Model construction for mortality trend of road traffic injury in Fujian Province

WANG Dengwei; HUANG Shaofen; YIN Yanrong; CHEN Tiehui; ZHONG Wenling

doi:10.16462/j.cnki.zhjbkz.2024.04.010

Volume 28 Issue 4

Apr. 2024

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WANG Dengwei, HUANG Shaofen, YIN Yanrong, CHEN Tiehui, ZHONG Wenling. Model construction for mortality trend of road traffic injury in Fujian Province[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 438-442. doi: 10.16462/j.cnki.zhjbkz.2024.04.010

Citation:

WANG Dengwei, HUANG Shaofen, YIN Yanrong, CHEN Tiehui, ZHONG Wenling. Model construction for mortality trend of road traffic injury in Fujian Province[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 438-442. doi: 10.16462/j.cnki.zhjbkz.2024.04.010

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Model construction for mortality trend of road traffic injury in Fujian Province

doi: 10.16462/j.cnki.zhjbkz.2024.04.010

Department of Chronic and Non-Communicable Disease Prevention and Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China

Funds:

Construction of Fujian Provincial Scientific and Technological Innovation Platform 2019Y2001

Pilot Project of Fujian Provincial Department of Science and Technology 2020Y0060

More Information

Corresponding author: ZHONG Wenling, E-mail: mbzwl@163.com
Received Date: 2023-08-17
Rev Recd Date: 2024-01-31

Available Online: 2024-05-17

Publish Date: 2024-04-10

Abstract

Abstract

Objective To analyze the status of road traffic injury deaths in Fujian province from 2014 to 2021, and to explore the applicable trend prediction model. Methods Seasonal autoregressive integrated moving average (SARIMA), support vector regression(SVR) and long short-term memory (LSTM) network were constructed using road traffic injury deaths data from January 2014 to June 2021 in Fujian province to predict the mortality rate from July to December in 2021, and its prediction effects were evaluated by comparison with the actual value. Results The annual reporting rate of road traffic injuies in Fujian Province showed a decreasing trand from 2014 to 2021 (AAPC=-6.29%, P < 0.001). LSTM network had highest prediction accuracy among three models, with root mean square error (RMSE) of 0.070 5, mean absolute error (MAE) of 0.061 2 and mean absolute percentage error (MAPE) of 8.72%. Conclusions The overall road traffic injury deaths in Fujian province showed a downward trend. LSTM network can be used to predict the short-term trend of road traffic injury deaths.
- Road traffic injury,
- Mortality,
- Trend,
- Seasonal autoregressive integrated moving average,
- Support vector regression,
- Long short-term memory network

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References(15)

References

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