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. 

Cost-effectiveness analysis (CEA) is a process that models the effects of interventions on a population, predicting their costs and benefits across time horizons and estimating a set of metrics for making informed decisions on the best resource allocation. 

This project team worked to advance cost-effectiveness analysis in public health by incorporating analysis techniques, abstraction languages and automation tools developed in mathematics and engineering for Markov models. Specifically, the team aimed to match the needs of CEA with the capabilities of the stochastic reward nets (SRNs) modeling formalism.

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 their practice or policymaking processes.

Timing

Summer 2022 – Summer 2023

Team Outputs

When Math Meets Public Health (2023 Fortin Foundation Bass Connections Virtual Showcase)

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, presented at Fortin Foundation Bass Connections Showcase, Duke University, April 19, 2023)

This Team in the News

Graduate and Professional Student Spotlight: Reflections from the Class of 2023

Image: Duke Kunshan University in March 2016, by Chris Hildreth