Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01z316q471s
Title: Geospatial Modeling for Antibiotic Resistance in Hospital-Acquired Infections in the United States
Authors: Song, Annie
Advisors: Levin, Simon
Laxminarayan, Ramanan
Department: Ecology and Evolutionary Biology
Certificate Program: Global Health and Health Policy Program
Class Year: 2021
Abstract: Antibiotic resistance is a major public health crisis due to the lack of new drugs in development and the continuous rise of resistance mechanisms. On the other hand, hospitalized patients are particularly vulnerable to obtaining a hospital-acquired infection, which increases the risk for death and injury. Unfortunately, the hospital setting often fosters the rise of resistance due to the high usage of antibiotics and the increased density of bacteria and vulnerable patients. Ultimately, this leads to higher risk of antibiotic resistance in hospital-acquired infections. Implementing infection control and antibiotic stewardship policies require sufficient knowledge of local resistance trends. However, data on antibiotic resistance specific to the hospital setting are sparse. This study analyzes point-prevalence data of antibiotic resistance rates in hospital-acquired infections utilizing spatial interpolation for five pathogens. Along with machine learning models and stacked generalization, environmental covariates are combined with unique location data points to make continuous predictions of resistance across the United States. I found that resistance rates were low in hospital-acquired infections in the United States, although high rates of resistance were identified for Acinetobacter spp. The map of predictions highlights potential hotspots of resistance in hospital-acquired infections, allowing for more targeted interventions and research.
URI: http://arks.princeton.edu/ark:/88435/dsp01z316q471s
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Ecology and Evolutionary Biology, 1992-2023
Global Health and Health Policy Program, 2017-2023

Files in This Item:
File Description SizeFormat 
SONG-ANNIE-THESIS.pdf1.62 MBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.