paper the authors, two German professors, say the Imperial College work involves circular reasoning and contradicts the data, but they misunderstood how the transmissibility factor which is a key was treated and made another error regarding Sweden.  But their basic critique is correct.

And unfortunately I have to mention a couple of notes from yesterday’s press briefing.  One is that the health department officials tried to claim that improvement in management of long term care facility risks had contributed to the drop in hospitalizations and deaths.  This is absurd, the truth is that cases in long-term care don’t seem to be dropping much faster than overall cases, especially when you consider relative testing rates.

And the last question related to “caution fatigue” and how people can fight it.  I laughed out loud (I am told that would be LOL for my younger readers).  People don’t get caution fatigue if there is actually some danger to be afraid off.  People have figured out that they were misled at best at first and know they think they are being lied to and they are right.  So people are appropriately ignoring the lies and false warnings.  So my advice to the “experts” is to stop lying and exaggerating.

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