Modelling the Extremes of Seasonal Viruses and Hospital Congestion: The Example of Flu in a Swiss Hospital
Creators
- 1. University of Lausanne
- 2. University of Geneva
- 3. University College London
- 4. City London
- 5. CHUV
Description
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. 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 empirical effectiveness of
the proposed method is assessed through a simulation study.
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