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Artificial intelligence predicts malaria outbreaks in South Asia
Last reviewed: 02.07.2025
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Researchers from NDORMS, in collaboration with international institutions, have demonstrated the potential of using environmental measurements and deep learning models to predict malaria outbreaks in South Asia. The study offers encouraging prospects for improving early warning systems for one of the world’s deadliest diseases.
Malaria remains a significant global health problem, with approximately half the world's population at risk of infection, particularly in Africa and South Asia. Although malaria is preventable, the variable nature of climate, sociodemographic and environmental risk factors makes predicting outbreaks difficult.
A team of researchers led by Associate Professor Sarah Khalid from the NDORMS Planetary Health Informatics Group, University of Oxford, in collaboration with the Lahore University of Management Sciences, sought to address this issue and investigate whether an environment-based machine learning approach could offer the potential for place-specific early warning tools for malaria.
They developed a multivariate LSTM (M-LSTM) model that simultaneously analyzed environmental metrics including temperature, rainfall, vegetation measurements, and night-time light data to predict malaria incidence in a South Asian belt spanning Pakistan, India, and Bangladesh.
The data were compared with district-level malaria incidence rates for each country between 2000 and 2017, obtained from the United States Agency for International Development's Demographic and Health Surveys datasets.
The results, published in The Lancet Planetary Health, show that the proposed M-LSTM model consistently outperforms the traditional LSTM model with 94.5%, 99.7%, and 99.8% lower errors for Pakistan, India, and Bangladesh, respectively.
Overall, higher accuracy and reduced errors were achieved with increasing model complexity, highlighting the effectiveness of the approach.
Sarah explained: “This approach is generalizable, and so our modelling has significant implications for public health policy. For example, it could be applied to other infectious diseases or scaled up to other high-risk areas with disproportionately high malaria morbidity and mortality in WHO regions in Africa. It could help decision-makers implement more proactive measures to manage malaria outbreaks early and accurately.
"The real appeal is the ability to analyze virtually anywhere on Earth thanks to rapid advances in Earth observation, deep learning and AI, as well as the availability of high-performance computers. This could lead to more targeted interventions and better allocation of resources in the ongoing effort to eradicate malaria and improve public health outcomes worldwide."