tetano
Editor, Senior Moderator
BMC Public Health
. 2024 May 25;24(1):1399.
doi: 10.1186/s12889-024-18583-x. Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models
Qingquan Chen[SUP] #[/SUP][SUP] 1 2 [/SUP], Xiaoyan Zheng[SUP] #[/SUP][SUP] 1 2 [/SUP], Huanhuan Shi[SUP] 1 2 [/SUP], Quan Zhou[SUP] 1 2 [/SUP], Haiping Hu[SUP] 1 2 [/SUP], Mengcai Sun[SUP] 1 2 [/SUP], Youqiong Xu[SUP] 3 4 [/SUP], Xiaoyang Zhang[SUP] 5 6 [/SUP]
Affiliations
Background: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions.
Method: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models.
Results: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models.
Conclusion: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.
Keywords: Holt-Winters; Influenza; Prediction model; Prophet; SARIMA; Time series; XGBoost.
. 2024 May 25;24(1):1399.
doi: 10.1186/s12889-024-18583-x. Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models
Qingquan Chen[SUP] #[/SUP][SUP] 1 2 [/SUP], Xiaoyan Zheng[SUP] #[/SUP][SUP] 1 2 [/SUP], Huanhuan Shi[SUP] 1 2 [/SUP], Quan Zhou[SUP] 1 2 [/SUP], Haiping Hu[SUP] 1 2 [/SUP], Mengcai Sun[SUP] 1 2 [/SUP], Youqiong Xu[SUP] 3 4 [/SUP], Xiaoyang Zhang[SUP] 5 6 [/SUP]
Affiliations
- PMID: 38796443
- PMCID: PMC11127308
- DOI: 10.1186/s12889-024-18583-x
Background: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions.
Method: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models.
Results: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models.
Conclusion: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.
Keywords: Holt-Winters; Influenza; Prediction model; Prophet; SARIMA; Time series; XGBoost.