Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic

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

Various bio-mathematical approaches have been proposed and pursued for epidemic-spread modeling. At the highest level one can categorize them into empirical/machine-learning, statistical and dynamical approaches [9] . Yet, due to the lack of sufficient data on Covid-19 pandemic (at this stage), it is more appropriate that we place our focus on the epidemic-dynamics models. Epidemic-dynamics modeling employs deterministic or stochastic methodologies to tackle the evolution of the epidemic inside a susceptible population. The former category belongs to deterministic descriptions where Susceptible-Infectious-Recovered (SIR), Susceptible-Infectious-Susceptible (SIS) and Susceptible-Exposed-Infectious-Susceptible (SEIS) models are among the manifested ones [4] . A more involved class of models incorporates the stochastic nature of the epidemic-spread via the framework of e.g. Ito-or Levy-type processes [3, 1, 14] . The discrete class of stochastic epidemic models involves random networks [2, 7] or agent based schemes [10] . Both deterministic and stochastic descriptions, at their fundamental level, rely on reaction mechanisms which characterize infections, recoveries and deaths within different sub-groups of the population.