Digital Twins and/or Synthetic Data Population Modeling for Whole-Person Health to Mitigate Chronic Disease Disparities Grand Challenge
NIH Challenges funds digital twin and synthetic data modeling research to advance whole-person health and reduce chronic disease disparities through integrated, AI-ready computational platforms.
Funder: NIH Challenges
Due Dates: February 19, 2027: Registration Deadline | April 19, 2027: Phase I Submission End | December 13, 2027: Phase II Submission End
Funding Amounts: Total prize purse: $1,000,000. Phase I: 4 awards ($110,000–$140,000 each); Phase II: 2 awards ($200,000 & $300,000).
Summary: Supports innovative digital twin and synthetic data modeling approaches to advance whole-person health and reduce chronic disease disparities through integrated, AI-ready computational platforms.
Key Information: Only Phase I winners may compete in Phase II; U.S. citizenship or U.S.-incorporated entities required for prize eligibility.
Description
This NIH Challenges Grand Challenge seeks to catalyze the development of digital twin and synthetic data population modeling approaches to address chronic disease disparities. The initiative aims to leverage data science, AI, and integrated biological, behavioral, environmental, social, and healthcare data to create advanced computational models for whole-person health. The goal is to transform static data repositories into dynamic research and innovation platforms, empowering researchers to simulate disease progression, predict health risks, evaluate interventions, and uncover actionable solutions to persistent health inequities.
The competition is structured in two phases: Phase I focuses on innovative design and prototyping of digital twin or synthetic population models, while Phase II supports the building, validation, and demonstration of operational platforms. Solutions should integrate real-world, multidimensional data and generate AI-ready, reusable resources to advance chronic disease research and public health decision-making, with a strong emphasis on reducing health disparities.
