Holme Petter, Masuda Naoki
Department of Energy Science, Sungkyunkwan University, Suwon, Korea; Department of Physics, Umeå University, Umeå, Sweden; Department of Sociology, Stockholm University, Stockholm, Sweden.
Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.
PLoS One. 2015 Mar 20;10(3):e0120567. doi: 10.1371/journal.pone.0120567. eCollection 2015.
The basic reproduction number R0--the number of individuals directly infected by an infectious person in an otherwise susceptible population--is arguably the most widely used estimator of how severe an epidemic outbreak can be. This severity can be more directly measured as the fraction of people infected once the outbreak is over, Ω. In traditional mathematical epidemiology and common formulations of static network epidemiology, there is a deterministic relationship between R0 and Ω. However, if one considers disease spreading on a temporal contact network--where one knows when contacts happen, not only between whom--then larger R0 does not necessarily imply larger Ω. In this paper, we numerically investigate the relationship between R0 and Ω for a set of empirical temporal networks of human contacts. Among 31 explanatory descriptors of temporal network structure, we identify those that make R0 an imperfect predictor of Ω. We find that descriptors related to both temporal and topological aspects affect the relationship between R0 and Ω, but in different ways.
基本再生数R0（即在其他条件均为易感人群的情况下，由一名感染者直接感染的个体数量）可以说是用于评估疫情爆发严重程度的最广泛使用的指标。这种严重程度可以更直接地用疫情结束后被感染人群的比例Ω来衡量。在传统的数学流行病学和静态网络流行病学的常见公式中，R0和Ω之间存在确定性关系。然而，如果考虑疾病在时间接触网络上的传播（即人们不仅知道谁之间有接触，还知道接触发生的时间），那么更大的R0并不一定意味着更大的Ω。在本文中，我们对一组人类接触的实证时间网络，从数值上研究了R0和Ω之间的关系。在时间网络结构的31个解释性描述符中，我们确定了那些使R0成为Ω的不完美预测指标的描述符。我们发现，与时间和拓扑方面相关的描述符都会影响R0和Ω之间的关系，但方式不同。
