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The paper develops a stochastic Agent-Based Model (ABM) mimicking the spreadof infectious diseases in geographical domains. The model is designed tosimulate the spatiotemporal spread of SARS-CoV2 disease, known as COVID-19. OurSARS-CoV2-based ABM framework (CoV-ABM) simulates the spread at anygeographical scale, ranging from a village to a country and considers uniquecharacteristics of SARS-CoV2 viruses such as its persistence in theenvironment. Therefore, unlike other simulators, CoV-ABM computes the densityof active viruses inside each location space to get the virus transmissionprobability for each agent. It also uses the local census and health data tocreate health and risk factor profiles for each individual. The proposed modelrelies on a flexible timestamp scale to optimize the computational speed andthe level of detail. In our framework each agent represents a personinteracting with the surrounding space and other adjacent agents inside thesame space. Moreover, families stochastic daily tasks are formulated to gettracked by the corresponding family members. The model also formulates thepossibility of meetings for each subset of friendships and relatives. The mainaim of the proposed framework is threefold: to illustrate the dynamics ofSARS-CoV diseases, to identify places which have a higher probability to becomeinfection hubs and to provide a decision-support system to design efficientinterventions in order to fight against pandemics. The framework employs SEIHRDdynamics of viral diseases with different intervention scenarios. The papersimulates the spread of COVID-19 in the State of Delaware, United States, withnear one million stochastic agents. The results achieved over a period of 15weeks with a timestamp of 1 hour show which places become the hubs ofinfection. The paper also illustrates how hospitals get overwhelmed as theoutbreak reaches its pick.

CoV-ABM: A stochastic discrete-event agent-based framework to simulate spatiotemporal dynamics of COVID-19