Africa’s First Genetics Study
September 23, 2020
16th H3Africa Consortium Meeting attendees
October 13, 2020

Harnessing Data Science for Health Discovery and Innovation in Africa (DS-I Africa)

Hello, Environmental Health Working group,

Just a reminder about DS-I Africa funding opportunities currently available.  In these FOAs, NIH is calling for applications in four areas: an open data science platform and coordinating center; research hubs; research training programs; and ethical, legal and social implications (ELSI) research.

NIEHS is participating in these FOAs and welcomes applications within the areas of environmental health including exposures to air pollution, pesticides, e-waste, metals, or other toxic chemical exposures.  Below are some ideas related to aspects of data science that NIEHS would be interested in:

  1. Data management and integration
    • Advance data readiness of existing environmental health datasets, including making data accessible, structured, quality-controlled, and well-annotated, with appropriate privacy/security protections;
    • Create or improve standards for the collection of new environmental health data derived from exposure science technologies (e.g., mobile health, sensors, biomarkers of environmental exposures);
    • Promote interoperability, aggregation, and harmonization of complex environmental health data to enable research and public health applications (e.g., integrating geospatial data from climate, satellite, or air monitoring sources with disease surveillance or other population data).
  2. Tool or methods development:
    • Improve environmental exposure surveillance or exposure science approaches by applying machine learning, AI, or related computational approaches;
    • Develop or expand informatic tools and statistical methodologies to analyze multi-dimensional environmental exposure data, gene-environment interaction (GxE) data, and related complex data (e.g. imputation techniques for missing observations, novel methodologies for chemical mixtures);
  3. Application to environmental health research questions and translation to public health:
    • Apply AI or related computational approaches to better understand the impact of the environmental exposures on health outcomes;
    • Develop or apply tools and technologies (e.g., mobile health approaches) for prevention/intervention such as community-based or similar strategies to reduce hazardous environmental exposures.
Please contact

Bonnie Joubert

Kim McAllister

Chris Duncan