University of Maine, Orono, ME, USA.
University of Illinois, Urbana-Champaign, IL, USA.
Math Biosci. 2023 May;359:108996. doi: 10.1016/j.mbs.2023.108996. Epub 2023 Mar 30.
Predicting and preparing for the trajectory of disease epidemics relies on a knowledge of environmental and socioeconomic factors that affect transmission rates on local and global spatial scales. This article discusses the simulation of epidemic outbreaks on human metapopulation networks with community structure, such as cities within national boundaries, for which infection rates vary both within and between communities. We demonstrate mathematically, through next-generation matrices, that the structures of these communities, setting aside all other considerations such as disease virulence and human decision-making, have a profound effect on the reproduction rate of the disease throughout the network. In high modularity networks, with high levels of separation between neighboring communities, disease epidemics tend to spread rapidly in high-risk communities and very slowly in others, whereas in low modularity networks, the epidemic spreads throughout the entire network as a steady pace, with little regard for variations in infection rate. The correlation between network modularity and effective reproduction number is stronger in population with high rates of human movement. This implies that the community structure, human diffusion rate, and disease reproduction number are all intertwined, and the relationships between them can be affected by mitigation strategies such as restricting movement between and within high-risk communities. We then test through numerical simulation the effectiveness of movement restriction and vaccination strategies in reducing the peak prevalence and spread area of outbreaks. Our results show that the effectiveness of these strategies depends on the structure of the network and the properties of the disease. For example, vaccination strategies are most effective in networks with high rates of diffusion, whereas movement restriction strategies are most effective in networks with high modularity and high infection rates. Finally, we offer guidance to epidemic modelers as to the ideal spatial resolution to balance accuracy and data collection costs.
预测和准备疾病疫情的轨迹依赖于对环境和社会经济因素的了解，这些因素影响着本地和全球空间尺度上的传播速度。本文讨论了在具有社区结构的人类元种群网络上模拟传染病爆发的问题，这些社区结构如国界内的城市，其感染率在社区内部和社区之间都有所不同。我们通过下一代矩阵从数学上证明了，这些社区的结构（不考虑疾病的毒力和人类决策等所有其他因素）对整个网络中疾病的繁殖率有深远的影响。在高模块性网络中，相邻社区之间的隔离程度较高，疾病疫情往往在高风险社区迅速传播，而在低模块性网络中，疫情则以稳定的速度在整个网络中传播，而不考虑感染率的变化。在人口流动率较高的情况下，网络模块性与有效繁殖数之间的相关性更强。这意味着社区结构、人类扩散率和疾病繁殖数是相互交织的，它们之间的关系可以通过限制高危社区之间和内部的流动等缓解策略来影响。然后，我们通过数值模拟测试了限制流动和接种疫苗策略在降低疫情峰值流行率和传播范围方面的有效性。我们的结果表明，这些策略的有效性取决于网络的结构和疾病的特性。例如，在扩散率较高的网络中，接种疫苗策略最为有效，而在模块性较高和感染率较高的网络中，限制流动策略最为有效。最后，我们为传染病模型制作者提供了关于平衡准确性和数据收集成本的理想空间分辨率的指导。
