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Sensitive near point-of-care detection of asymptomatic and submicroscopic Plasmodium falciparum infections in African endemic countries

  • NIHR Global Health Research Group on Digital Diagnostics for African Health Systems
  • London School of Hygiene & Tropical Medicine
  • Imperial College London
  • ProtonDx Ltd.
  • Health Sciences Research Institute (IRSS)
  • University of Ghana
  • Imperial College Healthcare NHS Trust
  • Malaria Consortium
  • Univ. of Energy and Natural Resources
  • Masinde Muliro University of Science and Technology
  • University of Khartoum
  • Ghana Health Service
  • National Health Research and Training Institute
  • Institute of Endemic Diseases Sudan
  • Institut Pasteur de Dakar
  • Patients Helping Fund
  • The Hague University of Applied Sciences
  • The Institute of Cancer Research
  • University of Nairobi
  • Durham University
  • University of New South Wales
  • Canterbury Christ Church University
  • Karolinska Institutet
  • Minohealth AI Labs

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Limited diagnostic capacity for detecting asymptomatic malaria infections with low parasite densities hinders elimination efforts in Africa. Here, we adapt a near point-of-care, LAMP-based diagnostic platform for malaria diagnosis using capillary blood. This Pan/Pf detection method meets the Malaria Eradication Research Agenda (malERA) criteria for community-level screening, with a limit of detection of 0.6 parasites/μL and a sample-to-result time under 45 minutes. We evaluate its performance on 672 capillary blood samples collected at the community level in The Gambia and Burkina Faso, including 146 Plasmodium falciparum positives confirmed by qPCR. The diagnostic platform achieved 95.2% sensitivity (95% CI: 90.4–98.1) and 96.8% specificity (95% CI: 94.9–98.0). It also detected 94.9% (130/137) of asymptomatic infections and 95.3% (41/43) of submicroscopic cases (<16 parasites/μL), outperforming expert microscopy (70.1% and 0%) and rapid diagnostic tests (49.6% and 4.7%). This field-deployable molecular diagnostic method offers a sensitive, scalable solution to support test-and-treat strategies for malaria elimination across Africa.

Original languageEnglish
Article number8925
JournalNature Communications
Volume16
Issue number1
DOIs
Publication statusPublished - Dec 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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