Project Summary
Title: Larval Ecology of Invasive Anopheles stephensi in Ethiopia
The invasion and rapid expansion of Anopheles stephensi in Africa have created major challenges
to effective malaria control and elimination in Africa, triggering calls for urgent action to stop its
spread. Since its first detection in Djibouti in 2012, An. stephensi has spread to Ethiopia, Sudan,
Kenya, and Somalia in Eastern Africa and to Nigeria in Western Africa. Modelling studies have
found that most cities in sub-Saharan Africa are highly suitable for An. stephensi invasion. Unlike
native African malaria vectors, such as An. gambiae, An. Arabiensis, and An. funestus, which
reside predominantly in rural areas, An. stephensi mosquitoes thrive in urban environments.
Furthermore, the invasion of An. stephensi has already caused local malaria outbreaks in Djibouti
and Ethiopia. Control of An. stephensi in Africa currently emphasizes larviciding. However,
effective larval control requires addressing major knowledge gaps in the factors regulating An.
stephensi populations as well as where and when to implement interventions. While mapping out
all habitats through visual inspection is infeasible and impractical, machine learning techniques
and models provide an effective approach for mapping An. stephensi larval habitats. The
development of effective machine learning models for larval habitat prediction requires better
understanding of the environmental and biological determinants of larval habitats. To accomplish
these objectives, two aims are proposed: 1) To examine population dynamics and environmental
regulation of An. stephensi, and 2) To develop and validate An. stephensi habitat models. Our
proposed work will have broad implications for the development of larval biological control
strategies to stop the spread of An. stephensi in Ethiopia, and our machine learning methods
utilizing multiscale data is applicable for guiding mosquito-borne disease controls in urban areas
in other countries.