Wang Yan, Qing Feng, Li Haozhan, Wang Xuteng
State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China.
School of Data Science and Media Intelligence Communication University of China Beijing China.
Math Methods Appl Sci. 2022 Sep 23. doi: 10.1002/mma.8732.
For all humanity, the sudden outbreak of Corona Virus Disease 2019 has been an important problem. Timely and effective media coverage is considered to be one of the effective approaches to control the spread of epidemic in early stage. In this paper, a Sentiment-enabled Susceptible-Exposed-Infected-Recovered (SEIR) model is established to reveal the relationship between the propagation of the epidemic and media coverage. The authors take the positive and negative media coverage into consideration when implementing the Sentiment-enabled SEIR model. This model is constructed by parameterizing the number of current confirmed cases, cumulative cured cases, cumulative deaths, and media coverage. The numerical simulation and sensitivity analysis are conducted based on the Sentiment-enabled SEIR model. The numerical analysis confirms the rationality of the Sentiment-enabled SEIR model. The sensitivity analysis shows that positive media coverage acts a pivotal part in reducing the figure for confirmed cases. Negative media coverage has an effect on the figure for confirmed cases is not as significant as that of positive media coverage, but it is not negligible.
对全人类而言，2019冠状病毒病的突然爆发是一个重大问题。及时有效的媒体报道被认为是在早期控制疫情传播的有效途径之一。本文建立了一个考虑舆情的易感-暴露-感染-康复（SEIR）模型，以揭示疫情传播与媒体报道之间的关系。作者在实施考虑舆情的SEIR模型时考虑了媒体报道的正负性。该模型通过对当前确诊病例数、累计治愈病例数、累计死亡数和媒体报道进行参数化构建。基于考虑舆情的SEIR模型进行了数值模拟和敏感性分析。数值分析证实了考虑舆情的SEIR模型的合理性。敏感性分析表明，正面媒体报道在降低确诊病例数方面起着关键作用。负面媒体报道对确诊病例数的影响不如正面媒体报道显著，但也不可忽视。
