Gallos Lazaros K, Fefferman Nina H
Department of Ecology, Evolution, and Natural Resources, Rutgers University - New Brunswick, NJ 08901, United States of America; DIMACS, Rutgers University - Piscataway, NJ 08854, United States of America.
PLoS One. 2015 Aug 27;10(8):e0136704. doi: 10.1371/journal.pone.0136704. eCollection 2015.
As the understanding of the importance of social contact networks in the spread of infectious diseases has increased, so has the interest in understanding the feedback process of the disease altering the social network. While many studies have explored the influence of individual epidemiological parameters and/or underlying network topologies on the resulting disease dynamics, we here provide a systematic overview of the interactions between these two influences on population-level disease outcomes. We show that the sensitivity of the population-level disease outcomes to the combination of epidemiological parameters that describe the disease are critically dependent on the topological structure of the population's contact network. We introduce a new metric for assessing disease-driven structural damage to a network as a population-level outcome. Lastly, we discuss how the expected individual-level disease burden is influenced by the complete suite of epidemiological characteristics for the circulating disease and the ongoing process of network compromise. Our results have broad implications for prediction and mitigation of outbreaks in both natural and human populations.
随着对社交接触网络在传染病传播中重要性的认识不断提高,人们对理解疾病改变社交网络的反馈过程的兴趣也与日俱增。虽然许多研究探讨了个体流行病学参数和/或潜在网络拓扑结构对由此产生的疾病动态的影响,但我们在此提供了这两种影响对人群层面疾病结果之间相互作用的系统概述。我们表明,人群层面疾病结果对描述疾病的流行病学参数组合的敏感性严重依赖于人群接触网络的拓扑结构。我们引入了一种新的指标,用于评估作为人群层面结果的疾病对网络造成的结构性损害。最后,我们讨论了循环疾病的全套流行病学特征以及网络受损的持续过程如何影响预期的个体层面疾病负担。我们的结果对预测和缓解自然人群和人类群体中的疫情具有广泛意义。