  The rapid transmission of the highly contagious novel coronavirus has been
represented through several data-guided approaches across targeted geographies,
in an attempt to understand when the pandemic will be under control and imposed
lockdown measures can be relaxed. However, these epidemiological models
predominantly based on training data employing number of cases and fatalities
are limited in that they do not account for the spatiotemporal population
dynamics that principally contributes to the disease spread. Here, a stochastic
cellular automata enabled predictive model is presented that is able to
accurate describe the effect of demography-dependent population dynamics on
disease transmission. Using the spread of coronavirus in the state of New York
as a case study, results from the computational framework remarkably agree with
the actual count for infected cases and deaths as reported across
organizations. The predictions suggest that an extended lockdown in some form,
for up to 180 days, can significantly reduce the risk of a second wave of the
outbreak. In addition, increased availability of medical testing is able to
reduce the number of infected patients, even when less stringent social
distancing guidelines and imposed. Equipping this stochastic approach with
demographic factors such as age ratio, pre-existing health conditions,
robustifies the model to predict the transmittivity of future outbreaks before
they transform into an epidemic.
