Εμφάνιση απλής εγγραφής

dc.creatorBastani H., Drakopoulos K., Gupta V., Vlachogiannis I., Hadjicristodoulou C., Lagiou P., Magiorkinis G., Paraskevis D., Tsiodras S.en
dc.date.accessioned2023-01-31T07:36:22Z
dc.date.available2023-01-31T07:36:22Z
dc.date.issued2021
dc.identifier10.1038/s41586-021-04014-z
dc.identifier.issn00280836
dc.identifier.urihttp://hdl.handle.net/11615/71158
dc.description.abstractThroughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting entry from select nations on the basis of population-level epidemiological metrics such as cases, deaths or testing positivity rates1,2. Here we report the design and performance of a reinforcement learning system, nicknamed Eva. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources on the basis of incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2–4 times as many during peak travel, and 1.25–1.45 times as many asymptomatic, infected travellers as testing policies that utilize only epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies3 that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.en
dc.language.isoenen
dc.sourceNatureen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115342234&doi=10.1038%2fs41586-021-04014-z&partnerID=40&md5=304f3b7ffc854243d97c90b7c8d773a8
dc.subjectCOVID-19en
dc.subjectdemographic methoden
dc.subjectinformationen
dc.subjectlearningen
dc.subjectreinforcementen
dc.subjectsevere acute respiratory syndromeen
dc.subjectAgnosticen
dc.subjectarticleen
dc.subjectcontrolled studyen
dc.subjectdemographyen
dc.subjectGreeceen
dc.subjecthumanen
dc.subjectnonhumanen
dc.subjectpredictive valueen
dc.subjectprevalenceen
dc.subjectpublic healthen
dc.subjectreinforcement (psychology)en
dc.subjectSevere acute respiratory syndrome coronavirus 2en
dc.subjectsummeren
dc.subjecttravelen
dc.subjectdiagnosisen
dc.subjectemporiatricsen
dc.subjectepidemiologyen
dc.subjectheterozygoteen
dc.subjectmachine learningen
dc.subjectprevention and controlen
dc.subjecttravelen
dc.subjectGreeceen
dc.subjectCoronavirusen
dc.subjectSARS coronavirusen
dc.subjectCarrier Stateen
dc.subjectCOVID-19en
dc.subjectGreeceen
dc.subjectHumansen
dc.subjectMachine Learningen
dc.subjectPrevalenceen
dc.subjectPublic Healthen
dc.subjectTravelen
dc.subjectTravel Medicineen
dc.subjectNature Researchen
dc.titleEfficient and targeted COVID-19 border testing via reinforcement learningen
dc.typejournalArticleen


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