Journal
COMMUNICATIONS BIOLOGY
Volume 5, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42003-022-04352-2
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Funding
- Wellcome Trust [200909, 204911, 206194/Z/17/Z]
- Australian Department of Foreign Affairs and Trade [TDCRRI 72904]
- Australian National Health and Medical Research Council (NHMRC) [APP2001083]
- Bill and Melinda Gates Foundation [OPP1164105]
- Charles Darwin University International PhD Scholarship (CDIPS)
- Asia-Pacific Malaria Elimination Network [108-07]
- Malaysian Ministry of Health [BP00500420]
- NHMRC [1037304, 1045156, 1042072, 1135820, 1088738, 1074795]
- 'Hot North' Earth Career Fellowship [1131932]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Brazil
- Medical Research Council
- UK Department for International Development [M006212]
- Australian Centre for Research Excellence on Malaria Elimination (ACREME) - NHMRC [APP 1134989]
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Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases. However, the presence of dormant liver stages of Plasmodium vivax complicates this approach. This study proposes molecular tools and machine learning methods to identify and map imported cases, offering an alternative approach to traditional methods.
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.
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