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

Tom Britton

of the peak, and infections being more spread out in time. Since healthcare systems are directly affected by incidence, and peak incidence (with some delay for severe symptoms to develop) being the most problematic time for hospitals, this effect is very important. Quantifying the positive effect of prevention is hence not straightforward: should it be in terms of how many fewer that ultimately get infected, how much the peak is reduced, or something else? We have no clear answer to the question but one comparison that can be misleading is to compare the number of infections for two different scenarios at a given time point [6] . As an illustration, out main predictive curve (the blue curve) is clearly favourable compared to no preventive measures (the black curve). The peak size is reduced by 65%, and the final number infected is reduced from 1.80 million to 1.32 million. However, if we compare the cumulative number of infections on e.g. April 10, there are 1.59 million infected with no preventive measures and 0.79 million according to our main prediction. However, to say that these preventive measures have reduced the number of infections by 0.8 million is misleading. The curve for the preventive scenario is shifted to the right, and at the end of the outbreak the number of saved infections is 0.48 million rather than 0.80 million (of course still a substantial reduction!). The same reasoning applies when comparing the number of infected having required intensive care or case fatalities: to compare on a specific date during an outbreak can be misleading [6] .