Journal article
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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(Preview, Version of record, pdf, 1.8MB, Terms of use)
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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
+ Centers for Disease Control and Prevention Foundation
More from this funder
- Funder identifier:
- 10.13039/100020365
- 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:
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2045-2322
- Language:
-
English
- Keywords:
- Pubs id:
-
1330734
- Local pid:
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pubs:1330734
- Source identifiers:
-
W4316810652
- Deposit date:
-
2026-05-05
- ARK identifier:
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Terms of use
- Copyright date:
- 2023
- Licence:
- CC Attribution (CC BY)
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