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dc.contributor.authorProverbio, Daniele
dc.contributor.authorKemp, Françoise
dc.contributor.authorMagni, Stefano
dc.contributor.authorGonçalves, Jorge
dc.date.accessioned2022-04-01T23:30:25Z
dc.date.available2022-04-01T23:30:25Z
dc.date.issued2022-03-30
dc.identifier.issn1553-734X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335692
dc.description.abstractDeveloping measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlePerformance of early warning signals for disease re-emergence: A case study on COVID-19 data.
dc.typeArticle
dc.date.updated2022-04-01T08:41:10Z
prism.endingPagee1009958
prism.issueIdentifier3
prism.publicationNamePLoS Comput Biol
prism.startingPagee1009958
prism.volume18
dc.identifier.doi10.17863/CAM.83128
dcterms.dateAccepted2022-02-23
rioxxterms.versionofrecord10.1371/journal.pcbi.1009958
rioxxterms.versionAM
dc.contributor.orcidProverbio, Daniele [0000-0002-0122-479X]
dc.contributor.orcidKemp, Françoise [0000-0001-5845-293X]
dc.contributor.orcidMagni, Stefano [0000-0001-8649-3616]
dc.contributor.orcidGonçalves, Jorge [0000-0002-5228-6165]
dc.identifier.eissn1553-7358
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-03-30
cam.orpheus.success2022-04-01 - Embargo set during processing via Fast-track
cam.depositDate2022-04-01
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2022-03-30


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