DOI: 10.57760/sciencedb.j00253.00755
title: Prediction of human papillomavirus vaccination volume in Shaanxi Province in 2025 based on Autoregressive moving average model
title: 基于自回归移动平均模型的陕西省2025年人乳头瘤病毒疫苗接种量预测

description: Objective The vaccination volume of human papillomavirus vaccine in Shaanxi Province was predicted by using the grey model, autoregressive moving average model, long short-term memory recurrent neural network and gated recurrent unit neural network.Method The vaccination doses of human papillomavirus vaccine in Shaanxi Province from January 2021 to December 2024 were collected. Descriptive epidemiology was used to analyze the temporal distribution of historical vaccination doses, and R software was used to fit the historical vaccination doses through four models to determine the optimal model. And predict the vaccination volume of human papillomavirus vaccine in Shaanxi Province from January 2025 to December 2025.Results From 2021 to 2023, the vaccination volumes of human papillomavirus vaccine among people of different age groups and the overall vaccination volume in Shaanxi Province showed an increasing trend year by year. In 2024, the vaccination volumes of vaccine among people of different age groups and the overall vaccination volume showed a decreasing trend. The autoregressive moving average model fitted well. The model predicts that the vaccination volume of human papillomavirus vaccine in Shaanxi Province will continue to decline from January 2025 to December 2025, among which the female population aged 15-19 will become the main vaccination group of human papillomavirus vaccine.Conclusion Using the Autoregressive moving average model to predict the vaccination amount of human papillomavirus vaccine in Shaanxi province has good feasibility, and provides theoretical support for rational formulation of vaccine procurement plan.
description: Objective The vaccination volume of human papillomavirus vaccine in Shaanxi Province was predicted by using the grey model, autoregressive moving average model, long short-term memory recurrent neural network and gated recurrent unit neural network.Method The vaccination doses of human papillomavirus vaccine in Shaanxi Province from January 2021 to December 2024 were collected. Descriptive epidemiology was used to analyze the temporal distribution of historical vaccination doses, and R software was used to fit the historical vaccination doses through four models to determine the optimal model. And predict the vaccination volume of human papillomavirus vaccine in Shaanxi Province from January 2025 to December 2025.Results From 2021 to 2023, the vaccination volumes of human papillomavirus vaccine among people of different age groups and the overall vaccination volume in Shaanxi Province showed an increasing trend year by year. In 2024, the vaccination volumes of vaccine among people of different age groups and the overall vaccination volume showed a decreasing trend. The autoregressive moving average model fitted well. The model predicts that the vaccination volume of human papillomavirus vaccine in Shaanxi Province will continue to decline from January 2025 to December 2025, among which the female population aged 15-19 will become the main vaccination group of human papillomavirus vaccine.Conclusion Using the Autoregressive moving average model to predict the vaccination amount of human papillomavirus vaccine in Shaanxi province has good feasibility, and provides theoretical support for rational formulation of vaccine procurement plan.