Human behavior during a pandemic can now be predicted using physics-like equations.
Using physics-constrained neural networks, researchers extracted a 'risk compensation' law from COVID data. It explains why vaccination triggers pandemic fatigue and leads to new waves, turning social science into a formal, predictive science.
Discovering Human Behavioral Transmission Laws in Epidemics via a Physics-Constrained Neural Network
SSRN · 6545175
Epidemic transmission is shaped not only by pathogen biology but by adaptive human behavioral responses, yet existing frameworks lack a mathematically explicit, data-derived formulation of this coupling. We introduce a physics-constrained neural network and symbolic regression (PCNN-SR) framework treating epidemic modeling as an inverse problem: recovering the time-varying transmission rate [[EQUATION]] from multimodal surveillance records while enforcing epidemiological consistency. Applied to