At a local health department, an epidemiologist scans fresh maps and lab results to decide which blocks need help first.
We’re turning regulated data and field surveillance into guidance that moves help where it’s needed, fast.
Data Platforms, Causal Inference and Machine Learning Methods for Identifying Social Determinants of Health in the US
Project Lead: Francesca Dominici
What we’re building
- Secure research spaces for sensitive health data, paired with analysis approaches that make causes and patterns easier to understand.
- Plain-language documentation and guardrails so results can be checked, repeated, and improved.
What it enables
- Faster, clearer evidence to inform air-quality, housing, and other policy decisions.
- More confidence that results are fair, transparent, and suitable for real-world use.
Development of a Deep Learning Model to Enable Serosurveillance of Vector-Borne Diseases and Their Hosts
Project Leads: Daniel Neafsey; Sarah Fortune; Junwei Lu
What we’re building
- An integrated lab-to-analytics pipeline that links mosquito and blood tests with environmental and clinical data to track mosquito-borne risks.
- Cloud-based data integration, quality checks, and automated updates to risk maps and dashboards.
What it enables
- Earlier, more targeted detection of signals that help communities prepare and respond.
- A foundation for surveillance systems that stay current as conditions change.
About the AWS Impact Computing Project at HDSI
Launched in 2022, the AWS Impact Computing Project is a collaboration between HDSI and AWS. Together, we are reimagining how data science and high-performance computing can be melded to confront society’s most complex challenges.