基于自我中心数据的家庭内部联系网络估计的惩罚似然方法
A penalized likelihood approach to estimate within-household contact networks from egocentric data
摘要：
急性传染病是通过社会接触网络传播的。流行病模型用于预测新出现的病原体的传播和比较干预策略。这些模型中的许多都假设混合群体（家庭、学校等）内的接触概率相等，但很少有工作推断出实际的接触网络，这可能会影响流行病的估计。我们开发了一种惩罚似然方法来推断家庭内的接触网络，这是疾病传播的一个关键领域。使用自我中心的调查在比利时的接触行为，我们估计在家庭内的接触网络为六个不同的年龄组成。我们的估计表明，在接触行为的依赖性和年龄构成的差异很大，与较少的接触发生在老年家庭。我们的研究结果与用于提出政策建议的流行病模型相关。
英文摘要：
Acute infectious diseases are transmitted over networks of social contacts. Epidemic models are used to predict the spread of emergent pathogens and compare intervention strategies. Many of these models assume equal probability of contact within mixing groups (homes, schools, etc.), but little work has inferred the actual contact network, which may influence epidemic estimates. We develop a penalized likelihood method to infer contact networks within households, a key area for disease transmission. Using egocentric surveys of contact behavior in Belgium, we estimate within-household contact networks for six different age compositions. Our estimates show dependency in contact behavior and vary substantively by age composition, with fewer contacts occurring in older households. Our results are relevant for epidemic models used to make policy recommendations.
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