  We developed a model and a software package for stochastic simulations of
transmission of COVID-19 and other similar infectious diseases, that takes into
account contact network structures and geographical distribution of population
density, detailed up to a level of location of individuals. Our analysis
framework includes a surrogate model 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) is 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 the canton of Geneva, Switzerland.
