Tuberculosis Surveillance Using a Hidden Markov Model.
Background: Routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. We aimed at using a Hidden Markov Model in order to detect the abnormal states of pulmonary tuberculosis in Iran. Methods:...
| Publicado en: | Iranian Journal of Public Health Vol. 41; no. 10; pp. 87 - 97 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Tehran University of Medical Sciences
2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=82716977&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 82716977 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22516085 6N6F jtl: Iranian Journal of Public Health issn: 22516085 maglogo: N pubinfo: dt: 2012 vid: 41 iid: 10 pid: 21783 pub: Tehran University of Medical Sciences artinfo: ui: 82716977 82716977 104429540 82716977 ppf: 87 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Tuberculosis Surveillance Using a Hidden Markov Model. aug: au: Rafei, A. Pasha, E. Jamshidi Orak, R. affil: Department of Mathematics and Statistics, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Tuberculosis, Pulmonary Disease Surveillance Methods Models, Statistical Iran Human Sputum Microbiology Descriptive Statistics Goodness of Fit Chi Square Test Sensitivity and Specificity Algorithms Record Review Multiple Linear Regression Funding Source ab: Background: Routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. We aimed at using a Hidden Markov Model in order to detect the abnormal states of pulmonary tuberculosis in Iran. Methods: Data for this study were the weekly number of newly diagnosed cases with sputum smear-positive pulmonary tuberculosis reported between April 2005 and March 2011 throughout Iran. In order to detect the unusual states of the disease, two Hidden Markov Models were applied to the data with and without seasonal trends as baselines. Consequently, the best model was selected and compared with the results of Serfling epidemic threshold which is typically used in the surveillance of infectious diseases. Results: Both adjusted R-squared and Bayesian Information Criterion (BIC) reflected better goodness-of-fit for the model with seasonal trends (0.72 and -1336.66, respectively) than the model without seasonality (0.56 and -1386.75). Moreover, according to the Serfling epidemic threshold, higher values of sensitivity and specificity suggest a higher validity for the seasonal model (0.87 and 0.94, respectively) than model without seasonality (0.73 and 0.68, respectively). Conclusion: A two-state Hidden Markov Model along with a seasonal trend as a function of the model parameters provides an effective warning system for the surveillance of tuberculosis. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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