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The copyright holder for this preprint (which was not peer-reviewed) is the .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 Equation (17) quantifies the average value of k k for a fractionn d,i of detected infected persons. Figure 3 shows k k as a function of the fraction of detected infected persons. One can see that the death rate k k is constant until the health care system is at maximum capacity atn i|d = 0.0008. Beyond that, it increases and asymptotically approaches a value which is more than two times larger. Figure 4 shows the model results for a period of 200 days. Dashed lines represent the immune (n s,init − n s (t)), dashdotted lines the infected (n i|u + n i|d ) and solid lines the deceased (n k ) population. The model predicts an equilibrium when approximately 53% of the population is immune, which would occur after roughly 150 days. Further it predicts that approximately 2.8% of the initial susceptible population would eventually be killed by the virus. The peak of infected persons (approximately 10% of the initial population) is reached after roughly 110 days. Note that we do not claim that these results are quantitatively correct, but since the model accounts for the important mechanisms, it can be expected that it captures the relevant dynamics to a high degree. Therefore, to study the response of containment and more frequent testing during specified phases it is an effective tool, provided the model parameters are chosen in a realistic range.