The University of Southampton
University of Southampton Institutional Repository

A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information

A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information
A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information
The effect of weather on health has been widely researched, and the ability to forecast meteorological events is able to offer valuable insights into the impact on public health services. In addition, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow hospital administrators to optimise resource allocation and service delivery. Using historical hospital admission data and several seasonal and meteorological variables for a site near the hospital, this paper develops a novel Bayesian model for short-term prediction of the numbers of admissions categorised by several factors such as age-group and sex. The proposed model is extended by incorporating the inherent uncertainty in the meteorological forecasts into the predictions for the number of admissions. The methods are illustrated with admissions data obtained from two moderately large hospital trusts in Cardiff and Southampton, in the United Kingdom, each admitting about 30-50 thousand non-elective patients every year. The Bayesian model, computed using Markov chain Monte Carlo methods, is shown to produce more accurate predictions of the number of hospital admissions than those obtained using a six-week moving average method similar to that widely used by the hospital managers. The gains are shown to be substantial during periods of rapid temperature changes, typically during the onset of cold, and highly variable winter weather
bayesian inference, emergency admissions, forecast validation, prediction
0964-1998
n/a
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Baffour, Bernard
0b1eaa15-f473-4bdd-b018-923907eb83e9
Minty, John
764f9bc1-c5fd-4051-9edf-1d3ad16b5d71
Harper, Paul
a7621384-7333-41b8-ab4f-8e3b9d181958
Sarran, Christophe
bb5258fd-5eac-4670-8c0e-8cd5174e9cb6
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Baffour, Bernard
0b1eaa15-f473-4bdd-b018-923907eb83e9
Minty, John
764f9bc1-c5fd-4051-9edf-1d3ad16b5d71
Harper, Paul
a7621384-7333-41b8-ab4f-8e3b9d181958
Sarran, Christophe
bb5258fd-5eac-4670-8c0e-8cd5174e9cb6

Sahu, Sujit K., Baffour, Bernard, Minty, John, Harper, Paul and Sarran, Christophe (2013) A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176, n/a. (doi:10.1111/rssa.12008).

Record type: Article

Abstract

The effect of weather on health has been widely researched, and the ability to forecast meteorological events is able to offer valuable insights into the impact on public health services. In addition, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow hospital administrators to optimise resource allocation and service delivery. Using historical hospital admission data and several seasonal and meteorological variables for a site near the hospital, this paper develops a novel Bayesian model for short-term prediction of the numbers of admissions categorised by several factors such as age-group and sex. The proposed model is extended by incorporating the inherent uncertainty in the meteorological forecasts into the predictions for the number of admissions. The methods are illustrated with admissions data obtained from two moderately large hospital trusts in Cardiff and Southampton, in the United Kingdom, each admitting about 30-50 thousand non-elective patients every year. The Bayesian model, computed using Markov chain Monte Carlo methods, is shown to produce more accurate predictions of the number of hospital admissions than those obtained using a six-week moving average method similar to that widely used by the hospital managers. The gains are shown to be substantial during periods of rapid temperature changes, typically during the onset of cold, and highly variable winter weather

Text
admissionmetsim.pdf - Accepted Manuscript
Download (842kB)

More information

e-pub ahead of print date: 23 April 2013
Keywords: bayesian inference, emergency admissions, forecast validation, prediction
Organisations: Mathematical Sciences, Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 347891
URI: https://eprints.soton.ac.uk/id/eprint/347891
ISSN: 0964-1998
PURE UUID: 54865fb0-13f5-4075-93bf-876883f15806
ORCID for Sujit K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598

Catalogue record

Date deposited: 01 Feb 2013 14:38
Last modified: 06 Jun 2018 12:52

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×