A Novel Causal Risk-Based Decision-Making Methodology: The Case of Coronavirus

Behavioral biases and heuristics in perceptions of COVID-19 risks and prevention decisions

This study adds to an emerging literature on the factors associated with individual perceptions of COVID-19 risks and decision-making processes related to prevention behaviors. We conducted a survey in the Netherlands (N = 3600) in June-July 2020 when the first peak of COVID-19 infections, hospitalizations, and deaths had passed, and lockdown measures had been eased. Dutch policies relied heavily on individual prevention behaviors to mitigate a second infection wave. We examine whether biases and heuristics that have been observed in how people perceive and respond to other risks also apply to the newly emergent risks posed by COVID-19. The results indicate that people simplify risk using threshold models and that risk perceptions are related with personal experiences with COVID-19 and experiences of close others, supporting the availability heuristic. We also observe that prevention behavior is more strongly associated with COVID-19 risk perceptions and feelings toward the risk than with local indicators of COVID-19 risks, and that prevention behavior is related with herding. Support for government lockdown measures is consistent with preferences that may contribute to the not-in-my-term-of-office bias. In addition, we offer insights into the role of trust, worry, and demographic characteristics in shaping perceptions of COVID-19 risks and how these factors relate with individual prevention behaviors and support for government prevention measures. We provide several lessons for the design of policies that limit COVID-19 risks, including risk communication strategies and appeals to social norms. Perhaps more importantly, our analysis allows for learning lessons to mitigate the risks of future pandemics.

Understanding Health Care Administrators’ Data and Information Needs for Decision Making during the COVID-19 Pandemic: A Qualitative Study at an Academic Health System

Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels.

Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19

This Special Issue is dedicated to issues and challenges related to pandemic risk and resilience, with a focus on policy and operations of global systems in the COVID-19 pandemic. The cascading effects of emerging and reemerging infectious diseases to the global economy are a critical interest. Measures to confront the ongoing pandemic are an urgent need. Data analysis at regional and global scales is helping to prioritize response and resilience across locations of high risks. The risk sciences are available for addressing human health and infection risks; the evaluation of risk management strategies and tradeoffs; risk perception as it relates to information processing and receiving risk communication; and tracking system resilience as it relates to various imposed measures.