Integrating Epidemiological Modeling and Surveillance Data Feeds: A Kalman Filter Based Approach

Infectious disease spread is difficult to accurately measure and model. Even for well-studied pathogens, uncertainties remain regarding dynamics of mixing behavior and how to balance simulation-generated estimates with empirical data. While Markov Chain Monte Carlo approaches sample posteriors given empirical data, health applications of such methods have not considered dynamics associated with model error. We present here an Extended Kalman Filter (EKF) approach for recurrent simulation regrounding as empirical data arrives throughout outbreaks. The approach simultaneously considers empirical data accuracy, growing simulation error between measurements, and supports estimation of changing model parameters. We evaluate our approach using a two-level system, with “ground truth” generated by an agent-based model simulating epidemics over empirical microcontact networks, and noisy measurements fed into an EKF corrected aggregate model. We find that the EKF solution improves outbreak peak estimation and can compensate for inaccuracies in model structure and parameter estimates.