摘要
基于隐马尔科夫模型(HMM)为中国疾病预防与控制中心发布的乙肝发病数量时间序列进行建模,通过似然函数的计算而建立起一个具有2状态的单变量正态分布隐马尔科夫模型.根据模型估计结果,发现两个状态对应的乙肝发病数量的分布规律有较大差异,分别对应着乙肝疫情的低发状态和高发状态.状态之间有可能发生转换,但是转换的概率比较低.基于所估计得到的隐马尔科夫模型,可以识别出特定时刻乙肝疫情所处的状态,也可以预测未来时刻乙肝疫情所处的状态.
This paper applies a hidden Markov model to the hepatitis B incidences series published by Chinese Center for Disease Control and Prevention. A two-state HMM with univariate normal distribution is built and estimated, where the number of states of the Markov chain is implied by maximum likelihood estimation. These two states, corresponding to different distribution laws, are interpreted as low incidence and high incidence accordingling.The probability of state transition is positive,albeit small. The historical states series can be inferred from the estimated HMM, which also can predict about the future states.
作者
刘琼
杨建华
LIU Qiong YANG Jian-hua(DonLinks School of Economics and Management, University of Science and Technology Beijing~ Beijing 100083 China)
出处
《数学的实践与认识》
北大核心
2017年第19期203-210,共8页
Mathematics in Practice and Theory
基金
资助项目:中央高校基本业务费(FRF-BR-16-002B)
关键词
乙肝
发病预测
隐马尔科夫模型
hepatitis B
disease indicence prediction
hidden Markov model
