Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.

翻译：基于真实接触数据的交互驱动疾病建模已被证明能促进对社区内疾病传播的理解。这种时间模型遵循接触的路径保留顺序和时间，这对于精确建模至关重要。然而，其他重要方面却被忽略了。各种空气传播病原体所需的感染暴露时间各不相同。此外，从个体角度来看，COVID-19的病程进展因人而异，其严重程度与年龄在统计学上相关。在此，我们通过引入（a）会面时长和（b）个体病程进展，丰富了一个针对COVID-19及类似空气传播病毒性疾病的交互驱动模型。该增强模型能够在群体和个体两个层面预测结果，并进一步允许根据病毒特征及其在人群中的流行程度预测个体参与社交互动的风险。我们还证明，无症状传播的神秘性源于网络密度对其传播的潜在影响，且仅在稀疏社区中，无症状传播才具有显著影响。