VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics.
Paulo Cesar Ventura1, Yong Dam Jeong2,3,4, Maria Litvinova5
1Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405.
Summary
A new multiscale model, VIBES, separates biological and social factors in infectious disease spread. It reveals how social interactions impact transmission dynamics and intervention effectiveness for diseases like COVID-19.
Area of Science:
- Epidemiology
- Computational Biology
- Network Science
Background:
- Infectious disease spread involves complex within-host (biological) and between-host (social) factors.
- Disentangling these drivers is crucial for accurate epidemic modeling and control.
Purpose of the Study:
- Introduce VIBES, a novel multiscale modeling framework.
- Quantify the distinct contributions of biological and social factors to epidemic dynamics.
- Analyze emergent properties like generation time, serial interval, and presymptomatic transmission for SARS-CoV-2.
Main Methods:
- Developed VIBES, integrating patient-level viral dynamics with population-level transmission on a social contact network.
- Established a biological baseline using within-host modeling.
- Incorporated social contact data to simulate population-level spread.
- Analyzed epidemic properties under varying transmissibility (R) and intervention scenarios.
Main Results:
- Estimated a biological generation time of 6.3 days with 43.1% presymptomatic transmission for symptomatic individuals.
- Found shorter generation times (5.4 days at R=3.0) and increased presymptomatic transmission (52.8% at R=3.0) when social contacts were included.
- Demonstrated that increased transmissibility shortens generation time and serial interval, while isolation increases presymptomatic transmission proportion.
- Estimated generation time for asymptomatic individuals (5.6 days at R=1.3).
Conclusions:
- VIBES effectively disentangles biological and social drivers of infectious disease spread.
- Multiscale modeling provides mechanistic insights into epidemic dynamics.
- The framework aids in assessing the impact of public health interventions by quantifying pathogen biology and social behavior interactions.
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