The spatio-temporal analysis and prediction model comparison of incidence rate of other infectious diarrhea diseases in Qinghai Province from 2017 to 2023

JIANG Yuqi; LONG Jiang; ZHAO Jinhua; ZHANG Huayi; DENG Ping; JIANG Wenqi

doi:10.16462/j.cnki.zhjbkz.2024.11.010

Volume 28 Issue 11

Nov. 2024

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2024 > 28(11): 1301-1307

JIANG Yuqi, LONG Jiang, ZHAO Jinhua, ZHANG Huayi, DENG Ping, JIANG Wenqi. The spatio-temporal analysis and prediction model comparison of incidence rate of other infectious diarrhea diseases in Qinghai Province from 2017 to 2023[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(11): 1301-1307. doi: 10.16462/j.cnki.zhjbkz.2024.11.010

Citation:

JIANG Yuqi, LONG Jiang, ZHAO Jinhua, ZHANG Huayi, DENG Ping, JIANG Wenqi. The spatio-temporal analysis and prediction model comparison of incidence rate of other infectious diarrhea diseases in Qinghai Province from 2017 to 2023[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(11): 1301-1307. doi: 10.16462/j.cnki.zhjbkz.2024.11.010

Citation:

JIANG Yuqi, LONG Jiang, ZHAO Jinhua, ZHANG Huayi, DENG Ping, JIANG Wenqi. The spatio-temporal analysis and prediction model comparison of incidence rate of other infectious diarrhea diseases in Qinghai Province from 2017 to 2023[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(11): 1301-1307. doi: 10.16462/j.cnki.zhjbkz.2024.11.010

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The spatio-temporal analysis and prediction model comparison of incidence rate of other infectious diarrhea diseases in Qinghai Province from 2017 to 2023

doi: 10.16462/j.cnki.zhjbkz.2024.11.010

JIANG Yuqi¹,
LONG Jiang^{2
,
,},
ZHAO Jinhua^{1, 3
,
,},
ZHANG Huayi³,
DENG Ping¹,
JIANG Wenqi¹

1.
Department of Public Health, Qinghai University Medical College, Xining 810001, China
2.
Institute for Communicable Disease Control and Prevention, Chongqing Center for Disease Control and Prevention, Chongqing 400707, China
3.
Institute for Communicable Disease Control and Prevention, Qinghai Center for Disease Control and Prevention, Xining 810007, China

Funds:

National Natural Science Foundation of China Project 12371503

"Kunlun Talents-Plateau Famous Doctors" Project Qing Health Offic [2021] No.104

More Information

Corresponding author: LONG Jiang, E-mail: 68803648@163.com; ZHAO Jinhua, E-mail: 99801973@qq.com
Received Date: 2024-06-11
Rev Recd Date: 2024-09-05

Available Online: 2024-12-23

Publish Date: 2024-11-10

Abstract

Abstract

Objective To analyze the epidemiological trends and characteristics of other infectious diarrheal diseases(OIDD) in Qinghai Province, and to provide predictions for these diseases in Qinghai Province for 2024. Methods Using monthly and annual incidence rates of OIDD in Qinghai Province from January 2017 to December 2023 as primary data, the study employed ArcGIS 10.8 software for map visualization of annual incidence rates in Qinghai Province, and GeoDa 1.16 software for spatial autocorrelation analysis. R 4.3.1 software was used to construct various models for OIDD in Qinghai Province, including seasonal autoregressive integrated moving average (SARIMA) model, triple exponential smoothing (Holt-Winters) model, neural network autoregression (NNAR) model, trigonometric seasonality, Box-Cox transformation (TBATS) model, and Prophet model. The models′ fitting effects were evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results All models, except the Holt-Winters model, effectively captured the incidence rate trends. Among them, the NNAR model performed best in the training set, with MAE of 0.90, RMSE of 1.25, and MAPE of 16.43, outperforming models such as TBATS. In the test set, while its RMSE value was higher than those of the SARIMA and TBATS models, its MAE and MAPE values were lower than other models, indicating the best overall predictive performance. Therefore, the NNAR model can be used to forecast the incidence rate of OIDD in Qinghai Province for 2024, providing insights for disease prevention strategies in high-altitude regions. Conclusions From 2017 to 2023, Xining City, Haidong City, and Huangnan Tibetan Autonomous Prefecture in Qinghai Province were high-incidence areas for OIDD. Among the predictive models, the NNAR model showed the best performance. However, in practical applications, it is necessary to develop corresponding prevention and control measures by considering the spatiotemporal characteristics and epidemic trends of each region.

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References(17)

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