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Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots

The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers—landscape change, host distribution, and human exposure—associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance an...

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Autores principales: Muylaert, Renata L., Wilkinson, David A., Kingston, Tigga, D’Odorico, Paolo, Rulli, Maria Cristina, Galli, Nikolas, John, Reju Sam, Alviola, Phillip, Hayman, David T. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611769/
https://www.ncbi.nlm.nih.gov/pubmed/37891177
http://dx.doi.org/10.1038/s41467-023-42627-2
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author Muylaert, Renata L.
Wilkinson, David A.
Kingston, Tigga
D’Odorico, Paolo
Rulli, Maria Cristina
Galli, Nikolas
John, Reju Sam
Alviola, Phillip
Hayman, David T. S.
author_facet Muylaert, Renata L.
Wilkinson, David A.
Kingston, Tigga
D’Odorico, Paolo
Rulli, Maria Cristina
Galli, Nikolas
John, Reju Sam
Alviola, Phillip
Hayman, David T. S.
author_sort Muylaert, Renata L.
collection PubMed
description The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers—landscape change, host distribution, and human exposure—associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.
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spelling pubmed-106117692023-10-29 Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots Muylaert, Renata L. Wilkinson, David A. Kingston, Tigga D’Odorico, Paolo Rulli, Maria Cristina Galli, Nikolas John, Reju Sam Alviola, Phillip Hayman, David T. S. Nat Commun Article The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers—landscape change, host distribution, and human exposure—associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions. Nature Publishing Group UK 2023-10-27 /pmc/articles/PMC10611769/ /pubmed/37891177 http://dx.doi.org/10.1038/s41467-023-42627-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Muylaert, Renata L.
Wilkinson, David A.
Kingston, Tigga
D’Odorico, Paolo
Rulli, Maria Cristina
Galli, Nikolas
John, Reju Sam
Alviola, Phillip
Hayman, David T. S.
Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
title Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
title_full Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
title_fullStr Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
title_full_unstemmed Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
title_short Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots
title_sort using drivers and transmission pathways to identify sars-like coronavirus spillover risk hotspots
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611769/
https://www.ncbi.nlm.nih.gov/pubmed/37891177
http://dx.doi.org/10.1038/s41467-023-42627-2
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