Malaria is a vector-borne parasitic disease affecting tropical and subtropical regions. Regardless control efforts, malaria incidence is still incredible high with 219 million clinical cases and an estimated 660,000 related deaths (WHO, 2012). In this project, different population genetic approaches were explored to characterize parasite populations. The goal was to create a framework that considered temporal and spatial changes of Plasmodium populations in malaria surveillance. This is critical in a vector borne disease in areas of low transmission where there is not accurate information of when and where a patient was infected. In this study, fragment analysis data and single nucleotide polymorphism (SNPs) from South American samples were used to characterize Plasmodium population structure, patterns of migration and gene flow, and discuss approaches to differentiate reinfection vs. recrudescence cases in clinical trials. A Bayesian approach was also applied to analyze the Plasmodium population history by inferring genealogies using microsatellites data. Specifically, fluctuations in the parasite population and the age of different parasite lineages were evaluated through time in order to relate them with the malaria control plan in force. These studies are important to understand the turnover or persistence of "clones" circulating in a specific area through time and consider them in drug efficacy studies. Moreover, this methodology is useful for assessing changes in malaria transmission and for more efficiently manage resources to deploy control measures in locations that act as parasite "sources" for other regions. Overall, these results stress the importance of monitoring malaria demographic changes when assessing the success of elimination programs in areas of low transmission.