Mathematical Recommendations to Fight Against COVID-19

13 Pages Posted: 9 Mar 2020

See all articles by Chenlin GU

Chenlin GU

École Normale Supérieure (ENS)

Wei Jiang

University of Paris-Saclay - Ecole Polytechnique

Tianyuan Zhao

University of Paris-Saclay - Ecole Polytechnique

Ban Zheng

Ecole Polytechnique; HSBC Global Asset Management

Date Written: March 9, 2020

Abstract

The statistics show that the mortality of COVID-19 is 20 times higher than seasonal flu and close to that of Spanish flu, hence it is becoming an absolute priority for every country to take efficient measure to limit the transmission of COVID-19. In this short paper, we propose a mathematical framework to model the contagion of COVID-19 by choosing three key parameters: infection rate, confirmation rate and quarantine efficiency ratio. We use the experience from China to calibrate the parameters, and then study the consequence of different measures. Our research suggest that working in distance and "distanciation sociale" (social distancing in French) is an efficient way to limit the contagion as soon as possible and highlight the risk of having a low confirmation rate which happens frequently when hospital is saturated.

Funding: None.

Declaration of Interest: None.

Keywords: COVID-19, epidemic modelling process

JEL Classification: I18, C68

Suggested Citation

GU, Chenlin and Jiang, Wei and Zhao, Tianyuan and Zheng, Ban, Mathematical Recommendations to Fight Against COVID-19 (March 9, 2020). Available at SSRN: https://ssrn.com/abstract=3551006 or http://dx.doi.org/10.2139/ssrn.3551006

Chenlin GU (Contact Author)

École Normale Supérieure (ENS) ( email )

45 rue d’Ulm
Paris Cedex 05, F-75230
France

Wei Jiang

University of Paris-Saclay - Ecole Polytechnique ( email )

55 Avenue de Paris
Versailles, 78000
France

Tianyuan Zhao

University of Paris-Saclay - Ecole Polytechnique ( email )

55 Avenue de Paris
Versailles, 78000
France

Ban Zheng

Ecole Polytechnique ( email )

Route de Saclay
Palaiseau, 91 91128
France

HSBC Global Asset Management ( email )

110, esplanade du Général de Gaulle
Paris La Défense, 92400
France

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