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

Tom Britton

is the (which was not peer-reviewed) The copyright holder for this preprint . The initial doubling time d = 3.5, and even more so the new doubling time after prevention, play a more significant role, more so than changing R 0 = 2.5 by 10% up or down. If the initial doubling time instead was set to d = 4 this would have slowed down the entire epidemic by about a week. The big effect of having different doubling times after preventive measures are put in place, corresponding to different magnitude of preventive measures, is illustrated by the alternative predictive curves. Finally, our model allows only for heterogeneity in terms of infectiousness. If heterogeneity in susceptibility as well as mixing patterns was included (thus making the model more realistic and complex) the result will typically be that the outbreak is slightly slower and less peaked. To investigate the exact effect, simulations from a realistic models are needed (and recommended!), but a rough estimate of the effect of heterogeneity is that the peak height is reduced by 25% and the left side of the peak is shifted about 1 week forward in 7 . CC-BY-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.