Development of Alliance Agreements in the Era of Outbreaks

Project Summary

This project summarizes the existing sample agreements from different institutions, analyzes the key contractual issues in the formation of alliances, and develops master charts of legal provisions to compare different approaches, to provide a reference for the formation of new alliances in the era of epidemic disease outbreaks. 

Themes and Categories

Key Contractual Issues in the Formation of New Alliances

Project Team

Beibei Sun, Duke University School of Law, J.D.’16 

Mentor: Professor Julie Barnes-Weise, Duke 

Funding: 

Methodology

Reviewed existing model and related agreements;

  • Identified applicable key terms;
  • Identified major approaches to specific issues;
  • Developed master chart of specific terms from designated agreements;
  • Adapted existing terms to the needs of a multi-party alliance for development of vaccines and therapies to treat and protect against an epidemic disease outbreak. 

Conclusion

The legal framework substantially affects the outcome and efficiency of the alliance formation.

  • The four key issues are usually central of the negotiation.
  • Which approaches to adopt is determined by the purpose and scope of the alliance. 

Download the poster with more details about the project

Related Projects

A team of students led by Janet Bettger and an interdisciplinary team with the 6th Vital Sign Study will use Census and other public data to examine the representativeness of people who participated in this smartphone based population health study. Students will design an online interactive map and other web-based tools that can be easily updated with new study participants illustrating key relationships such as health status with rurality, medical service availability, and sociodemographics. The online tools will be used to direct education efforts on the importance of walking speed as a marker of health and as the sixth vital sign. Findings from the data analysis will be used by GANDHI to direct scale-up of smartphone based research in target geographic areas and with specific population subgroups such as older adults and those with chronic illness.

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.

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.