Simple Formulae, Deep Learning and Elaborate Modelling for the COVID-19 Pandemic
Publication Date
2022-04-06Journal Title
Encyclopedia
ISSN
2673-8392
Publisher
MDPI AG
Volume
2
Issue
2
Pages
679-689
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Fokas, A. S., Dikaios, N., Tsiodras, S., & Kastis, G. A. (2022). Simple Formulae, Deep Learning and Elaborate Modelling for the COVID-19 Pandemic. Encyclopedia, 2 (2), 679-689. https://doi.org/10.3390/encyclopedia2020047
Abstract
<jats:p>Predictive modelling of infectious diseases is very important in planning public health policies, particularly during outbreaks. This work reviews the forecasting and mechanistic models published earlier. It is emphasized that researchers’ forecasting models exhibit, for large t, algebraic behavior, as opposed to the exponential behavior of the classical logistic-type models used usually in epidemics. Remarkably, a newly introduced mechanistic model also exhibits, for large t, algebraic behavior in contrast to the usual Susceptible-Exposed-Infectious-Removed (SEIR) models, which exhibit exponential behavior. The unexpected success of researchers’ simple forecasting models provides a strong support for the validity of this novel mechanistic model. It is also shown that the mathematical tools used for the analysis of the first wave may also be useful for the analysis of subsequent waves of the COVID-19 pandemic.</jats:p>
Keywords
COVID-19, forecasting model, SARS-CoV-2, variant of concern, VOC
Identifiers
External DOI: https://doi.org/10.3390/encyclopedia2020047
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335901
Rights
Licence:
https://creativecommons.org/licenses/by/4.0/
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