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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 2024:1-6. [PMID: 38404133 DOI: 10.1017/ice.2024.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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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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3
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Evans S, Stimson J, Pople D, Wilcox MH, Hope R, Robotham JV. Evaluating the impact of testing strategies for the detection of nosocomial COVID-19 in English hospitals through data-driven modeling. Front Med (Lausanne) 2023; 10:1166074. [PMID: 37928455 PMCID: PMC10622791 DOI: 10.3389/fmed.2023.1166074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/07/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction During the first wave of the COVID-19 pandemic 293,204 inpatients in England tested positive for SARS-CoV-2. It is estimated that 1% of these cases were hospital-associated using European centre for disease prevention and control (ECDC) and Public Health England (PHE) definitions. Guidelines for preventing the spread of SARS-CoV-2 in hospitals have developed over time but the effectiveness and efficiency of testing strategies for preventing nosocomial transmission has not been explored. Methods Using an individual-based model, parameterised using multiple datasets, we simulated the transmission of SARS-CoV-2 to patients and healthcare workers between March and August 2020 and evaluated the efficacy of different testing strategies. These strategies were: 0) Testing only symptomatic patients on admission; 1) Testing all patients on admission; 2) Testing all patients on admission and again between days 5 and 7, and 3) Testing all patients on admission, and again at days 3, and 5-7. In addition to admissions testing, patients that develop a symptomatic infection while in hospital were tested under all strategies. We evaluated the impact of testing strategy, test characteristics and hospital-related factors on the number of nosocomial patient infections. Results Modelling suggests that 84.6% (95% CI: 84.3, 84.7) of community-acquired and 40.8% (40.3, 41.3) of hospital-associated SARS-CoV-2 infections are detectable before a patient is discharged from hospital. Testing all patients on admission and retesting after 3 or 5 days increases the proportion of nosocomial cases detected by 9.2%. Adding discharge testing increases detection by a further 1.5% (relative increase). Increasing occupancy rates, number of beds per bay, or the proportion of admissions wrongly suspected of having COVID-19 on admission and therefore incorrectly cohorted with COVID-19 patients, increases the rate of nosocomial transmission. Over 30,000 patients in England could have been discharged while incubating a non-detected SARS-CoV-2 infection during the first wave of the COVID-19 pandemic, of which 3.3% could have been identified by discharge screening. There was no significant difference in the rates of nosocomial transmission between testing strategies or when the turnaround time of the test was increased. Discussion This study provides insight into the efficacy of testing strategies in a period unbiased by vaccines and variants. The findings are relevant as testing programs for SARS-CoV-2 are scaled back, and possibly if a new vaccine escaping variant emerges.
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Affiliation(s)
- Stephanie Evans
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
- Statistics, Modelling and Economics, UK Health Security Agency, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling and Health Economics at Imperial College London in Partnership With UKHSA and the London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - James Stimson
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
- Statistics, Modelling and Economics, UK Health Security Agency, London, United Kingdom
| | - Diane Pople
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
- Statistics, Modelling and Economics, UK Health Security Agency, London, United Kingdom
| | - Mark H Wilcox
- Healthcare-Associated Infections Research Group, Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
- Microbiology, Leeds Teaching Hospitals, Leeds, United Kingdom
- NIHR Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with UKHSA, Oxford, United Kingdom
| | - Russell Hope
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
| | - Julie V Robotham
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling and Health Economics at Imperial College London in Partnership With UKHSA and the London School of Hygiene and Tropical Medicine, London, United Kingdom
- NIHR Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with UKHSA, Oxford, United Kingdom
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4
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Cascante-Vega J, Galanti M, Schley K, Pei S, Shaman J. Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease. PLoS Comput Biol 2023; 19:e1011564. [PMID: 37889910 PMCID: PMC10655980 DOI: 10.1371/journal.pcbi.1011564] [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: 04/18/2023] [Revised: 11/17/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.
