  The paper develops a stochastic Agent-Based Model (ABM) mimicking the spread
of infectious diseases in geographical domains. The model is designed to
simulate the spatiotemporal spread of SARS-CoV2 disease, known as COVID-19. Our
SARS-CoV2-based ABM framework (CoV-ABM) simulates the spread at any
geographical scale, ranging from a village to a country and considers unique
characteristics of SARS-CoV2 viruses such as its persistence in the
environment. Therefore, unlike other simulators, CoV-ABM computes the density
of active viruses inside each location space to get the virus transmission
probability for each agent. It also uses the local census and health data to
create health and risk factor profiles for each individual. The proposed model
relies on a flexible timestamp scale to optimize the computational speed and
the level of detail. In our framework each agent represents a person
interacting with the surrounding space and other adjacent agents inside the
same space. Moreover, families stochastic daily tasks are formulated to get
tracked by the corresponding family members. The model also formulates the
possibility of meetings for each subset of friendships and relatives. The main
aim of the proposed framework is threefold: to illustrate the dynamics of
SARS-CoV diseases, to identify places which have a higher probability to become
infection hubs and to provide a decision-support system to design efficient
interventions in order to fight against pandemics. The framework employs SEIHRD
dynamics of viral diseases with different intervention scenarios. The paper
simulates the spread of COVID-19 in the State of Delaware, United States, with
near one million stochastic agents. The results achieved over a period of 15
weeks with a timestamp of 1 hour show which places become the hubs of
infection. The paper also illustrates how hospitals get overwhelmed as the
outbreak reaches its pick.
