Modeling COVID-19 spread in the Russian Federation using global VAR approach

Abstract

The aim of this study is to analyse the spread of COVID‐19 in Russia taking into account various connections between regions and the effectiveness of isolation strategies using global vector autoregression (GVAR). We use regional data on new cases of coronavirus, self‐isolation index, Google trends index reflecting social awareness of pandemic and passenger turnover in buses, trains and planes. It was found that the number of new COVID‐19 cases reacts to Moscow outbreak shock significantly in most regions. During the second wave, the speed of reaction was faster but of a smaller size. The forecasts of new cases dynamics during the rising of the second wave turn out to be rather close to the actual dynamics in many regions. More rigorous social distancing strategy in Moscow reduced the number of cases in some regions but at the same time raised that number in some others.