Defining hotspots of malaria transmission
Lead Research Organisation:
University of Oxford
Department Name: Clinical Medicine
Abstract
Malaria transmission is patchy at a local level, with hotspots of intense transmission. This hinders control measures, but also means that targeting additional interventions on hotspots will be highly effective. At present, we do not know how best to detect these hotspots, or how to apply the interventions available. For example, we need to know how much transmission in the surrounding area results from the hotspot, and how focal the point source is.
I will analyse 19 years of historical data on malaria from coastal Kenya, supplemented by data from the Gambia in West Africa, to determine the spatial patterns of hotspots and how they might be detected. I will extend my findings by collaborations with investigators collecting spatial data on malaria cases in Gambia, Indonesia and elsewhere in Africa.
In collaboration with Dominic Kwiatkowski in the Wellcome Trust Sanger Institute, I will conduct detailed genotyping studies to assign a bar-code to malaria parasites. This will allow me to distinguish the recent origin of malaria parasites isolated in the field, in order to inform the design of targeted interventions against hotspots.
I will analyse 19 years of historical data on malaria from coastal Kenya, supplemented by data from the Gambia in West Africa, to determine the spatial patterns of hotspots and how they might be detected. I will extend my findings by collaborations with investigators collecting spatial data on malaria cases in Gambia, Indonesia and elsewhere in Africa.
In collaboration with Dominic Kwiatkowski in the Wellcome Trust Sanger Institute, I will conduct detailed genotyping studies to assign a bar-code to malaria parasites. This will allow me to distinguish the recent origin of malaria parasites isolated in the field, in order to inform the design of targeted interventions against hotspots.
Technical Summary
Malaria transmission is spatially heterogeneous, and groups of homesteads that form hotspots or clusters of transmission can be identified. The presence of these hotspots makes malaria control measures less effective than they might be. However, adding targeted interventions to interrupt these hotspots will be highly effective. At present, we lack detailed epidemiological descriptions of the properties of hotspots and the ways in which they might be identified by malaria control programmes. Furthermore, in order to rationally design targeted interventions, we need to understand their transmission dynamics. For example, we need to know how much transmission in the surrounding area results from the hotspot, and how focal the point source is.
I will analyse 19 years of historical data on severe malaria, mild malaria and asymptomatic infection in Kilifi, Kenya. I will use datasets from cohorts under active surveillance in the field, and passive dispensary and hospital level surveillance, to describe the spatial and temporal limits of individual clusters of transmission, and the epidemiological markers of them. I will obtain external validation of my findings by collaborations with investigators collecting spatial data on malaria cases in Africa, including the Gambia and Indonesia.
In collaboration with Dominic Kwiatkowski in the Wellcome Trust Sanger Institute, I will conduct detailed genotyping studies to assign a bar-code to parasites. High resolution spatial and genotyping data will be combined to accurately identify transmission in and around hotspots, in order to predict the likely outcomes of intervening in hotspots. I will use a descriptive statistical approach for my primary analysis, but will also collaborate with Gil McVean (Oxford University), Dave Smith (Florida University) and Azra Ghani (Imperial College) to conduct post hoc analyses of the population genetic structure, potential indirect effects of interventions and Bayesian approaches to cluster determination, respectively.
I will analyse 19 years of historical data on severe malaria, mild malaria and asymptomatic infection in Kilifi, Kenya. I will use datasets from cohorts under active surveillance in the field, and passive dispensary and hospital level surveillance, to describe the spatial and temporal limits of individual clusters of transmission, and the epidemiological markers of them. I will obtain external validation of my findings by collaborations with investigators collecting spatial data on malaria cases in Africa, including the Gambia and Indonesia.
In collaboration with Dominic Kwiatkowski in the Wellcome Trust Sanger Institute, I will conduct detailed genotyping studies to assign a bar-code to parasites. High resolution spatial and genotyping data will be combined to accurately identify transmission in and around hotspots, in order to predict the likely outcomes of intervening in hotspots. I will use a descriptive statistical approach for my primary analysis, but will also collaborate with Gil McVean (Oxford University), Dave Smith (Florida University) and Azra Ghani (Imperial College) to conduct post hoc analyses of the population genetic structure, potential indirect effects of interventions and Bayesian approaches to cluster determination, respectively.