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

TL;DR

This paper analyzes emergency call data to track Covid-19's progression in Paris, revealing epidemic phases, delays in intervention effects, and proposing a resurgence detection algorithm using mathematical modeling.

Contribution

It introduces a novel approach combining emergency call analysis with PDE epidemiological modeling and advanced mathematical methods to monitor and detect Covid-19 epidemic dynamics.

Findings

Identification of distinct epidemic phases in Paris

Development of an algorithm to detect resurgences

Quantification of delays between measures and EMS load

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.