Recoverable static heterogeneity and weakly constrained coupling in sparse county-level epidemic inference

This repository contains the processed data, computational results, source code, and figure assets supporting the manuscript:

“Recoverable static heterogeneity and weakly constrained coupling in sparse county-level epidemic inference”.

The study investigates a sparse inverse problem in county-level epidemic modeling, focusing on which structural components of a spatial SIR-type model remain practically recoverable under limited observational information.

A low-dimensional static heterogeneity field, constructed from hazard and vulnerability descriptors and represented through an R–I–C decomposition, is embedded into the transmission operator and compared against explicit spatial coupling formulations.

The package includes:- processed county-level data for California- model calibration outputs across multiple configurations- R–I–C ablation experiments- gamma robustness and comparability analyses- incident-scale scoring results- sensitivity and identifiability diagnostics- scripts required to reproduce numerical summaries and figures

This archive is intended as a reproducibility package accompanying the submitted manuscript, rather than a standalone forecasting system.