Basic estimation-prediction techniques for Covid-19, and a prediction for Stockholm

Tom Britton

Once the calibration is performed it is possible to predict relevant quantities of the outbreak, both with and without preventive measures of different magnitudes. The results are summarized in Figure 1 and Figure 2 below. Figure 1 reports the daily incidence of new infections for the different scenarios. For example, considering our main prediction curve, assuming the new doubling time equals d e = 9 days (blue curve) shows that the peak day when most individuals get infected is April 3 when as many as 27 000 (=1.3%) get infected. Further, 75% of all infections have happened by April 19 and 90% of all infections have occurred by May 5. Comparing the different scenarios, without preventions and with preventions of different magnitude, it is seen that the peak is reduced the higher magnitude of preventive measures, and slightly shifted forward in time. Had the preventive measures happened earlier in relation to the outbreak, as for example was the case with other parts of Sweden, the peak heights would have been lower and further shifted forward in time. Figure 2 shows the cumulative numbers of infected (with overall percentages to the right) for the different scenarios. It is seen that without preventive measures slightly more 1.8 5 .