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

## TL;DR

VIBES is a new modeling framework that combines biological and social factors to better understand and predict how epidemics spread.

## Contribution

VIBES introduces a multiscale modeling approach that integrates within-host viral dynamics with social contact data to disentangle biological and behavioral drivers of epidemics.

## Key findings

- The within-host model estimated a generation time of 6.3 days for symptomatic individuals and 43.1% presymptomatic transmission.
- Incorporating social contacts shortened the generation time to 5.4 days and increased presymptomatic transmission to 52.8% at R = 3.0.
- Higher transmissibility (R) shortened generation time and serial interval, while isolation increased presymptomatic transmission by about 30%.

## Abstract

Predicting an epidemic’s course requires understanding how pathogen biology and human behavior independently shape transmission. While these scales are inextricably connected, traditional epidemiological methods often struggle to isolate biological traits from the influence of social contact patterns and public health interventions, limiting our ability to characterize novel pathogens. We introduce VIBES, a computational framework that integrates patient-level viral dynamics with data-driven contact patterns to bridge this gap. Our analysis quantifies how social contact patterns alter a pathogen’s biological baseline, accelerating the epidemic pace and expanding the window of silent presymptomatic transmission. By disentangling these drivers, VIBES provides a mechanistic foundation for identifying which transmission trends are biological versus behavioral, enabling better design of targeted public health interventions.

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 asymptomatic individuals (5.6 d; 95%CI: 5.1 to 6.0 at R = 1.3). Our findings establish multiscale modeling as a powerful tool for mechanistically quantifying how pathogen biology and human social behavior shape epidemic dynamics as well as for assessing public health interventions.