VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics

作者：Paulo C. Ventura, Yong Dam Jeong, Maria Litvinova, Allisandra G. Kummer, Shingo Iwami, Hongjie Yu, Stefano Merler, Alessandro Vespignani, Keisuke Ejima, Marco Ajelli · 发表于：Proceedings of the National Academy of Sciences · 年份：2026 · DOI：10.1073/pnas.2523055123 · 被引用次数：2 · 研究领域：COVID-19 epidemiological studies、Complex Network Analysis Techniques、Zoonotic diseases and public health

Infectious disease spread is a multiscale process composed of within-host (biological) and between-host (social) drivers and disentangling them from each other is a central challenge in epidemiology. Here, we introduce VIBES, a multiscale modeling framework that explicitly integrates viral dynamics based on patient-level data with population-level transmission on a data-driven network of social contacts. Using SARS-CoV-2 as a case study, we analyze three emergent epidemic properties, namely the generation time, serial interval, and presymptomatic transmission. First, we established a purely biological baseline, thus independent of the reproduction number ( R ), from the within-host model, estimating a generation time of 6.3 d for symptomatic individuals and 43.1% presymptomatic transmission. Then, using the full model incorporating social contacts, we found a shorter generation time (5.4 d at R = 3.0) and an increase in presymptomatic transmission (52.8% at R = 3.0), disentangling the impact of social drivers from a purely biological baseline. We further show that as pathogen transmissibility increases ( R from 1.3 to 6), competition among infectious individuals shortens the generation time and serial interval by up to 21% and 13%, respectively. Conversely, a social intervention, like isolation, increases the proportion of presymptomatic transmission by about 30%. Our framework also estimates metrics that are challenging to obtain empirically, such as the generation time for ...