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. 2025 Jun 6;15(1):19878.
doi: 10.1038/s41598-025-04682-1.

Spatial distribution and population structure of the invasive Anopheles stephensi in Kenya from 2022 to 2024

Jeanne N Samake #  1 , Duncan K Athinya #  2   3 , Sylvia Milanoi  4 , Edith Ramaita  5 , Margaret Muchoki  4 , Seline Omondi  4 , Bernard Abong'o  4 , Damaris Matoke-Muhia  6 , Charles Mbogo  7 , Kibor Keitany  5 , Wolfgang Richard Mukabana  2 , Florence Oyieke  2 , Mildred Shieshia  8 , Monica Mburu  9 , Sheila Ogoma  9 , Elizabeth Nyawira  9 , Celestine Wekesa  9 , Brian Bartilol  10 , Martin Rono  10 , Marta Maia  10   11 , Wendy O'Meara  12 , Samuel Kahindi  13 , Cristina Rafferty  1   14 , Jonathan S Schultz  15 , Julie R Gutman  16 , John E Gimnig  1   14 , Sarah Zohdy  1   14 , Eric Ochomo  17   18   19
Affiliations

Spatial distribution and population structure of the invasive Anopheles stephensi in Kenya from 2022 to 2024

Jeanne N Samake et al. Sci Rep. .

Abstract

This study analyzes the distribution, genetic diversity, and spread of Anopheles stephensi in Kenya following initial detection in December 2022. A total of 114 larval and 33 adult An. stephensi samples were confirmed in 7 of 18 surveyed counties majorly along transportation routes. Genetic analyses revealed three distinct genetic compositions with different levels of genetic diversity, suggesting multiple introductions into the country. The genetic composition of mosquitoes in most counties resembled southern Ethiopian populations, while those from Turkana showed a unique haplotype. A species distribution model predicts a more extensive range than currently observed, with low precipitation and minimal seasonal temperature variations as key factors influencing distribution. Challenges in adult sampling were noted, with larval sampling revealing co-occurrence with native Anopheles species. The findings have implications for surveillance and control strategies, emphasizing the need for continued monitoring, refined sampling techniques to inform bionomics, and cross-border collaboration.

Keywords: Anopheles stephensi; Kenya; Population structure; Spatial distribution.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethical approval: The methods used in this study were conducted according to the study protocol reviewed and approved by the Scientific and Ethics Review Unit (SERU) at the Kenya Medical Research Institute, SERU 2776. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent from all subjects was obtained for both participation and publication of identifying information/images in an online open-access publication. Consent for publication: This manuscript is published with the permission of the Director-General of the Kenya Medical Research Institute.

Figures

Fig. 1
Fig. 1
Map of Kenya showing the road network (blue dashed lines), areas sampled and sites where An. stephensi has been detected as of August 2024. The red dots represent areas where Anopheles stephensi has been confirmed while those with black dots are areas where An. stephensi has not been detected. The figure was generated using QGIS Version 3.34.11 “Prizren” .
Fig. 2
Fig. 2
The range of Anopheles larval collection sites: (A) used car tire, (B) discarded plastic jerrican, (C) runoff from a community tank, (D) shallow pit dug in a gold mining site, (E) cut out water tank, (F) plastic water storage tanks.
Fig. 3
Fig. 3
Pictures showing the range of adult collection methods evaluated in Marsabit county: (A) Prockopack aspiration in hidden areas outside the house, (B) indoor UV light trap, (C) outdoor UV light trap, (D) Prockopack aspiration Inside a half-filed water storage tank, (E) set up of a BG Sentinel trap outdoors and (F) a volunteer conducting human landing catches outdoors.
Fig. 4
Fig. 4
Multiple sampling efforts in Marsabit shows fluctuations in An. stephensi densities in Marsabit county.
Fig. 5
Fig. 5
Phylogenetic analysis of the invasive An. stephensi COI sequenced data. Haplotype numbering was based on haplotypes list from Carter et al. 2021. Posterior probabilities > 70 are shown.
Fig. 6
Fig. 6
(A) Kenyan An. stephensi COI haplotype map. (B) Kenya An. stephensi COI haplotype compared to Horn of Africa An. stephensi COI haplotypes.
Fig. 7
Fig. 7
Predictor importance by (a) Percent contribution of individual predictors to the ensemble output by mean (EMmean) and by committee averaging (EMca). Outputs were similar, EMmean was selected for the final model (b) Ensemble model TSS from each predictor.
Fig. 8
Fig. 8
Probability of An. stephensi occurrence from the mean model ensemble. The number on the map matches the county number in the table for identification. The figure was generated using R version 4.1.2.
See this image and copyright information in PMC

References

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