Basic prediction methodology for covid-19: estimation and sensitivity considerations

Tom Britton

is the (which was not peer-reviewed) The copyright holder for this preprint . experienced 50 case fatalities (Λ D (t 1 ) = 50). A prediction for the cumulative number of deaths by t = April 3 (10 days later) hence equalsΛ I (t) = Λ D (t 1 )2 (t−t 1 )/d = 50 * 2 10/3 = 500. We assume that s d , the typical time from getting infected to dying (assuming the individual dies from covid-19), equals 21 days, and that the infection fatality risk equals f = 0.3%. With these assumptions, the estimate of how many people that had been infected by t = March 3 (3 weeks earlier) equalsΛ I (t − s d ) = Λ D (t)/f = 16 700. If we want to estimate how infected there are at t = March 15, this number has to be scaled up using the doubling times: Λ I (t) = Λ D (t 1 ) * 2 (15−3)/3 /f = 267 000, thus approaching the limit of 10% where predictions should stop using the current method. Nedless to say, there is of course quite a lot of uncertainty in such a prediction: first we estimated the number of infected 3 weeks back using case fatalities of present time and then projected this number three weeks forward in time to present using the doubling times.