Adaptive Interventions for Global Health: A Case Study of Malaria
This addresses malaria control for public health systems and populations in affected regions, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of malaria's high incidence and preventable deaths in low- and middle-income countries by proposing mobile health applications with machine-learning-based adaptive interventions to strengthen surveillance, treatment adherence, and public health support, aiming to reduce cases and deaths.
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 by means of mobile health applications, machine-learning-based adaptive interventions can strengthen malaria surveillance and treatment adherence, increase testing, measure provider skills and quality of care, 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.