Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution.

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Pandemic modeling requires integrating pathogen evolution, human interactions, and public health responses.
• Existing models often struggle to capture the complex feedback loops between these scales.
• Timely and accurate pandemic simulation is crucial for effective disease control.

Purpose of the Study:

• To develop a novel computational framework for multi-scale pandemic modeling.
• To simulate the interplay between pathogen evolution, population dynamics, and control measures.
• To create a scalable and computationally tractable model for real-world application.

Main Methods:

• A stochastic agent-based model for disease spread was coupled with a phylodynamic model for pathogen evolution.
• The framework incorporates pathogen evolution, heterogeneous human interactions, and public health interventions.
• Validation was performed using a case study of SARS-CoV-2 evolution and COVID-19 pandemic data.

Main Results:

• The developed model successfully replicates key features of the COVID-19 pandemic and SARS-CoV-2 evolution.
• It captures punctuated pathogen evolution driven by variant emergence.
• The framework demonstrates computational tractability and scalability for large populations.

Conclusions:

• The new multi-scale modeling framework provides a powerful tool for understanding and predicting pandemic dynamics.
• Integrating pathogen evolution with population and intervention dynamics is essential for accurate pandemic simulation.
• This approach supports evidence-based public health decision-making during infectious disease outbreaks.