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Spatial Effects on the Multiplicity of Plasmodium falciparum Infections

Author

Listed:
  • Stephan Karl
  • Michael T White
  • George J Milne
  • David Gurarie
  • Simon I Hay
  • Alyssa E Barry
  • Ingrid Felger
  • Ivo Mueller

Abstract

As malaria is being pushed back on many frontiers and global case numbers are declining, accurate measurement and prediction of transmission becomes increasingly difficult. Low transmission settings are characterised by high levels of spatial heterogeneity, which stands in stark contrast to the widely used assumption of spatially homogeneous transmission used in mathematical transmission models for malaria. In the present study an individual-based mathematical malaria transmission model that incorporates multiple parasite clones, variable human exposure and duration of infection, limited mosquito flight distance and most importantly geographically heterogeneous human and mosquito population densities was used to illustrate the differences between homogeneous and heterogeneous transmission assumptions when aiming to predict surrogate indicators of transmission intensity such as population parasite prevalence or multiplicity of infection (MOI). In traditionally highly malaria endemic regions where most of the population harbours malaria parasites, humans are often infected with multiple parasite clones. However, studies have shown also in areas with low overall parasite prevalence, infection with multiple parasite clones is a common occurrence. Mathematical models assuming homogeneous transmission between humans and mosquitoes cannot explain these observations. Heterogeneity of transmission can arise from many factors including acquired immunity, body size and occupational exposure. In this study, we show that spatial heterogeneity has a profound effect on predictions of MOI and parasite prevalence. We illustrate, that models assuming homogeneous transmission underestimate average MOI in low transmission settings when compared to field data and that spatially heterogeneous models predict stable transmission at much lower overall parasite prevalence. Therefore it is very important that models used to guide malaria surveillance and control strategies in low transmission and elimination settings take into account the spatial features of the specific target area, including human and mosquito vector distribution.

Suggested Citation

  • Stephan Karl & Michael T White & George J Milne & David Gurarie & Simon I Hay & Alyssa E Barry & Ingrid Felger & Ivo Mueller, 2016. "Spatial Effects on the Multiplicity of Plasmodium falciparum Infections," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-20, October.
    • Handle: RePEc:plo:pone00:0164054
    DOI: 10.1371/journal.pone.0164054
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    References listed on IDEAS

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    1. Amanda Ross & Cristian Koepfli & Xiaohong Li & Sonja Schoepflin & Peter Siba & Ivo Mueller & Ingrid Felger & Thomas Smith, 2012. "Estimating the Numbers of Malaria Infections in Blood Samples Using High-Resolution Genotyping Data," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.
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    3. T Alex Perkins & Thomas W Scott & Arnaud Le Menach & David L Smith, 2013. "Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-16, December.
    4. Christopher J Thomas & Dónall E Cross & Claus Bøgh, 2013. "Landscape Movements of Anopheles gambiae Malaria Vector Mosquitoes in Rural Gambia," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-7, July.
    5. David L. Smith & Chris J. Drakeley & Christinah Chiyaka & Simon I. Hay, 2010. "A quantitative analysis of transmission efficiency versus intensity for malaria," Nature Communications, Nature, vol. 1(1), pages 1-9, December.
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    1. Khelifa, Amira & El Saadi, Nadjia, 2024. "The impact of aquatic habitats on the malaria parasite transmission: A view from an agent-based model," Ecological Modelling, Elsevier, vol. 487(C).

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