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Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.
J Infect Public Health. 2018 Sep - Oct; 11(5):707-712.JI

Abstract

OBJECTIVES

The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources.

METHODS

Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box-Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months.

RESULTS

During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1)12, which demonstrated adequate information extraction (white noise test, p>0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively.

CONCLUSIONS

According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics.

Authors+Show Affiliations

Institute of Occupational Health and Environmental Hygiene, School of Public Health, Lanzhou University, Lanzhou 730000, PR China. Electronic address: xingzhebutui@163.com.Institute of Occupational Health and Environmental Hygiene, School of Public Health, Lanzhou University, Lanzhou 730000, PR China.Institute of Social Medical and Health Management, School of Public Health, Lanzhou University, Lanzhou 730000, PR China.Institute of Occupational Health and Environmental Hygiene, School of Public Health, Lanzhou University, Lanzhou 730000, PR China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29730253

Citation

Mao, Qiang, et al. "Forecasting the Incidence of Tuberculosis in China Using the Seasonal Auto-regressive Integrated Moving Average (SARIMA) Model." Journal of Infection and Public Health, vol. 11, no. 5, 2018, pp. 707-712.
Mao Q, Zhang K, Yan W, et al. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Public Health. 2018;11(5):707-712.
Mao, Q., Zhang, K., Yan, W., & Cheng, C. (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public Health, 11(5), 707-712. https://doi.org/10.1016/j.jiph.2018.04.009
Mao Q, et al. Forecasting the Incidence of Tuberculosis in China Using the Seasonal Auto-regressive Integrated Moving Average (SARIMA) Model. J Infect Public Health. 2018 Sep - Oct;11(5):707-712. PubMed PMID: 29730253.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. AU - Mao,Qiang, AU - Zhang,Kai, AU - Yan,Wu, AU - Cheng,Chaonan, Y1 - 2018/05/03/ PY - 2017/09/02/received PY - 2018/03/23/revised PY - 2018/04/08/accepted PY - 2018/5/8/pubmed PY - 2018/11/7/medline PY - 2018/5/7/entrez KW - China KW - Forecasting KW - SARIMA KW - Tuberculosis SP - 707 EP - 712 JF - Journal of infection and public health JO - J Infect Public Health VL - 11 IS - 5 N2 - OBJECTIVES: The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources. METHODS: Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box-Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months. RESULTS: During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1)12, which demonstrated adequate information extraction (white noise test, p>0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively. CONCLUSIONS: According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics. SN - 1876-035X UR - https://www.unboundmedicine.com/medline/citation/29730253/Forecasting_the_incidence_of_tuberculosis_in_China_using_the_seasonal_auto_regressive_integrated_moving_average__SARIMA__model_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1876-0341(18)30045-5 DB - PRIME DP - Unbound Medicine ER -