Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: The example of the Paris area

Stéphane Gaubert; Marianne Akian; Xavier Allamigeon; Marin Boyet; Baptiste Colin; Théotime Grohens; Laurent Massoulié; David P. Parsons; Frédéric Adnet; Érick Chanzy; Laurent Goix; Frédéric Lapostolle; Éric Lecarpentier; Christophe Leroy; Thomas Loeb; Jean Sébastien Marx; Caroline Télion; Laurent Tréluyer; Pierre Carli

doi:10.5802/crmath.99

Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: The example of the Paris area

Titre traduit de la contribution: Comprendre et surveiller l’évolution de l’épidémie de Covid-19 à partir des appels au numéro 15: L’exemple de l’agglomération parisienne

Stéphane Gaubert
, Marianne Akian
, Xavier Allamigeon
, Marin Boyet
, Baptiste Colin
, Théotime Grohens
, Laurent Massoulié
, David P. Parsons
, Frédéric Adnet
, Érick Chanzy
, Laurent Goix
, Frédéric Lapostolle
, Éric Lecarpentier
, Christophe Leroy
, Thomas Loeb
, Jean Sébastien Marx
, Caroline Télion
, Laurent Tréluyer
, Pierre Carli

CNRS
GEMPPM-INSA Lyon
Université PSL
INRIA Institut National de Recherche en Informatique et en Automatique
Groupe Hospitalier Lariboisiere-Fernand Widal Assistance Publique-Hopitaux de Paris (AP-HP)

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

We portray the evolution of the Covid-19 epidemic during the crisis of March–April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This also leads to an algorithm, allowing one to detect epidemic resurgences. We rely on a transport PDE epidemiological model, and we use methods from Perron–Frobenius theory and tropical geometry.

Titre traduit de la contribution	Comprendre et surveiller l’évolution de l’épidémie de Covid-19 à partir des appels au numéro 15: L’exemple de l’agglomération parisienne
langue originale	Anglais
Pages (de - à)	843-875
Nombre de pages	33
journal	Comptes Rendus Mathematique
Volume	358
Numéro de publication	7
Les DOIs	https://doi.org/10.5802/crmath.99
état	Publié - 1 janv. 2020

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10.5802/crmath.99

Contient cette citation

Gaubert, S., Akian, M., Allamigeon, X., Boyet, M., Colin, B., Grohens, T., Massoulié, L., Parsons, D. P., Adnet, F., Chanzy, É., Goix, L., Lapostolle, F., Lecarpentier, É., Leroy, C., Loeb, T., Marx, J. S., Télion, C., Tréluyer, L., & Carli, P. (2020). Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: The example of the Paris area. Comptes Rendus Mathematique, 358(7), 843-875. https://doi.org/10.5802/crmath.99

@article{2f96f97997704b0993d427c4205df30c,

title = "Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: The example of the Paris area",

abstract = "We portray the evolution of the Covid-19 epidemic during the crisis of March–April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This also leads to an algorithm, allowing one to detect epidemic resurgences. We rely on a transport PDE epidemiological model, and we use methods from Perron–Frobenius theory and tropical geometry.",

author = "St{\'e}phane Gaubert and Marianne Akian and Xavier Allamigeon and Marin Boyet and Baptiste Colin and Th{\'e}otime Grohens and Laurent Massouli{\'e} and Parsons, \{David P.\} and Fr{\'e}d{\'e}ric Adnet and {\'E}rick Chanzy and Laurent Goix and Fr{\'e}d{\'e}ric Lapostolle and {\'E}ric Lecarpentier and Christophe Leroy and Thomas Loeb and Marx, \{Jean S{\'e}bastien\} and Caroline T{\'e}lion and Laurent Tr{\'e}luyer and Pierre Carli",

note = "Publisher Copyright: {\textcopyright} Acad{\'e}mie des sciences, Paris and the authors, 2020. Some rights reserved.",

year = "2020",

month = jan,

day = "1",

doi = "10.5802/crmath.99",

language = "English",

volume = "358",

pages = "843--875",

journal = "Comptes Rendus Mathematique",

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Gaubert, S , Akian, M , Allamigeon, X, Boyet, M, Colin, B, Grohens, T, Massoulié, L, Parsons, DP, Adnet, F, Chanzy, É, Goix, L, Lapostolle, F, Lecarpentier, É, Leroy, C, Loeb, T, Marx, JS, Télion, C, Tréluyer, L & Carli, P 2020, 'Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: The example of the Paris area', Comptes Rendus Mathematique, VOL. 358, Numéro 7, p. 843-875. https://doi.org/10.5802/crmath.99

TY - JOUR

T1 - Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls

T2 - The example of the Paris area

AU - Gaubert, Stéphane

AU - Akian, Marianne

AU - Allamigeon, Xavier

AU - Boyet, Marin

AU - Colin, Baptiste

AU - Grohens, Théotime

AU - Massoulié, Laurent

AU - Parsons, David P.

AU - Adnet, Frédéric

AU - Chanzy, Érick

AU - Goix, Laurent

AU - Lapostolle, Frédéric

AU - Lecarpentier, Éric

AU - Leroy, Christophe

AU - Loeb, Thomas

AU - Marx, Jean Sébastien

AU - Télion, Caroline

AU - Tréluyer, Laurent

AU - Carli, Pierre

PY - 2020/1/1

Y1 - 2020/1/1

N2 - We portray the evolution of the Covid-19 epidemic during the crisis of March–April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This also leads to an algorithm, allowing one to detect epidemic resurgences. We rely on a transport PDE epidemiological model, and we use methods from Perron–Frobenius theory and tropical geometry.

AB - We portray the evolution of the Covid-19 epidemic during the crisis of March–April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This also leads to an algorithm, allowing one to detect epidemic resurgences. We rely on a transport PDE epidemiological model, and we use methods from Perron–Frobenius theory and tropical geometry.

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DO - 10.5802/crmath.99

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JO - Comptes Rendus Mathematique

JF - Comptes Rendus Mathematique

IS - 7

ER -

Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: The example of the Paris area

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