Interpreting, Analysing and Modelling COVID-19 Mortality Data

109 Pages Posted: 30 Apr 2020 Last revised: 4 Aug 2020

See all articles by Didier Sornette

Didier Sornette

Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Tokyo Institute of Technology

Michael Schatz

ETH Zürich

Euan Mearns

Academic Guest at the Department of Management, Technology, and Economics, ETH Zurich

Ke Wu

ETH Zurich - Department of Management, Technology, and Economics (D-MTEC); Southern University of Science and Technology

Didier Darcet

Gavekal Intelligence Software (Gavekal-IS)

Date Written: April 26, 2020

Abstract

We present results on the mortality statistics of the COVID-19 epidemic in a number of countries. Our data analysis suggests classifying countries in five groups, 1) Western countries, 2) East Block , 3) developed South East Asian countries, 4) Northern Hemisphere developing countries and 5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerges in which the Western countries exhibit the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries reveals several subgroups within the Western countries and further emphasises the difference between the five groups. Analysing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries is found to be their larger population of elderly people, with exceptions such as Norway and Japan, for which other factors seem to dominate. Our comparison between countries at the same level of outbreak progress allows us to identify and quantify a measure of efficiency of the level of stringency of confinement measures. We find that increasing the stringency from 20 to 60 decreases the death count by about 50 lives per million in a time window of 20 days. Finally, we perform logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the ``first wave'' and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allows us to assess the outbreak progress in different countries, differentiating between those that are at a quite advanced stage and close to the end of the epidemic from those that are still in the middle of it. This raises many questions in terms of organisation, preparedness, governance structure, and so on.

Keywords: COVID-19 epidemic; mortality; life expectancy; stringency of confinement measures; logistic equation; outbreak progress

JEL Classification: I12, I18, I30, M14, Q50

Suggested Citation

Sornette, Didier and Schatz, Michael and Mearns, Euan and Wu, Ke and Wu, Ke and Darcet, Didier, Interpreting, Analysing and Modelling COVID-19 Mortality Data (April 26, 2020). Swiss Finance Institute Research Paper No. 20-27, Available at SSRN: https://ssrn.com/abstract=3586411 or http://dx.doi.org/10.2139/ssrn.3586411

Didier Sornette (Contact Author)

Risks-X, Southern University of Science and Technology (SUSTech) ( email )

1088 Xueyuan Avenue
Shenzhen, Guangdong 518055
China

Swiss Finance Institute ( email )

c/o University of Geneva
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CH-1211 Geneva 4
Switzerland

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

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Zurich, ZURICH CH-8092
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HOME PAGE: http://www.er.ethz.ch/

Tokyo Institute of Technology ( email )

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Tokyo 152-8550, 52-8552
Japan

Michael Schatz

ETH Zürich ( email )

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ZUE F7
Zürich, 8092
Switzerland

Euan Mearns

Academic Guest at the Department of Management, Technology, and Economics, ETH Zurich ( email )

ETH-Zentrum
Zurich, CH-8092
United States

Ke Wu

Southern University of Science and Technology ( email )

No 1088, xueyuan Rd.
Xili, Nanshan District
Shenzhen, Guangdong 518055
China

ETH Zurich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
Zürich, Zurich 8092
Switzerland
0446322445 (Phone)

Didier Darcet

Gavekal Intelligence Software (Gavekal-IS) ( email )

27 Bis rue Copernic
Paris, 75116
France

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