Assessment of COVID-19 Hospitalization Forecasts from a Simplified SIR Model

Keywords: COVID-19 prediction, COVID-19 forecast, SARS-CoV-2, coronavirus, SIR model, hospitalization prediction


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 (e.g., the root relative squared error is below 10% for Belgium over the period from 2020-03-15 to 2020-07-15). Moreover, we observed that, when the model is trained on a suitable three-week period around the hospitalization peak for Belgium, it forecasts the subsequent two months with mean absolute percentage error (MAPE) under 4%. We repeated the experiment for each French department and found 14 of them where the MAPE was below 20%. However, when the model is trained in the increase phase, it is less successful at forecasting the subsequent evolution.

How to Cite
Absil, Pierre-Antoine, Ousmane Diao, and Mouhamadou Diallo. 2021. “Assessment of COVID-19 Hospitalization Forecasts from a Simplified SIR Model”. Letters in Biomathematics 8 (1), 215–228.