The goals of the Geospatial Health Research Laboratory, housed at Saint Louis University's College of Public Health and Social Justice, are to:
- Apply geospatial data and technology to public health and medicine research to better understand predictors of health outcomes.
- Leverage nontraditional data sources to better know how individuals within communities live, work, and play
- Advocate for the implementation of actionable results informed by community members, data, and research findings
Using location technology and nontraditional health-related data sources, we aim to identify causes of inequity throughout communities and work with local leaders to affect actionable results and sustainable changes that address those inequities. We conduct research through the lens of community and equity, while leveraging geospatial data sources to better understand community members and the environments where they live, work and play.
Geospatial health can provide insights to communities throughout the world by identifying patterns of mobility, exposure, and environment and how those may impact the health behaviors and outcomes community members experience.
Geospatial Health Data Analytics Core
The Institute of Clinical and Translational Science (ICTS) at Washington University School of Medicine supports the SLU-housed Geospatial Health Data Analytic Core. The opportunities this core provides include consultation and thought leadership from geospatial health researchers to create actionable results in the communities we serve, help developing and implementing research projects with nontraditional health data sources, and assist in conducting spatial analyses.
Geospatial Modeling for Climate, Environment and Health
SLU’s College for Public Health and Social Justice conducts extensive research related to health, including projects that seek to predict and prevent the spread of infectious diseases through multisystem early-detection efforts. This work has focused on the interaction between mobility and the spread of both COVID-19 and Middle Eastern Respiratory Syndrome-Coronavirus (MERS-CoV).
To build additional models that can identify shifts in climate, human mobility and animal mobility, we have explored diverse and disparate data sources, and aim to cross-reference them with location data to develop environmental risk assessments. The cross-national and -disciplinary efforts of our One Health modeling of MERS-CoV risk, for example, highlight Kenya as an environment where significant infection risk can occur. By assessing and monitoring camel caravans, interactions with water sources, and contact with humans, the associated risk of MERS-CoV will be modeled using geospatial infectious disease modeling and tested in similar environments throughout the world.
The assessment of COVID-19 risk is anchored in human mobility. These models are informed by human discussions, geomobility and policy. We are using a multi-sensor approach to assess human patterns of communication via social media and Google searching to better identify where and when COVID-19 infections occurred to more comprehensively predict future infectious disease outbreaks.
These geospatial models, built from nontraditional data sources, when spatially anchored, collated, and analyzed, can provide prevention and care insights in community settings throughout the world and for a diverse set of infectious diseases. Our effort is to build, test and implement these types of multi-sensor systems to better know community needs and share actionable results with public health and healthcare partners.
Data sources used include anonymized smartphone data for geomobility, spatially anchored Google searches, social media mentions, other environmental data and satellite imagery.
We also study how different environments can become more equitable. Topics in this area include chronic conditions such as hypertension, diabetes and asthma; infectious diseases including HIV infection and sexually transmitted infections; the COVID-19 pandemic, Zika, and climate change and its impact on health.
Partners and Team Members
We could not do this work without our team and partners in this effort.
- Matt Ellis
- Steve Scroggins
- Adam Gilmore
- Germysha Little
- Geoffrey Kangogo
- Rashaad Adams
- Shafeel Umam
- Shreya Nagendra
- Bryce Takenaka
- Odiraa Okala
- Sydney Grellner
- Alyssa Coleman
- Caroline Hoyniak
- John Felder
- Rachel Skladman
- Ganeseh Babelul
- Julie Heyd
- Beau Ances
- Marie Philipneri
- Min Lian