March 31st, 2020: An analysis of SARS – CoV2 Data and Forecasts

March 31st, 2020: An analysis of SARS – CoV2 Data and Forecasts

Categories: COVID-19
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This is an update as of March 31st, 2020.
We are analyzing the existing available data on daily deaths caused by the SARS-CoV2 virus and use that in conjunction with certain simple models to predict the evolution of the disease in certain geographical areas. You can read the full introduction here.

Today the most extensive changes are made to the algorithm:

  • Regions grouped by 4 and plotted together on the same plot with a log scale.
  • As the outbreak matures, all of the markets are run on the Erf fit.

Exponential Fit

DE stands for Germany, not Delaware.

Error function Fit

Key take aways from the results

We have reached a point where the outbreak matures and because of certain policies, we see a slowing down in number of deaths in all regions.

The “a” parameter of the Erf fit shows when we expect to peak in number of deaths.

For Italy we are 2.5 days past the peak.

For Spain, we should reach the peak tomorrow.

For France we should reach the peak in 2 days.

New Jersey gives some signs of reaching a peak soon too.

For all the regions, we are away from the peak, so the a parameter still can have large errors in it.

The “c” parameter describes the size of the outbreak and 2*c is the total estimated number of deaths.

The largest size of the outbreak was in Italy and second was in Spain and New York. Regions with a large size of the outbreak are likely to build heard immunity and are less susceptible to subsequent outbreaks.

California, New Jersey and Colorado have the smallest size, so there a small percentage of population was exposed to the virus making them susceptible to subsequent waves.

Parameter “b” describes the speed of growth of the outbreak.

The fastest growth was in Spain, New Jersey and New York.  That puts them on a steep curve with high chances of over-running the medical system. New Jersey and Colorado have a high speed too, but given the small size of outbreak the risk of over-running the health care system is low.

Washington has the slowest growth speed, meaning it is a success story in flattening the curve, while at the same time being on track towards obtaining heard immunity and making it immune to subsequent outbreaks.

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