Mosquito Detection and Habitat Mapping
for Improved Malaria Vector Modelling

A partnership project between Royal Botanic Gardens Kew and Oxford University


Mosquito-borne disease has a major impact on human health, income and mortality in over 100 countries affecting over half the world’s population. More than 70 species of the Anopheles genus are able to spread human malaria (with approximately 40 species able to do so at a level of specific concern for human health), which alone caused 430,000 child deaths worldwide in 2014 (World Health Organisation, Malaria Report 2014).

Control programmes, such as those distributing insecticide treated bed nets, have lead to significant reductions in malaria transmission. Indeed, recent estimates suggest that incident of clinical disease in Africa had fallen by 40% in just the last 5 years. In Africa the main parasite responsible for the malaria burden is Plasmodium falciparum and it is the transmission of this (most deadly) species that has been most significantly reduced. Yet there are four other malaria parasites, the most notable of which, Plasmodium vivax has a dormant stage where it can remain hidden from drug therapies within the liver. This species is most commonly found, although not restricted to, Asia.

With emerging insecticide resistance and the wide distribution of species that naturally avoid indoor based interventions, current vector control regimes may begin to struggle. Their reliance on mapping and modelling tools based on sparse, poorly distributed static data may no longer be enough to deal with the residual and more resilient transmission nor the more complex, multi-parasite species found out of Africa.

Utilising portable devices, such as mobile phones, wristbands or other acoustic monitoring devices we are developing a real-time detection system that can alert users to the presence of a vector species and transmit real time occurrence data to a dynamic on-line mapping and data platform. These data will be available for real-time analysis by researchers, who will combine it with high resolution remote imaging to determine associated environmental factors such as vegetation composition and structure and distance to water bodies. This multi-layered data will provide invaluable insights into the ecology of these vector species and support the design of better-targeted, more effective vector control programmes.

On these pages you can find more about this project, the research team and our outputs. If you are interested in working with us, if you have data that you can share, or if you would like to know more about this research, please don't hesitate to contact us.