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Cui J, Heavey J, Klein E, Madden GR, Vullikanti A, Prakash BA. Identifying Importation and Asymptomatic Spreaders of Multi-drug Resistant Organisms in Hospital Settings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.14.24310393. [PMID: 39072020 PMCID: PMC11275683 DOI: 10.1101/2024.07.14.24310393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Healthcare-associated infections (HAIs) due to multi-drug resistant organisms (MDROs) are a significant burden to the healthcare system. Patients are sometimes already infected at the time of admission to the hospital (referred to as "importation"), and additional patients might get infected in the hospital through transmission ("nosocomial infection"). Since many of these importation and nosocomial infection cases may present no symptoms (i.e., "asymptomatic"), rapidly identifying them is difficult since testing is limited and incurs significant delays. Although there has been a lot of work on examining the utility of both mathematical models of transmission and machine learning for identifying patients at risk of MDRO infections in recent years, these methods have limited performance and suffer from different drawbacks: Transmission modeling-based methods do not make full use of rich data contained in electronic health records (EHR), while machine learning-based methods typically lack information about mechanistic processes. In this work, we propose N eur ABM, a new framework which integrates both neural networks and agent-based models (ABM) to combine the advantages of both modeling-based and machine learning-based methods. N eur ABM simultaneously learns a neural network model for patient-level prediction of importation, as well as the ABM model which is used for identifying infections. Our results demonstrate that N eur ABM identifies importation and nosocomial infection cases more accurately than existing methods.
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2
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Cui J, Heavey J, Lin L, Klein EY, Madden GR, Sifri CD, Lewis B, Vullikanti AK, Prakash BA. Modeling relaxed policies for discontinuation of methicillin-resistant Staphylococcus aureus contact precautions. Infect Control Hosp Epidemiol 2024; 45:833-838. [PMID: 38404133 DOI: 10.1017/ice.2024.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
OBJECTIVE To evaluate the economic costs of reducing the University of Virginia Hospital's present "3-negative" policy, which continues methicillin-resistant Staphylococcus aureus (MRSA) contact precautions until patients receive 3 consecutive negative test results, to either 2 or 1 negative. DESIGN Cost-effective analysis. SETTINGS The University of Virginia Hospital. PATIENTS The study included data from 41,216 patients from 2015 to 2019. METHODS We developed a model for MRSA transmission in the University of Virginia Hospital, accounting for both environmental contamination and interactions between patients and providers, which were derived from electronic health record (EHR) data. The model was fit to MRSA incidence over the study period under the current 3-negative clearance policy. A counterfactual simulation was used to estimate outcomes and costs for 2- and 1-negative policies compared with the current 3-negative policy. RESULTS Our findings suggest that 2-negative and 1-negative policies would have led to 6 (95% CI, -30 to 44; P < .001) and 17 (95% CI, -23 to 59; -10.1% to 25.8%; P < .001) more MRSA cases, respectively, at the hospital over the study period. Overall, the 1-negative policy has statistically significantly lower costs ($628,452; 95% CI, $513,592-$752,148) annually (P < .001) in US dollars, inflation-adjusted for 2023) than the 2-negative policy ($687,946; 95% CI, $562,522-$812,662) and 3-negative ($702,823; 95% CI, $577,277-$846,605). CONCLUSIONS A single negative MRSA nares PCR test may provide sufficient evidence to discontinue MRSA contact precautions, and it may be the most cost-effective option.
