Monitoring and forecasting the COVID-19 epidemic in Moscow: model selection by balanced identification technology - version: September 2021

Abstract

AbstractA mathematical model is a reflection of knowledge on the real object studied. The paper shows how the accumulation of data (statistical data and knowledge) about the COVID-19 pandemic lead to gradual refinement of mathematical models, to the expansion of the scope of their use. The resulting model satisfactorily describes the dynamics of COVID-19 in Moscow from 19.03.2020 to 01.09.2021 and can be used for forecasting with a horizon of several months. The dynamics of the model is mainly determined by herd immunity. Monitoring the situation in Moscow has not yet (as of 01.09.2021) revealed noticeable seasonality of the disease nor an increase in infectivity (due to the Delta strain). The results of using balanced identification technology to monitor the COVID-19 pandemic are:models corresponding to the data available at different points in time (from March 2020 to August 2021);new knowledge (dependencies) acquired;forecasts for the third and fourth waves in Moscow.Discrepancies that manifested after 01.09.2021 and possible further modifications of the model are discussed