This project was started during the New Lab COVID-19 Data Hack and aims to provide a variety of indices to assess community vulnerability to COVID-19 infections, adverse health outcomes, and disparate socio-economic harms. We intend this to be usuable by a variety of stakeholders: by healthcare systems to identify testing and service deserts and effectively allocate existing resources, by community organizations to find outreach areas and understand needs, and by policy makers to inform funding and decision making.
Our dataset contains a range of health care, public health, physical and community infrastructure, socio-economic, demographic, and COVID-19 specific information at the US county level. We include data from the following open access sources:
- The New York Times COVID-19 Data Repository
- The Robert Wood Johnson Foundation County Health Rankings and Roadmaps Program
- The Federal Communications Commission Form 477 County Data on Internet Access Services
We implement a variety of vulnerability scores to help stakeholders assess needs in their area and plan how to best allocate their efforts and resources. The currently available scores are:
- Severe COVID Complications
- Food Access Difficulties
- Risk of Economic Harm
- Community Connectedness
- Mental Health Support Needs
- Mobile Healthcare Impact
- Overwhelming Healhcare Systems
- Information Deserts
Full definitions of each of these scores can be found in the data dictionary
 Savannah Thais |  Cassandra Durkee |  Olga Karabinech |  Ioana Anghel |  Yedi |
- Savannah Thais: Metrics, Data Cleaning/Processing, Visualizations
- Annina Christensen: Documentation, Platform Development, User Experience, Publicity
- Olga Karabinech: Visualizations, Platform Development, User Experience
- Nataly Rios: Literature Review, Identify Metric Data, User Experience, Publicity
- Alexandra Passarelli: Literature Review, Identify Metric Data, Visualizations, User Experience, Funding
- Cassandra Durkee: Documentation, Project Management
- Ioana Anghel: Incorporating New Data, Visualizations
- Sarah Boufelja: Metrics, Data Cleaning/Processing, Machine Learning
- Yedi: