文章摘要
Zhihang Peng,Changjun Bao,Yang Zhao,Honggang Yi,Letian Xia,Hao Yu,Hongbing Shen,Feng Chen.[J].南京医科大学学报,2010,(3):207~214
Weighted Markov chains for forecasting and analysis in Incidence of infectious diseases in jiangsu Province, China
投稿时间:2010-02-03  
DOI:10.7655
中文关键词: 
英文关键词: weighted Markov chains, sequential cluster, infectious diseases, forecasting and analysis, Markov chain Monte Carlo
基金项目:
作者单位
Zhihang Peng Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029,Jiangsu Province, China 
Changjun Bao Center for Disease Control and Prevention of Jiangsu Province, Nanjing 210029, Jiangsu Province, China 
Yang Zhao Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029,Jiangsu Province, China 
Honggang Yi Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029,Jiangsu Province, China 
Letian Xia Applied Mathematics Department, Hohai University, Nanjing 210029, Jiangsu Province, China 
Hao Yu Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029,Jiangsu Province, China 
Hongbing Shen Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029,Jiangsu Province, China 
Feng Chen Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029,Jiangsu Province, China 
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中文摘要:
      
英文摘要:
      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.
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