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Safari M, Sadeghifar M, Roshanaei G, Zahiri A. Forecasting new cases of tuberculosis in the Hamadan province using hidden Markov model based on the record data during 1384-1394 years. irje. 2018; 1111
URL: http://irje.tums.ac.ir/article-1-5916-en.html
1- Department of Biostatistics, School of Public Health, Hamadan University of Medical ‎ Sciences, Hamadan, Iran
2- Assistant professor in Statistics, Department of Mathematics, Bu-Ali-Sina University, Hamadan, Iran
3- Associate professor in Biostatistics, Modeling of Noncommunicable Disease Research Canter, Hamadan University of Medical Sciences, Hamadan, Iran , gh.roshanaei@umsha.ac.ir
4- BSc of Public Health Center for Disease Control & Prevention, Deputy of Health Services, Hamadan University of Medical Sciences, Hamadan, Iran
Abstract:   (151 Views)
Background: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality and caused by TB involves the collection of Mycobacterium tuberculosis. Awareness of the incidence and number of new cases of the disease is valuable information for revising of the provided programs and indicatores of development. The aim of this study is the forecast of the number of new cases using hidden Markov model.
Materials and Methods: The used data in this study was the monthly number of new cases of tuberculosis during 2006-2016 years that is identified and recorded in Hamedan province. forecasting number of new cases of tuberculosis is down using hidden Markov models using HiddenMarkov packages in R software.
Results: According to the AIC and BIC criterion, two states was the best fit to the data, i.e. the data in this study contain a mixture of two Poisson distributions with parameters 5.96 and 10.2. The results also predict the number of new cases over the next 24 months based on the hidden Markov model between 8 and 9 new cases in each month.
Conclusion: Hidden Markov model is the best model for prediction using of markov chain. This model       morever detection of appropriate model for available data, can be able to determine the transition probability matrix. This probability can help to medics to predict future state of disease and do preventive preceeding befor entrance to  progressive state.
     
Type of Study: Research | Subject: General
Received: 2018/04/23 | Accepted: 2018/04/23 | Published: 2018/04/23

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