Social stress drives the multi-wave dynamics of COVID-19 outbreaks

TL;DR

This paper introduces a sociophysics-based model combining social stress dynamics with classical epidemic models to accurately predict multi-wave COVID-19 outbreaks across different countries.

Contribution

It develops a novel multi-wave epidemic model incorporating social stress and the general adaptation syndrome, improving prediction accuracy over traditional SIR models.

Findings

Model fits data from 13 countries with high accuracy.

Kinetic constants reveal societal resilience and response capacity.

Model captures multiple epidemic waves driven by social fatigue and adaptation.

Abstract

The dynamics of epidemics depend on how people's behavior changes during an outbreak. At the beginning of the epidemic, people do not know about the virus, then, after the outbreak of epidemics and alarm, they begin to comply with the restrictions and the spreading of epidemics may decline. Over time, some people get tired/frustrated by the restrictions and stop following them (exhaustion), especially if the number of new cases drops down. After resting for a while, they can follow the restrictions again. But during this pause the second wave can come and become even stronger then the first one. Studies based on SIR models do not predict the observed quick exit from the first wave of epidemics. Social dynamics should be considered. The appearance of the second wave also depends on social factors. Many generalizations of the SIR model have been developed that take into account the weakening of immunity over time, the evolution of the virus, vaccination and other medical and biological details. However, these more sophisticated models do not explain the apparent differences in outbreak profiles between countries with different intrinsic socio-cultural features. In our work, a system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models. Social stress is described by the tools of sociophysics. The combination of a dynamic SIR-type model with the classical triad of stages of the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible to describe with high accuracy the available statistical data for 13 countries. The sets of kinetic constants corresponding to optimal fit of model to data were found. They characterize the ability of society to mobilize efforts against epidemics and maintain this concentration over time, and can further help in the development of strategies specific to a particular society.