A Machine Learning Framework for Constructing Heterogeneous Contact Networks: Implications for Epidemic Modelling
This paper presents a machine learning framework that constructs scalable, heterogeneous contact networks from survey data to more accurately simulate epidemic dynamics, demonstrating that incorporating both age structure and contact heterogeneity significantly reduces projected outbreak sizes and improves the targeting of public health interventions.