Inferring contact network characteristics from epidemic data via compact mean-field models

TL;DR

This paper introduces a framework combining the edge-based compartmental model with dynamical survival analysis to infer contact network properties from epidemic data, improving understanding of disease spread and aiding intervention strategies.

Contribution

It develops a novel method for inferring contact network characteristics from epidemic data using a compact, analytically tractable model integrated with survival analysis.

Findings

Robust inference of network properties from synthetic epidemic data.

Accurate estimation of disease and network parameters from real-world outbreaks.

Good fit and reliable short-term forecasts for real epidemic data.

Abstract

Modelling epidemics using contact networks provides a significant improvement over classical compartmental models by explicitly incorporating the network of contacts. However, while network-based models describe disease spread on a given contact structure, their potential for inferring the underlying network from epidemic data remains largely unexplored. In this work, we consider the edge-based compartmental model (EBCM), a compact and analytically tractable framework, and we integrate it within dynamical survival analysis (DSA) to infer key network properties along with parameters of the epidemic itself. Despite correlations between structural and epidemic parameters, our framework demonstrates robustness in accurately inferring contact network properties from synthetic epidemic simulations. Additionally, we apply the framework to real-world outbreaks, namely the 2001 UK foot-and-mouth disease outbreak and the COVID-19 epidemic in Seoul, to estimate both disease parameters and network characteristics. Our results show that our framework achieves good fits to real-world epidemic data and reliable short-term forecasts. These findings highlight the potential of network-based inference approaches to uncover hidden contact structures, providing insights that can inform the design of targeted interventions and public health strategies.