NeuralSEIR: Modeling uncertainty in non-pharmaceutical interventions with neural epidemic dynamics
Hu Cao, Longbing Cao · Pattern Recognition · 2025
Understanding how non-pharmaceutical interventions (NPIs) influence epidemic trajectories is critical for evidence-based public health planning. Most COVID-19 models either treat NPIs as fixed effects, ignoring behavioral fatigue, or use black-box learning approaches that lack epidemiological transparency. We propose NeuralSEIR , a hybrid modeling framework that links daily human mobility to time-varying disease transmission in a mechanistically interpretable way. The model integrates a vaccination-aware compartmental core (SVEIC), which estimates a baseline transmission rate, with a shallow multilayer perceptron (MLP) that adjusts this baseline using Google mobility data to produce a behavior-aware rate. These components are coupled via ordinary differential equations, ensuring epidemiological consistency while allowing adaptive learning. Applied to nine COVID-19 waves across Germany, Japan, and the Philippines, NeuralSEIR reduces 14-day mean absolute percentage error by up to 60 % compared to a mobility-free baseline. It also reveals country-specific correlations between mobility patterns and transmission, highlighting the role of retail, transit, and residential activity in shaping NPI effectiveness. Benchmarks on U.S. state-level data against six CDC-tracked models further demonstrate its accuracy. By capturing mobility-driven changes in transmission, NeuralSEIR offers a transparent, data-informed tool for tailoring NPIs to local behavioral dynamics-bridging mechanistic epidemiology and explainable AI.