# Collaborative Research: ACED: Track 1: PanAX: Accelerating Epidemic Science through Causally-Informed Domain Generalization and Knowledge Transfer

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Georgia State University Research Foundation, Inc. (GA) · $167,000

## Abstract

The understanding and containment of epidemics involves many factors, such as how people behave and interact, how they move around, and the decisions made by governments and organizations to control the spread. Because of this complexity, building accurate and timely models to predict and manage outbreaks in near real time is challenging and time consuming. It also requires copious data, which are often unavailable when it is urgently needed. To address these challenges, this project develops PanAX, a new computational system to improve how we prepare for and respond to evolving epidemics. The core idea is to use existing data and models in smarter ways and based on situational awareness, so we do not have to start from scratch every time. The focus is on identifying and leveraging the key underlying patterns and relationships that drive the spread of diseases, allowing models and data to be adapted to new situations more easily. Bringing together experts from computational and data sciences with epidemiologists,  PanAX explores the deeper causes of how epidemics spread in different contexts, helping to create models that are more adaptable, accurate and reliable based on real-time conditions. The project develops methods to reuse parts of existing data and models, applying them to new outbreaks in different locations or circumstances. Consequently, new tools will be created to better plan, inform, prepare the public, and respond to outbreaks. The project also trains students

## Key facts

- **NSF award ID:** 2435887
- **Awardee organization:** Georgia State University Research Foundation, Inc. (GA)
- **SAM.gov UEI:** MNS7B9CVKDN7
- **PI:** Gerardo Chowell-Puente
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** —
- **Estimated total:** $167,000
- **Funds obligated:** $167,000
- **Transaction type:** Standard Grant
- **Period:** 06/15/2025 → 11/30/2026

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2435887

## Citation

> US National Science Foundation, Award 2435887, Collaborative Research: ACED: Track 1: PanAX: Accelerating Epidemic Science through Causally-Informed Domain Generalization and Knowledge Transfer. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2435887. Licensed CC0.

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