Research Center of Network Public Opinion Governance, China People's Police University, Langfang, China.
Front Public Health. 2023 Jan 30;11:1018378. doi: 10.3389/fpubh.2023.1018378. eCollection 2023.
This research focuses on the research problem of eliminating COVID-19 vaccine hesitancy through web search. A dynamic model of eliminating COVID-19 vaccine hesitancy through web search is constructed based on the Logistic model, the elimination degree is quantified, the elimination function is defined to analyze the dynamic elimination effect, and the model parameter estimation method is proposed. The numerical solution, process parameters, initial value parameters and stationary point parameters of the model are simulated, respectively, and the mechanism of elimination is deeply analyzed to determine the key time period. Based on the real data of web search and COVID-19 vaccination, data modeling is carried out from two aspects: full sample and segmented sample, and the rationality of the model is verified. On this basis, the model is used to carry out dynamic prediction and verified to have certain medium-term prediction ability. Through this research, the methods of eliminating vaccine hesitancy are enriched, and a new practical idea is provided for eliminating vaccine hesitancy. It also provides a method to predict the quantity of COVID-19 vaccination, provides theoretical guidance for dynamically adjusting the public health policy of the COVID-19, and can provide reference for the vaccination of other vaccines.
本研究聚焦于通过网络搜索消除 COVID-19 疫苗犹豫这一研究问题。基于 Logistic 模型构建了通过网络搜索消除 COVID-19 疫苗犹豫的动态模型，量化了消除程度，定义了消除函数来分析动态消除效果，并提出了模型参数估计方法。模拟了模型的数值解、过程参数、初始值参数和稳定点参数，深入分析了消除机制，确定了关键时期。基于网络搜索和 COVID-19 疫苗接种的真实数据，从全样本和分段样本两个方面进行数据建模，并验证了模型的合理性。在此基础上，利用模型进行动态预测，并验证其具有一定的中期预测能力。通过本研究，丰富了消除疫苗犹豫的方法，为消除疫苗犹豫提供了新的实用思路。它还提供了一种预测 COVID-19 疫苗接种数量的方法，为动态调整 COVID-19 公共卫生政策提供了理论指导，并可为其他疫苗的接种提供参考。
