Modelling the Extremes of Seasonal Viruses and Hospital Congestion: The Example of Flu in a Swiss Hospital

TL;DR

This paper models the extremes of hospital visits due to seasonal flu using a robust statistical framework linked to meteorological factors, providing tools for hospital management and potential applications to other seasonal diseases.

Contribution

It introduces a novel modeling approach using Generalized Pareto distributions linked to meteorological covariates for analyzing hospital congestion due to seasonal viruses.

Findings

Meteorological conditions significantly influence hospital visit extremes.

The proposed model effectively captures the tail behavior of hospital congestion data.

The approach is applicable to other seasonal diseases like COVID-19.

Abstract

Viruses causing flu or milder coronavirus colds are often referred to as "seasonal viruses" as they tend to subside in warmer months. In other words, meteorological conditions tend to impact the activity of viruses, and this information can be exploited for the operational management of hospitals. In this study, we use three years of daily data from one of the biggest hospitals in Switzerland and focus on modelling the extremes of hospital visits from patients showing flu-like symptoms and the number of positive cases of flu. We propose employing a discrete Generalized Pareto distribution for the number of positive and negative cases, and a Generalized Pareto distribution for the odds of positive cases. Our modelling framework allows for the parameters of these distributions to be linked to covariate effects, and for outlying observations to be dealt with via a robust estimation approach. Because meteorological conditions may vary over time, we use meteorological and not calendar variations to explain hospital charge extremes, and our empirical findings highlight their significance. We propose a measure of hospital congestion and a related tool to estimate the resulting CaRe (Charge-at-Risk-estimation) under different meteorological conditions. The relevant numerical computations can be easily carried out using the freely available GJRM R package. The introduced approach could be applied to several types of seasonal disease data such as those derived from the new virus SARS-CoV-2 and its COVID-19 disease which is at the moment wreaking havoc worldwide. The empirical effectiveness of the proposed method is assessed through a simulation study.