title: Weighted Markov chains for forecasting and analysis in Incidence of infectious diseases in jiangsu Province, China.
abstract: 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.
authors.affiliation: Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029, Jiangsu Province, China.
authors.forename: Zhihang
authors.initials: Z
authors.lastname: Peng
authors.forename: Changjun
authors.initials: C
authors.lastname: Bao
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.forename: Honggang
authors.initials: H
authors.lastname: Yi
authors.forename: Letian
authors.initials: L
authors.lastname: Xia
authors.forename: Hao
authors.initials: H
authors.lastname: Yu
authors.forename: Hongbing
authors.initials: H
authors.lastname: Shen
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
keywords: Markov chain Monte Carlo; forecasting and analysis; infectious diseases; sequential cluster; weighted Markov chains
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