# Adaptive Interventions for Global Health: A Case Study of Malaria

**Authors:** África Periáñez, Andrew Trister, Madhav Nekkar, Ana Fernández del Río, Pedro L. Alonso  
**arXiv:** [2303.02075](https://arxiv.org/abs/2303.02075)  
**Date:** 2023-03-03  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2303.02075v2/

## 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 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.