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Enhancing global preparedness during an ongoing pandemic from partial and noisy data

As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecuti...

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Autores principales: Klamser, Pascal P, d’Andrea, Valeria, Di Lauro, Francesco, Zachariae, Adrian, Bontorin, Sebastiano, Di Nardo, Antonello, Hall, Matthew, Maier, Benjamin F, Ferretti, Luca, Brockmann, Dirk, De Domenico, Manlio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282504/
https://www.ncbi.nlm.nih.gov/pubmed/37351112
http://dx.doi.org/10.1093/pnasnexus/pgad192
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author Klamser, Pascal P
d’Andrea, Valeria
Di Lauro, Francesco
Zachariae, Adrian
Bontorin, Sebastiano
Di Nardo, Antonello
Hall, Matthew
Maier, Benjamin F
Ferretti, Luca
Brockmann, Dirk
De Domenico, Manlio
author_facet Klamser, Pascal P
d’Andrea, Valeria
Di Lauro, Francesco
Zachariae, Adrian
Bontorin, Sebastiano
Di Nardo, Antonello
Hall, Matthew
Maier, Benjamin F
Ferretti, Luca
Brockmann, Dirk
De Domenico, Manlio
author_sort Klamser, Pascal P
collection PubMed
description As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.
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spelling pubmed-102825042023-06-22 Enhancing global preparedness during an ongoing pandemic from partial and noisy data Klamser, Pascal P d’Andrea, Valeria Di Lauro, Francesco Zachariae, Adrian Bontorin, Sebastiano Di Nardo, Antonello Hall, Matthew Maier, Benjamin F Ferretti, Luca Brockmann, Dirk De Domenico, Manlio PNAS Nexus Biological, Health, and Medical Sciences As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2. Oxford University Press 2023-06-07 /pmc/articles/PMC10282504/ /pubmed/37351112 http://dx.doi.org/10.1093/pnasnexus/pgad192 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Biological, Health, and Medical Sciences
Klamser, Pascal P
d’Andrea, Valeria
Di Lauro, Francesco
Zachariae, Adrian
Bontorin, Sebastiano
Di Nardo, Antonello
Hall, Matthew
Maier, Benjamin F
Ferretti, Luca
Brockmann, Dirk
De Domenico, Manlio
Enhancing global preparedness during an ongoing pandemic from partial and noisy data
title Enhancing global preparedness during an ongoing pandemic from partial and noisy data
title_full Enhancing global preparedness during an ongoing pandemic from partial and noisy data
title_fullStr Enhancing global preparedness during an ongoing pandemic from partial and noisy data
title_full_unstemmed Enhancing global preparedness during an ongoing pandemic from partial and noisy data
title_short Enhancing global preparedness during an ongoing pandemic from partial and noisy data
title_sort enhancing global preparedness during an ongoing pandemic from partial and noisy data
topic Biological, Health, and Medical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282504/
https://www.ncbi.nlm.nih.gov/pubmed/37351112
http://dx.doi.org/10.1093/pnasnexus/pgad192
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