Sociocultural determinants of global mask-wearing behavior.

Behavioral responses influence the trajectories of epidemics. During the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) reduced pathogen transmission and mortality worldwide. However, despite the global pandemic threat, there was substantial cross-country variation in the adoption of protective behaviors that is not explained by disease prevalence alone. In particular, many countries show a pattern of slow initial mask adoption followed by sharp transitions to high acceptance rates. These patterns are characteristic of behaviors that depend on social norms or peer influence. We develop a game-theoretic model of mask wearing where the utility of wearing a mask depends on the perceived risk of infection, social norms, and mandates from formal institutions. In this model, increasing pathogen transmission or policy stringency can trigger social tipping points in collective mask wearing. We show that complex social dynamics can emerge from simple individual interactions and that sociocultural variables and local policies are important for recovering cross-country variation in the speed and breadth of mask adoption. These results have implications for public health policy and data collection.

Personal hardship narrows the partisan gap in COVID-19 and climate change responses.

The COVID-19 pandemic in the United States was characterized by a partisan gap. Democrats were more concerned about this novel health threat, more willing to socially distance, and more likely to support policies aimed at mitigating the spread of the virus than Republicans. In cross-sectional analyses of three nationally representative survey waves in 2020, we find that adverse experience with COVID-19 is associated with a narrowing of the partisan gap. The mean difference between Republicans and Democrats in concern, policy support, and behavioral intentions narrows or even disappears at high levels of self-reported adverse experience. Reported experience does not depend on party affiliation and is predicted by local COVID-19 incidence rates. In contrast, analyses of longitudinal data and county-level incidence rates do not show a consistent relationship among experience, partisanship, and behavior or policy support. Our findings suggest that self-reported personal experience interacts with partisanship in complex ways and may be an important channel for concern about novel threats such as the COVID-19 pandemic. We find consistent results for self-reported experience of extreme weather events and climate change attitudes and policy preferences, although the association between extreme weather and experience and climate change is more tenuous.

Examining evidence for the Finite Pool of Worry and Finite Pool of Attention hypotheses

The Finite Pool of Worry (FPW) hypothesis states that humans have finite emotional resources for worry, so that when we become more worried about one threat, worry about other threats decreases. Despite its relevance, no conclusive empirical evidence for the hypothesis exists. We leverage the sudden onset of new worries introduced by the COVID-19 pandemic as a natural experiment to test the FPW hypothesis and a related hypothesis, the Finite Pool of Attention (FPA) hypothesis. The FPA hypothesis proposes that when we pay more attention to one threat, our attention to other threats decreases. To test these two hypotheses, we assessed self-reported attention, self-reported worries, and Twitter/news attention to various threats (climate change, terrorism, economic problems, and others) throughout the pandemic in three countries (USA, Italy, and China). We find that as attention to and worry about COVID-19 increases, attention to climate change decreases, but worry does not. Our results are confirmed by further analysis of a large, longitudinal U.S. sample. We find that public perceptions that COVID-19 and climate change are related do not fully explain the positive relationship in worry between the two hazards. In summary, our findings suggest that while there may be a Finite Pool of Attention to threats, there is limited evidence for a Finite Pool of Worry.

Understanding the coevolution of mask wearing and epidemics: A network perspective.

Nonpharmaceutical interventions (NPIs) such as mask wearing can be effective in mitigating the spread of infectious diseases. Therefore, understanding the behavioral dynamics of NPIs is critical for characterizing the dynamics of disease spread. Nevertheless, standard infection models tend to focus only on disease states, overlooking the dynamics of "beneficial contagions," e.g., compliance with NPIs. In this work, we investigate the concurrent spread of disease and mask-wearing behavior over multiplex networks. Our proposed framework captures both the competing and complementary relationships between the dueling contagion processes. Further, the model accounts for various behavioral mechanisms that influence mask wearing, such as peer pressure and fear of infection. Our results reveal that under the coupled disease-behavior dynamics, the attack rate of a disease-as a function of transition probability-exhibits a critical transition. Specifically, as the transmission probability exceeds a critical threshold, the attack rate decreases abruptly due to sustained mask-wearing responses. We empirically explore the causes of the critical transition and demonstrate the robustness of the observed phenomena. Our results highlight that without proper enforcement of NPIs, reductions in the disease transmission probability via other interventions may not be sufficient to reduce the final epidemic size.

Voting in a global pandemic: Assessing dueling influences of Covid-19 on turnout

Abstract Objective We investigate the impact of a global health crisis on political behavior. Specifically, we assess the impact of Covid-19 incidence rates, and the impact of temporal and spatial proximity to the crisis, on voter turnout in the 2020 Brazilian municipal elections. Methods We use Ordinary Least Squares and Spatial Durbin Error models to evaluate sub-national variation in municipal-level Covid-19 incidence and voter turnout. We include controls for political, economic, health, and state context. Results Ceteris paribus, increasing deaths in the month leading up to the election from 0.01 to 1 per 1000 people is associated with a 5 percentage point decrease in turnout;higher cases and deaths earlier in the pandemic are generally associated with higher turnout. Covid-19 incidence rates in nearby municipalities affect local turnout in the same directions. Conclusion Higher Covid-19 incidence near the time of the election decreases voter turnout, while incidence farther from the election increases voter turnout.