Mathematicians have analysed global COVID-19 data to identify two constants which can drastically change a country’s rate of infection.

An international team of researchers led by Professor Alexander Gorban from the University of Leicester used available data from 13 countries to determine the rate of stress response, or ‘mobilisation’ and the rate of spontaneous exhaustion, or ‘demobilisation’.

Their findings, published in Scientific Reports, show that social stress — which varied broadly across the countries studied — drives the multi-wave dynamics of COVID-19 outbreaks.

The study analysed data from China, the USA, UK, Germany, Colombia, Italy, Spain, Israel, Russia, France, Brazil, India, and Iran — and contributed to the research team’s proposed new system of models, which combine the dynamics of the established concept of social stress with classical epidemic models.

Alexander Gorban is a Professor of Applied Mathematics at the University of Leicester, and Director of the Centre for Artificial Intelligence, Data Analysis and Modelling. Professor Gorban said:

«We tried to use the pandemic for research and quantify the social and cultural differences between countries. We measured how variable countries are in two processes: mobilisation of people for rational protective behaviour and exhaustion of this mobilisation with destroying of rational behaviour.

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