Target acquired
The newest weapon against mosquitoes: computer vision
The tech behind self-driving cars is also helping fight malaria.
Can computers see? The answer is complicated. I've been following the field of computer vision for decades—ever since Paul Allen and I started dreaming about what you could do with a personal computer—and we're only now reaching the point where they can really understand visual inputs. We still have a long way to go, but the ability of computers to see things is already revolutionizing many parts of our lives. It makes autonomous vehicles possible. It’s used to read x-rays quickly and accurately, and it’s what allows a mobile phone to translate street signs from one language to another.
Lately I’ve been especially enthused about a different application (and one my teenage self never would’ve imagined caring about): scanning pictures of mosquitoes.
Mosquitoes are responsible for spreading malaria, which kills more than 600,000 people every year and is a major focus of the Gates Foundation’s health work. Although scientists have learned a lot about them in the past few decades, one challenge has been especially stubborn: telling one mosquito from another. There are around 3,500 different species of them, and many look alike. Even a highly trained entomologist has to examine one for several minutes under a microscope to identify it accurately.
Why do we care about mosquito species? Most importantly, because different species can carry different diseases, and some don’t carry any diseases at all. (The ones that carry malaria belong to the genus Anopheles.) There are other differences too: Some bite people indoors, while others feed outdoors. Some dine at dusk while others take their meals during the day. And only females bite—the blood gives them the energy needed to lay eggs.
All this variation means we need different tools for different mosquitoes. For example, indoor insecticides and bednets work well against species that primarily bite indoors. But for the ones that mainly live and feed outside, you’ll need to take other steps too, such as eliminating the outdoor spaces where they breed.
Fortunately, some novel uses of computer vision are supercharging the process of identification. They’re not only helping us know our opponent, they’re helping us target its weak spots, save more lives, and move even closer to eradicating malaria.
One of the most exciting innovations is called VectorCam—an app that lets someone with minimal training identify mosquito species in a matter of seconds.
VectorCam was developed by Dr. Soumya Acharya and his team of bioengineers at Johns Hopkins University, with support from Uganda’s malaria control program, Makerere University, and the Gates Foundation. Using a smartphone, the VectorCam app, and an inexpensive lens attached to the phone, you simply take a picture of a mosquito and get it identified right away. The app can distinguish among the different species that transmit malaria. It can also determine the sex of the mosquito and, if the insect is a female, whether it has recently fed on blood or developed eggs. And with further refinement, VectorCam could identify species that carry other diseases, like dengue and Zika.
