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The COVID-19 pandemic has spread uncertainty and social disruption, and exacerbated political divides in the United States. Most studies of the drivers of the epidemic focus on victim characteristics without consideration of drivers in the general population. This study presents statistical models that track the underlying factors in the general population associated with the spread of the pandemic and addresses how social learning mechanisms have led people to adopt perspectives and behaviors depending on their social context. Despite many social, physiological and economic factors, the statistical drivers of the pandemic primarily relate to the presence of vectors and the probability of transmission. However, the relationship between these drivers and COVID-19 deaths is weak and variable outside of the New York metropolitan area. Furthermore, the per capita death rate in much of the country has been much lower than the New York metropolitan area. There have been two very different experiences with the pandemic, one where the signals of its danger have been obvious from the start and one where the signals have been much weaker. Social learning mechanisms (in-group information sharing, imitation, costly punishment) have amplified the effect of people’s experiences with the pandemic. Sheltering in cities and protesting shutdowns in rural areas probably were initially adaptive somatic efforts in the evolutionary sense, given the different realities of the pandemic versus its economic costs in urban versus rural environments. These adaptations, however, have deepened the political divides in an already Balkanized country. The paper concludes with practical recommendations for how to use social learning theory for disseminating information on how to combat the pandemic.

A tale of two pandemics: evolutionary psychology, urbanism, and the biology of disease spread deepen sociopolitical divides in the U.S.