COVID-19 March 31, 2020

If you take the numbers at face value, there has been a definite and unarguable flattening of the curve for the number of cases.  The total number of deaths does not reflect this flattening, but it looks to be starting even in the death rate data. I would caution taking all the numbers at face value, however.  There are still obvious issues with testing and reporting, and it's quite possible these are affecting the U.S. aggregated data.  It is certainly affecting some states, as the data are contrary to the behavior of a natural system.  The slowing death rate is a good thing, since the dead are almost always reported, and those data are likely to be more robust than the data for the number of infections.

The usual plots of total cases, daily change in cases, death, and death rate are below.  After that, I provide a quick analysis of the ongoing "New York Effect" and introduce a plot generated by a colleague for Colorado.

In a previous post, I introduced the "New York Effect", which was the result of the New York cases being such a large fraction of the U.S. total.  As went New York so went the U.S.   The fraction of total cases coming from New York has dropped with time.  In the graph below, I plot the whole U.S., New York, and the U.S. after removing New York cases.

The data (diamonds) do not trace out straight lines; the data is flattening over time.  The dashed lines show a 4th order polynomial fit to the data.  Note that New York (orange) is falling away faster than the rest of the data.  The rest of the U.S. (gray) is flattening, but not as quickly.  As the fraction of New York cases continues to decrease, all of the U.S. (blue) will be less and less influenced by the numbers in New York.  

The figure below for Colorado was provided courtesy of my colleague Dr. Scott Anderson and used with his permission.  It shows hints that the testing in Colorado is falling short.  Over the last few days, note that the total number of tests conducted to date is falling away from the best fit curve. If that trend continues, it suggests that the testing cannot keep up with the demand.  The number of positives is also falling away, which is exactly what you'd expect if there are insufficient numbers of tests being given.  It is also possible that the demand has simply decreased, but that doesn't seem like a good explanation.  All indications are that there is far more demand than available tests.  The number of hospitalized continues to rise with a doubling time of about 2.7 days.  This is a lagging indicator of initial infection since an individual must go through the incubation period and then become sick enough to be hospitalized.  In very rough numbers, it is also clear that each of the lines is separated by about a factor of 10.  So, of all those tested, about 10% end up positive.  Of those, about 10% are hospitalized.  And of those, about 10% die.  Again, these are very rough numbers.  Those rough numbers suggest a mortality rate on the order of a few percent for those known to be positive, which is consistent with global calculations.