Publication:
The turning point and end of an expanding epidemic cannot be precisely forecast

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dc.affiliation.dptoUC3M. Departamento de Matemáticases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Interdisciplinar de Sistemas Complejos (GISC)es
dc.contributor.authorCastro, Mario
dc.contributor.authorAres, Saúl
dc.contributor.authorCuesta, José A.
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2021-02-15T16:57:47Z
dc.date.available2021-02-15T16:57:47Z
dc.date.issued2020-10-20
dc.description.abstractEpidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. A susceptible-infected-removed (SIR) model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slowdown in the number of newly infected individuals during the expansion phase allows one to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the midterm is not due to wrong or incomplete data, since it persists in error-free, synthetically produced datasets and does not necessarily improve by using larger datasets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given.en
dc.format.extent7es
dc.identifier.bibliographicCitationProceedings of the National Academy of Sciences of th United States of America, 117(42), Oct. 2020, Pp. 26190-26196en
dc.identifier.doihttps://doi.org/10.1073/pnas.2007868117
dc.identifier.issn1091-6490
dc.identifier.issn0027-8424 (online)
dc.identifier.publicationfirstpage26190es
dc.identifier.publicationissue42es
dc.identifier.publicationlastpage26196es
dc.identifier.publicationtitleProceedings of the National Academy of Sciences of th United States of Americaen
dc.identifier.publicationvolume117es
dc.identifier.urihttps://hdl.handle.net/10016/31931
dc.identifier.uxxiAR/0000026258
dc.language.isoengen
dc.publisherNational Academy of Sciences of the United States of Americaen
dc.relation.ispartofhttps://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2007868117/-/DCSupplemental
dc.relation.projectIDGobierno de España. FIS2016-78883-C2-2-Pes
dc.relation.projectIDGobierno de España. PID2019-106339GB-I00es
dc.relation.projectIDGobierno de España. PGC2018-098186-B-I00es
dc.relation.projectIDGobierno de España. FIS2017-89773-Pes
dc.relation.projectIDGobierno de España. FIS2016-78313-Pes
dc.relation.projectIDGobierno de España. PID2019-109320GB-100/AEI/10.13039/501100011033es
dc.rightsCopyright © 2020 the Author(s). Published by PNAS.en
dc.rightsThis open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaMatemáticases
dc.subject.otherPredictabilityen
dc.subject.otherEpidemicsen
dc.subject.otherForecasten
dc.subject.otherBayesianen
dc.titleThe turning point and end of an expanding epidemic cannot be precisely forecasten
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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