# Analysis and Simulation of Bacterial Infections and Resource Strain in Hospitals during the COVID-19 Pandemic

> **NIH ALLCDC U01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $1,200,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Antimicrobial resistant (AR) pathogens remain a major cause of healthcare associated infections (HAIs) in the
United States. Indeed, the prevalence of these existing and emerging drug-resistant agents continues to
impose a heavy burden on U.S. healthcare systems. To better control existing AR pathogen-associated HAIs
and prepare for the possible emergence of a novel AR organism, better, more targeted identification and
intervention strategies need to be developed. Here, for this Modeling Infectious Diseases in Healthcare
Research Projects to Improve Prevention Research and Healthcare Delivery (MInD Healthcare) network
project, we propose to develop a hierarchy of new model-inference systems capable of simulating and
forecasting HAI outbreaks, quantifying individual patient colonization risk, and identifying optimal intervention
approaches. Specifically, we will use hospitalization records and diagnostic data for multiple AR pathogens
from four major hospitals in New York City to conduct a series of modeling studies. We will develop two
mathematical modeling structures: 1) a metapopulation model capable of simulating AR pathogen transmission
dynamics across multiple healthcare facilities; and 2) an agent-based model capable of simulating individual-
level patient infection status, transmission dynamics, and movements within multiple hospitals. These models
will be used in conjunction with Bayesian inference methods to simulate observed outbreaks of AR pathogens,
estimate critical epidemiological characteristics and asymptomatic carriage probabilities among individual
patients, and support development of an AR pathogen forecasting system. As the models are high dimension
and the observations are sparse, new inference methods, capable of data augmentation and efficient model
optimization, will also be developed. Additionally, we will use the optimized model structures to run free
simulations testing the effectiveness of six interventions: 1) hand hygiene and barrier precautions; 2) isolation
of infections; 3) environmental cleaning; 4) active patient screening within hospitals; 5) contact tracing; and 6)
screening at admission. These interventions will be tested singly and in bundles and used to inform targeted
control approaches. Further, we will develop a framework for identifying intervention bundles that maximally
reduce HAI rates given cost and logistical constraints. Lastly, we propose to collaborate with the CDC and the
other research groups in the MInD Healthcare network to develop standardized intervention scenarios and
inter-comparisons of simulated intervention outcomes among the different model forms used across the
network.

## Key facts

- **NIH application ID:** 10220744
- **Project number:** 5U01CK000592-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Sen Pei
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2021
- **Award amount:** $1,200,000
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10220744

## Citation

> US National Institutes of Health, RePORTER application 10220744, Analysis and Simulation of Bacterial Infections and Resource Strain in Hospitals during the COVID-19 Pandemic (5U01CK000592-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10220744. Licensed CC0.

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