This AI Startup’s Field Service Solution Could Help Eradicate Malaria for Good

Despite global containment efforts, the Malaria parasite is still very much a threat to today’s world, infecting over 219 million people as of 2017 according to the World Health Organization. But it could be totally eliminated by existing tools. The problem lies in the efficacy and efficiency of operations,and the use of the right means in the right context. Solving that gap is Israeli startup Zzapp with their AI-based mobile application designed both to tailor the right mix of interventions for each community, and help design, implement and monitor large-scale elimination campaigns. 

Founded in 2017 by Arnon Houri-Yafin (CEO), Zzapp’s goal is to lead the full elimination of malaria in the near future.

“We know malaria can be eradicated” explained Arbel Vigodny, COO of Zzapp. “We’ve seen it here in Israel  and also in countries like Cyprus, Egypt, Sri Lanka and many more. In almost all successful campaigns, treating the water bodies effectively stopped mosquitos reproduction and broke the malaria transmission cycle.” But in many developing countries today, eliminating Malaria, or even just controlling the disease, proves challenging explained Vigodny. In some countries Malaria is rising. The application of bed nets and spraying houses have been successful and cost effective, but those interventions are not enough. “Not all mosquitoes bite indoors, and even the species that do have been adapting to biting outdoors and have been developing resistance to the insecticide currently used. The way to address the root of the problem is to spray the water bodies, and the challenge therein lies in identifying the ones that are susceptible to mosquito breeding.” To address this problem, Zzapp’s AI factors many variables, like population distribution, location of water bodies, climate and the biology of the prevalent mosquito species, in order to tailor the exact mix of intervention for a given village; which houses to spray, where to look for water bodies, and when interventions should take place according to the season.

Once a plan has been set, the area is divided into manageable chunks each assigned to a field worker. The mobile application directs each of the workers in their assigned area and enables them to easily report the exact location of water bodies and their content. It also helps campaign managers monitor the process even in campaigns with thousands of workers.

Zzapp’s dashboard displaying the areas being treated and the means of intervention that should be used in each area. Graphic: Zzapp

The startup’s technology is a spin-off of Israeli AI startup Sight Diagnostics, for which Arnon Houri-Yafin (CEO) headed their research and was an early team member. The technology is geared towards tailoring interventions to individual communities according to its unique parameters. The system also supports the implementation of the chosen interventions and enables real-time monitoring of campaign progress during which it recommends dynamic adjustments. With satellite imagery (courtesy of , they use neural networks to identify the locations of villages and towns, predict the abundance of stagnant water bodies around them, and tailor the intervention strategies to individual communities in the area. “Our AI identifies buildings and villages, and instructs the field workers to explore specific areas with the highest likelihood of water bodies forming” explained Houri-Yafin. “Our app increases the detection of water bodies to the degree required for full elimination. This is crucial, since thoroughness remains a key challenge in current malaria eradication operations, where even experienced field workers overlook about 40% of the water bodies in their assigned treatment areas.”

The startup is funded by Sight Diagnostics (which also provided R&D support) and grants from the Israel Innovation Authority and the Bill and Melinda Gates Foundation through IVCC, which funds their operation with the Zanzibar government.

Over the last three years, the startup has been meeting with governments and NGOs across Africa, such as Mozambique, Kenya, and Ghana, to onboard them with their solution. And during the same time, they’ve been working towards winning the prestigious IBM Watson AI XPRIZE ‘AI for Impact’, a four-year-long competition for AI-based startups making the world a better place. “We joined in 2017 as a ‘wild card’ team and have been reporting our progress every year, wherein judges shortlist the cohort. One of the requirements is to pass an annual technical review by a board of machine learning professors, including the likes of Yoshua Bengio, who evaluate the innovative aspect of their technology against evolving and competing techniques. At NeurIPS last December, the XPRIZE competition announced Zzapp as one of the top 10 startups.

This Thursday, the three leading teams will be selected to speak at the TED Talks in Vancouver in April 2020. The winning team of the XPRIZE competition will receive $3 million.

IBM Watson XPRIZE, AI for Impact, competition.

Regardless of Zzapp’s performance in the competition, they are planning to expand their efforts and raise a funding round. While the WHO organization recorded a 18% reduction in Malaria cases from 2010 to 2017, and a 28% reduction in related deaths over the same period, 92 countries are still at risk of being infected with the Malaria disease. The death toll stands at 405,000 people in 2018

“At the current rate, the total amount spent per capita to simply contain the Malaria disease roughly adds up to $2 annually per country, in some countries. We believe that with the power of a solution like Zzapp’s, the cost to eradicate Malaria entirely could be $8 per person” said Houri-Yafin.

In line with the XPRIZE ethos AI for Impact, Zzapp’s solution is certainly fitting in their efforts to eliminate Malaria, as well as “other vector-borne [mosquito] illnesses” explained Houri-Yafin, who mentioned interest from a South-East Asian country in using Zzapp’s technology to combat mosquito-borne diseases like Zika and Dengue virus.

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