Spatio-temporal analysis of malaria within a transmission season in Bandiagara, Mali.

Drissa Coulibaly,Modibo Daou,Karim Traore,Issa Diarra,Stanislas Rebaudet,Mody Sissoko,Jean Gaudart,Youssouf Tolo,Amadou Niangaly,Renaud Piarroux,Nadine Dessay,Abdoulaye K Koné ,Ahmadou Dembélé ,Abdoulaye Guindo ,Matthew B Laurens ,Mark A Travassos ,Bouréma Kouriba ,Christopher V Plowe ,Mahamadou A Thera ,Ogobara K Doumbo

doi:10.1186/1475-2875-12-82

Abstract

BackgroundHeterogeneous patterns of malaria transmission are thought to be driven by factors including host genetics, distance to mosquito breeding sites, housing construction, and socio-behavioural characteristics. Evaluation of local transmission epidemiology to characterize malaria risk is essential for planning malaria control and elimination programmes. The use of geographical information systems (GIS) techniques has been a major asset to this approach. To assess time and space distribution of malaria disease in Bandiagara, Mali, within a transmission season, data were used from an ongoing malaria incidence study that enrolled 300 participants aged under six years old”.MethodsChildren’s households were georeferenced using a handheld global position system. Clinical malaria was defined as a positive blood slide for Plasmodium falciparum asexual stages associated with at least one of the following signs: headache, body aches, fever, chills and weakness. Daily rainfall was measured at the local weather station.Landscape features of Bandiagara were obtained from satellite images and field survey. QGIS™ software was used to map malaria cases, affected and non-affected children, and the number of malaria episodes per child in each block of Bandiagara. Clusters of high or low risk were identified under SaTScan® software according to a Bernoulli model.ResultsFrom June 2009 to May 2010, 296 clinical malaria cases were recorded. Though clearly temporally related to the rains, Plasmodium falciparum occurrence persisted late in the dry season. Two “hot spots” of malaria transmission also found, notably along the Yamé River, characterized by higher than expected numbers of malaria cases, and high numbers of clinical episodes per child. Conversely, the north-eastern sector of the town had fewer cases despite its proximity to a large body of standing water which was mosquito habitat.ConclusionThese results confirm the existence of a marked spatial heterogeneity of malaria transmission in Bandiagara, providing support for implementation of targeted interventions.

Highlights

Heterogeneous patterns of malaria transmission are thought to be driven by factors including host genetics, distance to mosquito breeding sites, housing construction, and socio-behavioural characteristics
In order to assess space and time distribution of malaria disease in children in Bandiagara, Mali, within a transmission season, the data from a malaria incidence study have been used. Study area This space-time description of malaria distribution among children in Bandiagara, Mali is part of a multi-year cohort survey conducted by the Malaria Research and Training Center (MRTC) as part of the Bandiagara Malaria Project (BMP)
Malaria episodes and rainfall times series In order to analyse the temporal distribution of malaria episodes, data were aggregated by week, and a time series of malaria episodes was plotted together with locally measured rainfalls (Figure 3)

Summary

Introduction

Heterogeneous patterns of malaria transmission are thought to be driven by factors including host genetics, distance to mosquito breeding sites, housing construction, and socio-behavioural characteristics. Malaria kills approximately 655,000 people per year; 91% of deaths occur in sub-Saharan Africa [1], mostly in Malaria parasite transmission and clinical disease are characterized by important microgeographic variation, often between adjacent villages, households or families [3,4,5,6,7,8] This local heterogeneity is driven by a variety of factors including human genetics [9,10], distance to potential breeding sites [11,12], housing construction [2,13,14,15,16], presence of domestic animals near the household [17,18], and socio-behavioural characteristics [6,12,19,20]. The development of Geographical Information Systems (GIS) has been an indispensable asset to this approach [24]

Methods

Results

Discussion

Conclusion