Charting and Mapping Vaccine Development Capacities

Project Summary

With the significant international consequences of recent outbreaks, the ITP Lab conducted extensive stakeholder interviews and macro-level health policy analysis to expose gaps in pandemic preparedness and develop legal frameworks for future threats. 

Themes and Categories

Project Team

  • Malcolm Nowlin, Public Policy & Chemistry
  • Nora Ghanem, Public Policy & Global Health
  • Niveen Hennein, Public Policy & Global Health
  • Julia Tuttle, Global Health & Cultural Anthropology
  • Courtney Scoufis. Public Policy & Global Health
  • Farrukh Jadoon, Computer Science
  • Christina Langmack, Public Policy & Global Health
  • Kushal Kadakhia|, Public Policy & Global Health 

Mentors: 

  • ProfessorJuliaBarnes-Weise, Duke
  • ProfessorAnaSantos-Rutschman, Duke 

Funding:

Project Details

Emerging danger:

  • Scientists and public health experts have identified several key pathogens that have caused or are likely to cause severe outbreaks and have few or no medical interventions available 

Updateable mapping tool:

  • Online geospatial database with search capabilities that allow for :
    • Filtering by region, country, and pathogen
    • Clickable markers that reveal further details of organization
    • Bubble Map that displays linkages between organizations and their roles in development
  • Able to provide critical knowledge of vaccine development landscape and facilitate the formation of alliances 

Download the poster presentation for more details (PDF).

 

Related Projects

A team of students led by Professors Jonathan Mattingly and Gregory Herschlag will investigate gerrymandering in political districting plans.  Students will improve on and employ an algorithm to sample the space of compliant redistricting plans for both state and federal districts.  The output of the algorithm will be used to detect gerrymandering for a given district plan; this data will be used to analyze and study the efficacy of the idea of partisan symmetry.  This work will continue the Quantifying Gerrymandering project, seeking to understand the space of redistricting plans and to find justiciable methods to detect gerrymandering. The ideal team has a mixture of members with programing backgrounds (C, Java, Python), statistical experience including possibly R, mathematical and algorithmic experience, and exposure to political science or other social science fields.

Read the latest updates about this ongoing project by visiting Dr. Mattingly's Gerrymandering blog.

A team of students led by faculty and researchers at the Social Science Research Institute will bring together data that will facilitate research using social determinants of health (SDH) to examine, understand, and ameliorate health disparities. This project will identify SDH variables that have the potential to be linked to data from the MURDOCK Study, a longitudinal health study based in Cabbarus County, NC. Much of this data – information relevant to understanding socioeconomic status, education, the physical and social environment, employment, and social support networks – is publicly available or easily obtained and its aggregation and analysis offer opportunities to significantly improve predictions of health risks and improve personalized care. Students will evaluate potential data sources, develop ethical policies to protect respondent privacy, clean and merge data, create documentation for data sharing and reuse, and use statistical tools and neighborhood mapping software to examine patterns of disparity.

A team of students will contribute to an effort to operationalize the application of distributed computing methodologies in the analysis of electronic medical records (EMR) at Duke.  Specifically, the team will compare and contrast conventional (Oracle Exadata) and distributed (Apache SPARK) systems in the analysis of EMR data, and create recommendations for implementation.  Students will then use these systems to execute natural language processing (NLP) on clinical narratives and radiology notes with existing, ongoing analyses of Duke data.  This Data+ team will work with the Duke Forge, an interdepartmental collaboration focused on data science research and innovation in health and biomedical sciences.