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
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DOI：
10.1111/rssa.12008
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发表时间：
2014-01-01
影响因子：
2
通讯作者：
Sarran, Christophe
中科院分区：
文献类型：
--
作者：
Sahu, Sujit K.;Baffour, Bernard;Sarran, Christophe
天气对健康的影响已经得到了广泛的研究，预测气象事件的能力可以为了解天气对公共卫生服务的影响提供有价值的见解。此外，更好地预测对天气波动更敏感的医院需求可以使医院管理人员优化资源分配和服务提供。使用历史入院数据和几个季节性和气象变量的医院附近的网站，本文开发了一种新的贝叶斯模型的入院人数的短期预测分类的几个因素，如年龄组和性别。提出的模型扩展到招生人数的预测，将固有的不确定性，在气象预报。该方法说明了从两个中等规模的医院信托在卡迪夫和南安普顿，在英国，每个承认约30000-50000非择期患者每年的入院数据。贝叶斯模型，通过使用马尔可夫链蒙特卡罗方法计算，产生更准确的预测入院人数比那些通过使用6周移动平均法，这是类似于医院管理者广泛使用的。在气温迅速变化的时期，特别是在寒冷和多变的冬季天气开始时，这种增益是很大的。
The effect of weather on health has been widely researched, and the ability to forecast meteorological events can offer valuable insights into the effect on public health services. In addition, better predictions of hospital demand that are more sensitive to fluctuations in weather can allow hospital administrators to optimize resource allocation and service delivery. Using historical hospital admission data and several seasonal and meteorological variables for a site near the hospital, the paper develops a novel Bayesian model for short-term prediction of the numbers of admissions categorized by several factors such as age group and sex. The model proposed 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 UK, each admitting about 30000-50000 non-elective patients every year. The Bayesian model, computed by using Markov chain Monte Carlo methods, is shown to produce more accurate predictions of the number of hospital admissions than those obtained by using a 6-week moving average method which is similar to that widely used by 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.