How AI is Helping Control Malaria – THINK Blog

How AI is Helping Control Malaria – THINK Blog
Bed nets, insecticides and repellents are all effective intervention strategies to control the spread of malaria, but with a continuously dwindling budget, how can public officials and policy makers know what to use, where and when, to be most effective? Malaria is caused by parasites that are transmitted to people through the bites of infected mosquitoes. While it’s endemic in sub-Saharan Africa (SSA), nearly half of the world’s population is at risk. According to the World Health Organization in 2015, there were roughly 212 million malaria cases and an estimated 429,000 malaria deaths

Malaria control measures have led to a 29% reduction in mortality rates since 2010, I’d personally like to see this statistic eradicated, the tools of machine learning (ML) and more recently artificial intelligence (AI) may provide some potential answers.

A teacher explains to students about mosquitoes and malaria. CREDIT: WHO/S. Hollyman

Today at the Innovative Applications for Artificial Intelligence (AAAI-IAAI) conference located in New Orleans, I am privileged to present our recent work ‘Novel Exploration Techniques (NETs) for Malaria Policy Interventions.’ Conducted in collaboration between IBM Research – Africa and the University of Oxford, we have begun to use machine intelligence to augment the decision-making abilities of officials, and explore more effective malaria policy interventions.

Our work proposes the use of different AI algorithms or ‘agents’ to determine the most effective intervention strategies for specific locations. This was possible due to publicly available research and models (OpenMalaria). The OpenMalaria simulation model can be used by computational agents to explore “what if” scenarios and learn new, more effective policies for the control of the disease.

Our approach applies multiple AI agents to determine more effective malaria policies based on a combination of; distributing long-lasting insecticide-treated nets; and performing indoor residual spraying programs. We ran these computational experiments to simulate a region in Western Kenya over a five-year intervention period into the future.