Detecting Imported Malaria Cases | Ethan Booth | GenRe-Mekong Scientific Forum 2025

Описание: Accurately identifying imported malaria cases is critical for elimination, yet travel histories are often incomplete or unreliable. Ethan Booth, a PhD student/research scientist working on the GenRe-Mekong project, explains how genetic surveillance combined with machine learning can infer the country or region of infection directly from parasite DNA. Using large Plasmodium falciparum and Plasmodium vivax datasets from the Greater Mekong Subregion and beyond, the talk shows that AI models trained on genetic barcodes and microhaplotypes can distinguish African from Southeast Asian parasites with near-perfect accuracy and predict country of origin with over 85–90% accuracy. It discusses strengths and limitations of this approach, its value for detecting long- and short-range importation, and ongoing efforts to integrate these models into practical reporting tools for national malaria control programs.

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GenRe-Mekong is a collaborative project of in-depth genomic surveillance of malaria parasites, aiming to provide National Malaria Control Programmes in the Greater Mekong Subregion and other public health stakeholders with timely and actionable knowledge to support their decision-making in activities relevant to malaria elimination efforts.

This clip is part of the Annual GenRe-Mekong Scientific Forum 2025 (24th November 2025)
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More information about GenRe-Mekong project, visit: https://genremekong.org/