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Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi

Abstract:
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
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41598-023-27439-0
Publication website:
https://research.lstmed.ac.uk/ws/files/25885932/s41598-025-04682-1.pdf

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-7145-3179
More by this author
Role:
Author
ORCID:
0000-0001-8709-8247
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Role:
Author
ORCID:
0000-0001-5316-0567



Publisher:
Nature Research
Journal:
Scientific Reports More from this journal
Volume:
13
Issue:
1
Pages:
876-876
Article number:
876
Publication date:
2023-01-17
DOI:
EISSN:
2045-2322
ISSN:
2045-2322


Language:
English
Keywords:
Pubs id:
1330734
Local pid:
pubs:1330734
Source identifiers:
W4316810652
Deposit date:
2026-05-05
ARK identifier:
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