Assessment of COVID-19 hospitalization forecasts from a simplified SIR model
Abstract
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.
Keywords
Cite
@article{arxiv.2007.10492,
title = {Assessment of COVID-19 hospitalization forecasts from a simplified SIR model},
author = {P. -A. Absil and Ousmane Diao and Mouhamadou Diallo},
journal= {arXiv preprint arXiv:2007.10492},
year = {2021}
}
Comments
Paper home page: https://sites.uclouvain.be/absil/2020.05