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Affiliation(s)
- Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, Georgia
| | - Jack Heavey
- Department of Computer Science, University of Virginia, Charlottesville, Virginia
| | - Leo Lin
- Department of Computer Science, University of Virginia, Charlottesville, Virginia
| | - Eili Y Klein
- Center for Disease Dynamics, Economics & Policy, Washington, DC
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Gregory R Madden
- Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Costi D Sifri
- Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia
- Office of Hospital Epidemiology/Infection Prevention & Control, UVA Health, Charlottesville, Virginia
| | - Bryan Lewis
- Biocomplexity Institute, University of Virginia, Charlottesville, Virginia
| | - Anil K Vullikanti
- Department of Computer Science, University of Virginia, Charlottesville, Virginia
- Biocomplexity Institute, University of Virginia, Charlottesville, Virginia
| | - B Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, Georgia
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3
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Cao Y, Wang B, Wang Y, Wang Y, Huai W, Bao X, Jin M, Jin Y, Jin Y, Zhang Z, Shan J. Construction of a postoperative infection outbreak investigation form: A tool for early detection and control measures. Am J Infect Control 2024; 52:588-594. [PMID: 38142776 DOI: 10.1016/j.ajic.2023.12.011] [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: 05/29/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
BACKGROUND To develop an investigation form for postoperative infection outbreak (PIO), and to identify sources of the outbreak in the early stage. METHODS After an exhaustive literature review, we used the Delphi method to determine the indicators and relative risk scores of the assessment tools through 2 rounds of specialist consultation and overall consideration of the opinions and suggestions of 20 specialists. RESULTS A total of 203 studies of PIO were eligible for inclusion. The mean authority coefficient (Cr) was 0.87. Kendall's W coefficient of the specialist consultation was 0.704 after 2 rounds of consultation (P < .005), suggesting that the specialists had similar opinions. Based on 4 primary items and 19 secondary items of the source of PIO, and tripartite distribution characteristics of infected patients, we constructed the PIO investigation form. CONCLUSIONS The PIO investigation form can be used in the investigation of the early-stage cluster of cases, it's a prerequisite for taking effective control measures, avoiding PIO occurrence. However, the effect of the investigation form needs to be further evaluated.
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Affiliation(s)
- Yulong Cao
- Department of Hospital-Acquired Infection Control, Peking University People's Hospital, Beijing, China
| | - Bin Wang
- Department of Neurosurgery, Peking University People's Hospital, Beijing, China
| | - Yanbin Wang
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing, China
| | - Yan Wang
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Wei Huai
- Department of Emergency, Peking University Third Hospital, Beijing, China
| | - Xiaoyuan Bao
- Medical Information Center, Peking University Health Science Center, Beijing, China
| | - Meng Jin
- Medical Information Center, Peking University Health Science Center, Beijing, China
| | - Yicheng Jin
- School of General Studies, Columbia University, New York, USA
| | - Yixi Jin
- Khoury College of Computer Science, Northeastern University, Seattle, USA
| | - Zexin Zhang
- Graduate School of Medicine Faculty of Medicine, Kyoto University, Kyoto, Japan
| | - Jiao Shan
- Department of Hospital-Acquired Infection Control, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
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4
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Uwanibe JN, Olawoye IB, Happi CT, Folarin OA. Genomic Characterization of Multidrug-Resistant Pathogenic Enteric Bacteria from Healthy Children in Osun State, Nigeria. Microorganisms 2024; 12:505. [PMID: 38543556 PMCID: PMC10974654 DOI: 10.3390/microorganisms12030505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 04/01/2024] Open
Abstract
Antimicrobial resistance (AMR) is responsible for the spread and persistence of bacterial infections. Surveillance of AMR in healthy individuals is usually not considered, though these individuals serve as reservoirs for continuous disease transmission. Therefore, it is essential to conduct epidemiological surveillance of AMR in healthy individuals to fully understand the dynamics of AMR transmission in Nigeria. Thirteen multidrug-resistant Citrobacter spp., Enterobacter spp., Klebsiella pneumoniae, and Escherichia coli isolated from stool samples of healthy children were subjected to whole genome sequencing (WGS) using Illumina and Oxford nanopore sequencing platforms. A bioinformatics analysis revealed antimicrobial resistance genes such as the pmrB_Y358N gene responsible for colistin resistance detected in E. coli ST219, virulence genes such as senB, and ybtP&Q, and plasmids in the isolates sequenced. All isolates harbored more than three plasmid replicons of either the Col and/or Inc type. Plasmid reconstruction revealed an integrated tetA gene, a toxin production caa gene in two E. coli isolates, and a cusC gene in K. quasivariicola ST3879, which induces neonatal meningitis. The global spread of AMR pathogenic enteric bacteria is of concern, and surveillance should be extended to healthy individuals, especially children. WGS for epidemiological surveillance will improve the detection of AMR pathogens for management and control.
