Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system

Abstract

Background In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks. Methods We consolidated the output of three epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds. Results We report on three key contributions by which our forecasting and reporting system has helped shaping Austria’s policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities. Conclusions Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

“…In Bicher [ 29 ] a harmonized model was developed based on three different models: SIR-X model, agent based SEIR model and an autoregressive model to predict the daily number of infections in Austria. With these projections, the authors provide forecasts for hospital occupation by estimating the hospitalization rate and the length of stay.…”