Experience in Modeling and Predicting the Incidence of Community-Acquired Pneumonia During the COVID-19 Pandemic

During the COVID-19 pandemic, the number of cases of community-acquired pneumonia (CAP) increased dramatically, which significantly changed the dynamics of its incidence time series (TS). Such changes overestimate the predicted values of the incidence of CAP and increase the forecast error. The purpose of this work was to evaluate methods for predicting the dynamics of CAP incidence during the COVID-19 pandemic. The CAP incidence data, registered within the time period from 2011 to 2022 was used. Two TS data were compiled, which did not include and included cases of CAP caused by COVID-19 in 2021-2022. TS data transformation was performed using outliers’ deletion, seasonal decomposition, or X-13-ARIMA-SEATS techniques. Typical monthly dynamics calculation method and several adaptive regression models (ETS, SARIMA, decSARIMA) were used for CAP incidence modeling and forecasting. For CAP incidence TS data that excluded cases of COVID-19 pneumonia, all analyzed transformation techniques effectively smoothed out the outlier period making the TS data suitable for modeling using adaptive regression models. For CAP incidence TS data that included cases of COVID-19 pneumonia, the methods of TS decomposition turned out to be ineffective. An acceptable forecast error was obtained when using typical monthly dynamics model based on the TS data with deleted outliers.