Pandemic Forecasting by Machine Learning in a Decision Support Problem

This paper presents an approach that allows us, based on fairly simple models, to propose a methodology for predicting the decision of the governing bodies on the number of medical centers (MCs) required to combat a pandemic. This approach is based on the idea that the decision to open a new center is not made immediately when the existing centers are overwhelmed, but with a delay. Thus, the government aims to minimize the risks of opening MCs unnecessarily and makes this decision with the understanding that the congestion of existing centers will not end in the short term. This decision can be predicted by training the model on the historical data obtained from open sources. We develop a model that can be trained on historical data and allows forecasting the number of MCs based on a forecast of the number of hospitalized patients over a period of 14 days. Approaches are proposed for sufficiently accurately predicting the number of hospitalized patients for the model to predict the number of MCs. The models are tested on the data from open sources obtained for Ryazan oblast. For the model of forecasting the number of open MCs in Ryazan oblast, penalty functions are determined and the corresponding coefficients are calculated.