Ma Weicai, Zhang Peng, Zhao Xin, Xue Leyang
School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China.
Physica A. 2022 Feb 15;588:126558. doi: 10.1016/j.physa.2021.126558. Epub 2021 Nov 1.
The outbreak of coronavirus disease 2019 (COVID-19) threatens the health and safety of all humanity. This disease has a prominent feature: the presymptomatic and asymptomatic viral carriers can spread the disease. It is crucial to estimate the impact of this undetected transmission on epidemic outbreaks. Currently, disease-related information has been widely disseminated by the mass media. To investigate the impact of both individuals and mass media information dissemination on the epidemic spreading, we establish a new UAU-SEIR (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Recovered) model with mass media on two-layer multiplex networks. In the model, E-state individuals denote asymptomatic infections, and a single node connecting to all individuals denotes the mass media. In this work, we use the Microscopic Markovian Chain Approach (MMCA) to derive the epidemic threshold. Comparing the MMCA theoretical results with Monte Carlo (MC) simulations, we find that the MMCA has a good consistency with MC simulations. In addition, we also analyze the impact of model parameters on epidemic spreading and epidemic threshold. The results show that reducing the proportion of asymptomatic infections, accelerating the dissemination of information between individuals and the dissemination of information via the mass media can effectively inhibit the epidemic spreading and raise the epidemic threshold.
2019年冠状病毒病（COVID-19）的爆发威胁着全人类的健康与安全。这种疾病有一个突出特点：症状出现前和无症状的病毒携带者能够传播疾病。评估这种未被发现的传播对疫情爆发的影响至关重要。目前，与疾病相关的信息已通过大众媒体广泛传播。为了研究个体和大众媒体信息传播对疫情传播的影响，我们在双层多重网络上建立了一个带有大众媒体的新型UAU-SEIR（未察觉-察觉-未察觉-易感-暴露-感染-康复）模型。在该模型中，E状态个体表示无症状感染，连接到所有个体的单个节点表示大众媒体。在这项工作中，我们使用微观马尔可夫链方法（MMCA）来推导疫情阈值。将MMCA理论结果与蒙特卡罗（MC）模拟进行比较，我们发现MMCA与MC模拟具有良好的一致性。此外，我们还分析了模型参数对疫情传播和疫情阈值的影响。结果表明，降低无症状感染比例、加速个体之间的信息传播以及通过大众媒体传播信息能够有效抑制疫情传播并提高疫情阈值。
