Real-time malaria detection in the Amazon rainforest via drone-collected eDNA and portable qPCR

  • Yin Cheong Aden Ip
  • , Luca Montemartini
  • , Jia Jin Marc Chang
  • , Andrea Desiderato
  • , Nicolás D. Franco-Sierra
  • , Christian Geckeler
  • , Mailyn Adriana Gonzalez Herrera
  • , Michele Gregorini
  • , Meret Jucker
  • , Steffen Kirchgeorg
  • , Martina Lüthi
  • , Elvira Mächler
  • , Frederik Bendix Thostrup
  • , Guglielmo Murari
  • , Marina Mura
  • , Paola Pulido-Santacruz
  • , Florencia Sangermano
  • , Tobias Schindler
  • , Claus Melvad
  • , Stefano Mintchev
  • Kristy Deiner

    Research output: Contribution to JournalResearch Articlepeer-review

    Abstract

    Zoonotic malaria risk at human-wildlife-environment interfaces requires surveillance that integrates signals from reservoirs, vectors and the environment. We coupled a drone-based environmental DNA (eDNA) canopy swabbing approach with portable quantitative PCR (qPCR) to detect Plasmodium DNA in situ during a 24-h field exercise in the Amazon rainforest. Drone-lowered sterile swabs into the canopy, which were then extracted and subjected to a multiplex pan-Plasmodium assay targeting five human-infecting Plasmodium species (limit of detection 0.2 parasites μL−1). Of 12 samples (10 canopy swabs, 2 field blanks; 13 total runs including repeats), one canopy swab amplified in duplicate (Ct = 28.7 and 29.23), while positive controls amplified as expected (Ct = 30.82 and 31.11) and all other environmental samples and blanks were negative. Passive acoustics confirmed co-occurring howler monkeys (Alouatta spp.), a known reservoir, whereas Anopheles mosquitoes were not recovered from concurrently deployed insect canopy traps. The end-to-end workflow, from drone deployment to qPCR diagnostic readout, averaged 1.5 h per assay, without requiring cold-chain logistics. This proof-of-concept demonstrates that intracellular parasite DNA can be recovered from canopy surfaces and read out in real-time, providing upstream, landscape-level intelligence to guide targeted vector surveillance in remote settings. Our approach operationalizes One Health by integrating environmental, wildlife, and vector signals within a single technological platform, representing a paradigm shift from reactive, sector-specific surveillance to proactive, integrated pathogen intelligence across the human-animal-environment interface.

    Original languageEnglish (US)
    Article number101167
    JournalOne Health
    Volume21
    DOIs
    StatePublished - 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

    All Science Journal Classification (ASJC) codes

    • Public Health, Environmental and Occupational Health
    • Infectious Diseases

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