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Affiliation(s)
- Jaime Cascante-Vega
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Marta Galanti
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | | | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- Columbia Climate School, Columbia University, New York, New York, United States of America
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5
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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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6
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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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Huang HN, Xie T, Chen WF, Wei YY. Parallel evolution and control method for predicting the effectiveness of non-pharmaceutical interventions in pandemics. ZEITSCHRIFT FUR GESUNDHEITSWISSENSCHAFTEN = JOURNAL OF PUBLIC HEALTH 2023:1-12. [PMID: 36844446 PMCID: PMC9942014 DOI: 10.1007/s10389-023-01843-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/02/2023] [Indexed: 02/23/2023]
Abstract
Aim Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making. Subject and methods Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics. Results The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds. Conclusion The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts. Supplementary Information The online version contains supplementary material available at 10.1007/s10389-023-01843-2.
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Affiliation(s)
- Hai-nan Huang
- Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China
- School of Management, Jinan University, Guangzhou, 510632 China
| | - Tian Xie
- Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China
| | - Wei-fan Chen
- Information Sciences and Technology, The Pennsylvania State University, State College, PA 16802 USA
| | - Yao-yao Wei
- Present Address: School of Economics, Management and Law, University of South China, Hengyang, 421001 Hunan Province China
- School of Education, Central China Normal University, Wuhan, 430079 China
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8
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Leclerc Q, Clements A, Dunn H, Hatcher J, Lindsay JA, Grandjean L, Knight GM. Quantifying patient- and hospital-level antimicrobial resistance dynamics in Staphylococcus aureus from routinely collected data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.15.23285946. [PMID: 36824943 PMCID: PMC9949191 DOI: 10.1101/2023.02.15.23285946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen Staphylococcus aureus . The reported prevalence of these resistances vary, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures. We explored S. aureus AMR diversity in 70,000 isolates from a UK paediatric hospital between 2000-2020, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms, hospitalisation information, and antibiotic consumption. At the hospital-level, the proportion of isolates that were meticillin-resistant (MRSA) increased between 2014-2020 from 25 to 50%, before sharply decreasing to 30%, likely due to a change in inpatient demographics. Temporal trends in the proportion of isolates resistant to different antibiotics were often correlated in MRSA, but independent in meticillin-susceptible S. aureus . Ciprofloxacin resistance in MRSA decreased from 70% to 40% of tested isolates between 2007-2020, likely linked to a national policy to reduce fluoroquinolone usage in 2007. At the patient level, we identified frequent AMR diversity, with 4% of patients ever positive for S. aureus simultaneously carrying, at some point, multiple isolates with different resistances. We detected changes over time in AMR diversity in 3% of patients ever positive for S. aureus . These changes equally represented gain and loss of resistance. Within this routinely collected dataset, we found that 65% of changes in resistance within a patient’s S. aureus population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single S. aureus clones.
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López-Goerne TM, Padilla-Godínez FJ, Castellanos M, Perez-Davalos LA. Catalytic nanomedicine: a brief review of bionanocatalysts. Nanomedicine (Lond) 2022; 17:1131-1156. [DOI: 10.2217/nnm-2022-0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Catalytic nanomedicine is a research area and source of disruptive technology that studies the application of bionanocatalysts (organically functionalized mesoporous nanostructured materials with catalytic properties) in diverse areas such as disinfection, tissue regeneration in chronic wounds and oncology. This paper reviews the emergence of catalytic nanomedicine in 2006, its basic principles, main achievements and future perspectives, as well as giving a summary of the knowledge gaps that need to be addressed to exploit the full potential of this novel discipline. This review intends to foster knowledge dissemination regarding catalytic nanomedicine, and to encourage further research to elucidate the mechanisms and possible applications of these nanomaterials.
