Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic

Patrick Jenny; David F Jenny; Hossein Gorji; Markus Arnoldini; Wolf-Dietrich Hardt

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.27.20045237 doi: medRxiv preprint infection rate is reduced by 29.42% (corresponding to a virus reproduction factor of R 0 = 1) from day 50 till day 200. In the logarithmic plot one can see that the number of infected persons remains constant during the social distancing phase (as expected when R 0 = 1), but the number of cases and deaths keeps increasing. Note that afterwards only a very small fraction of the population is immune. With some delay after social distancing is abandoned, the numbers raise exponentially until more than half of the population becomes immune. Figure 5c shows the case with 70% lower infection rate in the period from 50 to 100 days. This time the virus reproduction factor R 0 is smaller than one and the number of infected persons declines exponentially; seen in the logarithmic plot. After the social distancing phase there is a delay of approximately 200 days before the numbers climb exponentially until more than half of the initial population is immune. In both cases one obtains the same endemic state as in the base case, i.e., temporary social distancing only delays the main outbreak. Therefore, the success of a containment strategy eventually relies on discovering an effective and cheap vaccine in due time. As additional information information regarding intensive care unit capacity is provided in the bottom logarithmic plots of Fig. 5 . The detected infected population (dotted lines) is shown together with the intensive care unit capacity (horizontal long dashed lines).