Funding Opportunity for Innovative Tools and Solutions to Address Public Health Emergencies

Funding Opportunity for Innovative Tools and Solutions to Address Public Health Emergencies is sponsored by New York City Pandemic Response Institute (PRI). This initiative funds pioneering projects that bring forward technology-driven, scalable solutions to enhance public health resilience, empower communities, and improve emergency preparedness across New York City.

NYC Pandemic Response Institute Awards Grants for Innovative Tools to Strengthen Public Health Emergency Response - NYC Preparedness & Recovery Institute NYC Pandemic Response Institute Awards Grants for Innovative Tools to Strengthen Public Health Emergency Response - NYC Preparedness & Recovery Institute NYC Pandemic Response Institute Awards Grants for Innovative Tools to Strengthen Public Health Emergency Response New York, NY – The New York City Pandemic Response Institute (PRI), in partnership with Columbia Engineering and the City University of New York (CUNY), has awarded grants to recipients of its first Funding Opportunity for Innovative Tools and Solutions to Address Public Health Emergencies .

This initiative funds pioneering projects that bring forward technology-driven, scalable solutions to enhance public health resilience, empower communities, and improve emergency preparedness across New York City.

The grants support projects that enhance New York City’s preparedness for health crises, emphasizing four key areas: improving access to care and social services , strengthening community engagement and communications , advancing data sharing and analytics , and promoting diagnostic solutions .

Chosen from a competitive pool of applicants, each awardee will receive funding to implement and expand their project over the coming year to address critical gaps and be adaptable in diverse public health scenarios.

Awardees and Project Descriptions: Genome Language Models for Pandemic Surveillance This project combines wastewater genomic analysis with biological language models to detect emerging pathogens before they become widespread. The system uses machine learning to analyze genetic sequences, capturing both the nature of pathogen variants and how they change over time.

Building on NYC’s wastewater monitoring program, the system can identify novel pathogenic sequences and predict outbreaks by detecting cryptic variants – genetic sequences found in wastewater that are absent from clinical samples but precede major disease waves, as demonstrated during COVID-19.

The system will be piloted using data from NYC Health + Hospitals and designed to integrate into public hospitals’ biosurveillance systems, enabling proactive, accessible health monitoring for underserved communities.

Lead Applicant: Noga Aharony, Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center (CUIMC) Co-Applicant(s): Mohammed AlQuraishi, Program for Mathematical Genomics, Department of Systems Biology, CUIMC; Harry Lee, Program for Mathematical Genomics, Department of Systems Biology, CUIMC; Jaeweon Shen, Program for Mathematical Genomics, Department of Systems Biology, CUIMC; Anne-Catrin Uhlermann, Division of Infectious Diseases, Department of Medicine, CUIMC; Medini K.

Annavajhala, Division of Infectious Diseases, Department of Medicine, CUIMC; John J. Dennehy, Department of Biology, Queens College and The Graduate Center, CUNY; Sherin Kannoly, Department of Biology, Queens College and The Graduate Center, CUNY; Monica Trujillo, Department of Biological Sciences and Geology, Queensborough Community College, CUNY.

Focus Area: Data Sharing, Analytics, Modeling, and Forecasting Solutions, Diagnostic Solutions Structuring Unstructured Health Data This project addresses the challenge of managing unstructured health data, which often requires manual data entry, consuming valuable staff time and potentially delaying public health responses, particularly impacting communities facing digital inequalities.

The project will develop a multimodal transformer model trained on representative public health use cases to automatically convert various formats of unstructured data (PDFs, faxes, images, free text) into machine-actionable data. This will enhance the speed and efficiency of public health emergency responses.

Lead Applicant : Analee Etheredge, PhD, MSPH, NYC Department of Health and Mental Hygiene, Center for Public Health Data Science Co-Applicant(s) : Jeanette Stingone, PhD, Columbia University, Mailman School of Public Health; Karmen S.

