Introduction
In an era characterized by rapid environmental changes, urbanization, and increasing human-animal interactions, the dynamics of infectious diseases are evolving at an unprecedented pace. Large-scale programs are dedicated to controlling or eliminating infectious diseases with the greatest global health impact, with many of these efforts focused on neglected tropical diseases (NTDs). While NTDs encompass fungal, viral, and bacterial infections, the majority are caused by parasites, particularly protozoa and helminths. Vector-borne parasitic diseases such as malaria, trypanosomiasis, leishmaniasis and filariasis cause greatest incidence and mortality globally .
Effective control of NTDs relies on the ability to monitor changes in pathogen populations, ensuring that interventions stay on track toward elimination goals and enabling targeted resource allocation. However, conventional monitoring techniques face challenges in many disease-endemic countries, where diagnostic tools are often limited. This task becomes increasingly difficult as disease prevalence decreases. Genomic epidemiology, however, can provide a deep understanding of parasite population dynamics, enabling strategic planning of control interventions, monitoring their effects, and raising alerts if necessary and hence, support disease eradication efforts by providing actionable knowledge .
While genetic data is most extensively used for diseases caused by prokaryotes and viruses , phylodynamic tools used in viral and bacterial genomics capture both epidemiological changes and evolutionary history, due to the high mutation rates in these pathogens and measurable genetic changes within the time frame of an outbreak or epidemic . However, in pathogens with a lower mutation rate and frequent recombination, such as eukaryotic parasites, inferring transmission events is more challenging . The application of genomic epidemiology for these parasitic diseases has lagged behind, hindered by the complexity of the parasite’s life cycle and the greater size of its genome. Genetic diversity is influenced by various factors such its life history, population dynamics, and recent changes in population size. It is crucial to have a comprehensive understanding of pathogen populations and an accurate assessment of their population structure over time to accurately evaluate the effectiveness of control interventions . This information allows for better understanding of inbreeding patterns and gene flow that can inform the development of improved strategies for controlling current populations.
While population genetics of several parasite species has been analysed using microsatellite regions, the rapid innovation and decreasing cost of whole-genome sequencing makes it the ideal tool, since genome-wide data have more resolution and are more comparable between populations and pathogens, eliminating the need for validated and standardized marker panels. For many key parasitic diseases, essential genomic resources like annotated reference genomes are already available. Genome-wide data can provide insights into sudden emergence and spread of new pathogen genotypes, reveal recent strong selection on certain genome regions, and population evolution in response to treatment and control interventions when signs of a significant bottleneck are detected. An example is the identification of emerging drug resistance in the malaria parasite Plasmodium falciparum .
Malaria, caused by Plasmodium parasites, contribute to a very high disease burden with an estimated 247 million malaria cases in 84 malaria endemic countries . However, in several countries across the world where control efforts have reduced overall malaria cases, there has been an increase in the proportion of P. vivax . Moreover, substantial reductions in P. vivax prevalence over 5-10 years in several locations have not consistently result in changes in population structure . P. vivax  accounts for 18.0% to 71.5% of malaria cases outside Africa, with the highest proportion in the Americas . Many countries in Latin America have made strong progress in malaria control, reducing the malaria burden from 1.5 to 0.6 million cases between 2000 and 2021 . However, high transmission areas remain predominantly concentrated in the Amazon rainforest regions, disproportionally affecting indigenous and remote communities. In 2021, Venezuela, Colombia, Brazil and Peru were in the top 4 countries contributing mostP. vivax cases (79%) in the region .
Genomic diversity in malaria parasites is generated through a combination of de novo mutations during asexual replication and sexual recombination within the mosquito vector. Plasmodiumparasites have a high recombination rate, and frequent infections with multiple genetically distinct clones, especially in the case of P. vivax . In addition, parasite genomes are polymorphic, with a diversity of phenotypic characteristics that impact disease severity . P. vivax often displays a higher genetic diversity than P. falciparum , due to key biological factors including frequent subpatent (i.e. , detectable by molecular methods but not by field diagnostics) and asymptomatic infections, along with a hidden reservoir of hypnozoites leading to a larger number of complex infections . The asymptomatic infections and hypnozoites contribute to this parasite’s resilience and facilitate its spread and gene flow across large regions, jeopardizing the effectiveness of local and targeted elimination strategies . Other factors contributing to the high genetic diversity ofP. vivax are its longer history of association with humans, larger effective population size and fewer population bottlenecks . Finally, sexual stages of P. vivax parasites appear early in the infection, facilitating effective transmission to mosquitoes before treatment, even at low-level parasitaemia, making the disease more difficult to eliminate .
In Latin America, the analysis of mitochondrial genomes has previously shown that the combined effects of geographical population structure and the relatively low incidence of P. vivax malaria has resulted in patterns of low local but high regional genetic diversity . In this study, we take a population genomic approach to investigate the spatial temporal dynamics of P. vivax in this region, using genome wide data identified through literature and supplemented with data from our own studies. Using high-resolution genome wide SNP variants of these P. vivax isolates, we first compare the Latin American P. vivax genomes to P. vivax genomes from around the world. Next, we investigate the population structure, admixture, relatedness and geneflow, and signatures of positive selection to study local adaptations of the parasites. With this study we investigate if and how the declining and more heterogenous transmission is impactingP. vivax population structure in this relatively recently expanded population and discuss the factors driving diversity and population structure in this ecologically diverse region. Not only is this informative for malaria control and elimination strategies, but it can also identify targets and key pathways important for P. vivaxsurvival.