COVID-19 epidemic: from data to mathematical models. Comment on "Data-driven mathematical modeling approaching for COVID-19: A survey" by Jacques Demongeot and Pierre Magal - Archive ouverte HAL
Article Dans Une Revue (Compte-Rendu De Lecture) Physics of Life Reviews Année : 2024

COVID-19 epidemic: from data to mathematical models. Comment on "Data-driven mathematical modeling approaching for COVID-19: A survey" by Jacques Demongeot and Pierre Magal

Résumé

In their extensive survey [5], based on the analysis of an impressive list of more than 250 references, the authors J. Demongeot and P. Magal review the most used mathematical models to describe the various phases of the COVID-19 waves in many places of the world. The COVID-19 outbreak is reminiscent of some old epidemics of infectious diseases, such as the plague epidemic in Europe in the 14th century, the London cholera epidemic in the 17th century, and the smallpox epidemic in the 18th century, which led Bernoulli and d'Alembert to create the first models for the prediction of further epidemic waves [1]. Epidemics are essentially made up of two distinct phases: an epidemic phase in which infections and cases first grow exponentially fast, and an endemic phase in which new cases are maintained at a low level.

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Dates et versions

hal-04784967 , version 1 (15-11-2024)

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  • HAL Id : hal-04784967 , version 1

Citer

François Hamel. COVID-19 epidemic: from data to mathematical models. Comment on "Data-driven mathematical modeling approaching for COVID-19: A survey" by Jacques Demongeot and Pierre Magal. Physics of Life Reviews, inPress. ⟨hal-04784967⟩
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