Social markers of a pandemic: modeling the association between cultural norms and COVID-19 spread data

Abstract

While cross-national differences of the epidemic curves of COVID-19 become evident, social markers of such variability are still unexplored. In order to investigate how certain social norms may underlie the heterogeneity of the spread of infections, global social data (including cultural values, indices of prosperity, and government effectiveness) and covariates (such as climate zone, economic indicator, and healthcare access and quality) of early transmission dynamics of COVID-19 were collected. Model-based clustering and random forest regression analysis were applied to identify distinct groups of societies and explore predictors of COVID-19 doubling time. Clustering revealed four groups: (1) reserved; (2) drifting; (3) assertive; and (4) compliant societies. Compliant societies from dry climate zones showed the highest doubling times in spite of increased population densities. Most relevant predictors of doubling time were population density, freedom of assembly and association, and agency, underlining the importance of social factors in the hetereogeneity of COVID-19 transmission rates. Our cluster typology might contribute to the explanation of cross-national variability in early transmission dynamics of highly infectious diseases.

Community movement and COVID-19: a global study using Google's Community Mobility Reports

“…Another study examined survey data on nationwide risk-taking attitudes, finding that this affected mobility; it also reported a clear effect on the mobility of the WHO pandemic announcement [33]. In another work, the importance of freedom of assembly and association was identified as the most important predictor of COVID-19 doubling time among cultural norms [26]. The factors affecting personal movement decisions are complex and inter-related; including the personal perceived risk of infection, socio-economic factors and peer group behaviour [27].…”

“…The main aim of this study was thus to examine the relationship between mobility and confirmed case numbers for COVID-19 globally, and to ascertain whether cross-country patterns in this relationship were apparent. Such patterns could reflect the range of movement restrictions implemented [10], but could also be due to other cultural or socio-economic differences [26, 27]. Another aim was to integrate CMR data into disease models, to assess whether it could enhance model quality and prediction.…”

COVID-19: An Opportunity of Systematic Integration for Chagas Disease. Example of A Community-Based Approach within the Bolivian Population in Barcelona

“…That can notably be explained, as stated earlier, by sociocultural factors such as work and living conditions in which lationamerican immigrants often nd themselves in their host country. Some interviews done during the study also highlight that members of that community are more likely to gather -fueling the spread of the virus -and be fearless of the disease (49). Because of their economic status, potentially infected people tend to live in dwellings and work in positions with major exposure to the COVID-19 virus.…”

Reflecting on prediction strategies for epidemics

“… 59 Empirical findings related to previous epidemics indicated that low educational level, non-White ethnicity, and poor socioenvironmental conditions are typically associated with increased hospitalization or the number of disease outbreaks owing to virus infections. 64 People in disadvantaged communities are generally more susceptible to occupational exposure to the virus and tend to have higher rates of comorbidities.…”