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Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data

As agent-based models (ABMs) are increasingly used for modeling infectious diseases, model validation is becoming more crucial. In this study, we present an alternate approach to validating hospital ABMs that focuses on replicating hospital-specific conditions and proposes a new metric for validatin...

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Autores principales: Scaria, Elizabeth, Safdar, Nasia, Alagoz, Oguzhan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120937/
https://www.ncbi.nlm.nih.gov/pubmed/37083629
http://dx.doi.org/10.1371/journal.pone.0284611
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author Scaria, Elizabeth
Safdar, Nasia
Alagoz, Oguzhan
author_facet Scaria, Elizabeth
Safdar, Nasia
Alagoz, Oguzhan
author_sort Scaria, Elizabeth
collection PubMed
description As agent-based models (ABMs) are increasingly used for modeling infectious diseases, model validation is becoming more crucial. In this study, we present an alternate approach to validating hospital ABMs that focuses on replicating hospital-specific conditions and proposes a new metric for validating the social-environmental network structure of ABMs. We adapted an established ABM representing Clostridioides difficile infection (CDI) spread in a generic hospital to a 426-bed Midwestern academic hospital. We incorporated hospital-specific layout, agent behaviors, and input parameters estimated from primary hospital data into the model, referred to as H-ABM. We compared the predicted CDI rate against the observed rate from 2013–2018. We used colonization pressure, a measure of nearby infectious agents, to validate the socio-environmental agent networks in the ABM. Finally, we conducted additional experiments to compare the performance of individual infection control interventions in the H-ABM and the generic model. We find that the H-ABM is able to replicate CDI trends during 2013–2018, including a roughly 46% drop during a period of greater infection control investment. High CDI burden in socio-environmental networks was associated with a significantly increased risk of C. difficile colonization or infection (Risk ratio: 1.37; 95% CI: [1.17, 1.59]). Finally, we found that several high-impact infection control interventions have diminished impact in the H-ABM. This study presents an alternate approach to validation of ABMs when large-scale calibration is not appropriate for specific settings and proposes a new metric for validating socio-environmental network structure of ABMs. Our findings also demonstrate the utility of hospital-specific modeling.
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spelling pubmed-101209372023-04-22 Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data Scaria, Elizabeth Safdar, Nasia Alagoz, Oguzhan PLoS One Research Article As agent-based models (ABMs) are increasingly used for modeling infectious diseases, model validation is becoming more crucial. In this study, we present an alternate approach to validating hospital ABMs that focuses on replicating hospital-specific conditions and proposes a new metric for validating the social-environmental network structure of ABMs. We adapted an established ABM representing Clostridioides difficile infection (CDI) spread in a generic hospital to a 426-bed Midwestern academic hospital. We incorporated hospital-specific layout, agent behaviors, and input parameters estimated from primary hospital data into the model, referred to as H-ABM. We compared the predicted CDI rate against the observed rate from 2013–2018. We used colonization pressure, a measure of nearby infectious agents, to validate the socio-environmental agent networks in the ABM. Finally, we conducted additional experiments to compare the performance of individual infection control interventions in the H-ABM and the generic model. We find that the H-ABM is able to replicate CDI trends during 2013–2018, including a roughly 46% drop during a period of greater infection control investment. High CDI burden in socio-environmental networks was associated with a significantly increased risk of C. difficile colonization or infection (Risk ratio: 1.37; 95% CI: [1.17, 1.59]). Finally, we found that several high-impact infection control interventions have diminished impact in the H-ABM. This study presents an alternate approach to validation of ABMs when large-scale calibration is not appropriate for specific settings and proposes a new metric for validating socio-environmental network structure of ABMs. Our findings also demonstrate the utility of hospital-specific modeling. Public Library of Science 2023-04-21 /pmc/articles/PMC10120937/ /pubmed/37083629 http://dx.doi.org/10.1371/journal.pone.0284611 Text en © 2023 Scaria et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Scaria, Elizabeth
Safdar, Nasia
Alagoz, Oguzhan
Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data
title Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data
title_full Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data
title_fullStr Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data
title_full_unstemmed Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data
title_short Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data
title_sort validating agent-based simulation model of hospital-associated clostridioides difficile infection using primary hospital data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120937/
https://www.ncbi.nlm.nih.gov/pubmed/37083629
http://dx.doi.org/10.1371/journal.pone.0284611
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