Partial Prediction of the Virus COVID-19 Spread in Russia Based on SIR and SEIR Models

The possibility to predict the spread of COVID-19 in Russia is studied. Particular goal is to predict the time instant when the number of infected achieves its maximum (peak). Such a partial prediction allows one to use simple epidemoics models: SIR and SEIR. Simplicity and small number of parameters are significant advantages of SIR and SEIR models under conditions of a lack of numerical initial data and structural incompleteness of models. The prediction is carried out according to public WHO datasets from March 10 to April 20, 2020. Comparison of forecast results by SIR and SEIR models are given. In both cases, the peak number of infected persons while maintaining the current level of quarantine measures is forecasted at the end of May 2020 or later. It coincides with the real data obtained in May-June, 2020. The results confirm usefulness of simple nonlinear dynamical models for partial prediction of complex epidemic processes.