Adaptive Interventions for Global Health: A Case Study of Malaria

Africa Perianez Santiago, Andrew Trister, Madhav Nekkar, Ana Fernandez del Rio, Pedro Alonso

Keywords: adaptive interventions, malaria, machine learning, global health

TL;DR: Using mobile health and adaptive interventions to reduce infections and deaths of malaria in low- and middle-income countries

Abstract: Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200.000 preventable deaths. Malaria remains a pressing public health concern in low- and middle-income countries, especially in sub-Saharan Africa. We describe how utilizing mobile health applications and machine-learning-based adaptive interventions can strengthen malaria surveillance and treatment adherence, increase testing, measure provider skills and quality of care, and improve public health by supporting front-line workers and patients (e.g., by capacity building and encouraging behavioral changes, like using bed nets), reduce test stockouts in pharmacies and clinics and informing public health for policy intervention.