# Center for Transformative Infectious Disease Research on Climate, Health and Equity in a Changing Environment (C-CHANGE)

> **NIH NIH P20** · CORNELL UNIVERSITY · 2024 · $258,042

## Abstract

LEAD-MC SUMMARY
The impact of climate change on human health via disease transmission remains a key need for study, even as
other linkages between climate change and human health, like those of thermal extremes and air pollution, are
better understood. The linking pathway is complex--it incorporates climate, ecosystems, vector biology, disease
biology, and human biology and behavior—and requires both a systems approach and a need to de-silo different
fields. To help C-CHANGE meet its goal of creating predictive epidemiological models linking climate change
and disease transmission, we propose a Living Evidence and Applied Data Modeling Core (LEAD-MC), which
will de-silo the relevant and diverse fields and accelerate the building of transdisciplinary models. We will build
a federated database overlaid with integrated disease modeling, geospatial analysis, climate modeling,
econometrics and policy analysis, epidemiological modeling, and modern data management and analysis (living
evidence review, artificial intelligence, machine learning) to facilitate the creation of predictive epidemiological
models in C-CHANGE. The overarching goal of LEAD-MC is to build capacity and tools for transdisciplinary
research initially among C-CHANGE participants and collaborators. In the short term, LEAD-MC will facilitate the
achievement of overall C-CHANGE goals by de-siloing skillsets, datasets, and institutions. In the long term,
methods used in LEAD-MC can be applied elsewhere to accelerate actionable climate change-health impact
research. To achieve our goal, we have assembled a team representing diverse skillsets, geographic experience,
institutions, and career levels. The LEAD-MC team will leverage their diverse expertise in direct support of C-
CHANGE through a collaborative model and through organizing trainings with support from the Administrative
Core. LEAD-MC will also investigate new methods of curating relevant datasets and building integrative models.
Specifically, we will test two hypotheses: 1) that applying the living-evidence model of systematic review will
improve the applicability of research products; 2)that methods of artificial intelligence, epidemiology, and
econometrics provide a framework for integrating data streams to generate actionable data and research.Each
member of the team is an expert in their field and some level of interdisciplinary collaboration, but LEAD-MC will
enable all members to gain experience working in a transdisciplinary setting. Expertise includes disease
modeling (Bento), geospatial analysis (Hayden, ESI), climate modeling (Jacobson), living evidence review
(Kibbee, ESI), artificial intelligence (Marivate, ESI; Pavicic, ESI), statistics (Ntozini), econometrics and policy
analysis (Sanders), and epidemiological modeling (Smith, ESI). We will also leverage already-collected data
contributed by other LEAD-MC members (Chaisi) and C-CHANGE collaborators, including human- and wildlife-
health datasets including genomic li...

## Key facts

- **NIH application ID:** 10982893
- **Project number:** 1P20AI186093-01
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Alistair Hayden
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $258,042
- **Award type:** 1
- **Project period:** 2024-09-17 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10982893

## Citation

> US National Institutes of Health, RePORTER application 10982893, Center for Transformative Infectious Disease Research on Climate, Health and Equity in a Changing Environment (C-CHANGE) (1P20AI186093-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10982893. Licensed CC0.

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