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Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.
Ann Epidemiol. 2015 Feb; 25(2):101-6.AE

Abstract

PURPOSE

Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China.

METHODS

A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012.

RESULTS

The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012.

CONCLUSIONS

This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China.

Authors+Show Affiliations

Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China. Electronic address: xjzhang@seu.edu.cn.Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.Colorado Injury Control Research Center, Department of Psychology, Colorado State University, Fort Collins, CO, USA.Center for Injury Research and Policy, The Research Institute at Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus; Center for Pediatric Trauma Research, The Research Institute at Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

25467006

Citation

Zhang, Xujun, et al. "Forecasting Mortality of Road Traffic Injuries in China Using Seasonal Autoregressive Integrated Moving Average Model." Annals of Epidemiology, vol. 25, no. 2, 2015, pp. 101-6.
Zhang X, Pang Y, Cui M, et al. Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann Epidemiol. 2015;25(2):101-6.
Zhang, X., Pang, Y., Cui, M., Stallones, L., & Xiang, H. (2015). Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Annals of Epidemiology, 25(2), 101-6. https://doi.org/10.1016/j.annepidem.2014.10.015
Zhang X, et al. Forecasting Mortality of Road Traffic Injuries in China Using Seasonal Autoregressive Integrated Moving Average Model. Ann Epidemiol. 2015;25(2):101-6. PubMed PMID: 25467006.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. AU - Zhang,Xujun, AU - Pang,Yuanyuan, AU - Cui,Mengjing, AU - Stallones,Lorann, AU - Xiang,Huiyun, Y1 - 2014/10/31/ PY - 2014/07/23/received PY - 2014/10/20/revised PY - 2014/10/24/accepted PY - 2014/12/4/entrez PY - 2014/12/4/pubmed PY - 2015/10/2/medline KW - China KW - Forecast KW - Mortality KW - Road traffic death KW - SARIMA model SP - 101 EP - 6 JF - Annals of epidemiology JO - Ann Epidemiol VL - 25 IS - 2 N2 - PURPOSE: Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. METHODS: A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. RESULTS: The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. CONCLUSIONS: This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. SN - 1873-2585 UR - https://www.unboundmedicine.com/medline/citation/25467006/Forecasting_mortality_of_road_traffic_injuries_in_China_using_seasonal_autoregressive_integrated_moving_average_model_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1047-2797(14)00457-8 DB - PRIME DP - Unbound Medicine ER -