Preliminaries on a Stochastic Cellular Automaton Based Framework for Studying the Population Dynamics of COVID-19

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Lima I.
Balbi P.P.
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© 2021, Springer Nature Switzerland AG.The propagation of infectious diseases through social interactions can be mitigated when health measures aim to reduce or remove the results of these interactions. This is the scenario of ongoing COVID-19 pandemic adopted quarantine policies, from social distancing to lockdown, and of immunization programs. When a sufficient number of interactions is suppressed, the spread of an infectious disease is ended achieving herd immunity, defined as the indirect protection given by immune individuals to susceptible individuals. Here we describe the preliminaries of a stochastic cellular automaton based framework designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via Moore neighbourhood of radius one, with a view to analyze the impact of initially immune individuals on the dynamics of COVID-19. This impact was measured comparing a progression of initial immunity ratio from 0 to 90% of the population with the number of susceptible individuals not contaminated, the peak value of active cases, the total number of deaths and the emulated pandemic duration in days. A herd immunity threshold of 60% was obtained from this procedure, which is in tune with the estimates of the currently available medical literature. Nevertheless, more accurate results demand more research efforts including better analysing the model probabilities of propagation and duration.
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Herd immunities , Immunization programs , Infectious disease , Medical literatures , Model probabilities , Research efforts , Social interactions , Stochastic cellular automata
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