Author or contributor

Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models

Resource type
Authors/contributors
Title
Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models
Abstract
A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.
Book Title
Epidemic Analytics for Decision Supports in COVID19 Crisis
Place
Cham
Publisher
Springer International Publishing
Date
2022
Pages
17-64
Language
en
ISBN
978-3-030-95281-5
Accessed
9/21/22, 2:33 AM
Library Catalog
Springer Link
Extra
DOI: 10.1007/978-3-030-95281-5_2
Citation
Fong, S. J., Marques, J. A. L., Li, G., Dey, N., Crespo, R. G., Herrera-Viedma, E., Gois, F. N. B., & Neto, J. X. (2022). Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 17–64). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_2