Department of Mathematics, Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu 41566, Republic of Korea.
School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China.
Chaos. 2024 May 1;34(5). doi: 10.1063/5.0204497.
The metapopulation network model is a mathematical framework used to study the spatial spread of epidemics with individuals' mobility. In this paper, we develop a time-varying network model in which the activity of a population is correlated with its attractiveness in mobility. By studying the spreading dynamics of the SIR (susceptible-infectious-recovered)-type disease in different correlated networks based on the proposed model, we theoretically derive the mobility threshold and numerically observe that increasing the correction between activity and attractiveness results in a reduced mobility threshold but suppresses the fraction of infected subpopulations. It also introduces greater heterogeneity in the spatial distribution of infected individuals. Additionally, we investigate the impact of nonpharmaceutical interventions on the spread of epidemics in different correlation networks. Our results show that the simultaneous implementation of self-isolation and self-protection is more effective in negatively correlated networks than that in positively correlated or non-correlated networks. Both self-isolation and self-protection strategies enhance the mobility threshold and, thus, slow down the spread of the epidemic. However, the effectiveness of each strategy in reducing the fraction of infected subpopulations varies in different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. Our study will provide insights into epidemic prevention and control in large-scale time-varying metapopulation networks.
复群网络模型是一种用于研究具有个体流动性的传染病空间传播的数学框架。在本文中，我们开发了一个时变网络模型，其中种群的活动与其在流动性中的吸引力相关联。通过基于所提出的模型研究不同相关网络中 SIR（易感染-感染-恢复）型疾病的传播动力学，我们从理论上推导出了流动性阈值，并通过数值观察到，增加活动和吸引力之间的校正会降低流动性阈值，但会抑制感染亚群的比例。它还会在感染个体的空间分布中引入更大的异质性。此外，我们研究了非药物干预措施对不同相关网络中传染病传播的影响。研究结果表明，在负相关网络中同时实施自我隔离和自我保护比在正相关或非相关网络中更有效。自我隔离和自我保护策略都提高了流动性阈值，从而减缓了传染病的传播。然而，在不同相关网络中，每种策略在减少感染亚群比例方面的有效性有所不同。自我保护在正相关网络中更有效，而自我隔离在负相关网络中更有效。我们的研究将为大规模时变复群网络中的传染病预防和控制提供新的见解。
