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

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

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ORCID for Sujit K. Sahu: orcid.org/0000-0003-2315-3598

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