Analyzing Epidemic Thresholds on Dynamic Network Structures
Analyzing Epidemic Thresholds on Dynamic Network Structures
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DOI：
10.1137/20s1368227
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Keegan Kresge
中科院分区：
文献类型：
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作者：
Keegan Kresge
美国和世界许多地区的 COVID-19 疫情显示出每日新增病例数从指数增长到线性增长的意外转变。配置模型网络上的流行病通常会产生指数增长，而格子上的流行病会产生线性增长。我们探索了一种基于网络的流行病模型，该模型在格状模型网络和配置模型网络之间进行插值，同时保持度分布和基本再生数（R0）恒定。该模型从在单位正方形中分配随机位置的节点开始，并连接到它们最近的邻居。在配置模型子网络中，比例 p 的边被断开并重新连接。随着 p 的增加，我们观察到从线性增长到指数增长的转变。现实的人类接触网络涉及大量本地交互和较少的远距离交互，因此社交距离既影响有效繁殖数Rt，又影响网络中远距离连接的比例。虽然 Rt 变化的影响已广为人知，但对网络结构更微妙变化的影响却知之甚少。我们的分析表明，即使只有一小部分重新配置的边缘，线性增长和指数增长之间的阈值也可能出现。此外，即使 R0 保持不变，流行病中的总感染人数也会在该阈值附近大幅增加。这项研究表明，实施和放松社交距离限制可能会对流行病动态产生比之前想象的更加复杂和戏剧性的影响。
COVID-19 epidemics in many parts of the United States and the world have shown unexpected shifts from exponential to linear growth in the number of daily new cases. Epidemics on configuration model networks typically produce exponential growth, while epidemics on lattices produce linear growth. We explore a network-based epidemic model that interpolates between lattice-like and configuration model networks while keeping the degree distribution and basic reproduction number (R0) constant. This model starts with nodes assigned random locations in a unit square and connected to their nearest neighbors. A proportion p of the edges are disconnected and reconnected in a configuration model subnetwork. As p increases, we observe a shift from linear to exponential growth. Realistic human contact networks involve many local interactions and fewer long-distance interactions, so social distancing affects both the effective reproduction number Rt and the proportion of long-distance connections in the network. While the impact of changes in Rt is well-understood, far less is understood about the effect of more subtle changes in network structure. Our analysis indicates that the threshold between linear and exponential growth may occur even with a small percentage of reconfigured edges. Additionally, the number of total infected individuals in an epidemic substantially increases around this threshold even when R0 remains constant. This study reveals that implementing and relaxing social distancing restrictions can have more complex and dramatic effects on epidemic dynamics than previously thought.