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网络社区中流行病的通用模型与疫苗接种的作用。

A generic model for pandemics in networks of communities and the role of vaccination.

机构信息

Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.

Department of Mathematics, Yazd University, Yazd 89195-741, Iran.

出版信息

Chaos. 2022 Jun;32(6):063127. doi: 10.1063/5.0082002.

DOI:10.1063/5.0082002
PMID:35778153
Abstract

The slogan "nobody is safe until everybody is safe" is a dictum to raise awareness that in an interconnected world, pandemics, such as COVID-19, require a global approach. Motivated by the ongoing COVID-19 pandemic, we model here the spread of a virus in interconnected communities and explore different vaccination scenarios, assuming that the efficacy of the vaccination wanes over time. We start with susceptible populations and consider a susceptible-vaccinated-infected-recovered model with unvaccinated ("Bronze"), moderately vaccinated ("Silver"), and very-well-vaccinated ("Gold") communities, connected through different types of networks via a diffusive linear coupling for local spreading. We show that when considering interactions in "Bronze"-"Gold" and "Bronze"-"Silver" communities, the "Bronze" community is driving an increase in infections in the "Silver" and "Gold" communities. This shows a detrimental, unidirectional effect of non-vaccinated to vaccinated communities. Regarding the interactions between "Gold," "Silver," and "Bronze" communities in a network, we find that two factors play a central role: the coupling strength in the dynamics and network density. When considering the spread of a virus in Barabási-Albert networks, infections in "Silver" and "Gold" communities are lower than in "Bronze" communities. We find that the "Gold" communities are the best in keeping their infection levels low. However, a small number of "Bronze" communities are enough to give rise to an increase in infections in moderately and well-vaccinated communities. When studying the spread of a virus in dense Erdős-Rényi and sparse Watts-Strogatz and Barabási-Albert networks, the communities reach the disease-free state in the dense Erdős-Rényi networks, but not in the sparse Watts-Strogatz and Barabási-Albert networks. However, we also find that if all these networks are dense enough, all types of communities reach the disease-free state. We conclude that the presence of a few unvaccinated or partially vaccinated communities in a network can increase significantly the rate of infected population in other communities. This reveals the necessity of a global effort to facilitate access to vaccines for all communities.

摘要

“没有人是安全的,直到每个人都是安全的”是一句口号,旨在提高人们的认识,即在一个相互关联的世界中,像 COVID-19 这样的大流行病需要采取全球方法。受持续的 COVID-19 大流行的启发,我们在这里对相互关联的社区中的病毒传播进行建模,并探索了不同的疫苗接种方案,假设疫苗的效力会随着时间的推移而减弱。我们从易感人群开始,考虑了一个具有未接种疫苗(“青铜”)、中度接种疫苗(“银”)和高度接种疫苗(“金”)社区的易感染-接种-感染-康复模型,通过不同类型的网络通过局部扩散的扩散线性耦合连接。我们表明,当考虑“青铜”-“金”和“青铜”-“银”社区中的相互作用时,“青铜”社区会导致“银”和“金”社区中的感染增加。这表明非接种社区对接种社区具有有害的单向影响。关于网络中“金”、“银”和“青铜”社区之间的相互作用,我们发现有两个因素起着核心作用:动力学中的耦合强度和网络密度。当考虑在 Barabási-Albert 网络中传播病毒时,“银”和“金”社区的感染率低于“青铜”社区。我们发现“金”社区在保持低感染水平方面表现最佳。然而,少数“青铜”社区足以导致中度和高度接种社区的感染增加。在研究在密集的 Erdos-Rényi 和稀疏的 Watts-Strogatz 和 Barabási-Albert 网络中传播病毒时,密集的 Erdos-Rényi 网络中的社区达到了无病状态,但稀疏的 Watts-Strogatz 和 Barabási-Albert 网络中则没有。然而,我们还发现,如果所有这些网络都足够密集,那么所有类型的社区都将达到无病状态。我们得出的结论是,网络中存在少数未接种或部分接种的社区会大大增加其他社区中感染人群的比例。这揭示了全球努力为所有社区提供疫苗接种的必要性。

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