Age Group Sensitivity Analysis of Epidemic Models: Investigating the Impact of Contact Matrix Structure

TL;DR

This paper introduces AGSA, a novel sensitivity analysis framework for epidemic models that assesses how age-specific contact patterns influence disease spread and model uncertainty, guiding targeted data collection.

Contribution

The study presents a new sensitivity analysis method combining epidemic modeling, LHS, and PRCC to identify influential age groups and reduce epistemic uncertainty in contact matrices.

Findings

AGSA identifies key age groups affecting epidemic outcomes.

Targeted data collection on influential groups improves model accuracy.

Framework enhances epidemic forecasting and intervention planning.

Abstract

Understanding the role of different age groups in disease transmission is crucial for designing effective intervention strategies. A key parameter in age-structured epidemic models is the contact matrix, which defines the interaction structure between age groups. However, accurately estimating contact matrices is challenging, as different age groups respond differently to surveys and are accessible through different channels. This variability introduces significant epistemic uncertainty in epidemic models. In this study, we introduce the Age Group Sensitivity Analysis (AGSA) method, a novel framework for assessing the impact of age-structured contact patterns on epidemic outcomes. Our approach integrates age-stratified epidemic models with Latin Hypercube Sampling (LHS) and the Partial Rank Correlation Coefficient (PRCC) method, enabling a systematic sensitivity analysis of age-specific interactions. Additionally, we propose a new sensitivity aggregation technique that quantifies the contribution of each age group to key epidemic parameters. By identifying the age groups to which the model is most sensitive, AGSA helps pinpoint those that introduce the greatest epistemic uncertainty. This allows for targeted data collection efforts, focusing surveys and empirical studies on the most influential age groups to improve model accuracy. As a result, AGSA can enhance epidemic forecasting and inform the design of more effective and efficient public health interventions.