Approaches for forecasting of socioeconomic impacts to the spread of COVID-19 with territorial differences of Russian regions

Abstract

The COVID-19 pandemic has brought severe demographical, socioeconomic, and territorial impacts. Those challenges require the world community to develop both response measures and anticipation of new threats. Therefore, creating the modern tools to forecast various indicators of the impact intensity pandemic becomes important and relevant for consideration and evaluation of interregional differences. This paper presents deep neural network models to predict a viral pandemic's effects in the regional cluster of Moscow and its neighbors. They are based on recurrent and Transformer-like architectures and utilize the attention mechanism to consider the features of the neighbor regions and dependencies between various indicators. These models are trained on heterogeneous data, including daily cases and deaths, the diseased age structure, transport, and hospital availability of the regions. The experimental evaluation shows that the demographic and healthcare features can significantly improve the accuracy of economic impact prediction. We also revealed that the neighboring regions' data helps predict the outburst's healthcare and economic impact. Namely, that data helps to improve accuracy for both the number of infected and the unemployment rate. The impact forecasting would help to develop strategies to reduce inter-territorial inequality due to the pandemic.