Britton Tom, Ball Frank
From the Department of Mathematics, Stockholm University, Stockholm, Sweden.
School of Mathematical Sciences, University of Nottingham, Nottingham, UK.
Epidemiology. 2025 Sep 1;36(5):660-667. doi: 10.1097/EDE.0000000000001876. Epub 2025 Jun 13.
Social contact studies are used in infectious disease epidemiology to infer a contact matrix , having the mean number of contacts between individuals of different age groups as elements. However, does not capture the (often large) variation in the number of contacts within each age group, information is also available in social contact studies. Here, we include such variation by separating each age group into two halves: the socially active (having many contacts) and the socially less active (having fewer contacts). The extended contact matrix and its associated epidemic model show that acknowledging variation in social activity within age groups has a substantial impact on the basic reproduction number, , and the final fraction getting infected if the epidemic takes off, . In fact, variation in social activity is more important for data fitting than allowing for different age groups. A difficulty with variation in social activity, however, is that social contact studies typically lack information on whether mixing with respect to social activity is assortative (when socially active mainly have contact with other socially active individuals) or not. Our analysis shows that accounting for variation in social activity improves model predictability, yielding more accurate expressions for and irrespective of whether such mixing is assortative, but different assumptions on assortativity give rather different outputs. Future social contact studies should, therefore, also try to infer the degree of assortativity (with respect to social activity) between peers and their contacts.
社交接触研究在传染病流行病学中用于推断接触矩阵,该矩阵以不同年龄组个体之间的平均接触次数为元素。然而,它没有捕捉到每个年龄组内接触次数(通常差异很大)的变化情况,而社交接触研究中也有相关信息。在这里,我们通过将每个年龄组分为两半来纳入这种变化:社交活跃的(有很多接触)和社交不太活跃的(接触较少)。扩展后的接触矩阵及其相关的流行病模型表明,承认年龄组内社交活动的变化对基本再生数(R_0)以及如果疫情爆发最终被感染的比例(I_f)有重大影响。事实上,社交活动的变化对于数据拟合比考虑不同年龄组更为重要。然而,社交活动变化的一个难点在于,社交接触研究通常缺乏关于社交活动方面的混合是否是 assortative 的信息(即社交活跃的人主要是否与其他社交活跃的个体接触)。我们的分析表明,考虑社交活动的变化可以提高模型的可预测性,无论这种混合是否是 assortative,都能得出关于(R_0)和(I_f)更准确的表达式,但关于 assortativity 的不同假设会给出相当不同的结果。因此,未来的社交接触研究也应该尝试推断同龄人与其接触者之间(关于社交活动的)assortativity 程度。