A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk
Edited by Nils Chr. Stenseth, University of Oslo, Norway, and approved July 27, 2020 (received for review March 26, 2020)
Significance
In 2012, an unusual outbreak of malaria occurred in Djibouti City followed by increasingly severe annual outbreaks. Investigations revealed the presence of an Asian mosquito species; Anopheles stephensi, which thrives in urban environments. An. stephensi has since been identified in Ethiopia and Sudan. By combining data for An. stephensi across its full range (Asia, Arabian Peninsula, Horn of Africa) with spatial models that identify the species’ preferred habitat, we provide evidence-based maps predicting the possible African locations where An. stephensi could establish if allowed to spread. Our results suggest over 126 million people in cities across Africa could be at risk. This supports the WHO’s call for targeted An. stephensi control and prioritized surveillance.
Abstract
In 2012, an unusual outbreak of urban malaria was reported from Djibouti City in the Horn of Africa and increasingly severe outbreaks have been reported annually ever since. Subsequent investigations discovered the presence of an Asian mosquito species; Anopheles stephensi, a species known to thrive in urban environments. Since that first report, An. stephensi has been identified in Ethiopia and Sudan, and this worrying development has prompted the World Health Organization (WHO) to publish a vector alert calling for active mosquito surveillance in the region. Using an up-to-date database of published locational records for An. stephensi across its full range (Asia, Arabian Peninsula, Horn of Africa) and a set of spatial models that identify the environmental conditions that characterize a species’ preferred habitat, we provide evidence-based maps predicting the possible locations across Africa where An. stephensi could establish if allowed to spread unchecked. Unsurprisingly, due to this species’ close association with man-made habitats, our maps predict a high probability of presence within many urban cities across Africa where our estimates suggest that over 126 million people reside. Our results strongly support the WHO’s call for surveillance and targeted vector control and provide a basis for the prioritization of surveillance.
Data Availability
The full occurrence dataset used to generate our maps are available via Dryad [An. stephensi occurrence data 1985 to 2019, Dryad, Dataset, https://doi.org/10.5061/dryad.3xsj3txcx (67)].
Acknowledgments
This work was funded by the Google Impact Challenge award (Mosquito Detection Project DFR01520 - HumBug) and Wellcome grant 108440/Z/15/Z. Additional thanks to Ian Ondo for mapping advice, Seth Irish, Dave Weetman and colleagues for providing additional data, Marybel Soto Gomez for assisting in running our final models, and to Dan Strickman for a query that inspired this work in the first place.
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Copyright © 2020 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Data Availability
The full occurrence dataset used to generate our maps are available via Dryad [An. stephensi occurrence data 1985 to 2019, Dryad, Dataset, https://doi.org/10.5061/dryad.3xsj3txcx (67)].
Submission history
Published online: September 14, 2020
Published in issue: October 6, 2020
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Acknowledgments
This work was funded by the Google Impact Challenge award (Mosquito Detection Project DFR01520 - HumBug) and Wellcome grant 108440/Z/15/Z. Additional thanks to Ian Ondo for mapping advice, Seth Irish, Dave Weetman and colleagues for providing additional data, Marybel Soto Gomez for assisting in running our final models, and to Dan Strickman for a query that inspired this work in the first place.
Notes
This article is a PNAS Direct Submission.
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The authors declare no competing interest.
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A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk, Proc. Natl. Acad. Sci. U.S.A.
117 (40) 24900-24908,
https://doi.org/10.1073/pnas.2003976117
(2020).
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