A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread.
Math Biosci Eng
; 18(6): 7685-7710, 2021 09 06.
Article
in English
| MEDLINE | ID: covidwho-1405479
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Math Biosci Eng
Year:
2021
Document Type:
Article
Affiliation country:
Mbe.2021381
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