title: Exploring the seasonality of reported treated malaria cases in Mpumalanga, South Africa.
abstract: South Africa, having met the World Health Organisation's pre-elimination criteria, has set a goal to achieve malaria elimination by 2018. Mpumalanga, one of three provinces where malaria transmission still occurs, has a malaria season subject to unstable transmission that is prone to sporadic outbreaks. As South Africa prepares to intensify efforts towards malaria elimination, there is a need to understand patterns in malaria transmission so that efforts may be targeted appropriately. This paper describes the seasonality of transmission by exploring the relationship between malaria cases and three potential drivers: rainfall, geography (physical location) and the source of infection (local/imported). Seasonal decomposition of the time series by Locally estimated scatterplot smoothing is applied to the case data for the geographical and source of infection sub-groups. The relationship between cases and rainfall is assessed using a cross-correlation analysis. The malaria season was found to have a short period of no/low level of reported cases and a triple peak in reported cases between September and May; the three peaks occurring in October, January and May. The seasonal pattern of locally-sourced infection mimics the triple-peak characteristic of the total series while imported infections contribute mostly to the second and third peak of the season (Christmas and Easter respectively). Geographically, Bushbuckridge municipality, which exhibits a different pattern of cases, contributed mostly to the first and second peaks in cases while Maputo province (Mozambique) experienced a similar pattern in transmission to the imported cases. Though rainfall lagged at 4 weeks was significantly correlated with malaria cases, this effect was dampened due to the growing proportion of imported cases since 2006. These findings may be useful as they enhance the understanding of the current incidence pattern and may inform mathematical models that enable one to predict the impact changes in these drivers will have on malaria transmission.
authors.affiliation: Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa.
authors.forename: Sheetal Prakash
authors.initials: SP
authors.lastname: Silal
authors.forename: Karen I
authors.initials: KI
authors.lastname: Barnes
authors.forename: Gerdalize
authors.initials: G
authors.lastname: Kok
authors.forename: Aaron
authors.initials: A
authors.lastname: Mabuza
authors.forename: Francesca
authors.initials: F
authors.lastname: Little
mesh_terms: D062306:Geography, Medical; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000188:drug therapy / Q000453:epidemiology* / Q000635:transmission; D011891:Rain; D012621:Seasons; D013019:South Africa / Q000453:epidemiology