Agent-based modeling in epidemiology of airborne infections

  • 1 Marchuk Institute of Numerical Mathematics, Russian Academy of Science, Moscow, Russia


Agent-based modeling proved to be a powerful tool for studying the complex multifactorial processes that take place in human population. In this paper we describe how this approach can be used to study the spread of airborne infections in a big city and the ways to control them. Agent-based modeling includes three main stages: a creating a synthetic population, simulation of disease spread in synthetic population during a fixed time period, and an analysis of the results. We created the population of 10 million agents and united them in a complex network according to their individual characteristics such as age, sex, marital status and occupation. In addition, each agent has a property that characterizes its state of health: susceptible, infected, and recovered. A susceptible agent can become infected if has an infected agent among its contacts and if an event of disease transmission occurs. Disease transmission is simulated as a random event with probability p which calculated for every pair susceptible-infected and depends on their individual characteristics and on the length of the contact. In so doing, the heterogeneity in a number of contacts and in resistance can be modelled. This approach was applied to model a dynamic of COVID-19 in Moscow during the period between October 2020 and December 2021.



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