Quantifying and mapping population response to the COVID-19 pandemic in different countries for the period 2020–2022

Aim: This study analyses the time series of daily increases in the number of diagnosed COVID-19 cases in Russia and countries from different continents. The aim of the study is to identify the specifics of the population response of different countries to the spread of the pandemic and anti-epidemic measures of public authorities to determine the most effective model to describe this process. This is a problematic, synoptic, and pilot study. Methods: To evaluate this response strategy, models and methods from reliability theory are used to describe the probability of health protection, the probability density function of an increasing number of cases, the integrated risk of infection, the risk of morbidity, the acceptable risk, and the manageability of the epidemic situation. To approximate infection curves, various daily incidence rate functions are used and compared, and their coefficients are calculated for various pandemic waves. Results: The results demonstrate that the Fréchet distribution function is the best model for the epidemic process. Indicators of variability in acceptable risk were identified during the first stage of pandemic development, showing the varying controllability of the situation by health systems. Through meta-analysis, country distributions were shown to appear as a single pattern, abstracted from local conditions. Estimated coefficients of reliability functions allow the construction of cartograms that reflect the peculiarities of state epidemic regulation and the stages of global pandemic deployment. Conclusions: The findings confirm the effectiveness of the selected model in terms of reliability theory and identify directions for model improvement, taking into account the dynamic nature of the pandemic and its specific characteristics in different countries. The study is based on the methodological approach of function stratification (geometric fiber bundle). It allows for a deeper understanding of the identified patterns within a broader knowledge system.