Volume 14, Issue 2 (Vol.14, No.2, 2018)                   irje 2018, 14(2): 126-135 | Back to browse issues page

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1- PhD Candidate, 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:   (3597 Views)
Background and Objectives: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality. It is caused by a Mycobacterium tuberculosis. Awareness of the incidence and number of new cases of the disease is valuable information for revising the implemented programs and development indicators. time series and regression are commonly used models for prediction but these methods require some assumptions. The purpose of this study was to predict new TB cases using the hidden Markov model which does not require many assumption.
Methods: The data used in this study was the monthly number of new TB cases during 2006-2016 identified and recorded in Hamedan Province. Rorecasting the number of new TB cases was done using hidden Markov models using the hidden Markov package in the R software.
Results: According to the AIC and BIC criterion, two states had the best fit to the data, i.e. the data of this study were a mixture of two Poisson distributions with average number of event 5.96 and 10.2 respectively. The results also predicted the number of new cases over the next 24 months based on the hidden Markov model would be between 8 and 9 new cases in each month.
Conclusion: The hidden Markov model is the best model for prediction using the Markov chain. This model, in addition to detection of an appropriate model for the available data, can determine the transition probability matrix, which can help physicians predict the future state of the disease and take preventive measures befor reaching advanced stages.
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Type of Study: Research | Subject: General
Received: 2018/09/25 | Accepted: 2018/09/25 | Published: 2018/09/25