SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data

JIN Xin; YU Jingbo; CUI Shuangshuang; WANG Yan; YU Hao

doi:10.16462/j.cnki.zhjbkz.2024.09.013

Volume 28 Issue 9

Sep. 2024

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JIN Xin, YU Jingbo, CUI Shuangshuang, WANG Yan, YU Hao. SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1075-1082. doi: 10.16462/j.cnki.zhjbkz.2024.09.013

Citation:

JIN Xin, YU Jingbo, CUI Shuangshuang, WANG Yan, YU Hao. SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1075-1082. doi: 10.16462/j.cnki.zhjbkz.2024.09.013

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SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data

doi: 10.16462/j.cnki.zhjbkz.2024.09.013

JIN Xin^{1, 2},
YU Jingbo^{3
,
,},
CUI Shuangshuang³,
WANG Yan³,
YU Hao^{4
,
,}

1.
School of Medicine, Tianjin University, Tianjin 300072, China
2.
Health Department of Tianjin Municipal Health Commission, Tianjin 300041, China
3.
Institute of Orthopedic Research, Tianjin Hospital, Tianjin 300202, China
4.
Institute of Business and Quality Control, Tianjin Center for Disease Control and Prevention, Tianjin 300171, China

Funds:

The Scientific and Technological Project of Tianjin Health TJWI2022MS046

More Information

Corresponding author: YU Jingbo, E-mail: yujingbo2333@163.com; YU Hao, E-mail: tjcdc_yuhao@163.com
Received Date: 2024-06-18
Rev Recd Date: 2024-08-21

Available Online: 2024-10-24

Publish Date: 2024-09-10

Abstract

Abstract

Objective To develop a prediction model for the epidemic trend of influenza-like illness using monitoring data from Tianjin City and quantitatively evaluate the impact of epidemic prevention and control measures on the medical burden caused by influenza-like illness. Methods The data from November 6, 2023 to November 15, 2023 were used for fitting the SEIHRS_gv model, and the data from November 15, 2023 to March 31, 2024 were using for validating. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination r-square (R²) were used to evaluate the predictive ability of the model. Results The SEIHRS_gv model could predict the trend, peak, and crucial point of influenza-like illness epidemics. Using 10 days of data for prediction, with an R² of 0.85 and an RMSE of 949.5. Increasing the intensity of epidemic prevention and control measures could reduce the number of patients seeking medical treatment. Conclusions The SEIHRS_gv model required a few days of data for prediction in this round of influenza-like illness epidemic prediction and had high accuracy in prediction results, which could serve as an efficient predictive model to evaluate the pressure of hospital visits and guide the implementation intensity of epidemic control measures.
- Influenza-like illness,
- SEIR model,
- Epidemic prevention and control

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References(32)

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