Forecasting COVID-19 hospitalizations using dynamic regression with ARIMA errors

For more than a year, COVID-19 has changed societies all over the world and put massive strains on its healthcare systems. In an attempt to aid in prioritizing medical resources, this thesis uses dynamic regression with ARIMA errors to forecast the number of hospitalizations related to COVID-19 two weeks ahead in Uppsala County. For this purpose, 100 models are created and their ability to forecast hospitalizations two weeks ahead for weeks 15-17 of 2021 for the different municipalities in Uppsala County is evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best performing models are then utilized to forecast hospitalizations for weeks 19-22. The results show that the models perform well during periods of increasing numbers of hospitalizations during early 2021, while they perform less well during the last weeks of May 2021 where hospitalizations numbers have been falling dramatically. This recent decrease in forecasting performance is believed to be caused by an increase in vaccination coverage, which is not accounted for in the models