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Although early concepts of risk perception measures distinguished cognitive from affective items, until recently multi-dimensional taxonomies were absent from risk perception studies, and even more from tests of their association with behavior or policy support. Six longitudinal panel surveys on U.S. COVID-19 views (n = 2004 February 2020, ending April 2021) allowed testing of these relationships among ≤ 10 risk perception items measured in each wave. Confirmatory factor analyses revealed consistent distinctions between personal (conditioning perceived risk on taking further or no further protective action), collective (U.S., global), affective (concern, dread), and severity (estimates of eventual total U.S. infections and deaths) measures, while affect (good-bad feelings) and duration (how long people expect the outbreak to last) did not fit with their assumed affective and severity (respectively) parallels. Collective and affective/affect risk perceptions most strongly predicted both behavioral intentions and policy support for mask wearing, avoidance of large public gatherings, and vaccination, controlling for personal risk perception (which might be partly reflected in the affective/affect effects) and other measures. These findings underline the importance of multi-dimensionality (e.g. not just asking about personal risk perceptions) in designing risk perception research, even when trying to explain personal protective actions. 

Identifying and understanding risk perceptions—“how bad are the harms” to humans or to what they value that people see as potentially or actually arising from entities or events—has been critical for risk analysis, both for its own sake, and for expected associations between risk perceptions and subsequent outcomes, such as risky or protective behavior, or support for hazard management policies. Cross-sectional surveys have been the dominant method for identifying and understanding risk perceptions, yielding valuable data. However, cross-sectional surveys are unable to probe the dynamics of risk perceptions over time, which is critical to do while living in a dynamically hazardous world and to build causal understandings. Building upon earlier longitudinal panel studies of Americans’ Ebola and Zika risk perceptions using multi-level modeling to assess temporal changes in these views and inter-individual factors affecting them, we examined patterns in Americans’ COVID-19 risk perceptions in six waves across 14 months. The findings suggest that, in general, risk perceptions increased from February 2020 to April 2021, but with varying trends across different risk perception measures (personal, collective, affective, affect, severity, and duration). Factors in baseline risk perceptions (Wave 1) and inter-individual differences across waves differed even more: baseline ratings were associated with how immediate the threat is (temporal distance) and how likely the threat would affect people like oneself (social distance), and following the United States news about the pandemic. Inter-individual trend differences were shaped most by temporal distance, whether local coronavirus infections were accelerating their upward trend, and subjective knowledge about viral transmission. Associations of subjective knowledge and risk trend with risk perceptions could change signs (e.g. from positive to negative) over time. These findings hold theoretical implications for risk perception dynamics and taxonomies, and research design implications for studying risk perception dynamics and their comparison across hazards. 

Two decades ago a research team clarified that cross-sectional associations of risk perceptions and protective behavior can only test an “accuracy” hypothesis: e.g., people with higher risk perceptions at Ti should also exhibit low protective behavior and/or high risky behavior at Ti. They argued that these associations are too often interpreted wrongly as testing two other hypotheses, only testable longitudinally: the “behavioral motivation” hypothesis, that high risk perception at Ti increases protective behavior at Ti+1, and the “risk reappraisal” hypothesis, that protective behavior at Ti reduces risk perception at Ti+1. Further, this team argued that risk perception measures should be conditional (e.g., personal risk perception if one’s behavior does not change). Yet these theses have garnered relatively little empirical testing. An online longitudinal panel study of U.S. residents’ COVID-19 views across six survey waves over 14 months in 2020–2021 tested these hypotheses for six behaviors (hand washing, mask wearing, avoiding travel to infected areas, avoiding large public gatherings, vaccination, and [for five waves] social isolation at home). Accuracy and behavioral motivation hypotheses were supported for both behaviors and intentions, excluding a few waves (particularly in February–April 2020, when the pandemic was new in the U.S.) and behaviors. The risk reappraisal hypothesis was contradicted—protective behavior at one wave increased risk perception later—perhaps reflecting continuing uncertainty about efficacy of COVID-19 protective behaviors and/or that dynamic infectious diseases may yield different patterns than chronic diseases dominating such hypothesis-testing. These findings raise intriguing questions for both perception- behavior theory and behavior change practice.