Determining Plasmodium falciparum Malaria Transmission Networks Through Sequenom And Capillary Electrophoresis Genotyping

Omedo, Irene Akinyi (2017). Determining Plasmodium falciparum Malaria Transmission Networks Through Sequenom And Capillary Electrophoresis Genotyping. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.0000cdb7

Abstract

Declining malaria transmission leads to infection hotspots which need to be targeted to eliminate and eradicate malaria. The degree of parasite mixing in and around transmission foci is likely to impact the effectiveness of targeted interventions and should be considered when developing control programmes. Few studies currently provide empiric evidence on parasite mixing over time and space, making it hard to predict the likely outcomes of targeted interventions. Here, spatio-temporal malaria transmission networks were inferred using genetic data. P. falciparum SNP data were analysed at micro-epidemiological scales in two sites in Kenya and one site in The Gambia, and in a subsequent study at macro-epidemiological scales in Western Kenya. Principal component analysis and linear regression were used to analyse population structure and genetic relatedness in time and space, respectively. Study sites were analysed for parasite genotype clusters, barriers to, and directionality in parasite movement. Parasite genetic relatedness was predicted by relatedness in time and space at micro-geographical scales, but no evidence of population structure was seen over larger areas. No barriers to parasite movement were detected at micro or macro-epidemiological scales, although directional movement was observed in two regions of Western Kenya.
PfAMA1 and surf4.2 capillary sequence data from parasites collected between 1995 – 2014 in Kilifi county were used to validate SNP data results. Sequence data showed high parasite mixing, with no clustering of distinct haplotypes in time or space. Time and distance interacted antagonistically such that distance no longer predicted genetic variation for parasites collected more than 1 year apart.
These findings show parasite populations that are well mixed in time and space, thus targeting hotspots is likely to benefit surrounding communities. However, this high parasite movement is likely to lead to re-introduction of infection from surrounding regions following “one-off” interventions, although repeated targeted interventions may be effective.

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About

  • Item ORO ID
  • 52663
  • Item Type
  • PhD Thesis
  • Project Funding Details
  • Funded Project NameProject IDFunding Body
    Not SetNot SetMRC (Medical Research Council)/DFID
  • Keywords
  • malaria transmission; disease hotspots; spatial heterogeneity
  • Academic Unit or School
  • Faculty of Science, Technology, Engineering and Mathematics (STEM)
  • Associated Research Centre
  • KEMRI - Wellcome Trust Research Programme, Kenya
  • Copyright Holders
  • © 2017 The Author
  • Depositing User
  • Irene Akinyi Omedo

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