Rapid whole-genome sequencing of Plasmodium DNA from cryptic malaria cases in UK travellers provides insights into infection origins, transmission and antimalarial resistance.

BACKGROUND

The UK reports approximately 2000 imported malaria cases annually, necessitating effective surveillance to determine infection sources and transmission routes, inform strategies for prevention and detect molecular markers of drug resistance that may compromise treatment outcomes. Defined by their unclear route of infection, cryptic malaria cases pose a particular challenge for malaria surveillance because they may signify undetected localized transmission or malaria re-introduction and therefore necessitate additional public health resources and epidemiological investigations. Here, we demonstrate the utility of near-real-time whole-genome sequencing (WGS) in providing high genomic resolution and detailed molecular characterization to help resolve cryptic malaria cases in the UK.

METHODS

Plasmodium DNA (9 isolates) sourced from clinical blood samples underwent WGS using either Illumina or Oxford Nanopore Technologies (ONT) platforms. Sequence data were rapidly analysed with Malaria-Profiler, which performs read mapping, variant calling, quality control, drug resistance prediction and artificial intelligence (AI)-based geographic origin inference using a reference database of more than 15 000 isolate genomes. Plasmodium ovale spp. and P. falciparum infections identified among family members were further analysed to assess parasite relatedness using identity-by-descent and multiplicity of infection approaches to investigate transmission clusters.

RESULTS

Using a combination of Illumina and ONT WGS platforms alongside Malaria-Profiler, we rapidly profiled parasites from four cryptic P. falciparum malaria cases in the UK, identifying drug resistance markers and predicting geographic origins through AI-based methods. We also applied WGS to family-related clusters of P. ovale spp. and P. falciparum cases, confirming (sub)species identities and enabling fine-scale transmission cluster analysis.

CONCLUSIONS

This study highlights the power of real-time WGS and AI-enhanced tools for high-resolution malaria genomic surveillance. By enabling rapid characterization of cryptic and imported cases, this approach supports timely public health responses, including targeted epidemiological investigations and, where appropriate, the de-escalation of entomological surveillance. In doing so, this approach helps sustain malaria elimination in non-endemic settings.