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<ns4:p><ns4:bold>Background: </ns4:bold>The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites.  Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models.</ns4:p><ns4:p> </ns4:p><ns4:p> <ns4:bold>Methods</ns4:bold>: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 <ns4:italic>P. falciparum</ns4:italic> isolates from two Kenyan sites and one Gambian site to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and relatedness in space and time for parasite pairs.</ns4:p><ns4:p> </ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>We show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes.  Genetic relatedness of sample pairs is predicted by relatedness in space and time.</ns4:p><ns4:p> </ns4:p><ns4:p> <ns4:bold>Conclusions</ns4:bold>: Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population.</ns4:p>

Original publication

DOI

10.12688/wellcomeopenres.10784.1

Type

Journal

Wellcome Open Research

Publisher

F1000 Research Ltd

Publication Date

14/02/2017

Volume

2

Pages

10 - 10