He Yongzhang, Ran Lingshi, Wang Yang, Huang Fengxiang, Xia Yixue
Research Center of Network Public Opinion Governance, China People's Police University, Langfang, China.
Front Public Health. 2025 Apr 4;13:1435513. doi: 10.3389/fpubh.2025.1435513. eCollection 2025.
This study uses a non-linear model to explore the impact mechanism of change rates between internet search behavior and confirmed COVID-19 cases. The research background focuses on epidemic monitoring, leveraging internet search data as a real-time tool to capture public interest and predict epidemic development. The goal is to establish a widely applicable mathematical framework through the analysis of long-term disease data.
Data were sourced from the Baidu Index for COVID-19-related search behavior and confirmed COVID-19 case data from the National Health Commission of China. A logistic-based non-linear differential equation model was employed to analyze the mutual influence mechanism between confirmed case numbers and the rate of change in search behavior. Structural and operator relationships between variables were determined through segmented data fitting and regression analysis.
The results indicated a significant non-linear correlation between search behavior and confirmed COVID-19 cases. The non-linear differential equation model constructed in this study successfully passed both structural and correlation tests, with dynamic data fitting showing a high degree of consistency. The study further quantified the mutual influence between search behavior and confirmed cases, revealing a strong feedback loop between the two: changes in search behavior significantly drove the growth of confirmed cases, while the increase in confirmed cases also stimulated the public's search behavior. This finding suggests that search behavior not only reflects the development trend of the epidemic but can also serve as an effective indicator for predicting the evolution of the pandemic.
This study enriches the understanding of epidemic transmission mechanisms by quantifying the dynamic interaction between public search behavior and epidemic spread. Compared to simple prediction models, this study focuses more on stable common mechanisms and structural analysis, laying a foundation for future research on public health events.
本研究使用非线性模型来探索互联网搜索行为与确诊的 COVID-19 病例之间变化率的影响机制。研究背景聚焦于疫情监测，利用互联网搜索数据作为实时工具来捕捉公众关注并预测疫情发展。目标是通过对长期疾病数据的分析建立一个广泛适用的数学框架。
数据来源于百度指数中与 COVID-19 相关的搜索行为以及中国国家卫生健康委员会的确诊 COVID-19 病例数据。采用基于逻辑斯蒂的非线性微分方程模型来分析确诊病例数与搜索行为变化率之间的相互影响机制。通过分段数据拟合和回归分析确定变量之间的结构和算子关系。
结果表明搜索行为与确诊的 COVID-19 病例之间存在显著的非线性相关性。本研究构建的非线性微分方程模型成功通过了结构和相关性检验，动态数据拟合显示出高度一致性。该研究进一步量化了搜索行为与确诊病例之间的相互影响，揭示了两者之间存在强烈的反馈回路：搜索行为的变化显著推动了确诊病例的增长，而确诊病例的增加也刺激了公众的搜索行为。这一发现表明搜索行为不仅反映了疫情的发展趋势，还可以作为预测疫情演变的有效指标。
本研究通过量化公众搜索行为与疫情传播之间的动态相互作用，丰富了对疫情传播机制的理解。与简单的预测模型相比，本研究更侧重于稳定的共同机制和结构分析，为未来公共卫生事件的研究奠定了基础。
