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

Stéphane Gaubert 1, 2 Marianne Akian 1, 2 Xavier Allamigeon 1, 2 Marin Boyet 1, 2 Baptiste Colin 1, 2 Théotime Grohens 3, 4, 5 Laurent Massoulié 6, 7 David Parsons 8, 5 Frederic Adnet 9 Érick Chanzy 9 Laurent Goix 10, 9 Frédéric Lapostolle 10, 9 Éric Lecarpentier 11 Christophe Leroy 12 Thomas Loeb 13 Jean-Sébastien Marx 14 Caroline Télion 14 Laurent Treluyer 12 Pierre Carli 14, 15
1 TROPICAL - TROPICAL
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
2 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
3 INSA Lyon - Institut National des Sciences Appliquées de Lyon
4 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
5 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
6 MSR - INRIA - Microsoft Research - Inria Joint Centre
7 DYOGENE - Dynamics of Geometric Networks
Inria de Paris, CNRS - Centre National de la Recherche Scientifique : UMR 8548, DI-ENS - Département d'informatique de l'École normale supérieure
8 SED [Grenoble]
9 SAMU 93 [Bobigny]
10 UP13 - Université Paris 13
11 Service d'anesthésie-réanimation SAMU94-SMUR94 [Mondor]
12 AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP)
13 SAMU 92 [Garches]
14 SAMU 75 [Paris]
15 UP - Université de Paris
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.
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https://hal.inria.fr/hal-02648075
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Submitted on : Friday, June 12, 2020 - 7:18:07 AM
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EC-LYON | INSMI | X-CMAP | UPEC-UPEM | UPEC | CMAP | TDS-MACS | BIOENVIS | INRIA | X | UNIV-LYON1 | INSA-LYON | LIRIS | X-DEP-MATHA | UNIV-LYON2 | INSA-GROUPE | CNRS | UDL | SORBONNE-PARIS-NORD | UNIV-PARIS13 | IP_PARIS | USPC | UNIV-PARIS | PSL | COVID19_INRIA | GS-COMPUTER-SCIENCE

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Stéphane Gaubert, Marianne Akian, Xavier Allamigeon, Marin Boyet, Baptiste Colin, et al.. Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: the example of the Paris area. Comptes Rendus. Mathématique, Centre Mersenne (2020-..) ; Elsevier Masson (2002-2019), 2020, 358 (7), pp.843-875. ⟨10.5802/crmath.99⟩. ⟨hal-02648075v2⟩

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