Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
This work addresses the need for localized, long-term epidemiological modeling to inform public health interventions, but it is incremental as it applies an existing PINN method to new COVID-19 data.
The authors tackled the problem of modeling COVID-19 dynamics in German states by using Physics-Informed Neural Networks (PINNs) to solve the inverse SIR model with infection data, resulting in estimates of state-specific transmission parameters and time-varying reproduction numbers over three years, revealing regional variations correlated with vaccination and pandemic phases.
The COVID-19 pandemic has highlighted the need for quantitative modeling and analysis to understand real-world disease dynamics. In particular, post hoc analyses using compartmental models offer valuable insights into the effectiveness of public health interventions, such as vaccination strategies and containment policies. However, such compartmental models like SIR (Susceptible-Infectious-Recovered) often face limitations in directly incorporating noisy observational data. In this work, we employ Physics-Informed Neural Networks (PINNs) to solve the inverse problem of the SIR model using infection data from the Robert Koch Institute (RKI). Our main contribution is a fine-grained, spatio-temporal analysis of COVID-19 dynamics across all German federal states over a three-year period. We estimate state-specific transmission and recovery parameters and time-varying reproduction number (R_t) to track the pandemic progression. The results highlight strong variations in transmission behavior across regions, revealing correlations with vaccination uptake and temporal patterns associated with major pandemic phases. Our findings demonstrate the utility of PINNs in localized, long-term epidemiological modeling.