Assessing Climate Driven Malaria Variability in Ghana Using a Regional Scale Dynamical Model
AbstractMalaria is a major public health challenge in Ghana and adversely affects the productivity and economy of the country. Although malaria is climate driven, there are limited studies linking climate variability and disease transmission across the various agro-ecological zones in Ghana. We used the VECTRI (vector-borne disease community model of the International Centre for Theoretical Physics, Trieste) model with a new surface hydrology scheme to investigate the spatio-temporal variability in malaria transmission patterns over the four agro-ecological zones in Ghana. The model is driven using temperature and rainfall datasets obtained from the GMet (Ghana Meteorological Agency) synoptic stations between 1981 and 2010. In addition, the potential of the VECTRI model to simulate seasonal pattern of local scale malaria incidence is assessed. The model results reveal that the simulated malaria transmission follows rainfall peaks with a two-month time lag. Furthermore, malaria transmission ranges from eight to twelve months, with minimum transmission occurring between February and April. The results further reveal that the intra- and inter-agro-ecological variability in terms of intensity and duration of malaria transmission are predominantly controlled by rainfall. The VECTRI simulated EIR (Entomological Inoculation Rate) tends to agree with values obtained from field surveys across the country. Furthermore, despite being a regional model, VECTRI demonstrates useful skill in reproducing monthly variations in reported malaria cases from Emena hospital (a peri urban town located within Kumasi metropolis). Although further refinements in this surface hydrology scheme may improve VECTRI performance, VECTRI still possesses the potential to provide useful information for malaria control in the tropics. View Full-Text
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Asare, E.O.; Amekudzi, L.K. Assessing Climate Driven Malaria Variability in Ghana Using a Regional Scale Dynamical Model. Climate 2017, 5, 20.
Asare EO, Amekudzi LK. Assessing Climate Driven Malaria Variability in Ghana Using a Regional Scale Dynamical Model. Climate. 2017; 5(1):20.Chicago/Turabian Style
Asare, Ernest O.; Amekudzi, Leonard K. 2017. "Assessing Climate Driven Malaria Variability in Ghana Using a Regional Scale Dynamical Model." Climate 5, no. 1: 20.
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