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Lanzas C, Jara M, Tucker R, Curtis S. A review of epidemiological models of Clostridioides difficile transmission and control (2009-2021). Anaerobe 2022; 74:102541. [PMID: 35217149 DOI: 10.1016/j.anaerobe.2022.102541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/09/2022] [Accepted: 02/20/2022] [Indexed: 02/08/2023]
Abstract
Clostridioides difficile is the leading cause of infectious diarrhea and one of the most common healthcare-acquired infections worldwide. We performed a systematic search and a bibliometric analysis of mathematical and computational models for Clostridioides difficile transmission. We identified 33 publications from 2009 to 2021. Models have underscored the importance of asymptomatic colonized patients in maintaining transmission in health-care settings. Infection control, antimicrobial stewardship, active testing, and vaccination have often been evaluated in models. Despite active testing and vaccination being not currently implemented, they are the most commonly evaluated interventions. Some aspects of C. difficile transmission, such community transmission and interventions in health-care settings other than in acute-care hospitals, remained less evaluated through modeling.
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Affiliation(s)
- Cristina Lanzas
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA.
| | - Manuel Jara
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA
| | - Rachel Tucker
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA
| | - Savannah Curtis
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA
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- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA
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Sulyok CJ, Fox L, Ritchie H, Lanzas C, Lenhart S, Day J. Mathematically modeling the effect of touch frequency on the environmental transmission of Clostridioides difficile in healthcare settings. Math Biosci 2021; 340:108666. [PMID: 34310932 DOI: 10.1016/j.mbs.2021.108666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 10/20/2022]
Abstract
Clostridioides difficile, formerly Clostridium difficile, is the leading cause of infectious diarrhea and one of the most common healthcare acquired infections in United States hospitals. C. difficile persists well in healthcare environments because it forms spores that can survive for long periods of time and can be transmitted to susceptible patients through contact with contaminated hands and fomites, objects or surfaces that can harbor infectious agents. Fomites can be classified as high-touch or low-touch based on the frequency they are contacted. The mathematical model in this study investigates the relative contribution of high-touch and low-touch fomites on new cases of C. difficile colonization among patients of a hospital ward. The dynamics of transmission are described by a system of ordinary differential equations representing four patient population classes and two pathogen environmental reservoirs. Parameters that have a significant effect on incidence, as determined by a global sensitivity analysis, are varied in stochastic simulations of the system to identify feasible strategies to prevent disease transmission. Results indicate that on average, under one-quarter of asymptomatically colonized patients are exposed to C. difficile via low-touch fomites. In comparison, over three-quarters of colonized patients are colonized through high-touch fomites, despite additional cleaning of high-touch fomites. Increased contacts with high-touch fomites increases the contribution of these fomites to the incidence of colonized individuals and decreasing the duration of a hospital visit reduces the amount of pathogen in the environment. Thus, enhanced efficacy of disinfection upon discharge and extra cleaning of high-touch fomites, reduced contact with high-touch fomites, and higher discharge rates, among other control measures, could lead to a decrease in the incidence of colonized individuals.
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Affiliation(s)
- Cara Jill Sulyok
- Department of Mathematics, University of Tennessee, Knoxville, 1403 Circle Drive, Knoxville, TN 37996, United States of America.
| | - Lindsey Fox
- Department of Mathematics, Eckerd College, 4200 54th Ave S, St. Petersburg, FL 33711, United States of America
| | - Hannah Ritchie
- Department of Population Health and Pathobiology, North Carolina State University, 1051 William Moore Drive, Raleigh, NC 27607, United States of America
| | - Cristina Lanzas
- Department of Population Health and Pathobiology, North Carolina State University, 1051 William Moore Drive, Raleigh, NC 27607, United States of America
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, Knoxville, 1403 Circle Drive, Knoxville, TN 37996, United States of America
| | - Judy Day
- Departments of Mathematics and Electrical Engineering and Computer Science, University of Tennessee, Knoxville, 1403 Circle Drive, Knoxville, TN 37996, United States of America
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Stephenson B, Lanzas C, Lenhart S, Ponce E, Bintz J, Dubberke ER, Day J. Comparing intervention strategies for reducing Clostridioides difficile transmission in acute healthcare settings: an agent-based modeling study. BMC Infect Dis 2020; 20:799. [PMID: 33115427 PMCID: PMC7594474 DOI: 10.1186/s12879-020-05501-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 10/12/2020] [Indexed: 12/15/2022] Open
Abstract
Background Clostridioides difficile infection (CDI) is one of the most common healthcare infections. Common strategies aiming at controlling CDI include antibiotic stewardship, environmental decontamination, and improved hand hygiene and contact precautions. Mathematical models provide a framework to evaluate control strategies. Our objective is to evaluate the effectiveness of control strategies in decreasing C. difficile colonization and infection using an agent-based model in an acute healthcare setting. Methods We developed an agent-based model that simulates the transmission of C. difficile in medical wards. This model explicitly incorporates healthcare workers (HCWs) as vectors of transmission, tracks individual patient antibiotic histories, incorporates varying risk levels of antibiotics with respect to CDI susceptibility, and tracks contamination levels of ward rooms by C. difficile. Interventions include two forms of antimicrobial stewardship, increased environmental decontamination through room cleaning, improved HCW compliance, and a preliminary assessment of vaccination. Results Increased HCW compliance with CDI patients was ranked as the most effective intervention in decreasing colonizations, with reductions up to 56%. Antibiotic stewardship practices were highly ranked after contact precaution compliance. Vaccination and reduction of high-risk antibiotics were the most effective intervention in decreasing CDI. Vaccination reduced CDI cases to up to 90%, and the reduction of high-risk antibiotics decreased CDI cases up to 23%. Conclusions Overall, interventions that decrease patient susceptibility to colonization by C. difficile, such as antibiotic stewardship, were the most effective interventions in reducing both colonizations and CDI cases.
