Eigenvector-Based Sensitivity Analysis of Contact Patterns in Epidemic Modeling

TL;DR

This paper introduces an eigenvector-based sensitivity analysis method for age-structured contact patterns in epidemic models, improving understanding of transmission pathways and aiding public health strategies.

Contribution

It develops a novel eigenvector framework to analyze the impact of age-specific interactions on disease spread, validated with real-world COVID-19 data from multiple regions.

Findings

Identifies key age groups influencing transmission

Validates the method against traditional sampling techniques

Provides insights applicable to different demographic contact structures

Abstract

Understanding how age-specific social contact patterns and susceptibility influence infectious disease transmission is crucial for accurate epidemic modeling. This study presents an eigenvector-based sensitivity analysis framework to quantify the impact of age-structured interactions on disease spread. By applying perturbation analysis to the Next Generation Matrix, we reformulate the basic reproduction number, $\mathcal{R}_0$, as a generalized eigenproblem, enabling the identification of key age group interactions that drive transmission. Using real-world COVID-19 contact data from Hungary, we demonstrate the framework's ability to highlight critical transmission pathways. We compare these findings with results obtained earlier using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC), validating the effectiveness of our approach. Additionally, we extend the analysis to contact structures in the UK and British Columbia, Canada, providing broader epidemiological insights. This work enhances our understanding of demographic interactions in epidemic propagation and offers a robust methodological foundation for improving infectious disease modeling and informing public health interventions.