A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations

  • Ernest O. Asare | eoheneasare@gmail.com Department of Physics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Abdus Salam International Centre for Theoretical Physics, Trieste, Italy.
  • Adrian M. Tompkins Abdus Salam International Centre for Theoretical Physics, Trieste, Italy.
  • Leonard K. Amekudzi Department of Physics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
  • Volker Ermert Institute of Geophysics and Meteorology, University of Cologne, Cologne, Germany.

Abstract

Daily observations of potential mosquito developmental habitats in a suburb of Kumasi in central Ghana reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The persistence of the ponds was strongly tied with rainfall, location and size of the puddles. A simple power-law relationship is found to fit the relationship between the average pond depth and area well. A prognostic water balance model is derived that describes the temporal evolution of the pond area and depth, incorporating the power-law geometrical relation. Pond area increases in response to rainfall, while evaporation and infiltration act as sink terms. Based on a range of evaluation metrics, the prognostic model is judged to provide a good representation of the pond coverage evolution at most sites. Finally, we demonstrate that the prognostic equation can be generalised and equally applied to a grid-cell to derive a fractional pond coverage, and thus can be implemented in spatially distributed models for relevant vector- borne diseases such as malaria.

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Published
2016-03-31
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Original Articles
Keywords:
Pond water area, Pond water depth, Area-depth relation, Water fraction
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How to Cite
Asare, E. O., Tompkins, A. M., Amekudzi, L. K., & Ermert, V. (2016). A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations. Geospatial Health, 11(s1). https://doi.org/10.4081/gh.2016.390

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