Nana Ama Browne Klutse1, Fred Aboagye-Antwi², Kwadwo Owusu³, Yaa Ntiamoa-Baidu^2,4

¹Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, Accra, Ghana
²Department of Animal Biology and Conservation Science, University of Ghana, Legon, Ghana
³Department of Geography and Resource Development, University of Ghana, Legon, Ghana
⁴Centre for African Wetlands, University of Ghana, Legon, Ghana
 

Abstract
Climate change is projected to impact human health, particularly incidence of water related and vector borne diseases, such as malaria. A better understanding of the relationship between rainfall patterns and malaria cases is thus required for effective climate change adaptation strategies involving planning and implementation of appropriate disease control interventions. We analyzed climatic data and reported cases of malaria spanning a period of eight years (2001 to 2008) from two ecological zones in Ghana (Ejura and Winneba in the transition and coastal savannah zones respectively) to determine the association between malaria cases, and temperature and rainfall patterns and the potential effects of climate change on malaria epidemiological trends. Monthly peaks of malaria caseloads lagged behind monthly rainfall peaks. Correlation between malaria caseloads and rainfall intensity, and minimum temperature were generally weak at both sites. Lag correlations of up to four months yielded better agreement between the variables, especially at Ejura where a two-month lag between malaria caseloads and rainfall was significantly high but negatively correlated (r = -0.72; p value < 0.05). Mean monthly maximum temperature and monthly malaria caseloads at Ejura showed a strong negative correlation at zero month lag (r = -0.70, p value < 0.05), with a similar, but weaker relationship at Winneba, (r = -0.51). On the other hand, a positive significant correlation (r = 0.68, p value < 0.05) between malaria caseloads and maximum temperature was observed for Ejura at a four-month lag, while Winneba showed astrong correlation (r = 0.70; p value < 0.05) between the parameters at a two-month lag. The results suggest maximum temperature as a better predictor of malaria trends than minimum temperature or precipitation, particularly in the transition zone. Climate change effects on malaria caseloads seem multi-factorial. For effective malaria control, interventions could be synchronized

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