Data Science Institute, Hasselt University, Hasselt, Belgium.
Center for Infectious Disease Control, National Institute for Public Health and the Environment, Blithoven, The Netherlands.
Proc Biol Sci. 2024 Aug;291(2027):20241296. doi: 10.1098/rspb.2024.1296. Epub 2024 Jul 24.
The spread of viral respiratory infections is intricately linked to human interactions, and this relationship can be characterized and modelled using social contact data. However, many analyses tend to overlook the recurrent nature of these contacts. To bridge this gap, we undertake the task of describing individuals' contact patterns over time by characterizing the interactions made with distinct individuals during a week. Moreover, we gauge the implications of this temporal reconstruction on disease transmission by juxtaposing it with the assumption of random mixing over time. This involves the development of an age-structured individual-based model, using social contact data from a pre-pandemic scenario (the POLYMOD study) and a pandemic setting (the Belgian CoMix study), respectively. We found that accounting for the frequency of contacts impacts the number of new, distinct, contacts, revealing a lower total count than a naive approach, where contact repetition is neglected. As a consequence, failing to account for the repetition of contacts can result in an underestimation of the transmission probability given a contact, potentially leading to inaccurate conclusions when using mathematical models for disease control. We, therefore, underscore the necessity of acknowledging contact repetition when formulating effective public health strategies.
病毒呼吸道感染的传播与人类的相互作用密切相关，这种关系可以通过社会接触数据来描述和建模。然而，许多分析往往忽略了这些接触的周期性。为了弥补这一差距，我们通过描述一周内与不同个体的交互来描述个体随时间的接触模式。此外，我们通过将其与随时间随机混合的假设进行对比，来衡量这种时间重构对疾病传播的影响。这涉及到使用来自大流行前场景（POLYMOD 研究）和大流行场景（比利时 CoMix 研究）的社会接触数据，开发一个基于个体的年龄结构模型。我们发现，考虑到接触的频率会影响新的、不同的接触数量，与忽略接触重复的简单方法相比，实际接触数量会减少。因此，如果不考虑接触的重复，可能会导致低估给定接触的传播概率，从而在使用数学模型进行疾病控制时得出不准确的结论。因此，我们强调在制定有效的公共卫生策略时，必须承认接触的重复性。
