Applications are invited to the Wellcome Data Re-use Prize: Malaria challenge 2019. This Challenge is organized by The WellcomeTrust with support from Sage Bionetworks. The World Health Organization (WHO) considers nearly half the world’s population at risk from Malaria, with an estimated 216 million new cases and 445,000 deaths in 2016, mostly in sub-Saharan Africa. But after decades of neglect, efforts to combat the disease have entered an unprecedented era – it is high on the policy agenda, and a growing body of evidence points towards important reductions in illness and death. The global effort to eradicate malaria depends on researchers and policymakers being able to access and use the data that is being generated about the disease and interventions to control it.
The Malaria Atlas Project launched its Repository of Open Access Data (MAP) resource in 2013, with support from a Wellcome Biomedical Resources grant, and then funding from the Bill & Melinda Gates Foundation. It contains a wealth of data on malaria risk and intervention coverage – all of which is free to be accessed, re-analysed and re-used by anyone. As part of its mission, MAP creates statistical models to estimate the true burden of malaria through time at a fine-scale resolution 5 km2 grids. Both parasite rate (prevalence) and clinical incidence rate (annual parasite index, or API) are modelled and mapped.
The winning team or individual will get a cash prize of £15,000. Two runners-up will receive £5,000 each.
Submissions to the prize should generate a new insight, tool, or health application from the vast amount of data held by MAP. Participants can pursue any research question or innovation that makes best use of the data. The MAP team has suggested several potential research questions or themes including:
- Exploring novel explanations for unattributed residual malaria transmission present in MAP’s statistical models.
- Novel approaches to ‘down-scaling’ of areal incidence data provided by MAP for three endemic countries for which case totals (corrected for treatment-seeking and reporting biases) are available at a range of spatial scales.
- Visualising the measures of uncertainty associated with MAP’s typical modelled outputs.
Submissions will take the form of a piece of code or analysis, plus a short narrative description of the work undertaken and how it meets the success criteria. The success criteria will include things like novelty, potential health impact, and robustness of methodology.
Deadline: March 15, 2019
To register for the competition, visit the official site