Monte Carlo simulation of the COVID-19 spread in early and peak stages in different regions of the Russian Federation using an agent-based modelling

Abstract

The COVID-19 outbreak of the beginning of 2020 has grown into a full-scale world crisis, which is still continuing. Minimization of losses requires adequate systemic aids for the disease spread prevention, which, in turn, requires adequate models allowing to predict the effect of different factors to disease spread. Traditional simulation approaches based on derivatives of a SIR model, although being quite efficient, suffer from not accounting for random factors. Agent-based models provide a convenient solution which allows accurately accounting for such factors as age structure of population, feature of self-isolation strategies and testing protocols, presence of super-spreaders etc. In this paper we report on the results of predicting the spread of COVID-19 in several representative regions of Russia. Our approach is based on an agent-based model with a general pool that includes a model of the population testing strategy. The model accounts for the following key epidemiologic characteristics: population age distribution, distributions of infestation period, manifestation period, and age-dependent probability of critical disease currency. It is demonstrated, that despite local features of different regions, the daily case curves can be predicted well for different territories with the same model parameters, except the initial number of infected, which serve as a tuning parameter of the model.

Speaker

Mikhail Kirillin
Institute of Applied Physics PRAS
Russia

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