RAPID: How interpersonal interaction and personal hygiene norms and their shift dynamics impact COVID-19 transmission

RAPID: How interpersonal interaction and personal hygiene norms and their shift dynamics impact COVID-19 transmission RAPID: How interpersonal interaction and personal hygiene norms and their shift dynamics impact COVID-19 transmission Overview: To reduce the devastation to lives and livelihoods from the COVID-19 pandemic until a vaccine is developed, it is critical to be able to modify peoples behavior in ways that block the spread of the disease. Although governments have suggested and enforced behavioral guidelines, their impact and longer-term sustainability is limited by the extent to which people comply with these practices and reinforce them through social pressures and convert them to longer-term norms. This study will utilize theories of norm shifts to investigate: 1) how population composition of demographics and personality, government policies, information that people receive, community norms, and normative pressures, determine aggregate behaviors in the context of interpersonal distance, manner of greeting, avoidance of potential patients, and mask wearing, and 2) how these behaviors and their changes in turn influence the pandemic transmission pattern in the populations. To develop a robust model of behavioral shifts during the pandemic waves in the next 12 months, we will collect longitudinal and time-series cross-sectional survey data from three distinct populations: Derung, a small-scale subsistence society in China where exposure risk is low, urban populations in China with stringent government directives, and urban populations in the US experiencing less stringent government directives. The data will be analyzed to illuminate how people make interpersonal interaction and personal hygiene behavioral decisions, how norms in these contexts change in each community, and how the prevailing norms and the norm shift trajectories impact COVID-19 transmission patterns. Intellectual Merit: This study is novel in its perspective and methodology. It aims to use norm shift theories to predict behavioral change, and can shed new light on the transmission patterns of COVID-19 to better assess the efficacy of different behavioral interventions. By studying norms as complex systems and examining not only behaviors, but also preferences, beliefs, expectations, and how they interact with social, political, medical, and economic pressures to produce behavioral choices, it can obtain a high-resolution depiction of individual norm decision making algorithm, an understudied critical predictor for individual norm behaviors and behavioral shifts. Such multi-dimensional measurements fitted to models of norm change dynamics can better predict population-level outcomes and how they may change. The project can thus contribute to anthropological theories and methodologies on norm shift. The Co-PI has developed mathematical models and conducted fieldwork with Derung to study norm decision algorithms and norm shift dynamics, co-supervised by the PI. She is currently in Derung for data collection. Broader Impacts: This study can suggest transformative pandemic control interventions by focusing on normative decision algorithms, a critical predictor for whether and when a person adopts preventive practices. The inclusion of the Derung site can capture a wider range of human behavioral responses to the current pandemic. This allows us to better predict future global spread and reemergence of COVID-19 and can suggest interventions that may be needed for small-scale, subsistence societies to survive the pandemic. The data and results will be made immediately available to governments, other institutions, researchers and the general public through preprint servers, social media and web forums, to inform further research, policy making, and individual pandemic prevention. The project will provide STEM training to Derung field assistants hired for data collection and undergraduate students at Arizona State University to be trained for data coding and analysis. It will also contribute to the Co-PIs doctoral dissertation.