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Dynamical footprints enable detection of disease emergence

Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm...

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Detalles Bibliográficos
Autores principales: Brett, Tobias S., Rohani, Pejman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239390/
https://www.ncbi.nlm.nih.gov/pubmed/32433658
http://dx.doi.org/10.1371/journal.pbio.3000697
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author Brett, Tobias S.
Rohani, Pejman
author_facet Brett, Tobias S.
Rohani, Pejman
author_sort Brett, Tobias S.
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description Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.
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spelling pubmed-72393902020-06-03 Dynamical footprints enable detection of disease emergence Brett, Tobias S. Rohani, Pejman PLoS Biol Research Article Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases. Public Library of Science 2020-05-20 /pmc/articles/PMC7239390/ /pubmed/32433658 http://dx.doi.org/10.1371/journal.pbio.3000697 Text en © 2020 Brett, Rohani http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Brett, Tobias S.
Rohani, Pejman
Dynamical footprints enable detection of disease emergence
title Dynamical footprints enable detection of disease emergence
title_full Dynamical footprints enable detection of disease emergence
title_fullStr Dynamical footprints enable detection of disease emergence
title_full_unstemmed Dynamical footprints enable detection of disease emergence
title_short Dynamical footprints enable detection of disease emergence
title_sort dynamical footprints enable detection of disease emergence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239390/
https://www.ncbi.nlm.nih.gov/pubmed/32433658
http://dx.doi.org/10.1371/journal.pbio.3000697
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