Development of methods for forecasting the dynamics of morbidity in the case of COVID-19

The COVID-19 pandemic has had global repercussions and has led to severe restrictive measures in all areas of activity that have changed the working and living conditions of the world's population. Even after the end of the pandemic, predicting the incidence of COVID-19 remains an important task as it is necessary to monitor the development of the situation and the results of research on this issue can be extrapolated to other epidemics. Scientific studies on the analysis of factors that have a significant impact on the course of the epidemic have a particular importance. This study proposes a set of models and machine learning algorithms based on big data processing to predict the dynamics of the spread of the COVID-19 virus at the mesolevel, which analyzes the impact of various exogenous factors on the incidence. As the initial data for building machine learning models, we use a depersonalized data set provided by Voronezh Regional Clinical Consultative and Diagnostic Center and containing information on all tests for COVID-19 conducted in Voronezh Oblast. To effectively combat epidemics, it is necessary to forecast the development of the incidence dynamics for a sufficiently long period of time, e.g. from two weeks or more, while various studies, in general, propose short-term methods that allow making a fairly accurate forecast only for 1–5 days. Therefore, the goal of this study is to find the optimal method for predicting incidence over an average period of time using exogenous factors. Information about the weather, day of the week and month, and the popularity of search queries related to COVID-19 were selected as exogenous variables to improve the quality of forecasting.

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