  Mathematical models are widely recognized as an important tool for analyzing
and understanding the dynamics of infectious disease outbreaks, predict their
future trends, and evaluate public health intervention measures for disease
control and elimination. We propose a novel stochastic metapopulation
state-space model for COVID-19 transmission, based on a discrete-time
spatio-temporal susceptible/exposed/infected/recovered/deceased (SEIRD) model.
The proposed framework allows the hidden SEIRD states and unknown transmission
parameters to be estimated from noisy, incomplete time series of reported
epidemiological data, by application of unscented Kalman filtering (UKF),
maximum-likelihood adaptive filtering, and metaheuristic optimization.
Experiments using both synthetic data and real data from the Fall 2020 Covid-19
wave in the state of Texas demonstrate the effectiveness of the proposed model.
