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

arXiv: https://arxiv.org/abs/2303.02075 · HTML: https://mlsanity.com/paper/2303.02075/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 101 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 22 of 22)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health](https://mlsanity.com/paper/2109.08075/) (2021)
- [Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling](https://mlsanity.com/paper/1810.12027/) (2018)
- [Power Constrained Bandits](https://mlsanity.com/paper/2004.06230/) (2020)
- [Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology](https://mlsanity.com/paper/1905.12767/) (2019)
- [Unbiased split variable selection for random survival forests using maximally selected rank statistics](https://mlsanity.com/paper/1605.03391/) (2016)
- [A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit](https://mlsanity.com/paper/1510.00757/) (2015)
- [Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs](https://mlsanity.com/paper/2107.02480/) (2021)
- [Survival trees for left-truncated and right-censored data, with application to time-varying covariate data](https://mlsanity.com/paper/1606.03033/) (2016)
- [Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale](https://mlsanity.com/paper/1709.07638/) (2017)
- [A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries](https://mlsanity.com/paper/2111.01786/) (2021)
- [Machine learning and behavioral economics for personalized choice architecture](https://mlsanity.com/paper/1907.02100/) (2019)
- [Policy Learning for Malaria Control](https://mlsanity.com/paper/1910.08926/) (2019)
- [Data-Efficient Reinforcement Learning for Malaria Control](https://mlsanity.com/paper/2105.01620/) (2021)
- [A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities](https://mlsanity.com/paper/2111.11203/) (2021)

## Abstract (arXiv)

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.