Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic

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

While a few pandemic-spread investigations of Covid-19 have already been published, e.g. using the SEIS approach [13] and in a more recent study of non-pharmaceutical interventions [6] , no studies which investigate the effect of mass testing are found in the literature. To achieve this, it is necessary to separate the category of detected cases from infected cases that remain undetected (e.g. by the sheer lack of test kits) and predict their coupled dynamics. Note that detected here refers to persons taken "out of the game", which comprises not only those being tested positive, but also those who feel strong symptoms and thus stay in self-quarantine. It is further important to notice that in the case of this specific virus, infected people can infect others unusually long before the onset of symptoms due to the long active infection phase (the time of infection to infectiousness, i.e., the latency period, is shorter than the time from infection to disease, i.e., the incubation period). Therefore, early detection and containment of infected but symptom-free people can be extremely relevant for the dynamic behavior. Hence, in this paper we devise a set of reaction equations focusing on both detected and undetected categories. Moreover, the impact of the shortage of intensive care units during peaks of the pandemic are integrated in the outcome of the scenarios. The model coefficients are calibrated based on existing data, and the model is employed to investigate two main mitigation approaches, one relying on social distancing and one on more frequent infection testing. Also, a combination of the two is studied.