Assessment of COVID-19 hospitalization forecasts from a simplified SIR model
This work addresses forecasting challenges for public health officials during the COVID-19 pandemic, but it is incremental as it builds on existing SIR models.
The authors tackled the problem of forecasting COVID-19 hospitalizations by proposing a simplified SIR model, achieving high accuracy with a root relative squared error below 10% for Belgium and a mean absolute percentage error under 4% for two-month forecasts in some cases.
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 first 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.