Weighted Markov chains for forecasting and analysis in Incidence of infectious diseases in jiangsu Province, China
Zhihang Peng
Changjun Bao
Yang Zhao
Honggang Yi
Letian Xia
Hao Yu
Hongbing Shen
Feng Chen
摘要：This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence course. Then the paper presents a weighted Markov chain, a method which is used to predict the future incidence state. This method assumes the standardized self-coefficients as weights based on the special characteristics of infectious disease incidence being a dependent stochastic variable. It also analyzes the characteristics of infectious diseases incidence via the Markov chain Monte Carlo method to make the long-term benefit of decision optimal, Our method is successfully validated using existing incidents data of infectious diseases in Jiangsu Province. In summation, this paper proposes ways to improve the accuracy of the weighted Markov chain, specifically in the field of infection epidemiology.
机标关键词：jiangsu Provinceinfectious diseasesanalysisMarkov chainsclassification standardMonte Carlo methodpaperbased
分类号：R3(基础医学)
资助基金：National S&T Major Project Foundation of China(2009ZX10004-904)Universities Natural Science Foundation of Jiangsu Province(09KJB330004)National Science Foundation(DMS-9971405)National Institutes of Health Contract(N01-HV-28183)
论文发表日期：2010-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
英文信息