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   Predicting Hepatitis B Monthly Incidence Rates Using Weighted Markov Chains and Time Series Methods  
   
نویسنده Shahdoust Maryam ,Sadeghifar Majid ,Poorolajal Jalal ,Javanrooh Niloofar ,Amini Payam
منبع journal of research in health sciences - 2015 - دوره : 15 - شماره : 1 - صفحه:28 -31
چکیده    Background: hepatitis b (hb) is a major global mortality. accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. this paper aimed to apply three different methods predict monthly incidence rates of hb.methods: this historical cohort study was conducted on the hb incidence data of hamadan province, the west of iran, from 2004 to 2012. weighted markov chain (wmc) method based on markov chain theory and two time series models including holt exponential smoothing (hes) and sarima were applied on the data. the results of different applied methods were compared to correct percentages of predicted incidence rates.results: the monthly incidence rates were clustered into two clusters as state of markov chain. the correct predicted percentage of the first and second clusters for wmc, hes and sarima methods was (100, 0), (84, 67) and (79, 47) respectively.conclusions: the overall incidence rate of hbv is estimated to decrease over time. the comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the hes had the most accurate prediction of the incidence rates.
کلیدواژه Hepatitis B ,Markov Chains ,Time series ,Incidence rate ,Prediction
آدرس hamadan university of medical sciences, ایران, bu ali sina university of hamadan, ایران, Chronic Diseases (Home care) Research Center and Department of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran, ایران, hamadan university of medical sciences, ایران, hamadan university of medical sciences, ایران
 
     
   
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