# Mathematical and Computational Predictive Modeling Core

> **NIH NIH P20** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2020 · $194,131

## Abstract

PROJECT SUMMARY – Mathematical and Computational Predictive (MCP) Modeling Core
Reducing the burden of global infectious disease presents a number of stubborn key challenges. Principle
among these are 1) to understand the dynamic interface (immunologic, inflammatory, microbiome-related,
pathophysiologic) between an infectious pathogen and its human host, particularly in terms of the complex
factors that determine host susceptibility to or protection from clinical disease, 2) to understand the dynamics
of disease transmission and how they are affected by environmental and behavioral pressures, and 3) to
predict and measure the impact of interventions (vaccine, therapeutic, public health) with the goal of working
toward global improvements in public health. Meeting these challenges requires not only a broad knowledge
base that includes the natural history of the disease, its intra-host mechanism of action, its inter-host mode of
transmission, and large sets of complex -omic and demographic data, but also the ability to integrate this
knowledge into quantitative predictions of outcomes. This can only be done effectively when the relevant
processes are translated into mathematical expressions or algorithms that are implemented computationally so
that their predictions can be exhaustively examined. The goal of the Mathematical and Computational
Predictive (MCP) Modeling Core is to provide the expertise and resources necessary to bring MCP modeling to
the COBRE, with special focus on the junior faculty projects. MCP services include: 1) design, innovation and
planning, in consultation with the “Brains Trust,” 2) curation and preparation of final analytic datasets, and 3)
predictive model building and testing. By direct interaction with the COBRE faculty, its educational
components, and use of the “Innovation and Collaboration” laboratory, the MCP Modeling core will bridge the
scientific “culture gap” between the scientists with biomedical backgrounds and those with computational
modeling expertise. This will greatly enhance the ability of the TGIR Center to advance the understanding and
management of global infectious diseases.

## Key facts

- **NIH application ID:** 10021010
- **Project number:** 5P20GM125498-03
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** Jason HT Bates
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $194,131
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021010

## Citation

> US National Institutes of Health, RePORTER application 10021010, Mathematical and Computational Predictive Modeling Core (5P20GM125498-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10021010. Licensed CC0.

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