Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Infectious disease surveillance relies on tracking pathogen dynamics.
• Viral genomic data integration revolutionizes disease tracking and forecasting.
• Molecular surveillance plays a pivotal role in understanding pathogen evolution.

Purpose of the Study:

• To present a methodological workflow for epidemic forecasting.
• To highlight the transformative role of molecular surveillance in public health.
• To discuss the integration of molecular epidemiology with mathematical modeling.

Main Methods:

• Utilizing ordinary differential equation (ODE)-based models for disease dynamics.
• Implementing a structured approach to epidemic model validation (calibration, identifiability, uncertainty propagation).
• Leveraging Bayesian phylogenetics and phylodynamics for transmission cluster estimation.

Main Results:

• Demonstrated improved forecast reliability using multiple data streams (simulated and COVID-19 data).
• Showcased the ability to estimate transmission clusters and reconstruct outbreak histories using genomic data.
• Validated epidemic models through systematic calibration and uncertainty assessment.

Conclusions:

• A structured validation process enhances epidemic model reliability.
• Viral genomic data significantly improves infectious disease forecasting and surveillance.
• Integrating molecular epidemiology with mathematical modeling optimizes public health interventions.