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The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China.
Asia Pac J Public Health. 2016 05; 28(4):336-46.AP

Abstract

The aim of this study is to forecast the incidence of bacillary dysentery with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2014. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast bacillary dysentery incidence in Jiangsu, China. The ARIMA (1, 1, 1) × (1, 1, 2)12 model fitted exactly with the number of cases during January 2004 to December 2014. The fitted model was then used to predict bacillary dysentery incidence during the period January to August 2015, and the number of cases fell within the model's CI for the predicted number of cases during January-August 2015. This study shows that the ARIMA model fits the fluctuations in bacillary dysentery frequency, and it can be used for future forecasting when applied to bacillary dysentery prevention and control.

Authors+Show Affiliations

Jiangnan University, Wuxi, Jiangsu, China.Nanchang Center for Disease Control and Prevention, Jiangxi, China.Jiangnan University, Wuxi, Jiangsu, China.Helie Street Community Health Service Center, Wuxi, Jiangsu, China.Lihu Street Community Health Service Center, Wuxi, Jiangsu, China.Jiangnan University, Wuxi, Jiangsu, China jdhxf168@163.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27106828

Citation

Wang, Kewei, et al. "The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China." Asia-Pacific Journal of Public Health, vol. 28, no. 4, 2016, pp. 336-46.
Wang K, Song W, Li J, et al. The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China. Asia Pac J Public Health. 2016;28(4):336-46.
Wang, K., Song, W., Li, J., Lu, W., Yu, J., & Han, X. (2016). The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China. Asia-Pacific Journal of Public Health, 28(4), 336-46. https://doi.org/10.1177/1010539516645153
Wang K, et al. The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China. Asia Pac J Public Health. 2016;28(4):336-46. PubMed PMID: 27106828.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China. AU - Wang,Kewei, AU - Song,Wentao, AU - Li,Jinping, AU - Lu,Wu, AU - Yu,Jiangang, AU - Han,Xiaofeng, Y1 - 2016/04/22/ PY - 2016/4/24/entrez PY - 2016/4/24/pubmed PY - 2017/5/11/medline KW - ARIMA KW - bacillary dysentery KW - incidence KW - model KW - prediction SP - 336 EP - 46 JF - Asia-Pacific journal of public health JO - Asia Pac J Public Health VL - 28 IS - 4 N2 - The aim of this study is to forecast the incidence of bacillary dysentery with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2014. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast bacillary dysentery incidence in Jiangsu, China. The ARIMA (1, 1, 1) × (1, 1, 2)12 model fitted exactly with the number of cases during January 2004 to December 2014. The fitted model was then used to predict bacillary dysentery incidence during the period January to August 2015, and the number of cases fell within the model's CI for the predicted number of cases during January-August 2015. This study shows that the ARIMA model fits the fluctuations in bacillary dysentery frequency, and it can be used for future forecasting when applied to bacillary dysentery prevention and control. SN - 1941-2479 UR - https://www.unboundmedicine.com/medline/citation/27106828/The_Use_of_an_Autoregressive_Integrated_Moving_Average_Model_for_Prediction_of_the_Incidence_of_Dysentery_in_Jiangsu_China_ L2 - http://journals.sagepub.com/doi/full/10.1177/1010539516645153?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -
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