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A quantitative study on the epidemic situation of tuberculosis based on the transmission disease dynamics in 14 prefectures of Xinjiang from 2005 to 2017 / 中国感染控制杂志
Chinese Journal of Infection Control ; (4): 945-950, 2018.
Article in Chinese | WPRIM | ID: wpr-701626
ABSTRACT
Objective To quantitatively analyze the epidemic situation of tuberculosis(TB)by modeling the data of tuberculosis in prefectures of Xinjiang,and predict the new cases of tuberculosis in prefectures of Xinjiang.Methods A dynamic model was used to fit the data of TB in 14 prefectures in Xinjiang from 2005 to 2014,the results of the fitting were verified by tuberculosis data in 2015-2017,verified results were evaluated,estimated values and basic reproductive numbers (R0)of parameters in each region were obtained,data of new TB in 2018-2022 were predic-ted.Results The verification of TB data in 2015-2017 showed that the actual values fell within the 95% confidence interval of the predictive value curve,model was fit well.R0in Southern Kashgar was 1 1.38 (95%CI1 1.33-11.50),R0in Urumqi City in Eastern Xinjiang and Ili Kazak Autonomous Prefecture in Northern Xinjiang were 5.46 (95% CI5.28-5.50)and 2.22 (95% CI2.18-2.28)respectively.The epidemic situation of TB in Southern Xinjiang was more serious than that in Northern and Eastern Xinjiang,epidemic situation of TB in Kash- gar Prefecture was most serious.The predicted results showed that the number of new TB from 2018 to 2022 will slowly grow in most prefectures.Conclusion The dynamical model of TB fits well and is feasible in this study,it can be used for prediction of new TB cases,intervention and management in Southern Xinjiang should be strength-ened to control the prevalence of TB.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Infection Control Year: 2018 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Infection Control Year: 2018 Type: Article