Landscape movements of Anopheles gambiae malaria vector mosquitoes in rural Gambia.

C. Thomas, Donall Eoin Cross, Claus Bogh

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BACKGROUND: For malaria control in Africa it is crucial to characterise the dispersal of its most efficient vector, Anopheles gambiae, in order to target interventions and assess their impact spatially. Our study is, we believe, the first to present a statistical model of dispersal probability against distance from breeding habitat to human settlements for this important disease vector.

We undertook post-hoc analyses of mosquito catches made in The Gambia to derive statistical dispersal functions for An. gambiae sensu lato collected in 48 villages at varying distances to alluvial larval habitat along the River Gambia. The proportion dispersing declined exponentially with distance, and we estimated that 90% of movements were within 1.7 km. Although a 'heavy-tailed' distribution is considered biologically more plausible due to active dispersal by mosquitoes seeking blood meals, there was no statistical basis for choosing it over a negative exponential distribution. Using a simple random walk model with daily survival and movements previously recorded in Burkina Faso, we were able to reproduce the dispersal probabilities observed in The Gambia.

Our results provide an important quantification of the probability of An. gambiae s.l. dispersal in a rural African setting typical of many parts of the continent. However, dispersal will be landscape specific and in order to generalise to other spatial configurations of habitat and hosts it will be necessary to produce tractable models of mosquito movements for operational use. We show that simple random walk models have potential. Consequently, there is a pressing need for new empirical studies of An. gambiae survival and movements in different settings to drive this development.
Original languageEnglish
Article numbere68679
JournalPLoS One
Issue number7
Publication statusPublished - 18 Jul 2013


  • Animal Distribution/physiology
  • Animals
  • Anopheles/physiology
  • Computer Simulation
  • Ecosystem
  • Gambia
  • Insect Vectors/physiology
  • Malaria/prevention & control
  • Models, Biological
  • Stochastic Processes


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