Abstract:
—In this work, we present an open-source
stochastic epidemic simulator calibrated with extant epidemic experience of COVID-19. The simulator models a
country as a network representing each node as an administrative region. The transportation connections between the nodes are modeled as the edges of this network. Each node runs a Susceptible-Exposed-InfectedRecovered (SEIR) model and population transfer between
the nodes is considered using the transportation networks
which allows modeling of the geographic spread of the disease. The simulator incorporates information ranging from
population demographics and mobility data to health care
resource capacity, by region, with interactive controls of
system variables to allow dynamic and interactive modeling
of events. The single-node simulator was validated using
the thoroughly reported data from Lombardy, Italy. Then,
the epidemic situation in Kazakhstan as of 31 May 2020 was
accurately recreated. Afterward, we simulated a number of
scenarios for Kazakhstan with different sets of policies.
We also demonstrate the effects of region-based policies
such as transportation limitations between administrative
units and the application of different policies for different
regions based on the epidemic intensity and geographic
location. The results show that the simulator can be used to
estimate outcomes of policy options to inform deliberations
on governmental interdiction policies