ArticleBMJ open2015
Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China.
Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Global burden of diseases attributable to childhood sexual abuse and bullying: findings from 1990 to 2019 and predictions to 2035.Social psychiatry and psychiatric epidemiology · 2026Article
- Characteristics and trends of unintentional injuries among children and adolescents in Kunshan, China: a hospital-based retrospective study, 2018-2023.Frontiers in public health · 2025Article
- Comparing the trend of colorectal cancer before and after the implementation of the Population-Based National Cancer Registry in Iran.Journal of preventive medicine and hygiene · 2024Article
- Epidemiological and clinical characteristics of hospitalized unintentional injuries among children in central China from 2017-2023.Frontiers in pediatrics · 2024Article
- Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.Frontiers in public health · 2024Article
- Model prediction of radioactivity levels in the environment and food around the world's first AP 1000 nuclear power unit.Frontiers in public health · 2024Article
- Characteristics of injury mechanisms in children and differences between urban and rural areas in central China.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2023Article
- Forecasting the rate of hand injuries in Singapore.Journal of occupational medicine and toxicology (London, England) · 2022Article
- Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.Epidemiology and infection · 2022Article
- Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022.Infection and drug resistance · 2022Article
- The injury mechanisms and injury pyramids among children and adolescents in Zhuhai City, China.BMC public health · 2021Article
- Forecasting the incidence of mumps in Chongqing based on a SARIMA model.BMC public health · 2021Article
- Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis.BMC health services research · 2020Article
- Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis.Epidemiology and infection · 2020Article
- The association between the seasonality of pediatric pandemic influenza virus outbreak and ambient meteorological factors in Shanghai.Environmental health : a global access science source · 2020Article
- 90-90-90 cascade analysis on reported CLHIV infected by mother-to-child transmission in Guangxi, China: a modeling study.Scientific reports · 2020Article
- Epidemiological characteristics of injury mortality in Guangdong Province, China, 2015.BMC public health · 2019Article
- Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses.Infection and drug resistance · 2019Article
- Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China.Epidemiology and infection · 2019Article
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5 authors.
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Abstract
objectiveInjury is currently an increasing public health problem in China. Reducing the loss due to injuries has become a main priority of public health policies. Early warning of injury mortality based on surveillance information is essential for reducing or controlling the disease burden of injuries. We conducted this study to find the possibility of applying autoregressive integrated moving average (ARIMA) models to predict mortality from injuries in Xiamen.
methodThe monthly mortality data on injuries in Xiamen (1 January 2002 to 31 December 2013) were used to fit the ARIMA model with the conditional least-squares method. The values p, q and d in the ARIMA (p, d, q) model refer to the numbers of autoregressive lags, moving average lags and differences, respectively. The Ljung-Box test was used to measure the 'white noise' and residuals. The mean absolute percentage error (MAPE) between observed and fitted values was used to evaluate the predicted accuracy of the constructed models.
resultsA total of 8274 injury-related deaths in Xiamen were identified during the study period; the average annual mortality rate was 40.99/100,000 persons. Three models, ARIMA (0, 1, 1), ARIMA (4, 1, 0) and ARIMA (1, 1, (2)), passed the parameter (p<0.01) and residual (p>0.05) tests, with MAPE 11.91%, 11.96% and 11.90%, respectively. We chose ARIMA (0, 1, 1) as the optimum model, the MAPE value for which was similar to that of other models but with the fewest parameters. According to the model, there would be 54 persons dying from injuries each month in Xiamen in 2014.
conclusionThe ARIMA (0, 1, 1) model could be applied to predict mortality from injuries in Xiamen.
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