Read Jonathan M, Eames Ken T D, Edmunds W John
Mathematics Institute and Department of Biological Sciences, University of Warwick, Coventry CV4 7AL, UK.
J R Soc Interface. 2008 Sep 6;5(26):1001-7. doi: 10.1098/rsif.2008.0013.
Understanding the nature of human contact patterns is crucial for predicting the impact of future pandemics and devising effective control measures. However, few studies provide a quantitative description of the aspects of social interactions that are most relevant to disease transmission. Here, we present the results from a detailed diary-based survey of casual (conversational) and close contact (physical) encounters made by a small peer group of 49 adults who recorded 8,661 encounters with 3,528 different individuals over 14 non-consecutive days. We find that the stability of interactions depends on the intimacy of contact and social context. Casual contact encounters mostly occur in the workplace and are predominantly irregular, while close contact encounters mostly occur at home or in social situations and tend to be more stable. Simulated epidemics of casual contact transmission involve a large number of non-repeated encounters, and the social network is well captured by a random mixing model. However, the stability of the social network should be taken into account for close contact infections. Our findings have implications for the modelling of human epidemics and planning pandemic control policies based on social distancing methods.
了解人类接触模式的本质对于预测未来大流行的影响以及制定有效的控制措施至关重要。然而，很少有研究对与疾病传播最相关的社会互动方面进行定量描述。在此，我们展示了一项基于详细日记的调查结果，该调查针对一个由49名成年人组成的小同伴群体的偶然（对话式）和密切接触（身体接触式）情况，这些成年人在14个非连续的日子里记录了与3528个不同个体的8661次接触。我们发现互动的稳定性取决于接触的亲密程度和社会背景。偶然接触大多发生在工作场所，且主要是不规律的，而密切接触大多发生在家里或社交场合，并且往往更稳定。偶然接触传播的模拟疫情涉及大量非重复接触，随机混合模型能很好地捕捉社会网络。然而，对于密切接触感染，应考虑社会网络的稳定性。我们的研究结果对人类流行病建模以及基于社交距离方法制定大流行控制政策具有启示意义。
