Modelling of COVID-19 Morbidity in Russia

Georgy Kopanitsa, Oleg Metsker, Alexey Yakovlev, Alexey Fedorenko, Nadezhda Zvartau
2020 Studies in Health Technology and Informatics  
The outbreak of COVID-19 has led to a crucial change in ordinary healthcare approaches. In comparison with emergencies re-allocation of resources for a long period of time is required and the peak utilization of the resources is also hard to predict. Furthermore, the epidemic models do not provide reliable information of the development of the pandemic's development, so it creates a high load on the healthcare systems with unforeseen duration. To predict morbidity of the novel COVID-19, we used
more » ... records covering the time period from 01-03-2020 to 25-05-2020 and include sophisticated information of the morbidity in Russia. Total of 45238 patients were analyzed. The predictive model was developed as a combination of Holt and Holt-Winter models with Gradient boosting Regression. As we can see from the table 2, the models demonstrated a very good performance on the test data set. The forecast is quite reliable, however, due to the many uncertainties, only a real-world data can prove the correctness of the forecast.
doi:10.3233/shti200653 pmid:33087624 fatcat:rv5puu3yrffb3p4sdhkyr3naye