Modelling spatio-temporal dynamics of malaria and mortality to develop optimised interventions and surveillance tools in Africa
The overarching goal of the project is to reduce malaria burden in Africa by developing and validating tools (methods, knowledge, software) to strengthen malaria surveillance for disease control and elimination. The project results will be translated to tools that can directly assist efforts of the control programmes towards malaria elimination and reduction of child and maternal mortality.
About the project
There is a strong commitment from most countries and donors to reach malaria targets set for the year 2015 by the Millennium Development Goals (MDG), the World Health Assembly (WHA), the Roll Back Malaria (RBM) Partnership and Global Malaria Action Plan (GMAP). In many countries, national surveys have been repeatedly conducted to monitor key health-related indicators such as malaria child and maternal mortality and relevant interventions. However, despite these efforts, the information extracted from the data is poor and rather limited to national averages, overlooking regional and smaller scale heterogeneities and disparities. Additionally, there is limited use of the data in evaluating on-going interventions because existing analyses do not relate variations in the health indicators, system performance and implementation of interventions at local scale. Bayesian geostatistical modelling is the state-of-the-art approach for estimating the disease burden or child mortality at local scales and for measuring the effects of interventions from national survey data. However, most analyses focus on the spatial distribution of the health outcomes without linking health intervention and system performance data. In addition, lack of advance statistical expertise in the control programmes and readily available software to perform routinely geostatistical analyses for surveillance limits the use of the methods to statisticians or specialised epidemiologists.
The overarching goal of the project is to reduce malaria burden in Africa by developing and validating tools (methods, knowledge, software) to strengthen malaria surveillance for disease control and elimination. The project will pursue the following interrelated specific objectives: assess spatio-temporal dynamics of malaria risk and measure effectiveness of related interventions at local scale in Africa; estimate the malaria-related mortality across all age groups in Africa; assess spatio-temporal dynamics and obtain up-to-date high resolution estimates of infant and child mortality across Africa; estimate the contribution of health systems performance and interventions in the spatio-temporal dynamics of malaria and mortality in Uganda (East Africa) and Burkina Faso (West Africa); propose strategies to optimize health systems performance and interventions to reduce malaria and mortality burden; develop and disseminate software for malaria surveillance (i.e. Bayesian geostatistical malaria mapping and early detection of malaria outbreaks).
The spatio-temporal estimates will evaluate success in achieving malaria and mortality related MDGs and other global targets considering regional disparities rather than national averages. The project results will be translated to tools (e.g. maps, burden estimates at different geographical resolution, cost-effectiveness of different interventions, strategies to improve health system performance, surveillance software) that can directly assist efforts of the control programmes towards malaria elimination and reduction of child and maternal mortality. Additionally, these products can be used to influence policy makers to choose spatially targeted, cost-effective interventions and improve local health systems in Uganda and Burkina Faso. The set of malaria interventions optimised at a given location will contribute to reduction of malaria burden. The estimates of malaria-related mortality will calibrate mathematical models of malaria transmission. The up-to-date, geo-referenced estimates of infant and under five mortality can be considered as socio-economic proxies at high spatial resolution across Africa and improve spatially explicit burden estimates of poverty-related neglected diseases in Africa. The project will build capacity in advance statistical modelling by giving the opportunity to four doctoral students from Africa to be trained in advanced disease mapping, Bayesian spatio-temporal modelling and computation.
- Burkin a Faso
- Ugand a
Project information on P3