Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 11, 17 November 2023
Open
Access | Article
Influenza incidence trend prediction based on ARIMA seasonal multiplicative model
* Author to whom correspondence should be addressed.
Abstract
This study utilizes a time series ARIMA seasonal multiplicative model to predict the incidence trend of influenza in China, providing valuable early warning references for influenza prevention and control. Monthly incidence data of influenza cases nationwide were collected from January 2014 to August 2022. The data from January 2014 to December 2020 were used as the training set to fit the time series model of influenza incidence. The data from January 2021 to August 2022 were utilized as the testing set to predict the influenza incidence from January 2021 to August 2022 using the fitted model. The predicted values were then compared with the testing set. Through residual white noise testing, significance testing of parameters, and examination of model fit, the final model was determined as , which demonstrated a good fit. The majority of actual data fell within the 95% confidence interval of the predicted values, and the predicted incidence trend aligned closely with the actual trend. The constructed model holds significant application value in early warning systems for influenza prevention and control, providing crucial insights for public health strategies. In practical applications, this model can be integrated with various factors such as social, natural, and geographic environments to formulate targeted prevention and control strategies, thus enhancing the efficiency and effectiveness of influenza prevention and control measures.
Keywords
influenza, ARIMA seasonal multiplicative model, trend prediction.
References
1. Historical chronicle - Influenza pandemic in the 20th century. (2009). Disease Surveillance, 24(12), 941.
2. Lv, X. Q., Zhang, L., Di, H. R., et al. (2018). Overview of research progress on influenza. World Chinese Medicine, 13(02), 278-282.
3. Liang, J. G., Feng, Y. L., Dong, L., et al. (2023). Application of ARIMA seasonal model in tuberculosis prediction in Shanxi Province. Disease Surveillance, 38(03), 332-338.
4. Yang, Y., & Zhang, Q. (2023). Application of ARIMA model in forecasting the incidence trend of varicella. Health Education and Health Promotion, 18(02), 149-152. DOI: 10.16117/j.cnki.31-1974/r.202302149.
5. Xu, L., & Li, X. Y. (2023). Establishment and prediction of ARIMA seasonal model for other infectious diarrheal diseases in Shangcheng District. Practical Preventive Medicine, 30(01), 111-115.
6. Dai, H. Y., Zhou, N., Ren, X., et al. (2022). Characteristics and trend prediction of various subtypes of influenza based on autoregressive moving average model. Disease Surveillance, 37(10), 1338-1345.
7. Shi, J. X., Zhang, W. Z., Ji, G. Q., et al. (2010). Application of ARIMA model in predicting and early warning of influenza-like cases. Capital Public Health, 4(01), 12-16. DOI: 10.16760/j.cnki.sdggws.2010.01.010.
8. Li, Y. T., Zhang, H. W., Ren, H., et al. (2007). Time series analysis and prediction model of influenza-like cases in Shanghai. Chinese Journal of Preventive Medicine, 41(06), 496-498.
9. Ou, C. Q., Deng, Z. H., Yang, L., et al. (2007). Predicting the variation trend of influenza-like cases using autoregressive models. Chinese Journal of Health Statistics, (06), 569-571.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-133-9
- ISBN (Online)
- 978-1-83558-134-6
- Published Date
- 17 November 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/11/20230386
- Copyright
- 17 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited