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Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) Framework for Describing and Reporting Multidrug-resistant Organism and Healthcare-Associated Infections Agent-based Modeling Methods
Author(s) -
Rachel B. Slayton,
Justin J. O’Hagan,
Sean Barnes,
Sarah Rhea,
Rainer Hilscher,
Michael Rubin,
Eric Lofgren,
Brajendra K. Singh,
Alberto M. Segre,
Prabasaj Paul
Publication year - 2020
Publication title -
clinical infectious diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.44
H-Index - 336
eISSN - 1537-6591
pISSN - 1058-4838
DOI - 10.1093/cid/ciaa234
Subject(s) - health care , generalizability theory , computer science , protocol (science) , infectious disease (medical specialty) , initialization , process (computing) , medicine , data science , management science , risk analysis (engineering) , disease , pathology , engineering , operating system , economics , programming language , economic growth , alternative medicine , mathematics , statistics
Mathematical modeling of healthcare-associated infections and multidrug-resistant organisms improves our understanding of pathogen transmission dynamics and provides a framework for evaluating prevention strategies. One way of improving the communication among modelers is by providing a standardized way of describing and reporting models, thereby instilling confidence in the reproducibility and generalizability of such models. We updated the Overview, Design concepts, and Details protocol developed by Grimm et al [11] for describing agent-based models (ABMs) to better align with elements commonly included in healthcare-related ABMs. The Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) framework includes the following 9 key elements: (1) Purpose and scope; (2) Entities, state variables, and scales; (3) Initialization; (4) Process overview and scheduling; (5) Input data; (6) Agent interactions and organism transmission; (7) Stochasticity; (8) Submodels; and (9) Model verification, calibration, and validation. Our objective is that this framework will improve the quality of evidence generated utilizing these models.

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