• 1 Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, NT, Australia
  • 2 Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
  • 3 Eijkman Institute for Molecular Biology, Jakarta, Indonesia
  • 4 International Training and Medical Research Center (CIDEIM), Cali, Colombia
  • 5 Malaria Group, Universidad de Antioquia, Medellin, Colombia
  • 6 Infectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia
  • 7 College of Natural Sciences, Addis Ababa University, Addis Ababa, Ethiopia
  • 8 Armauer Hansen Research Institute, Addis Ababa, Ethiopia
  • 9 Ethiopian Public Health Institute, Addis Ababa, Ethiopia
  • 10 Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • 11 Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
  • 12 Infectious Diseases Division, International Centre for Diarrheal Diseases Research, Dhaka, Bangladesh
  • 13 Royal Center for Disease Control, Department of Public Health, Ministry of Health, Thimphu, Bhutan
  • 14 Infectious and Tropical Diseases Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Hormozgan Province, Iran
  • 15 Faculty of Medicine, University of Khartoum, Khartoum, Sudan
  • 16 National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China
  • 17 Shoklo Malaria Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
  • 18 Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
  • 19 Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea
  • 20 Deakin University, Victoria, Australia
  • 21 Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Victoria, Australia
  • 22 Fundação de Medicina Tropical, Manaus, Brazil
  • 23 Universidad Peruana Cayetano Heredia, Lima, Peru
  • 24 Mahidol University, Bangkok, Thailand
  • 25 Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
  • 26 University of Gondar, Gondar, Ethiopia
  • 27 Jimma University, Jimma, Ethiopia
  • 28 Centro de Pesquisa em Medicina Tropical, Porto Velho, Brazil
  • 29 Research Institute for Tropical Medicine, Manilla, Philippines
  • 30 Umphang Hospital, Tak, Thailand
  • 31 Centro de Investigaciones Clinicas, Cali, Colombia
  • 32 GlaxoSmithKline, Brentford, UK
  • 33 Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, NT, Australia.
Commun Biol, 2022 Dec 23;5(1):1411.
PMID: 36564617 DOI: 10.1038/s42003-022-04352-2


Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection's country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.