Covid-19 | Using chaos theory to predict the course of the epidemic

Results & impact 20 May 2020
How can we predict the course of an epidemic when data are in short supply? To answer that question, a team of scientists has developed an approach based on chaos theory. The results bear out field data, and the approach is due to be rolled out in Africa and also used for other diseases.
© Illustration from Freepik

Most of the approaches commonly used in epidemiology to assess disease spread rely on proven, tested knowledge. However, in the case of an emerging virus epidemic, reliable data on the disease and its spread are in short supply, and it is sometimes worth looking at original alternative approaches.

This is what a team of scientists [1] did, using data on the Covid-19 epidemics in China, Japan, South Korea and Italy. They used chaos theory to build predictive models for disease spread without strong assumptions. The models were subsequently applied to the situation in France, Spain, Belgium and the UK, to identify the most likely epidemic scenarios and monitor their actual course. In the thick of the crisis, datasets were only available for a very short period, but the scientists succeeded in predicting the course of the epidemic in several countries: Italy, Spain, France and the UK.

As long ago as 5 February, a chaotic model obtained for China suggested that without control measures, the epidemic would continue to spread. By 26 March, a model built for Italy served to predict the various stages of the slowing down of the epidemic in Italy and France. The work also demonstrated that the extent of the epidemic was directly linked to the early introduction of control measures.

This approach can be used in situations for which limited datasets are available. It is particularly suitable for situations in which behaviour is highly sensitive to initial conditions, as is the case for animal health and certain zoonoses. This type of modelling is also due to be developed to address disease emergence at the interfaces between wildlife, domestic animals and humans, and to study the link between biodiversity and health.

The same team of scientists had already tested the approach successfully for the Ebola epidemic [2].

This project was part-funded by several programmes: "Les enveloppes fluides et l’environnement" (INSU-CNRS), the "Programme national de télédétection spatiale" (PNTS) and "Défi InFiNiTi" (CNRS).

The next stage will be funded by Montpellier University of Excellence (MUSE) . It will include extending the analysis of the Covid-19 epidemic under way in Africa, to support decision making in terms of animal health management policy. There are also plans to develop a generic tool for modelling emergence processes that are difficult to address using conventional modelling approaches.


[2] Mangiarotti S., Peyre M., Huc M., A chaotic model for the epidemic of Ebola virus disease in West Africa (2013–2016). Chaos 2016, 26, 113112

This news item is an adaptation of an article initially published on the Institut national des sciences de l’Univers (INSU) website.