title: Could Malaria Control Programmes be Timed to Coincide with Onset of Rainfall?
abstract: Malaria cases in South Africa's Northern Province of Limpopo have surpassed known endemic KwaZulu Natal and Mpumalanga Provinces. This paper applies statistical methods: regression analysis and impulse response function to understand the timing of impact and the length that such impacts last. Climate data (rainfall and temperature) are obtained from South African Weather Services (SAWs); global data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province). Data collected span from January 1998 to July 2007. Signs of the coefficients are positive for rainfall and temperature and negative for their exponents. Three out of five independent variables consistently maintain a very high statistical level of significance. The coefficients for climate variables describe an inverted u-shape: parameters for the exponents of rainfall (-0.02, -0.01, -0.02, -0.00) and temperature (-46.61, -47.46, -48.14, -36.04) are both negative. A one standard deviation rise in rainfall (rainfall onset) increases malaria cases, and the effects become sustained for at least 3 months and conclude that onset of rainfall therefore triggers a 'malaria season'. Malaria control programme and early warning system should be intensified in the first 3 months following the onset of rainfall.
authors.affiliation: , City of Tshwane, City Sustainability Unit, Office of the Executive Mayor, P.O. Box 440, Pretoria, 0001, South Africa. kibii.komen@gmail.com.|Department of geography, Geoinformatics and Meteorology, Center for Environmental Studies, University of Pretoria, Pretoria, 0002, South Africa. kibii.komen@gmail.com.
authors.forename: Kibii
authors.initials: K
authors.lastname: Komen
mesh_terms: D002980:Climate; D006801:Humans; D008288:Malaria / Q000453:epidemiology* / Q000517:prevention & control*; D011891:Rain; D013019:South Africa; D013696:Temperature; D014887:Weather
keywords: Limpopo Province; South Africa; climate change; impulse response functions (IRF); malaria