Social stress is the key to population VOC levels

Mathematicians analyzed global COVID-19 data to identify two constants that can drastically alter a country’s infection rate.

An international team of researchers led by Professor Alexander Gorban of the University of Leicester used available data from 13 countries to determine the rate of stress response, or “mobilization”, and the rate of spontaneous exhaustion, or “demobilization “.

Their findings, published in Scientific reportsshow that social stress – which varied widely across countries – drives the multi-wave dynamics of COVID-19 outbreaks.

The study analyzed data from China, the United States, the United Kingdom, Germany, Colombia, Italy, Spain, Israel, Russia, France, Brazil, India and Iran – and contributed to the new model system proposed by the research team, which combines the dynamics of the established concept of social stress with classical epidemic models.

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

“We tried to use the pandemic for research and to quantify social and cultural differences between countries. We measured country variability in two processes: the mobilization of people for rational protective behavior and the exhaustion of this mobilization with the destruction of rational behavior.

“This is a serious lesson for the development of education, for the planning of real politics and similar things. Why was the mobilization in Germany and Israel significantly faster than in the UK Why, according to published epidemic data, did some countries quickly mobilize, but also very quickly demobilize (Iran)?

“How do you convince people to mobilize and maintain their rational behavior? When and how should we teach these skills to our children? And what are we willing to pay for these capabilities? Our study shows that we need to answer these and other questions.

For each country analyzed in the study, the researchers looked at 200 days of data, from 100 confirmed cases of COVID-19 in each country. They developed the SIR model for disease spread, which considers the number of infected individuals versus the number of “recovered” and “susceptible” members of the population, taking into account various patterns of human behavior.

Each country has demonstrated some form of first- and second-wave pattern, although the pattern does not account for factors that become important later, including continued improvement in biological protection methods (such as vaccination), trends economic and viral mutations. During the first 200 days, the dynamics of the epidemic are mainly determined by the contagiousness of the virus and the behavior of people.

Therefore, the researchers determined that the big difference between the spread of COVID-19 between countries is caused by social differences, in response to the established sociological concept of social stress.

This states that “sensitive” individuals will go through a cycle of three modes; ignorance (living without restrictions); resistance (individuals consciously and actively practicing social distancing measures; and exhaustion (exhaustion of the person’s ability to follow social distancing measures).

The rate at which this cycle repeats itself is largely determined by the stress response rate of a population and the rate at which a population becomes depleted due to social distancing measures. Colombia, Iran and the United States had the highest “burnout rates” of the countries in the study, with the United Kingdom at the median rate.

China’s stress response rate was the highest of the 13 countries analyzed, reflecting a rapid and dramatic spread of the virus among the human population – after a large initial spike, cases and morbidity rates fell sharply due to ‘a response from the highly unified society.

Professor Victor Kazantsev, leader of the Lobachevsky University team that contributed to the study, and head of the Department of Neurotechnology, Nizhny Novgorod, Russia, said:

“COVID-19 pandemics have given people a better understanding of our behavior in situations of global stress. This knowledge will help humanity to survive more. Our work is a step in extracting this new knowledge from COVID-19 data.

Dr Innokentiy Kastalskiy of Lobachevsky University and the Institute of Applied Physics of the Russian Academy of Sciences, added:

“This work is a first step in combining the modeling of social stress with the dynamics of epidemics. We also need to consider the dynamics of immunity, viral evolution and economics. Such models will provide us with tools to quantify different situations, evaluate solutions and play different “in silico” scenarios to develop anti-epidemic strategies specific to a particular society: country, region or social group.

Researchers say that ranking countries according to their ability to mobilize people for protective anti-epidemic behavior and to maintain this mobilization for a considerable time can help predict the dynamics of future epidemic outbreaks and manage their impact on the population. .

Leicester students on the Data Analysis for Business Intelligence MSc program will also use the models presented in the paper to analyze outbreaks in all UN countries in more detail.

“Social stress drives the multi-wave dynamics of COVID-19 outbreaks” is published in Scientific reports.

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