The Social and Political Impact of the COVID-19 Pandemic: An Introduction

The COVID-19 pandemic highlights the importance of responsive institutions: governments and communities coordinating policy changes;media, social networks, and officials swiftly and accurately conveying information;and an engaged public. This special issue explores social and political factors that both shaped initial response to the pandemic, and were altered by it. Institutional inequalities and variations in government response created significant differences in health outcomes even as the contagious nature of the pandemic linked spaces and people. Thus COVID-19 created new crises, exacerbated inequalities, and led to broad social changes. Social scientists will spend decades unraveling the consequences of COVID-19. This issue challenges scholars to apply existing theories and frameworks, but also to see the pandemic as an event that stimulates us to reevaluate settled paradigms.

Mobility network models of COVID-19 explain inequities and inform reopening.

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.