Integrated framework to study genomic surveillance of selective sweeps in multivariants dynamics

Area of Science:

• Epidemiology
• Genomic Epidemiology
• Mathematical Modeling

Background:

• Pandemics involve complex transmission dynamics where surveillance alone is insufficient for containment due to emerging variants.
• Pathogen evolution, driven by variants with differing transmissibility and immune escape, creates dynamic immune landscapes that influence variant dominance.

Purpose of the Study:

• To develop an integrated modeling framework coupling epidemic, genomic, and surveillance models.
• To examine how variant emergence timing, infectiousness, and cross-infection impact epidemic trajectories, immune landscapes, and genomic composition.

Main Methods:

• Developed an integrated modeling framework combining multivariant mean-field epidemic modeling, a mechanistic genomic dominance model, and a probabilistic surveillance model.
• Coupled epidemiological and genomic data from Ancestral, Alpha, Gamma, and Delta variant spread across multiple countries for validation.

Main Results:

• Variant dominance follows a selective sweep model driven by population immunity, described by multilogistic equations.
• Variant detection time is influenced by emergence conditions and the existing variant landscape, showing potential for acceleration or delay.
• Response strategy effectiveness is critically dependent on the evolving genomic composition of the outbreak.

Conclusions:

• The study provides a quantitative link between epidemic dynamics, genomic surveillance, and immune history.
• The findings advance genomic epidemiology for managing multivariant outbreaks by highlighting trade-offs in surveillance and intervention.
• The integrated framework offers a foundation for proactive public health strategies during evolving pandemics.