Williams, DrPH, MBA, MSPH, MA, CPH, City University of New York, CUNY Graduate School of Public Health and Health Policy Focus Area : Data Sharing, Analytics, Modeling, and Forecasting Solutions Towards Transformative Data-Driven Decision Platforms for Healthcare Crisis Response: From Prediction Models to AI-Digital-Twinning Integration This project will develop a methodology that integrates AI and digital twinning to inform data-driven decisions that are timely, reliable, and scalable to aid crisis response in NYC and other large cities.

The methodology involves building digital twins of hospital operations as an offline platform for optimization, and enabling real-time reactions to crisis scenarios by pre-training an AI-based metamodel based on the digital twin. With this integrated approach, the project aims to create a pipeline for robust decision-making to optimize patient welfare that efficiently utilizes both data trends and healthcare system knowledge.

Lead Applicant: Henry Lam, Associate Professor, Department of Industrial Engineering and Operations Research, Columbia Engineering Co-Applicant(s): Jay Sethuraman, Professor and Chair, Columbia Engineering; Charles Branas, Gelman Professor and Chair, Columbia Mailman School of Public Health; Alexis Zebrowski, Associate Professor, Emergency Medicine; Girish Nadkarni, Fishberg Professor of Medicine, Icahn School of Medicine; Donald Apakama, Assistant Professor and Physician, Mount Sinai; Sarah McCuskee, Assistant Professor, Emergency Medicine and Global Health, Icahn School of Medicine; Akhil Vaid, Assistant Professor, Icahn School of Medicine; Rachael Piltch-Loeb, Assistant Professor, CUNY Graduate School of Public Health and Health Policy Focus Area: Data Sharing, Analytics, Modeling, and Forecasting Solutions Conducting and Evaluating an AI-Enabled Community Based Participatory Preparedness Pilot This project evaluates the efficacy of Preppr.

ai, an AI-powered disaster exercise design software, in enhancing community engagement in government-led exercise design efforts. The project focuses on low-resourced communities that often struggle with traditional preparedness planning due to limited access, resources, and expertise.

By leveraging AI, the project aims to make disaster exercise design more accessible, efficient, and inclusive, leading to better preparedness and resilience in these communities. Lead Applicant : Justin Snair, CEO & Founder, Preppr.

ai Co-Applicant(s) : Principal Investigator: Rachael Piltch-Loeb, PhD, Assistant Professor, CUNY Graduate School of Public Health and Health Policy; Daniel Daugherty, Senior Software Engineer, Applied AI Consultant, Founder, Accelery.

ai Focus Area : Community Engagement and Communications Solutions A portion of this funding initiative is supported by Amazon, which has contributed both financial resources and technical support to help drive innovative public health solutions.

Amazon’s commitment to public health innovation extends to providing technical expertise and cloud infrastructure support to enhance the awarded projects, allowing them to scale more effectively and reach broader communities. This partnership reinforces the collective goal of advancing community resilience and equipping New York City with cutting-edge tools to address health crises.

“Amid the tragedy of COVID-19 and the increasing threat of climate change, public health preparedness and response must innovate,” said Mitch Stripling, Director of the New York City Pandemic Response Institute. “These projects blend the latest technology, community inspiration, and civic spirit to help New Yorkers meet future public health threats.

” Through this funding opportunity, PRI and its partners, Columbia Engineering and CUNY, seek to catalyze interdisciplinary collaboration, accelerate the development of practical public health tools, and prepare New York City to respond rapidly and equitably to future public health challenges.

About the NYC Pandemic Response Institute (PRI) The New York City Pandemic Response Institute (PRI) is a landmark initiative designed to proactively prepare NYC for future public health threats – from infectious disease to climate-related health emergencies. Its mission is to advance racial equity, build resilience, and promote public health preparedness throughout NYC and around the globe.

PRI is led by ICAP at Columbia University with its key partner, the CUNY Graduate School of Public Health and Health Policy.

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