Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2015 Mar 12

PMID 25760345

Citations 42

Authors

Yan-Ling Zheng

Li-Ping Zhang

Xue-Liang Zhang

Kai Wang

Yu-Jian Zheng

Affiliations

Soon will be listed here.

Abstract

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.

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References

Li Q, Guo N, Han Z, Zhang Y, Qi S, Xu Y . Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am J Trop Med Hyg. 2012; 87(2):364-70. PMC: 3414578. DOI: 10.4269/ajtmh.2012.11-0472. View

Zhang L, Zheng Y, Wang K, Zhang X, Zheng Y . An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China. Comput Biol Med. 2014; 49:67-73. DOI: 10.1016/j.compbiomed.2014.02.008. View

Earnest A, Chen M, Ng D, Sin L . Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore. BMC Health Serv Res. 2005; 5:36. PMC: 1274243. DOI: 10.1186/1472-6963-5-36. View

Reichert T, Simonsen L, Sharma A, Pardo S, Fedson D, Miller M . Influenza and the winter increase in mortality in the United States, 1959-1999. Am J Epidemiol. 2004; 160(5):492-502. DOI: 10.1093/aje/kwh227. View

Cao S, Wang F, Tam W, Tse L, Kim J, Liu J . A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak. 2013; 13:56. PMC: 3653787. DOI: 10.1186/1472-6947-13-56. View

Wongkoon S, Jaroensutasinee M, Jaroensutasinee K . Development of temporal modeling for prediction of dengue infection in northeastern Thailand. Asian Pac J Trop Med. 2012; 5(3):249-52. DOI: 10.1016/S1995-7645(12)60034-0. View

Nsoesie E, Mekaru S, Ramakrishnan N, Marathe M, Brownstein J . Modeling to predict cases of hantavirus pulmonary syndrome in Chile. PLoS Negl Trop Dis. 2014; 8(4):e2779. PMC: 3998931. DOI: 10.1371/journal.pntd.0002779. View

Gharbi M, Quenel P, Gustave J, Cassadou S, Ruche G, Girdary L . Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect Dis. 2011; 11:166. PMC: 3128053. DOI: 10.1186/1471-2334-11-166. View

Liu Q, Liu X, Jiang B, Yang W . Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infect Dis. 2011; 11:218. PMC: 3169483. DOI: 10.1186/1471-2334-11-218. View

10.

Guo Z, Wang H, Liu Q, Yang J . A feature fusion based forecasting model for financial time series. PLoS One. 2014; 9(6):e101113. PMC: 4074191. DOI: 10.1371/journal.pone.0101113. View

11.

Olsson G, Hjertqvist M, Lundkvist A, Hornfeldt B . Predicting high risk for human hantavirus infections, Sweden. Emerg Infect Dis. 2009; 15(1):104-6. PMC: 2660694. DOI: 10.3201/eid1501.080502. View

12.

Luz P, Mendes B, Codeco C, Struchiner C, Galvani A . Time series analysis of dengue incidence in Rio de Janeiro, Brazil. Am J Trop Med Hyg. 2008; 79(6):933-9. View

13.

Guan P, Huang D, Zhou B . Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol. 2004; 10(24):3579-82. PMC: 4611996. DOI: 10.3748/wjg.v10.i24.3579. View

14.

Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S . Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PLoS One. 2014; 9(6):e98241. PMC: 4043537. DOI: 10.1371/journal.pone.0098241. View

15.

Kuhn L, Davidson L, Durkin M . Use of Poisson regression and time series analysis for detecting changes over time in rates of child injury following a prevention program. Am J Epidemiol. 1994; 140(10):943-55. DOI: 10.1093/oxfordjournals.aje.a117183. View

16.

Yi J, Du C, Wang R, Liu L . [Applications of multiple seasonal autoregressive integrated moving average (ARIMA) model on predictive incidence of tuberculosis]. Zhonghua Yu Fang Yi Xue Za Zhi. 2007; 41(2):118-21. View

17.

Cunha G, Luitgards-Moura J, Naves E, Andrade A, Pereira A, Milagre S . [Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima]. Rev Soc Bras Med Trop. 2010; 43(5):567-70. DOI: 10.1590/s0037-86822010000500019. View

18.

Catalano R, Frank J . Detecting the effect of medical care on mortality. J Clin Epidemiol. 2001; 54(8):830-6. DOI: 10.1016/s0895-4356(01)00348-1. View

19.

Bi P, Wu X, Zhang F, Parton K, Tong S . Seasonal rainfall variability, the incidence of hemorrhagic fever with renal syndrome, and prediction of the disease in low-lying areas of China. Am J Epidemiol. 1998; 148(3):276-81. DOI: 10.1093/oxfordjournals.aje.a009636. View

20.

Gaudart J, Toure O, Dessay N, Dicko A, Ranque S, Forest L . Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali. Malar J. 2009; 8:61. PMC: 2686729. DOI: 10.1186/1475-2875-8-61. View