Trend prediction of rubella incidence based on ARIMA model and Holt-Winters multiplicative model

Zhiqian Zhu

doi:10.1117/12.3012918

8 January 2024 Trend prediction of rubella incidence based on ARIMA model and Holt-Winters multiplicative model

Zhiqian Zhu

Proceedings Volume 12924, Third International Conference on Biological Engineering and Medical Science (ICBioMed2023); 129242V (2024) https://doi.org/10.1117/12.3012918
Event: 3rd International Conference on Biological Engineering and Medical Science (ICBioMed2023), 2023, ONLINE, United Kingdom

Abstract

This paper predicts the trend of rubella incidence in China using the ARIMA model and the Holt-Winters multiplicative model. By collecting monthly rubella incidence data from January 2012 to December 2018, with the data from January 2012 to December 2017 as the training set, we fit the time series models of rubella incidence using both models. The data from January 2018 to December 2018 is used as the test set to compare the predictive performance of the two models. Based on the characteristics of the data, this study constructs an ARIMA(2,1,0)(0,1,0)₁₂ model and a Holt-Winters multiplicative model. By observing the Symmetric Mean Absolute Percentage Error (SMAPE), it is found that the Holt-Winters exponential smoothing model outperforms the ARIMA model and is more suitable for short-term prediction of rubella incidence in China. This model contributes to a deeper understanding of the epidemic trend of rubella in China and provides reliable prediction information for disease control authorities to formulate corresponding prevention and control strategies and public health measures.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiqian Zhu "Trend prediction of rubella incidence based on ARIMA model and Holt-Winters multiplicative model", Proc. SPIE 12924, Third International Conference on Biological Engineering and Medical Science (ICBioMed2023), 129242V (8 January 2024); https://doi.org/10.1117/12.3012918

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