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...