Background: Malaria programs estimate changes in prevalence to evaluate their efficacy. In this study, parasite genetic data was used to explore how the demography of the parasite population can inform about the processes driving variation in prevalence. In particular, how changes in treatment and population movement have affected malaria prevalence in an area with seasonal malaria.

Methods: Samples of Plasmodium falciparum collected over 8 years from a population in Turbo, Colombia were genotyped at nine microsatellite loci and three drug-resistance loci. These data were analyzed using several population genetic methods to detect changes in parasite genetic diversity and population structure. In addition, a coalescent-based method was used to estimate substitution rates at the microsatellite loci.

Results: The estimated mean microsatellite substitution rates varied between 5.35 × 10^-3 and 3.77 × 10^-2 substitutions/locus/month. Cluster analysis identified six distinct parasite clusters, five of which persisted for the full duration of the study. However, the frequencies of the clusters varied significantly between years, consistent with a small effective population size.

Conclusions: Malaria control programs can detect re-introductions and changes in transmission using rapidly evolving microsatellite loci. In this population, the steadily decreasing diversity and the relatively constant effective population size suggest that an increase in malaria prevalence from 2004 to 2007 was primarily driven by local rather than imported cases.

Background: Regardless of the growing interest in detecting population structures in malarial parasites, there have been limited discussions on how to use this concept in control programs. In such context, the effects of the parasite population structures will depend on interventions’ spatial or temporal scales. This investigation explores the problem of identifying genetic markers, in this case microsatellites, to unveil Plasmodium genetic structures that could affect decisions in the context of elimination. The study was performed in a low-transmission area, which offers a good proxy to better understand problems associated with surveillance at the final stages of malaria elimination.

Methods: Plasmodium vivax samples collected in Tumeremo, Venezuela, between March 2003 and November 2004 were analyzed. Since Plasmodium falciparum also circulates in many low endemic areas, P. falciparum samples from the same locality and time period were included for comparison. Plasmodium vivax samples were assayed for an original set of 25 microsatellites and P. falciparum samples were assayed for 12 microsatellites.

Results: Not all microsatellite loci assayed offered reliable local data. A complex temporal-cluster dynamics is found in both P. vivax and P. falciparum. Such dynamics affect the numbers and the type of microsatellites required for identifying individual parasites or parasite clusters when performing cross-sectional studies. The minimum number of microsatellites required to differentiate circulating P. vivax clusters differs from the minimum number of hyper-variable microsatellites required to distinguish individuals within these clusters. Regardless the extended number of microsatellites used in P. vivax, it was not possible to separate all individual infections.

Conclusions: Molecular surveillance has great potential; however, it requires preliminary local studies in order to properly interpret the emerging patterns in the context of elimination. Clonal expansions and clusters turnovers need to be taken into account when using molecular markers. Those affect the number and type of microsatellite markers, as well as, the expected genetic patterns in the context of operational investigations. By considering the local dynamics, elimination programs could cost-effectively use molecular markers. However, population level studies need to consider the local limitations of a given set of loci in terms of providing epidemiologically relevant information.