Assessment of COVID-19 hospitalization forecasts from a simplified SIR model
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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 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 trained in the increase phase, it is less successful at forecasting the subsequent evolution.
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