Malaria Vector Genomic Surveillance

Genomic Surveillance Unit

A team within the Genomic Surveillance Unit, we generate and curate genomic public health data about the mosquitoes that transmit malaria, while developing new cloud-native software tools and training resources that allow our partners to use large-scale genomic data sets for vector surveillance.

About us

We are data scientists and software developers with a strong interest in entomology and genomics. We are passionate about making genomic data accessible and usable by public health partners around the world. We primarily use Python, Jupyter notebooks, and Google CoLab cloud computing to manipulate malaria vector data. 

Our work

Malaria vector control, particularly in Africa, is going through a period of major change. New insecticides are being brought into use through a new generation of long-lasting insecticide-treated nets (LLINs) and indoor residual spraying (IRS) products. These new tools are currently effective, but we know based on past experience that mosquitoes will evolve resistance, and so without preemptive action, these tools will not remain effective for long. 

To keep on top of insecticide resistance, we build data products that track genetic changes in Anopheles mosquito populations — the mosquitoes which transmit malaria — in Africa and Southeast Asia.

The most recent versions of the largest Anopheles dataset — for the An. gambiae complex of species in Africa — contain more than 13,000 whole genome sequences from samples collected by partners working in 33 institutions across 25 countries. These are continually updated, with regular new data releases. 

The mosquito genome contains hundreds of millions of bases. That makes it about ten times larger than the malaria parasite genome, and ten times smaller than the human genome. This presents a unique set of challenges when it comes to collecting, storing, and analysing malaria vector data. To make the data more accessible, we created a set of cloud-native software tools, coded in Python, that allow anyone with an internet connection and a laptop to access and analyse the data. In association with the Pan-African Mosquito Control Association (PAMCA), we have also developed a set of online training workshops and established a bioinformatics fellowship program to help to build genomic surveillance capacity in Africa.

Another major challenge in vector control is that, in many cases, public health authorities don’t have a clear idea of which species are present and transmitting malaria. This is especially true in Southeast Asia, where mosquito species diversity is very high. The ANOSPP project focuses on mosquito diversity in Africa, whereas the Vector Observatory – Asia focuses on Asia.