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Title: A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

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, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and 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.

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Award ID(s):
2028632
NSF-PAR ID:
10293892
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Mathematical Biosciences and Engineering
Volume:
18
Issue:
6
ISSN:
1551-0018
Page Range / eLocation ID:
7685 to 7710
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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