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dc.contributor.authorPritchard, AJ
dc.contributor.authorSilk, MJ
dc.contributor.authorCarrignon, Simon
dc.contributor.authorBentley, RA
dc.contributor.authorFefferman, NH
dc.description.abstractReporting of epidemiological data requires coordinated action by numerous agencies, across a multitude of logistical steps. Using collated and reported information to inform direct interventions can be challenging due to associated delays. Mitigation can, however, occur indirectly through the public generation of concern, which facilitates adherence to protective behaviors. We utilized a coupled-dynamic multiplex network model with a communication- and disease-layer to examine how variation in reporting delay and testing probability are likely to impact adherence to protective behaviors, such as reducing physical contact. Individual concern mediated adherence and was informed by new- or active-case reporting, at the population- or community-level. Individuals received information from the communication layer: direct connections that were sick or adherent to protective behaviors increased their concern, but absence of illness eroded concern. Models revealed that the relative benefit of timely reporting and a high probability of testing was contingent on how much information was already obtained. With low rates of testing, increasing testing probability was of greater mitigating value. With high rates of testing, maximizing timeliness was of greater value. Population-level reporting provided advanced warning of disease risk from nearby communities; but we explore the relative costs and benefits of delays due to scale against the assumption that people may prioritize community-level information. Our findings emphasize the interaction of testing accuracy and reporting timeliness for the indirect mitigation of disease in a complex social system.
dc.publisherElsevier BV
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.titleBalancing timeliness of reporting with increasing testing probability for epidemic data.
dc.publisher.departmentMcdonald Institute For Archaeological Research
prism.publicationNameInfectious Disease Modelling
dc.contributor.orcidCarrignon, Simon [0000-0002-4416-1389]
rioxxterms.typeJournal Article/Review
cam.orpheus.successVoR added to record
pubs.licence-display-nameApollo Repository Deposit Licence Agreement

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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International