The mission of the Arnhold Institute’s Big Data and Global Health team is to improve the health of vulnerable populations by generating insights from complex health data.
The call for healthcare transformation coincides with unprecedented availability of data from electronic health records, genomics, insurance databases, clinical trials, and other sources. The Arnhold Institute data science team is an interdisciplinary group of computer scientists, economists, software developers, and clinician researchers that leverages data for knowledge discovery by applying recent advances in computer science and econometrics in novel ways. Computers have the ability to find hidden population health insights without being explicitly programmed where to look. This process, commonly referred to as machine learning, gives greater visibility to the invisible people and forces that drive health impact.
ATLAS: A Comprehensive Data Engine for Global Health Equity and Security
The world’s most marginalized populations are undercounted, and underserved. Information gaps exacerbate vulnerabilities to health crises. As a result, blind spots can become disease hot spots.
The ATLAS solution
ATLAS is a digital platform that uses predictive algorithms, mobile technology, and satellite imagery to analyze underreported communities so that health systems and policymakers can make better decisions on how to allocate resources and manage disease outbreaks where and when they occur.
The Arnhold Institute, along with its partners, Dimagi and TulaSalud, receives support from USAID and Digital Globe to locate and assess areas in Guatemala most at risk for a potential Zika virus epidemic.
Precision is key
ATLAS analyzes inputs from frontline workers and satellite images--making blind spots in demographic and health information visible. The platform is optimized for communities where information sources are out of date or absent, so ATLAS is primarily designed to be used by low-literacy frontline workers in low-resource settings.