# Dynamical modeling of hospital transmission and antibiotic resistance evolution in a multidrug resistant nosocomial pathogen

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $521,024

## Abstract

PROJECT SUMMARY/ABSTRACT
Enterococcus faecium is a leading cause of hospital acquired infections that has proven refractory to infection
prevention measures and has evolved increasing levels of antibiotic resistance over the last 40 years. How
resistance evolves and spreads in this pathogen is uncertain because transmission and selection are hidden
processes: transmission occurs silently between asymptomatically colonized patients, which obscures the
signal of selection observed from clinical isolates.
The proposed work will develop and deploy powerful new statistical inference techniques to assimilate
data from electronic medical records, microbiological samples, and whole genome sequences into explicit,
mechanistic models of transmission and antibiotic resistance evolution in E. faecium. The work is made
possible by unique features of the study system: we have documented ongoing transmission and resis-
tance evolution in the pathogen E. faecium and possess both a nearly perfect record of patient movement
and antibiotic exposure and a large collection of patient samples from a thorough and active surveillance
protocol.
The specific aims of the proposal are: (I) To develop and fit a detailed E. faecium transmission model to
medical record data to precisely quantify: (i) transmission rates, (ii) recovery rates, (iii) the rate of evo-
lution of resistance, (iv) drivers of these rates, including contact precautions and antibiotic exposure, and
(v) potential interactions between resistance and transmissibility. (II) Bioinformatic approaches that utilize
whole genome sequences for c. 600 E. faecium isolates/yr and electronic medical records will be used to es-
timate size, structure, and location of transmission chains and characterize patterns of resistance evolution
across the resulting transmission network. (III) Hypotheses based on the transition model from Aim I will
be directly tested by using the genetic data from Aim II.
The methods developed herein will be applicable to a broad array of pathogens and clinical settings, and
will facilitate the rational design of strategies to slow or even reverse the evolution of antibiotic resistance.
In particular, the models and protocols will be portable to hospitals generally, where they will be useful for
designing interventions.

## Key facts

- **NIH application ID:** 9857544
- **Project number:** 5R01AI143852-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Robert J Woods
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $521,024
- **Award type:** 5
- **Project period:** 2019-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9857544

## Citation

> US National Institutes of Health, RePORTER application 9857544, Dynamical modeling of hospital transmission and antibiotic resistance evolution in a multidrug resistant nosocomial pathogen (5R01AI143852-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9857544. Licensed CC0.

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