Eliciting beliefs about COVID-19 prevalence and mortality: epidemiological models compared with the street

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dc.check.date2022-04-08
dc.contributor.authorHarrison, Glenn W.
dc.contributor.authorHofmeyr, Andre
dc.contributor.authorKincaid, Harold
dc.contributor.authorMonroe, Brian
dc.contributor.authorRoss, Don
dc.contributor.authorSchneider, Mark
dc.contributor.authorSwarthout, J. Todd
dc.date.accessioned2021-10-28T09:36:43Z
dc.date.available2021-10-28T09:36:43Z
dc.date.issued2021-04-08
dc.description.abstractSubjective belief elicitation about uncertain events has a long lineage in the economics and statistics literatures. Recent developments in the experimental elicitation and statistical estimation of subjective belief distributions allow inferences about whether these beliefs are biased relative to expert opinion, and the confidence with which they are held. Beliefs about COVID-19 prevalence and mortality interact with risk management efforts, so it is important to understand relationships between these beliefs and publicly disseminated statistics, particularly those based on evolving epidemiological models. The pandemic provides a unique setting over which to bracket the range of possible COVID-19 prevalence and mortality outcomes given the proliferation of estimates from epidemiological models. We rely on the epidemiological model produced by the Institute for Health Metrics and Evaluation together with the set of epidemiological models summarised by FiveThirtyEight to bound prevalence and mortality outcomes for one-month, and December 1, 2020 time horizons. We develop a new method to partition these bounds into intervals, and ask subjects to place bets on these intervals, thereby revealing their beliefs. The intervals are constructed such that if beliefs are consistent with epidemiological models, subjects are best off betting the same amount on every interval. We use an incentivised experiment to elicit beliefs about COVID-19 prevalence and mortality from 598 students at Georgia State University, using six temporally-spaced waves between May and November 2020. We find that beliefs differ markedly from epidemiological models, which has implications for public health communication about the risks posed by the virus.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHarrison, G. W., Hofmeyr, A., Kincaid, H., Monroe, B., Ross, D., Schneider, M. and Swarthout, J. T. (2021) 'Eliciting Beliefs about COVID-19 Prevalence and Mortality: Epidemiological Models Compared with The Street', Methods, 195, pp. 103-112. doi: 10.1016/j.ymeth.2021.04.003en
dc.identifier.doi10.1016/j.ymeth.2021.04.003en
dc.identifier.endpage112en
dc.identifier.issn1046-2023
dc.identifier.journaltitleMethodsen
dc.identifier.startpage103en
dc.identifier.urihttps://hdl.handle.net/10468/12123
dc.identifier.volume195en
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2021 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectSubjective beliefsen
dc.subjectBeliefsen
dc.subjectCOVID-19 mortalityen
dc.subjectCOVID-19 prevalenceen
dc.titleEliciting beliefs about COVID-19 prevalence and mortality: epidemiological models compared with the streeten
dc.typeArticle (peer-reviewed)en
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