Global Flu View Spark Program
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. We are offering project funding opportunities to three students, 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.
Q&A Session
Funding Details
Number of selected candidates 3
Funding amount
$5,000 each
Duration
12 months
Funding type
Stipend
Topics and Expected Impact
GFV AI
The goal of this project is to enhance the functionality and impact of Global Flu View (GFV) by leveraging artificial intelligence (AI) to support real-time surveillance, predictive analytics, and actionable insights for influenza-like illnesses (ILI). Students are expected to contribute to the development and implementation of AI-driven features and tools aimed at improving the accuracy and utility of GFV. Potential contributions could include:
- Developing machine learning models to predict influenza outbreaks based on historical and real-time data
- Collaborating with state, local, or tribal health departments and universities to integrate AI-driven analytics from GFV into their influenza surveillance efforts
Expected impact: The integration of AI into GFV will significantly enhance the platform’s ability to forecast and monitor flu activity, leading to improved public health response times and resource allocation. By developing predictive models, students will help create an early warning system capable of detecting flu outbreaks before they peak. Enhanced data visualization and geospatial tools will also improve the user experience, empowering public health officials to make more informed decisions. Additionally, integrating AI-driven analytics into existing public health monitoring systems will streamline flu surveillance and boost the overall effectiveness of disease control efforts.
GFV Hyperlocal
The goal of GFV Hyperlocal is to empower local health departments and communities by providing tools to generate customized, localized flu surveillance surveys. This feature is designed to enhance community-level monitoring and response to influenza-like illnesses (ILI) by offering real-time data collection and actionable insights specific to defined geographic areas. Students are expected to contribute to the development and rollout of GFV Hyperlocal by focusing on:
- Designing and implementing customizable survey templates that can be tailored by local health departments to address specific community health needs
- Facilitating partnerships with community organizations and health agencies to deploy Hyperlocal surveys for real-time flu tracking
Expected impact: The deployment of GFV Hyperlocal will enable health departments and communities to monitor flu activity at the neighborhood or city level, allowing for more targeted and timely public health interventions. By generating surveys that capture real-time, community-specific data, GFV Hyperlocal will enhance the responsiveness of local health officials, improve engagement with residents, and foster participatory surveillance. This localized approach will strengthen the overall flu surveillance infrastructure, creating a more resilient and adaptable public health system.
What we offer
- Fully-Funded Opportunity: A $5,000 internship stipend to support your work over the course of the 12-month project.
- Skills Development in Digital Epidemiology and Participatory Surveillance: Participants will receive guided mentorship from the research faculty leading the project, with a focus on implementing digital tools for epidemiological research.
- Inspiration for Future Research and Dissertation Themes: Exposure to cutting-edge research and global health challenges will help students generate innovative dissertation topics that contribute to advancing public health knowledge.
- Opportunity to Co-Author Peer-Reviewed Papers: This opportunity enhances students’ research and writing skills, boosting their credibility and visibility in the academic community. It also prepares students for future scholarly pursuits, positioning them as knowledgeable contributors in their field of study.
- Internship Credits Eligibility: Students can earn internship credits through the GFV Spark Program, gaining professional public health experience, building a professional network, and refining post-graduation career plans.
What we expect
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Time Commitment: Selected candidates must dedicate approximately 15 hours per week to the project. This commitment is essential for meaningful engagement with the project’s objectives and activities, ensuring substantial contributions and the achievement of set goals.
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In-Person Meetings: Students are required to attend regular in-person meetings at the Mel and Enid Zuckerman College of Public Health (Drachman Hall). These meetings are crucial for collaborative planning, progress updates, and direct mentorship from program leaders and peers. For students living outside the Tucson metropolitan area, flexibility may be considered for in-person attendance.
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Engagement with Public Health Departments: Students must be prepared to engage with state, local, or tribal public health departments as part of their project activities. These interactions are critical for understanding real-world public health challenges, integrating GFV into existing health monitoring systems, and fostering partnerships for participatory surveillance programs.
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Diligence and Commitment: We expect all participants to approach the project with diligence and a strong commitment to developing and executing their proposed activities. This includes conducting thorough research, strategic planning, and implementing initiatives that expand and enhance the utility of GFV.
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Communication Skills: Effective communication is vital to the success of the GFV Spark program. Students must possess excellent written and verbal communication skills, enabling them to clearly articulate ideas, collaborate with team members and stakeholders, and disseminate project outcomes. This also includes the ability to produce engaging promotional materials and reports.
Eligibility
Open exclusively to graduate students (MS, MPH, PhD, DrPH) of the Mel and Enid Zuckerman College of Public Health.
Application Process
Interested applicants should submit a detailed application via this application form outlining their project idea. Additionally, candidates are required to submit a 2-minute video explaining why they should be selected for this opportunity.
Evaluation Criteria
Applications will be evaluated based on the clarity of ideas, innovative approach, feasibility, potential impact, and alignment with the objectives of the program.
Deadline
All proposals must be submitted by December 10th. Late submissions will not be considered.
Award Notification
Successful applicants will be notified by January 15th
Useful Resources
Contact Information
For further inquiries about the GFV Sparks program, please contact Prof. Onicio Leal at onicio@arizona.edu.
We look forward to your innovative proposals and to collaborating in advancing Global Flu View for a healthier future.
2024 - 2025 GFV Spark Cohort
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.
Paulina's project aims to harness the power of artificial intelligence to predict flu outbreaks in Arizona and help hospitals prepare for sudden surges in patients. By 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, these 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 U.S. 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.