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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11818

Title: Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa
Authors: Asare, Ernest O.
Tompkins, Adrian M.
Amekudzi, Leonard K.
Ermert, Volker
Redl, Robert
Keywords: Water temperature
Energy balance model
Larvae development time
Issue Date: 18-Feb-2016
Publisher: Geospatial Health
Citation: Geospatial Health 2016; volume 11(s1):391
Abstract: An energy budget model is developed to predict the water temperature of typical mosquito larval developmental habitats. It assumes a homogeneous mixed water column driven by empirically derived fluxes. The model shows good agreement at both hourly and daily time scales with 10-min temporal resolution observed water temperatures, monitored between June and November 2013 within a peri-urban area of Kumasi, Ghana. There was a close match between larvae development times calculated using either the model-derived or observed water temperatures. The water temperature scheme represents a significant improvement over assuming the water temperature to be equal to air temperature. The energy budget model requires observed minimum and maximum temperatures, information that is generally available from weather stations. Our results show that hourly variations in water temperature are important for the simulation of aquatic-stage development times. By contrast, we found that larval development is insensitive to sub-hourly variations. Modelling suggests that in addition to water temperature, an accurate estimation of degree-day development time is very important to correctly predict the larvae development times. The results highlight the potential of the model to predict water temperature of temporary bodies of surface water. Our study represents an important contribution towards the improvement of weather-driven dynamical disease models, including those designed for malaria early forecasting systems
Description: An article published by Geospatial Health 2016; volume 11(s1):391
URI: http://hdl.handle.net/123456789/11818
Appears in Collections:College of Science

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