论文标题
一个极化的时间网络模型，用于研究部分接种人群中复发性流行病的传播
A Polarized Temporal Network Model to Study the Spread of Recurrent Epidemic Diseases in a Partially Vaccinated Population
论文作者
论文摘要
即使在高疫苗吸收的种群中，也发生了大规模的Covid-19，我们提出了一种新型的多人种时间网络模型，用于复发性流行病的传播。我们研究人类行为，测试和疫苗接种运动对控制局部暴发和感染率的影响。我们的建模框架将疫苗在防止传播和严重症状的发展方面的疫苗有效性。此外，该框架解释了对疫苗接种和捕获同质疫苗的两极分化作用，即人们与志趣相投的人互动的趋势。通过平均场方法，我们从分析得出流行阈值。我们的理论结果表明，尽管疫苗接种运动减轻了医院的压力，但它们可能促进复活的暴发，强调测试运动可能在消除该疾病中的关键作用。然后，使用数值模拟来确认并将我们的理论发现扩展到更现实的情况。我们的数值和分析结果一致认为，疫苗接种不足以完全消除，而无需采用大规模的测试运动或依靠人群的责任。此外，我们表明同质性在控制局部暴发的控制中起着至关重要的作用，突出了两极化网络结构的危险。
Motivated by massive outbreaks of COVID-19 that occurred even in populations with high vaccine uptake, we propose a novel multi-population temporal network model for the spread of recurrent epidemic diseases. We study the effect of human behavior, testing, and vaccination campaigns on the control of local outbreaks and infection prevalence. Our modeling framework decouples the vaccine effectiveness in protecting against transmission and the development of severe symptoms. Furthermore, the framework accounts for the polarizing effect of the decision to vaccinate and captures homophily, i.e., the tendency of people to interact with like-minded individuals. By means of a mean-field approach, we analytically derive the epidemic threshold. Our theoretical results suggest that, while vaccination campaigns reduce pressure on hospitals, they might facilitate resurgent outbreaks, highlighting the key role that testing campaigns may have in eradicating the disease. Numerical simulations are then employed to confirm and extend our theoretical findings to more realistic scenarios. Our numerical and analytical results agree that vaccination is not sufficient to achieve full eradication, without employing massive testing campaigns or relying on the population's responsibility. Furthermore, we show that homophily plays a critical role in the control of local outbreaks, highlighting the peril of a polarized network structure.
