publication . Article . 2013

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

Paul Harper;
  • Published: 23 Apr 2013
  • Country: United States
Abstract
type="main" xml:id="rssa12008-abs-0001"> 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 a...
Subjects
free text keywords: Statistics, Probability and Uncertainty, Economics and Econometrics, Statistics and Probability, Social Sciences (miscellaneous), Resource allocation, XML, computer.internet_protocol, computer, Winter weather, Bayesian inference, Service delivery framework, Operations research, Econometrics, Moving average, Statistics, Mathematics, Markov chain Monte Carlo, symbols.namesake, symbols
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publication . Article . 2013

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

Paul Harper;