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Affiliation(s)
- Brittany Stephenson
- Department of Engineering, Computing, and Mathematical Sciences, Lewis University, 1 University Parkway, Romeoville, 60446, IL, USA.
| | - Cristina Lanzas
- Department of Population Health and Pathobiology, North Carolina State University, 1052 William Moore Drive, Raleigh, 27606, NC, USA
| | - Suzanne Lenhart
- Department of Mathematics, University of Tennessee, 1403 Circle Drive, Knoxville, 37996, TN, USA
| | - Eduardo Ponce
- Department of Electrical Engineering and Computer Science, University of Tennessee, 1520 Middle Drive, Knoxville, 37996, TN, USA
| | - Jason Bintz
- School of Arts and Sciences, Johnson University, Knoxville, 37998, TN, USA
| | - Erik R Dubberke
- Division of Infectious Disease, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, 63110, MO, USA
| | - Judy Day
- Department of Mathematics, University of Tennessee, 1403 Circle Drive, Knoxville, 37996, TN, USA.,Department of Electrical Engineering and Computer Science, University of Tennessee, 1520 Middle Drive, Knoxville, 37996, TN, USA
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Nguyen LKN, Megiddo I, Howick S. Simulation models for transmission of health care-associated infection: A systematic review. Am J Infect Control 2020; 48:810-821. [PMID: 31862167 PMCID: PMC7161411 DOI: 10.1016/j.ajic.2019.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 11/01/2019] [Accepted: 11/03/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND Health care-associated infections (HAIs) are a global health burden because of their significant impact on patient health and health care systems. Mechanistic simulation modeling that captures the dynamics between patients, pathogens, and the environment is increasingly being used to improve understanding of epidemiological patterns of HAIs and to facilitate decisions on infection prevention and control (IPC). The purpose of this review is to present a systematic review to establish (1) how simulation models have been used to investigate HAIs and their mitigation and (2) how these models have evolved over time, as well as identify (3) gaps in their adoption and (4) useful directions for their future development. METHODS The review involved a systematic search and identification of studies using system dynamics, discrete event simulation, and agent-based model to study HAIs. RESULTS The complexity of simulation models developed for HAIs significantly increased but heavily concentrated on transmission dynamics of methicillin-resistant Staphylococcus aureus in the hospitals of high-income countries. Neither HAIs in other health care settings, the influence of contact networks within a health care facility, nor patient sharing and referring networks across health care settings were sufficiently understood. CONCLUSIONS This systematic review provides a broader overview of existing simulation models in HAIs to identify the gaps and to direct and facilitate further development of appropriate models in this emerging field.
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Bussell EH, Cunniffe NJ. Applying optimal control theory to a spatial simulation model of sudden oak death: ongoing surveillance protects tanoak while conserving biodiversity. J R Soc Interface 2020; 17:20190671. [PMID: 32228402 DOI: 10.1098/rsif.2019.0671] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Sudden oak death has devastated tree populations across California. However, management might still slow disease spread at local scales. We demonstrate how to unambiguously characterize effective, local management strategies using a detailed, spatially explicit simulation model of spread in a single forest stand. This pre-existing, parameterized simulation is approximated here by a carefully calibrated, non-spatial model, explicitly constructed to be sufficiently simple to allow optimal control theory (OCT) to be applied. By lifting management strategies from the approximate model to the detailed simulation, effective time-dependent controls can be identified. These protect tanoak-a culturally and ecologically important species-while conserving forest biodiversity within a limited budget. We also consider model predictive control, in which both the approximating model and optimal control are repeatedly updated as the epidemic progresses. This allows management which is robust to both parameter uncertainty and systematic differences between simulation and approximate models. Including the costs of disease surveillance then introduces an optimal intensity of surveillance. Our study demonstrates that successful control of sudden oak death is likely to rely on adaptive strategies updated via ongoing surveillance. More broadly, it illustrates how OCT can inform effective real-world management, even when underpinning disease spread models are highly complex.
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Affiliation(s)
- E H Bussell
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
| | - N J Cunniffe
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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Huang Q, Huo X, Ruan S. Optimal control of environmental cleaning and antibiotic prescription in an epidemiological model of methicillin-resistant Staphylococcus aureus infections in hospitals. Math Biosci 2019; 311:13-30. [DOI: 10.1016/j.mbs.2019.01.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 01/22/2019] [Accepted: 01/22/2019] [Indexed: 01/16/2023]
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Camacho A, Jerez S. Bone metastasis treatment modeling via optimal control. J Math Biol 2018; 78:497-526. [DOI: 10.1007/s00285-018-1281-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 07/29/2018] [Indexed: 12/14/2022]
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Abstract
Introduction Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens. Methods We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments. Results We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens. Conclusions Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.
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