A stochastic geospatial epidemic model and simulation using an event modulated Gillespie algorithm

We developed a model and an instrument for stochastic simulations of spreading of COVID-19 and other similar infectious diseases, that takes into account both contact network structures and geographical distribution of population density, detailed up to a level of location of individuals. Our analysis framework includes the surrogate model (SuMo) optimization process for quick fitting of the model's parameters to the observed epidemic curves for cases, hospitalizations and deaths. This set of instruments (the model, the simulation code, and the optimizer) can be a useful tool for policymakers and epidemic response teams who can use it to forecast epidemic development scenarios in local environments (on the scale from towns to large countries) and design optimal response strategies. The simulation code also includes a geospatial visualization subsystem, presenting detailed views of epidemic scenarios directly on population density maps. We used the developed framework to draw predictions for COVID-19 spreading in Switzerland, on the level of individual cantons; their difference in population density distribution accounts to significant variety in epidemic curves and consequently the choice of optimal response strategy.