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

e-Archivo Repository

Show simple item record Castro, Mario Ares, Saúl Cuesta, José A. 2021-02-15T16:57:47Z 2021-02-15T16:57:47Z 2020-10-20
dc.identifier.bibliographicCitation Proceedings of the National Academy of Sciences of th United States of America, 117(42), Oct. 2020, Pp. 26190-26196
dc.identifier.issn 1091-6490
dc.identifier.issn 0027-8424 (online)
dc.description.abstract Epidemic 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.
dc.format.extent 7
dc.language.iso eng
dc.publisher National Academy of Sciences of the United States of America
dc.rights Copyright © 2020 the Author(s). Published by PNAS.
dc.rights This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Predictability
dc.subject.other Epidemics
dc.subject.other Forecast
dc.subject.other Bayesian
dc.title The turning point and end of an expanding epidemic cannot be precisely forecast
dc.type article
dc.subject.eciencia Matemáticas
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. FIS2016-78883-C2-2-P
dc.relation.projectID Gobierno de España. PID2019-106339GB-I00
dc.relation.projectID Gobierno de España. PGC2018-098186-B-I00
dc.relation.projectID Gobierno de España. FIS2017-89773-P
dc.relation.projectID Gobierno de España. FIS2016-78313-P
dc.relation.projectID Gobierno de España. PID2019-109320GB-100/AEI/10.13039/501100011033
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 26190
dc.identifier.publicationissue 42
dc.identifier.publicationlastpage 26196
dc.identifier.publicationtitle Proceedings of the National Academy of Sciences of th United States of America
dc.identifier.publicationvolume 117
dc.identifier.uxxi AR/0000026258
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades (España)
dc.affiliation.dpto UC3M. Departamento de Matemáticas
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Interdisciplinar de Sistemas Complejos (GISC)
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record