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arXiv · 2309.17087

Data-Driven Mathematical Modeling Approaches for COVID-19: a survey

J. Demongeot·P. Magal
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Abstract

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 230 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population.

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J. Demongeot, P. Magal. 2023-09-29. Data-Driven Mathematical Modeling Approaches for COVID-19: a survey. https://arxiv.org/abs/2309.17087

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