# CK20-003 Modeling Infectious Diseases in Healthcare Research Projects to Im

> **NIH ALLCDC U01** · NORTH CAROLINA STATE UNIVERSITY RALEIGH · 2024 · $338,231

## Abstract

Healthcare-associated infections (HAI) are a significant source of preventable morbidity and mortality.
Transmission models for HAI are a cornerstone method to both understand pathogen spread and evaluate
control interventions. Models have been particularly helpful in addressing transmission-blocking interventions,
for elucidating the connectivity among facilities, and their implications for controlling HAI. Mechanisms
underlying antimicrobial resistance, such as co-selection, have received less attention in transmission models.
In addition, key metrics—such as population-level fitness of resistant bacteria and the effect of resistant traits
on fitness—are often unknown. This limits our understanding of the complex relationship between antimicrobial
drug use and resistance, as well as the effectiveness of interventions aimed at changing drug selection
pressure. The objective of this proposal is to develop models that more explicitly address resistance traits and
modeling tools that support the identification of transmission sources and pathways for HAI. We will use the
models to further identify HAI sources and evaluate and optimize interventions. In particular, we will address
the following thematic areas: antimicrobial resistance (A), surveillance (A), genomics (B), and simulation of
epidemiological studies (B). We have assembled an interdisciplinary group of researchers with expertise in
infectious disease modeling, HAI hospital epidemiology and clinics, applied mathematics, and genomics
located at North Carolina State University, Washington University (WU) and University of Tennessee. We plan
to build on our previous and current collaborations among this team to: develop modeling approaches for
addressing HAI transmission; extend phylodynamics methods; and model antimicrobial resistance dynamics.
The CDC-Epi Center at WU and Barnes-Jewish Hospital in St. Louis, Missouri, will be the main source of data.
Additionally, we will use nation-level publicly available data sources. We will carry out the following aims: 1)
Develop improved approaches for inferring routes of acquisition of HAI and optimizing HAI surveillance and
control: We will develop ward- and hospital- level network models that take into account the main routes of HAI
acquisition and patient connectivity. We will apply optimization methods to identify environmental sampling
protocols and cost-effective control strategies. 2) Phylodynamics to estimate fitness of antimicrobial resistance
pathogens: We will apply and refine multi-type birth-death models to explore the fitness effects of a large
number of antimicrobial-resistant traits on pathogen phylogenies, and speed the methods to quantify fitness for
large numbers of strains, and 3) Multi-scale models for multidrug-resistant organisms: extended-spectrum
beta-lactamase (ESBL)- producing Enterobacteriaceae as case study: We will develop both agent- and
equation-based models that account for multi-scale dynamics of resistance transmi...

## Key facts

- **NIH application ID:** 10976404
- **Project number:** 5U01CK000587-05
- **Recipient organization:** NORTH CAROLINA STATE UNIVERSITY RALEIGH
- **Principal Investigator:** Cristina Lanzas
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2024
- **Award amount:** $338,231
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10976404

## Citation

> US National Institutes of Health, RePORTER application 10976404, CK20-003 Modeling Infectious Diseases in Healthcare Research Projects to Im (5U01CK000587-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10976404. Licensed CC0.

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