Inferring COVID-19 spreading rates and potential change points for case number forecasts

Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.02.20050922 doi: medRxiv preprint Also, we still observe an exponential rise of new infections after the intervention becomes effective, because the growth rate remains positive, λ * 1 = λ 1 − µ > 0. (III) Strong social distancing; Here, the spreading rate decreases to 10%, (λ 1 = λ 0 / 10 with median λ 1 = 0.04 < µ). The assumptions here are that contacts are severely limited, but even when people stay at home as much as possible, some contacts are still unavoidable. Even under such drastic policy changes, no effect is visible until the reporting delay D is over. Thereafter, a quick decrease in daily new infections manifests within two weeks (delay plus change point duration), and the total number of cases reaches a stable plateau. Only in this last scenario a plateau is reached, because here the growth rate becomes negative, λ * < 0, which leads to decreasing numbers of new infections.