Weather data predicts the risk of hospital congestion

A team of researchers, including GSEM Professor Eva Cantoni and Dr. Setareh Ranjbar (GSEM Ph.D. graduate), has developed a mathematical model that anticipates flu peaks in hospitals based on weather data.

When too many people fall ill at the same time, hospitals run the risk of congestion, as the COVID-19 pandemic showed to a certain extent. The flu virus can cause the same problems. A seasonal virus, influenza is mainly present during the winter season in our latitudes. The research team, therefore, compared certain meteorological data – precipitation, humidity, temperature, and sunshine – with the cases of influenza recorded daily for three years at Lausanne University Hospital (CHUV).

For the first time, the team focused on the extreme values of cases of influenza recorded because it is these values that may indicate a risk of congestion for hospitals and are therefore useful for resource planning. They were able to develop a model that uses weather data to predict the risk of congestion three days later – the incubation time of the flu.

The findings of this team of researchers from the University of Lausanne, the University of Geneva, the University College London, and the University of London, supported by the Swiss NSF, are published in the Journal of the Royal Statistical Society: Series C (Applied Statistics) in the article: "Modelling the extremes of seasonal viruses and hospital congestion: The example of flu in a Swiss hospital". 

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