GeoID-PINN: Identifiability-Aware Regional Epidemic Inference with Geographic Coupling
Regional surveillance data reflect local transmission, reporting, seeding, and external infection pressure, which are difficult to identify separately. We introduce GeoID-PINN, a physics-informed neural network (PINN) for susceptible-infectious-recovered-deceased (SIRD) dynamics. The model represents spatial dependence with a row-stochastic source-composition matrix whose rows assign nonnegative source weights that sum to one. We regularize this matrix toward a spatial prior constructed from distance, adjacency, commuting, or lead-lag information. In a four-region simulation with known truth, a compatible distance prior gives source-composition error 0.099. The error rises to 0.159 withou...
| Authors | Weixiong Hua, Fan Bu |
|---|---|
| Primary category | cs.LG |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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