A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria

Area of Science:

• Genomics
• Epidemiology
• Computational Biology

Background:

• Distinguishing imported from autochthonous malaria cases is crucial for control, but Plasmodium vivax liver stages complicate traditional travel history methods.
• Molecular epidemiology offers a promising alternative for accurately identifying and mapping malaria importation.

Purpose of the Study:

• To develop and validate novel molecular barcodes for determining the country of origin for Plasmodium vivax infections.
• To create an accessible computational tool to support malaria surveillance and control programs.

Main Methods:

• Machine learning, including hierarchical fixation index and decision tree analyses, was applied to 799 Plasmodium vivax genomes from 21 countries.
• Development of single-nucleotide polymorphism (SNP) barcodes (GEO33, GEO50, GEO55) and comparison with an existing barcode (BR38).
• Establishment of an open-access, likelihood-based online classifier (vivaxGEN-geo).

Main Results:

• The newly developed GEO SNP panels (GEO33, GEO50, GEO55) demonstrated high accuracy in predicting infection origin.
• GEO panels significantly outperformed the BR38 barcode, achieving higher median Matthews correlation coefficients (MCCs) across more countries.
• GEO33, GEO50, and GEO55 achieved median MCCs > 0.80 in 90% and 95% of countries, respectively, compared to 62% for BR38.

Conclusions:

• Novel SNP barcodes and a user-friendly classifier provide a robust molecular method for identifying the geographic origin of Plasmodium vivax infections.
• These tools enhance the ability to track and manage imported malaria cases, supporting global malaria elimination strategies.
• The methodology is adaptable for other applications within malaria control programs.