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Affiliation(s)
- Jessica N. Uwanibe
- African Center of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Oshogbo 232102, Osun State, Nigeria; (J.N.U.); (I.B.O.); (C.T.H.)
- Department of Biological Sciences, College of Natural Sciences, Redeemer’s University, Oshogbo 232102, Osun State, Nigeria
| | - Idowu B. Olawoye
- African Center of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Oshogbo 232102, Osun State, Nigeria; (J.N.U.); (I.B.O.); (C.T.H.)
- Department of Biological Sciences, College of Natural Sciences, Redeemer’s University, Oshogbo 232102, Osun State, Nigeria
| | - Christian T. Happi
- African Center of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Oshogbo 232102, Osun State, Nigeria; (J.N.U.); (I.B.O.); (C.T.H.)
- Department of Biological Sciences, College of Natural Sciences, Redeemer’s University, Oshogbo 232102, Osun State, Nigeria
| | - Onikepe A. Folarin
- African Center of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Oshogbo 232102, Osun State, Nigeria; (J.N.U.); (I.B.O.); (C.T.H.)
- Department of Biological Sciences, College of Natural Sciences, Redeemer’s University, Oshogbo 232102, Osun State, Nigeria
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5
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Böttcher L, Chou T, D’Orsogna MR. Forecasting drug-overdose mortality by age in the United States at the national and county levels. PNAS NEXUS 2024; 3:pgae050. [PMID: 38725534 PMCID: PMC11079616 DOI: 10.1093/pnasnexus/pgae050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/25/2024] [Indexed: 05/12/2024]
Abstract
The drug-overdose crisis in the United States continues to intensify. Fatalities have increased 5-fold since 1999 reaching a record high of 108,000 deaths in 2021. The epidemic has unfolded through distinct waves of different drug types, uniquely impacting various age, gender, race, and ethnic groups in specific geographical areas. One major challenge in designing interventions and efficiently delivering treatment is forecasting age-specific overdose patterns at the local level. To address this need, we develop a forecasting method that assimilates observational data obtained from the CDC WONDER database with an age-structured model of addiction and overdose mortality. We apply our method nationwide and to three select areas: Los Angeles County, Cook County, and the five boroughs of New York City, providing forecasts of drug-overdose mortality and estimates of relevant epidemiological quantities, such as mortality and age-specific addiction rates.
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Affiliation(s)
- Lucas Böttcher
- Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany
| | - Tom Chou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095-1766, USA
| | - Maria R D’Orsogna
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095-1766, USA
- Department of Mathematics, California State University at Northridge, Los Angeles, CA 91330-8313, USA
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6
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Robin TT, Cascante-Vega J, Shaman J, Pei S. System identifiability in a time-evolving agent-based model. PLoS One 2024; 19:e0290821. [PMID: 38271401 PMCID: PMC10810497 DOI: 10.1371/journal.pone.0290821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 08/16/2023] [Indexed: 01/27/2024] Open
Abstract
Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.
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Affiliation(s)
- Tal T. Robin
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America
| | - Jaime Cascante-Vega
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America
- Columbia Climate School, Columbia University, New York, NY, United States of America
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America
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7
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Cui J, Cho S, Kamruzzaman M, Bielskas M, Vullikanti A, Prakash BA. Using spectral characterization to identify healthcare-associated infection (HAI) patients for clinical contact precaution. Sci Rep 2023; 13:16197. [PMID: 37758756 PMCID: PMC10533902 DOI: 10.1038/s41598-023-41852-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Healthcare-associated infections (HAIs) are a major problem in hospital infection control. Although HAIs can be suppressed using contact precautions, such precautions are expensive, and we can only apply them to a small fraction of patients (i.e., a limited budget). In this work, we focus on two clinical problems arising from the limited budget: (a) choosing the best patients to be placed under precaution given a limited budget to minimize the spread (the isolation problem), and (b) choosing the best patients to release when limited budget requires some of the patients to be cleared from precaution (the clearance problem). A critical challenge in addressing them is that HAIs have multiple transmission pathways such that locations can also accumulate 'load' and spread the disease. One of the most common practices when placing patients under contact precautions is the regular clearance of pathogen loads. However, standard propagation models like independent cascade (IC)/susceptible-infectious-susceptible (SIS) cannot capture such mechanisms directly. Hence to account for this challenge, using non-linear system theory, we develop a novel spectral characterization of a recently proposed pathogen load based model, 2-MODE-SIS model, on people/location networks to capture spread dynamics of HAIs. We formulate the two clinical problems using this spectral characterization and develop effective and efficient algorithms for them. Our experiments show that our methods outperform several natural structural and clinical approaches on real-world hospital testbeds and pick meaningful solutions.
