As countries across the globe grapple with limited public health budgets, policymakers are seeking to optimize the impact of public health expenses and investments. Choosing which interventions to deploy based on reliable predictions of cost and effectiveness can save countries money and increase access to care.

Through joint efforts from computer engineering, global health and public policy at both Duke and Duke Kunshan University, this team provided public health practitioners and policy makers with a user-friendly tool supporting CEA using SRNs. Through follow-on work, they aim to develop a domain-specific language for users without programming knowledge to easily conduct CEA to provide information for their decisions during practice or policymaking process.

Predictive Modeling for Decision-Making in Public Health

Poster by Ivan Mura, Meifang Chen, Truls Ostbye, Kishor Trivedi, Shruti Pandey, Shuyi Qiu, Xueting Li, Haowen Ji and Zhexu (Alex) Jin