COVID-19 epidemic modeling - advantages of an agent-based approach

The article presents the authors’ approach to creating a model tool for predicting epidemiological development depending on quarantine measures with an assessment of peak loads on the health system. An agent-based model is proposed as such a tool, where agents-people go through the stages of diseasefrom infection to recovery or death. The difference of an agent-based epidemiological model from the classical one is that these transitions are modeled not at the group level but at the individual one, which makes it possible to account for the heterogeneity of the population by the characteristics related to people’s sensitivity to the infection and their involvement in the spread of the disease. Thus, the probability of the agents’ severe disease complications depends on the individual basic level of health, and the infection spread is simulated taking into account the agents’ social (family) relationships. The novelty of the presented agent-based model of epidemics lies in the use of the mechanism of family formation, which makes the simulation of contacts at the level of an individual agent as close to reality as possible. The model was tested on the example of the COVID-19 epidemic in the city of Moscow. For a plausible simulation of the agents’ disease, the epidemiological characteristics of COVID-19 were used, set by expert practitioners involved in the examination and treatment of patients. Using computer simulations, the researchers obtained estimates of the epidemic course for various values of the model parameters, including the impact of quarantine measures on such characteristics as the number of infected and dead over the entire period of the epidemic, the date of the infection peak and its scope, and the peak need for beds, including intensive care. The used socio-demographic structure of the population and epidemiological characteristics of a specific infection are the parameters of the model, which allows it to be adjusted to the particular qualities of other regions and infections for its further practical use as a tool for supporting management decisions in regional and sectoral situation centers. A supercomputer version of the model is planned to be developed for this purpose.