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

05/12/2020
by   Setareh Ranjbar, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 16

page 17

page 18

page 23

page 39

page 40

page 41

page 42

09/18/2019

Analysis of the hospital records from AOK Plus

We present analysis of anonymised admission/discharge data from insuranc...
11/10/2020

Estimating Risk-Adjusted Hospital Performance

The quality of healthcare provided by hospitals is subject to considerab...
08/01/2020

What can we learn about SARS-CoV-2 prevalence from testing and hospital data?

Measuring the prevalence of active SARS-CoV-2 infections is difficult be...
08/28/2020

Causal mediation analysis decomposition of between-hospital variance

Causal variance decompositions for a given disease-specific quality indi...
02/11/2021

Using exoskeletons to assist medical staff during prone positioning of mechanically ventilated COVID-19 patients: a pilot study

We conducted a pilot study to evaluate the potential and feasibility of ...
05/14/2021

A causal learning framework for the analysis and interpretation of COVID-19 clinical data

We present a workflow for clinical data analysis that relies on Bayesian...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.