Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of na\"ive populations faced with epidemics

In the context of natural disasters, human responses inevitably intertwine with natural factors. The COVID-19 pandemic, as a significant stress factor, has brought to light profound variations among different countries in terms of their adaptive dynamics in addressing the spread of infection outbreaks across different regions. This emphasizes the crucial role of cultural characteristics in natural disaster analysis. The theoretical understanding of large-scale epidemics primarily relies on mean-field kinetic models. However, conventional SIR-like models failed to fully explain the observed phenomena at the onset of the COVID-19 outbreak. These phenomena included the unexpected cessation of exponential growth, the reaching of plateaus, and the occurrence of multi-wave dynamics. In situations where an outbreak of a highly virulent and unfamiliar infection arises, it becomes crucial to respond swiftly at a non-medical level to mitigate the negative socio-economic impact. Here we present a theoretical examination of the first wave of the epidemic based on a simple SIRSS model (SIR with Social Stress). We conduct an analysis of the socio-cultural features of na\"ive population behaviors across various countries worldwide. The unique characteristics of each country/territory are encapsulated in only a few constants within our model, derived from the fitted COVID-19 statistics. These constants also reflect the societal response dynamics to the external stress factor, underscoring the importance of studying the mutual behavior of humanity and natural factors during global social disasters. Based on these distinctive characteristics of specific regions, local authorities can optimize their strategies to effectively combat epidemics until vaccines are developed.Comment: 29 pages, 16 figures, 1 table, 2 appendice

Моделирование распространения эпидемий с использованием сетей

В представленной работе разработана и реализована модель, позволяющая прогнозировать динамику распространения новой коронавирусной инфекции с применение сетевых данных. Модель реализована в виде программного продукта, который способен спрогнозировать эпидемиологическую обстановку в отдельном городе, используя статические данные по данному населенному пункту.In the presented work, a model has been developed and implemented that allows predicting the dynamics of the spread of a new coronavirus infection using network data. The model is implemented as a software product that is able to predict the epidemiological situation in a particular city using static data for a given locality