Deadline: 26 April 2019
Grand Challenges Africa (GCA) is seeking applications for its “Data Science Approaches to Improve Maternal and Child Health in Africa”, a programme of the African Academy of Sciences (AAS) supported through the AAS funding and programmes implementation platform, the Alliance for Accelerating Excellence in Science in Africa (AESA) – and the African Academy of Sciences with the South African Medical Research Council (SAMRC), in collaboration with the Bill & Melinda Gates Foundation’s Knowledge Integration (Ki) initiative.
The purpose of this call for proposals is to promote new quantitative approaches to synthesizing and analyzing data and evidence obtained from African public health surveys, longitudinal observational studies, clinical trials, and other relevant data sources – including combined datasets – to produce novel insights which can be used to improve maternal and child health in the African context and around the world. By enabling and fostering access to diverse datasets, GCA intend to engage a broad spectrum of collaborators – including research and clinical scientists working with data scientists, bioinformaticians, statisticians, epidemiologists, engineers, and computer programmers – to identify how innovative data analytic approaches can be used to develop more effective solutions for the maternal and child health challenges in Africa.
Key areas that have been identified by which to improve maternal and child health in Africa are: 1) better care during pregnancy, 2) better care at birth, 3) better postnatal care for women and their newborns, and 4) better hospital care of sick newborns. Nevertheless, developing and validating approaches to foster maternal and child health is challenging due to the complex interaction of biological, environmental, and social factors. Furthermore, policy recommendations for such approaches frequently lack sufficient supporting scientific evidence, while clinical trials are expensive, time-consuming, and increasingly difficult to implement. There is now a key opportunity to accelerate research in this area by establishing robust collaborative research networks in Africa (especially those focused on pregnancy and the peripartum period) and analyzing data from multiple African sources to guide public health recommendations that are data-driven and cost effective.
What GCA is looking for
GCA seeks proposals designed to answer critical scientific questions related to maternal, neonatal and child health and development outcomes. Proposals should use innovative quantitative analytics and modeling approaches that can be applied to the relevant existing data sets and should yield actionable results with a potential to significantly impact pan-African public health policy or that of specific African countries.
They will give highest priority to proposals that:
- support innovative collaborations between African researchers, healthcare professionals, and data scientists that could contribute to a sustainable African maternal, neonatal, and child health research network
- answer critical scientific questions identified in this call for proposals, while building and strengthening data science capacity for Africa
- consider African genetic diversity by accessing genomics databases, gene sequences, genome-wide association studies, or genetic cohorts that include Africans such as Hapmap
- take into account social, environmental and cultural determinants of outcomes and incorporate an understanding of the target community that includes barriers and constraints to delivery of interventions and to implementation of public health programs
- contribute to a portfolio of funded projects that addresses regional diversity and the need to provide health equity for diverse vulnerable populations
- explain how answers will have the highest likelihood of being relevant for implementation broadly in the public health system
- employ innovative and interdisciplinary analytical methods
How to Apply
Applicants can apply via given website.
For more information, please visit https://bit.ly/2FdGKuV