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The turning point and end of an expanding epidemic cannot be precisely forecast
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585017/ https://www.ncbi.nlm.nih.gov/pubmed/33004629 http://dx.doi.org/10.1073/pnas.2007868117 |
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author | Castro, Mario Ares, Saúl Cuesta, José A. Manrubia, Susanna |
author_facet | Castro, Mario Ares, Saúl Cuesta, José A. Manrubia, Susanna |
author_sort | Castro, Mario |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7585017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-75850172020-10-30 The turning point and end of an expanding epidemic cannot be precisely forecast Castro, Mario Ares, Saúl Cuesta, José A. Manrubia, Susanna Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2020-10-20 2020-10-01 /pmc/articles/PMC7585017/ /pubmed/33004629 http://dx.doi.org/10.1073/pnas.2007868117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Castro, Mario Ares, Saúl Cuesta, José A. Manrubia, Susanna The turning point and end of an expanding epidemic cannot be precisely forecast |
title | The turning point and end of an expanding epidemic cannot be precisely forecast |
title_full | The turning point and end of an expanding epidemic cannot be precisely forecast |
title_fullStr | The turning point and end of an expanding epidemic cannot be precisely forecast |
title_full_unstemmed | The turning point and end of an expanding epidemic cannot be precisely forecast |
title_short | The turning point and end of an expanding epidemic cannot be precisely forecast |
title_sort | turning point and end of an expanding epidemic cannot be precisely forecast |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585017/ https://www.ncbi.nlm.nih.gov/pubmed/33004629 http://dx.doi.org/10.1073/pnas.2007868117 |
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