- Dr. Liudmila Sergeevna Mainzer, Technical Program Manager, National Center for Supercomputing Applications
- Dr. Aiman Soliman, Research Scientist, National Center for Supercomputing Applications
- Dr. Kenton McHenry, Principal Research Scientist, National Center for Supercomputing Applications
- Maxwell Burnette, Senior Research Programmer, National Center for Supercomputing Applications
- Dr. Zeynep Madak-Erdogan, Assistant Professor, Dept. of Food Sciences and Human Nutrition
- Dr. Rebecca Smith, Assistant Professor, Dept. of Pathobiology
We would like to partner on a project that combines computational analysis with geospatial, biomolecular and environmental metrics to identify and ameliorate health disparities across the rural/urban axis in our region. The focus of our pilot project is health in rural communities and farmer families along the Champaign-Urbana/Rantoul axis, particularly agriculture-related health risks resulting from exposure to pollutants, fertilizers and other environmental dangers that accompany rural life, and exacerbated by poverty.
We propose to develop a rich data collection program that will cut across multiple facets of rural life and will provide sufficient information to train a machine learning engine to look for comorbidities among health, environment and socioeconomic status of the individuals. We will pool measurements of contaminants in ground and surface water, sediments, and farm soil, from existing databases, and also collect individually via soil and water samples in key locations. We will recruit individuals from rural areas for various biological assays (blood and saliva) and questionnaires (self-reported health and socioeconomic information) that will allow us to establish metrics of physical health, emotional well-being and potential health risks. Additionally, we will build relationships with farmer families to collaborate on acquisition of both environmental and health data. This information will be combined with geospatial analyses that take advantage of satellite imaging to track the evolution of homesteads and their vicinity, suburban housing and neighborhood across the rural/urban gradient, via the metrics of standing water, roof damage, vegetation overgrowth, state of the sewers, and garbage accumulation.
Our long-term goal is the development of a quantitative predictive tool that will help identify locations and individuals at risk of poor health outcomes. We believe this approach has potential for scalability and replicability in environments beyond Illinois. The work fits within the Center for Digital Agriculture under the theme of People in Agriculture.