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Read MoreAccessibility to Healthcare
Current Project
Housing In Africa
Current Project
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New research reveals a transformation of housing in sub-Saharan Africa, with the prevalence of improved housing doubling between 2000 and 2015. Read More
Catchment modelling for area-averaged malaria data
Current Project
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Until recently, the limited availability and low quality of routine health care data on the incidence of febrile malaria in low resource settings has meant that the vast majority of risk mapping has relied upon direct observations of parasite prevalence as a proxy for disease burden, particularly those from the large-scale Malaria Indicator Survey program. Under the leadership of Director of Malaria Risk Stratification Dr Ewan Cameron, MAP is developing a statistical framework for simultaneous estimation of malaria risk maps and health facility catchments based on a modified ‘gravity model’. Read More
Semi-mechanistic modelling for serological survey data
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As countries near malaria elimination, serological measurements become increasingly important for malaria burden estimation. MAP is working to integrate serological data within our risk mapping framework using novel semi-mechanistic models based.
Read MoreImproved intervention coverage models
Current Project
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The Global Malaria Epidemiology team generates detailed models of interventions to improve burden estimation models and attribute declines in malaria to specific measures.
Read MorePixel-level modelling in low burden areas
Current Project
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In low burden areas of the world, surveillance data is often a more informative measurement of malaria burden than parasite rate surveys, but these data are challenging to use in pixel-level modelling. We are developing disaggregation models that use these data alongside pixel-level environmental and human covariates in order to produce high-resolution burden estimates.
Read MoreReducing malaria transmission in Uganda through improvements to housing
Current Project
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Rapid population growth in Africa requires an urgent expansion and improvement of housing options. Improving housing presents a promising opportunity for malaria control by reducing indoor exposure to mosquitoes. A new study led by Dr Lucy Tusting measures recent changes in house design in rural Uganda and evaluates their association with malaria in relation to a mass scale-up of control efforts. Read More
Accessibility to Cities
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The ease with which people are able to connect with the services, institutions, and individuals supportive of socioeconomic success, good health, and overall wellbeing can ultimately separate communities that thrive from those left behind. Regardless of recent advances in electronic and online communications, inequalities persist in physical access to resources and opportunities that are primarily concentrated in urban centres. Understanding where the largest gaps in accessibility remain is of critical importance to a broad range of policymakers, investors, and development partners. Under the leadership Dr Daniel Weiss, Director of Global Malaria Epidemiology at MAP, we offer a first, crucial step toward tracking exactly where gaps in accessibility remain in 2015 and where the world can collectively address the most fundamental inequalities still experienced today. Read More
Quantifying malaria syndromes and treatment rates
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Malaria symptoms can often be quite general, overlapping with symptoms of other diseases. Malaria Atlas Project researchers aim to quantify the prevalence of two key symptoms of malaria (fever and anaemia) across Africa, and generate a better understanding of how much malaria contributes to each of these syndromes on the continent. In addition to this, by quantifying symptomatic and asymptomatic illness from malaria, we aim to measure the treatment seeking rate and effective treatment coverage for P. falciparum malaria in Africa.
Read MorePredicting the effect of seasonality on malaria transmission
Current Project
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Most malaria endemic countries experience seasonal variation in transmission. The aim of this work is to be able to accurately predict seasonal malaria transmission patterns where malaria survey data are sparse.
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