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Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi
Anopheles stephensi, an invasive malaria vector native to South Asia and the Arabian Peninsula, was detected in Djibouti’s seaport, followed by Ethiopia, Sudan, Somalia, and Nigeria. If An. stephensi introduction is facilitated through seatrade, similar to other invasive mosquitoes, the identificati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843102/ https://www.ncbi.nlm.nih.gov/pubmed/36650185 http://dx.doi.org/10.1038/s41598-023-27439-0 |
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author | Ahn, Jordan Sinka, Marianne Irish, Seth Zohdy, Sarah |
author_facet | Ahn, Jordan Sinka, Marianne Irish, Seth Zohdy, Sarah |
author_sort | Ahn, Jordan |
collection | PubMed |
description | Anopheles stephensi, an invasive malaria vector native to South Asia and the Arabian Peninsula, was detected in Djibouti’s seaport, followed by Ethiopia, Sudan, Somalia, and Nigeria. If An. stephensi introduction is facilitated through seatrade, similar to other invasive mosquitoes, the identification of at-risk countries are needed to increase surveillance and response efforts. Bilateral maritime trade data is used to (1) identify coastal African countries which were highly connected to select An. stephensi endemic countries, (2) develop a prioritization list of countries based on the likelihood of An. stephensi introduction through maritime trade index (LASIMTI), and (3) use network analysis of intracontinental maritime trade to determine likely introduction pathways. Sudan and Djibouti were ranked as the top two countries with LASIMTI in 2011, which were the first two coastal African countries where An. stephensi was detected. With Djibouti and Sudan included as source populations, 2020 data identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top countries with LASIMTI. Network analysis highlight South Africa, Mauritius, Ghana, and Togo. These tools can prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of identified countries may limit further expansion of An. stephensi by serving as an early warning system. |
format | Online Article Text |
id | pubmed-9843102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98431022023-01-17 Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi Ahn, Jordan Sinka, Marianne Irish, Seth Zohdy, Sarah Sci Rep Article Anopheles stephensi, an invasive malaria vector native to South Asia and the Arabian Peninsula, was detected in Djibouti’s seaport, followed by Ethiopia, Sudan, Somalia, and Nigeria. If An. stephensi introduction is facilitated through seatrade, similar to other invasive mosquitoes, the identification of at-risk countries are needed to increase surveillance and response efforts. Bilateral maritime trade data is used to (1) identify coastal African countries which were highly connected to select An. stephensi endemic countries, (2) develop a prioritization list of countries based on the likelihood of An. stephensi introduction through maritime trade index (LASIMTI), and (3) use network analysis of intracontinental maritime trade to determine likely introduction pathways. Sudan and Djibouti were ranked as the top two countries with LASIMTI in 2011, which were the first two coastal African countries where An. stephensi was detected. With Djibouti and Sudan included as source populations, 2020 data identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top countries with LASIMTI. Network analysis highlight South Africa, Mauritius, Ghana, and Togo. These tools can prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of identified countries may limit further expansion of An. stephensi by serving as an early warning system. Nature Publishing Group UK 2023-01-17 /pmc/articles/PMC9843102/ /pubmed/36650185 http://dx.doi.org/10.1038/s41598-023-27439-0 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 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 Ahn, Jordan Sinka, Marianne Irish, Seth Zohdy, Sarah Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi |
title | Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi |
title_full | Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi |
title_fullStr | Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi |
title_full_unstemmed | Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi |
title_short | Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi |
title_sort | modeling marine cargo traffic to identify countries in africa with greatest risk of invasion by anopheles stephensi |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843102/ https://www.ncbi.nlm.nih.gov/pubmed/36650185 http://dx.doi.org/10.1038/s41598-023-27439-0 |
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