Information Systems, University of Haifa, Haifa, Israel.
Software and Information Systems Engineering, Ben Gurion University, Beer Sheva, Israel.
Sci Rep. 2023 Aug 10;13(1):12955. doi: 10.1038/s41598-023-39817-9.
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.
基于真实世界接触数据的疾病交互作用模型已被证明可以促进对社区中疾病传播的理解。这种时间建模遵循接触的路径保持顺序和时间，这对于准确建模至关重要。然而，其他重要方面被忽视了。各种空气传播病原体感染所需的暴露时间不同。此外，从个体角度来看，Covid-19 的进展在个体之间存在差异，其严重程度与年龄呈统计学相关。在这里，我们用（a）会议持续时间和（b）个人疾病进展丰富了 Covid-19 和类似空气传播病毒疾病的交互作用模型。丰富的模型能够在人群和个体水平上预测结果。它还可以预测个体参与社交互动的风险，这是作为病毒特征及其在人群中的流行程度的函数。我们进一步表明，无症状传播的神秘性质源于网络密度对这种传播的潜在影响，并且无症状传播仅在稀疏社区中才具有重大影响。
