Center for Transformative Infectious Disease Research (CTIDR)

The impact of extreme weather events on human health via disease transmission remains a key need for study. The linking pathway is complex--it incorporates the weather event and its aftermath (e.g., standing water), ecosystems, vector biology, disease biology, human biology, effects on built and institutional infrastructure, and human behavior—and requires both a systems approach and a need to de-silo different fields. To help CTIDR meet its goal of creating predictive epidemiological models, we propose a Living Evidence and Applied Data Modeling Core (LEAD-MC), which will desilo the relevant and diverse fields and accelerate the building of transdisciplinary models. We will integrate different disciplinary datasets and provide transdisciplinary modeling expertise to enable researchers to integrate weather, land-use, geospatial analysis, animal and human health, econometrics and policy analysis, genomic data, epidemiological modeling, and modern data management and analysis (living evidence review, artificial intelligence, machine learning) to help identify generalizable vs contextspecific relationships, with a goal of facilitating the creation of predictive epidemiological models in CTIDR. The overarching goal of LEAD-MC is to build capacity and tools for transdisciplinary research initially among CTIDR participants and collaborators. In the short term, LEAD-MC will facilitate the achievement of overall CTIDR goals by de-siloing skillsets, datasets, and institutions. In the long term, methods used in LEAD-MC can be applied elsewhere to accelerate actionable health impact research. To achieve our goal, we have assembled a team representing diverse skillsets, experiences, and career levels. The LEAD-MC team will leverage their expertise in direct support of CTIDR 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; and 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, ESI), geospatial analysis (Hayden, ESI), living evidence review (Kibbee, ESI), econometrics and policy analysis (Sanders), and epidemiological modeling (Smith, ESI). We will also leverage already-collected data contributed by other LEAD-MC members and CTIDR collaborators, including human- and wildlife-health datasets including genomic libraries, remote sensing data, and biobanked human and animal samples. Additional members can be added to the LEAD-MC effort during the project as well. LEAD-MC function is supported by world-class research facilities including data storage and computation.