Exponential-growth prediction bias and compliance with safety measures related to COVID-19

Abstract

Objective We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x -weeks hence when presented with y -weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB. Rationale The scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols. Method To collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others’ behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Results Respondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Conclusion Behavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors.

Anticipating trajectories of exponential growth

“…Although from a slightly different perspective, this effect is of new relevance with respect to the coronavirus disease 2019 (COVID-19) [ 5 ]. The inability to anticipate exponential growth left many, the public [ 6 ] as well as political decision makers, unprepared for the rapid increase in COVID-19 cases.…”

What containment strategy leads us through the pandemic crisis? An empirical analysis of the measures against the COVID-19 pandemic

“…Our analysis also assumes that the implemented measures have the same impact in observed countries and states. Several studies have shed light on the differences in compliance with measures dependent on the stage of the COVID-19 pandemic [43], the trust in the health care system [44], or country differences [45]. Furthermore, we "only" observe measures implemented by the government and law institutions.…”

Optimizing Decision-Making Processes in Times of COVID-19: Using Reflexivity to Counteract Information-Processing Failures

“…Early estimates stated that seven billion infections and forty million deaths could arise ( Joffe, 2021 ) with estimates of case fatality rates ranging from 0.17% to as high a 20% (the latter was claimed in an article of Baud et al, 2020 ; for a review see Caduff, 2020 ). Moreover, early models predicted that the spread would be exponential ( Banerjee et al, 2021 ; Ferguson et al, 2020 ). Based on these early estimates, many governments decided to take decisive action and enforce a combination of strict lockdowns, curfews, and the closing of “non-essential businesses” (cf.…”

“…For instance, a pervasive bias that is highly vulnerable to framing effects is exponential growth prediction bias, the phenomenon whereby people underestimate exponential growth when presented with numerical information ( Wagenaar and Sagaria, 1975 ; Wagenaar and Timmers, 1979 ). In the context of COVID-19, this bias has been shown to lead to a systematic tendency to underestimate the number of COVID-19 cases or fatality rates in the future based on current numbers ( Wagenaar and Sagaria, 1975 ; Banerjee et al, 2021 ). This bias, may also have contributed to more risky decision-making, by potentially leading to unwarranted lax policy-measures (e.g., when current infection rates were low but likely to grow exponentially) or to the late introduction of stricter policy-measures (e.g., when current infection rates were already too high).…”