This is an update as of April 3rd, 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 2 more regions were added to the fit, with also the aim to see how this contributes to the correlation of the parameters with the external data. The 2 new regions are Texas and Massachusetts.
We included the AICc characteristic of the fit. Looking at that, the best parameters are in some European regions like Italy and France. The best AICc parameter in the US is for New York. The worst parameter is for Texas where we don’t have too much data yet.
Italy and Spain are already passed the peak of the outbreak. Netherlands is very close, about at the peak level. New York, Louisiana and Colorado are close to the peak.
Correlation between those external data and the parameters of the fit
We see that the temperature and humidity are negatively correlated with the speed of the outbreak.
Population density and especially the mass transit ridership are positively correlated with the speed of the outbreak.
The correlations of the size of the outbreak changed more significantly since yesterday, mainly because we added 2 more data points that influenced that correlation.
We will likely have 1 more datapoint added tomorrow.
Error Function Fit
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