New group of student researchers funded by the Global Flu View Spark program
The ‘Global Flu View Spark’ program funds students to work on research projects that expand or enhance the impact of the Global Flu View digital disease tracking platform, and projects proposed by the new cohort of students will use AI and digital epidemiology tools for research.
The Global Flu View Spark (GFV Spark) program funds students to work on projects that expand or enhance the impact of the Global Flu View (GFV) digital disease tracking platform. Three students in the new cohort of the GFV Spark program have been funded for promising research projects involving AI and digital epidemiology.
To kick off the new cohort, the GFV team conducted a workshop focused on digital epidemiology, Artificial Intelligence (AI), and participatory surveillance. The workshop, titled “GFV Spark Workshop – Digital Epidemiology & AI in Public Health,” was offered college-wide, and attracted students from various degree programs, so it gave all the participants a foundation in these vital public health areas and also fostered interdisciplinary collaboration.
Paulina Colombo
PhD Student in Epidemiology
Project Title
Influenza Forecasting in Arizona: An AI-powered approach to predict local flu cases and plan hospital resources
This project aims to harness the power of Artificial Intelligence to predict flu outbreaks in Arizona and help hospitals prepare for sudden surges in patients. Using machine learning techniques, the project will analyze large sets of flu data—from nationwide statistics to local Arizona trends—to anticipate flu cases 2–4 weeks in advance. First, the system is trained on comprehensive data from Global Flu View to capture overall patterns in flu activity. It then fine-tunes these insights with historical flu numbers specific to Arizona, making the forecasts more accurate for our region. Next, the predictions are converted into practical estimates of hospital bed usage in Arizona, ensuring local health facilities have the information they need to manage potential spikes in flu patients. This AI-driven forecasting tool promises to provide timely alerts for healthcare providers, making it easier to allocate resources and respond effectively to flu outbreaks across Arizona.
Seunghoon (Kelly) Lee
PhD Student in Biostatistics
Project Title
AI-Driven Influenza Forecasting: Leveraging ensemble learning for accurate ILI predictions in U.S.
Predicting Influenza-like Illness (ILI) in real-time is challenging due to flu’s seasonal and unpredictable nature. While machine learning (ML) models have improved prediction accuracy, issues like data quality, feature selection, and regional adaptability remain. This project aims to develop a machine learning-based ILI forecasting model for specific regions in the United States using ensemble learning techniques. The process will involve selecting and testing different ML algorithms to identify the best models for ensemble integration. The model will be tailored to region’s specific characteristics by carefully selecting relevant features. Finally, it will be trained on global data from the Global Flu View program to assess its ability to generalize across different states.
Royani Saha
PhD Student in Environmental Health Sciences
Project Title
GFV Hyperlocal: Integrating environmental factors for Omni-channel flu surveillance
The goal of GFV Hyperlocal is to improve flu surveillance by integrating real-time environmental data to predict and prevent outbreaks more effectively. When we utilize factors like air quality, weather conditions, wastewater analysis, and community reports, traditional surveillance systems can have a clearer picture of how flu spreads. And these additions are possible by using GFV Hyperlocal. With real-time monitoring, health officials and communities can act faster, launching data-driven interventions, and tailoring responses to local needs. This innovative approach connects environmental science with public health, creating a smarter, more adaptive system that keeps communities healthier and better prepared for flu season.
Pushing Digital Innovation in Public Health
Designed to drive innovation and research in public health, the Global Flu View (GFV) Spark program invites ambitious students to contribute to the expansion and impact of the Global Flu View platform. GFV Spark offers project funding opportunities to three students per cohort, supporting initiatives aimed at enhancing GFV’s effectiveness and reach. This is a unique chance to influence public health outcomes on both local and global scales. Through the GFV Spark program, students gain hands-on experience with data analysis and digital epidemiology platform management.
The Global Flu View platform was developed by Dr. Onicio Leal and the team at the nonprofit Ending Pandemics. In 2023, the GFV platform was awarded to the University of Arizona to become a program at the Global Health Institute in the Zuckerman College of Public Health. GFV continues to serve its purpose as a participatory disease surveillance platform globally, and at the same time provides a unique public health education and research opportunity within the college. Ending Pandemics will become part of the Zuckerman College of Public Health at the University of Arizona in 2025.