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Affiliation(s)
- Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Sungjun Cho
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Methun Kamruzzaman
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Matthew Bielskas
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA
| | - Anil Vullikanti
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA
| | - B Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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8
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Zhang R, Tai J, Pei S. Ensemble inference of unobserved infections in networks using partial observations. PLoS Comput Biol 2023; 19:e1011355. [PMID: 37549190 PMCID: PMC10434926 DOI: 10.1371/journal.pcbi.1011355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 08/17/2023] [Accepted: 07/12/2023] [Indexed: 08/09/2023] Open
Abstract
Undetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in both model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, despite that the model is mis-specified. Additionally, the computational complexity of this algorithm scales almost linearly with the number of nodes in the network and the number of observations, respectively, exhibiting the potential to apply to large-scale networks. The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks.
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Affiliation(s)
- Renquan Zhang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Jilei Tai
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
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9
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Huang S, Sun J, Feng L, Xie J, Wang D, Hu Y. Identify hidden spreaders of pandemic over contact tracing networks. Sci Rep 2023; 13:11621. [PMID: 37468540 DOI: 10.1038/s41598-023-32542-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/29/2023] [Indexed: 07/21/2023] Open
Abstract
The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.
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Affiliation(s)
- Shuhong Huang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China
- Institute of Neuroscience, Technical University of Munich, Munich, 80802, Germany
| | | | - Ling Feng
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, 138632, Singapore
- Department of Physics, National University of Singapore, Singapore, 117551, Singapore
| | - Jiarong Xie
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China
| | - Dashun Wang
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Yanqing Hu
- Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, 518055, Shenzhen, China.
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10
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Alnimr A. Antimicrobial Resistance in Ventilator-Associated Pneumonia: Predictive Microbiology and Evidence-Based Therapy. Infect Dis Ther 2023:10.1007/s40121-023-00820-2. [PMID: 37273072 DOI: 10.1007/s40121-023-00820-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Ventilator-associated pneumonia (VAP) is a serious intensive care unit (ICU)-related infection in mechanically ventilated patients that is frequent, as more than half of antibiotics prescriptions in ICU are due to VAP. Various risk factors and diagnostic criteria for VAP have been referred to in different settings. The estimated attributable mortality of VAP can go up to 50%, which is higher in cases of antimicrobial-resistant VAP. When the diagnosis of pneumonia in a mechanically ventilated patient is made, initiation of effective antimicrobial therapy must be prompt. Microbiological diagnosis of VAP is required to optimize timely therapy since effective early treatment is fundamental for better outcomes, with controversy continuing regarding optimal sampling and testing. Understanding the role of antimicrobial resistance in the context of VAP is crucial in the era of continuously evolving antimicrobial-resistant clones that represent an urgent threat to global health. This review is focused on the risk factors for antimicrobial resistance in adult VAP and its novel microbiological tools. It aims to summarize the current evidence-based knowledge about the mechanisms of resistance in VAP caused by multidrug-resistant bacteria in clinical settings with focus on Gram-negative pathogens. It highlights the evidence-based antimicrobial management and prevention of drug-resistant VAP. It also addresses emerging concepts related to predictive microbiology in VAP and sheds lights on VAP in the context of coronavirus disease 2019 (COVID-19).
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Affiliation(s)
- Amani Alnimr
- Department of Microbiology, College of Medicine, King Fahad Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia.
