Assessment of COVID-19 hospitalization forecasts from a simplified SIR model

We propose the SH model, a simplified version of the well-known SIR compartmental model of infectious diseases. With optimized parameters and initial conditions, this time-invariant two-parameter two-dimensional model is able to fit COVID-19 hospitalization data over several months with high accuracy (mean absolute percentage error below 15%). Moreover, we observed that, when the model is trained on a suitable two-week period around the hospitalization peak for Belgium, it forecasts the subsequent three-month decrease with mean absolute percentage error below 10%. However, when it is trained in the increase phase, it is less successful at forecasting the subsequent evolution.