Adaptive Interventions for Global Health: A Case Study of Malaria

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ID: 283597
2023
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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.
Reference Key
alonso2023adaptive Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors África Periáñez; Andrew Trister; Madhav Nekkar; Ana Fernández del Río; Pedro L. Alonso
Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2303.02075v2
Keywords
stat.ap cs.lg cs.cy stat.ml

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