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11
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Böttcher L, Chou T, D’Orsogna MR. Modeling and forecasting age-specific drug overdose mortality in the United States. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2023; 232:1-10. [PMID: 37359186 PMCID: PMC10132445 DOI: 10.1140/epjs/s11734-023-00801-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/24/2023] [Indexed: 06/28/2023]
Abstract
Drug overdose deaths continue to increase in the United States for all major drug categories. Over the past two decades the total number of overdose fatalities has increased more than fivefold; since 2013 the surge in overdose rates is primarily driven by fentanyl and methamphetamines. Different drug categories and factors such as age, gender, and ethnicity are associated with different overdose mortality characteristics that may also change in time. For example, the average age at death from a drug overdose has decreased from 1940 to 1990 while the overall mortality rate has steadily increased. To provide insight into the population-level dynamics of drug overdose mortality, we develop an age-structured model for drug addiction. Using an augmented ensemble Kalman filter (EnKF), we show through a simple example how our model can be combined with synthetic observation data to estimate mortality rate and an age-distribution parameter. Finally, we use an EnKF to combine our model with observation data on overdose fatalities in the United States from 1999 to 2020 to forecast the evolution of overdose trends and estimate model parameters.
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Affiliation(s)
- Lucas Böttcher
- Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany
| | - Tom Chou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, 90095 CA USA
- Department of Mathematics, University of California, Los Angeles, Los Angeles, 90095 CA USA
| | - Maria R. D’Orsogna
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, 90095 CA USA
- Department of Mathematics, California State University at Northridge, Los Angeles, 91330 CA USA
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12
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Pei S, Blumberg S, Vega JC, Robin T, Zhang Y, Medford RJ, Adhikari B, Shaman J. Challenges in Forecasting Antimicrobial Resistance. Emerg Infect Dis 2023; 29:679-685. [PMID: 36958029 PMCID: PMC10045679 DOI: 10.3201/eid2904.221552] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023] Open
Abstract
Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.
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13
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Zhang R, Wang Y, Lv Z, Pei S. Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread. BMC Infect Dis 2022; 22:648. [PMID: 35896977 PMCID: PMC9326419 DOI: 10.1186/s12879-022-07636-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 07/19/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, stay-at-home and quarantine measures were widely adopted and enforced. Understanding the effectiveness of stay-at-home and quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks. METHODS In this study, we use mathematical models to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with components of self-isolation and quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of stay-at-home and quarantine rates on COVID-19 spread after lockdown using counterfactual model simulations. RESULTS Results indicate that self-isolation of susceptible population is necessary to contain the outbreak. At a given rate, self-isolation of susceptible population induced by stay-at-home orders is more effective than quarantine of SARS-CoV-2 contacts in reducing effective reproductive numbers [Formula: see text]. Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London. CONCLUSIONS Our findings underscore the essential role of stay-at-home orders and quarantine of SARS-CoV-2 contacts during the early phase of the pandemic.
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Affiliation(s)
- Renquan Zhang
- School of Mathematical Sciences, Dalian University of Technology, 116024 Dalian, China
| | - Yu Wang
- School of Mathematical Sciences, Dalian University of Technology, 116024 Dalian, China
| | - Zheng Lv
- School of Control Science and Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 10032 New York, USA
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Mietchen MS, Short CT, Samore M, Lofgren ET. Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus. PLoS Comput Biol 2022; 18:e1010352. [PMID: 35877686 PMCID: PMC9352208 DOI: 10.1371/journal.pcbi.1010352] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 08/04/2022] [Accepted: 07/03/2022] [Indexed: 11/18/2022] Open
Abstract
Background
Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated.
Methods
Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied.
Results
The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure.
Discussion
Inclusion of complex population interactions within a modeled hospital ICU has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different colonization rates across relatively similar populations. The non-linearity of the model’s response to differing values of a parameter gamma (γ) suggests simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments.
Conclusion
Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most, cases more complex models that represent population mixing with higher granularity are justified.
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Affiliation(s)
- Matthew S. Mietchen
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America
| | - Christopher T. Short
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America
| | - Matthew Samore
- Department of Internal Medicine, University of Utah School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
- VA Salt Lake City Healthcare System, Salt Lake City, Utah
| | - Eric T. Lofgren
- Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America
- * E-mail:
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