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Affiliation(s)
- Tessy M López-Goerne
- Laboratory of Nanotechnology & Nanomedicine, Department of Health Care, Autonomous Metropolitan University-Xochimilco, Mexico City, 04960, Mexico
- Department of Molecular Neuropathology, Institute of Cell Physiology, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | - Francisco J Padilla-Godínez
- Laboratory of Nanotechnology & Nanomedicine, Department of Health Care, Autonomous Metropolitan University-Xochimilco, Mexico City, 04960, Mexico
- Department of Molecular Neuropathology, Institute of Cell Physiology, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | - Mariana Castellanos
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | - Luis A Perez-Davalos
- Laboratory of Nanotechnology & Nanomedicine, Department of Health Care, Autonomous Metropolitan University-Xochimilco, Mexico City, 04960, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, 04510, Mexico
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10
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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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11
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Zhang L, Zhang D, Tang H, Zhu Y, Liu H, Yu R. Bacteria Wear ICG Clothes for Rapid Detection of Intracranial Infection in Patients After Neurosurgery and Photothermal Antibacterial Therapy Against Streptococcus Mutans. Front Bioeng Biotechnol 2022; 10:932915. [PMID: 35875493 PMCID: PMC9298881 DOI: 10.3389/fbioe.2022.932915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/09/2022] [Indexed: 11/24/2022] Open
Abstract
Bacterial infection is one of the most serious physiological conditions threatening human health. There is an increasing demand for more effective bacterial diagnosis and treatment through non-invasive approaches. Among current antibacterial strategies of non-invasive approaches, photothermal antibacterial therapy (PTAT) has pronounced advantages with properties of minor damage to normal tissue and little chance to trigger antimicrobial resistance. Therefore, we developed a fast and simple strategy that integrated the sensitive detection and photothermal therapy of bacteria by measuring adenosine triphosphate (ATP) bioluminescence following targeted photothermal lysis. First, 3-azido-d-alanine (d-AzAla) is selectively integrated into the cell walls of bacteria, photosensitizer dibenzocyclooctyne, and double sulfonic acid-modified indocyanine green (sulfo-DBCO-ICG) are subsequently designed to react with the modified bacteria through in vivo click chemistry. Next, the sulfo-DBCO-ICG modified bacteria under irradiation of 808 nm near-infrared laser was immediately detected by ATP bioluminescence following targeted photothermal lysis and even the number of bacteria on the infected tissue can be significantly reduced through PTAT. This method has demonstrated the ability to detect the presence of the bacteria for ATP value in 32 clinical samples. As a result, the ATP value over of 100 confirmed the presence of bacteria in clinical samples for 22 patients undergoing craniotomy and ten otitis media patients. Overall, this study paves a brand new avenue to facile diagnosis and a treatment platform for clinical bacterial infections.
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Affiliation(s)
- Long Zhang
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China.,Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.,Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Deyun Zhang
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
| | - Hai Tang
- Epilepsy Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yufu Zhu
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China.,Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hongmei Liu
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China.,Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.,Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Rutong Yu
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China.,Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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12
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Salamanca CH, Barrera-Ocampo Á, Oñate-Garzón J. Development, Characterization, and Antimicrobial Evaluation of Ampicillin-Loaded Nanoparticles Based on Poly(maleic acid- co-vinylpyrrolidone) on Resistant Staphylococcus aureus Strains. Molecules 2022; 27:molecules27092943. [PMID: 35566294 PMCID: PMC9102852 DOI: 10.3390/molecules27092943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 04/20/2022] [Accepted: 04/27/2022] [Indexed: 02/01/2023] Open
Abstract
This study was focused on synthesizing, characterizing, and evaluating the antimicrobial effect of polymer nanoparticles (NPs) loaded with ampicillin. For this, the NPs were produced through polymeric self-assembly in aqueous media assisted by high-intensity sonication, using anionic polymers corresponding to the sodium salts of poly(maleic acid-co-vinylpyrrolidone) and poly(maleic acid-co-vinylpyrrolidone) modified with decyl-amine, here named as PMA-VP and PMA-VP-N10, respectively. The polymeric NPs were analyzed and characterized through the formation of polymeric pseudo-phases utilizing pyrene as fluorescent probe, as well as by measurements of particle size, zeta potential, polydispersity index, and encapsulation efficiency. The antimicrobial effect was evaluated by means of the broth microdilution method employing ampicillin sensitive and resistant Staphylococcus aureus strains. The results showed that PMA-VP and PMA-VP-N10 polymers can self-assemble, forming several types of hydrophobic pseudo-phases with respect to the medium pH and polymer concentration. Likewise, the results described that zeta potential, particle size, polydispersity index, and encapsulation efficiency are extremely dependent on the medium pH, whereas the antimicrobial activity displayed an interesting recovery of antibiotic activity when ampicillin is loaded in the polymeric NPs.
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Affiliation(s)
- Constain H. Salamanca
- Grupo de investigación Biopolimer, Departamento de Farmacia, Facultad de Ciencias Farmacéuticas y Alimentarias, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia
- Grupo de Investigación Natura, Facultad de Ciencias Naturales, Universidad ICESI, Calle 18 No. 122-135, Cali 760035, Colombia;
- Correspondence:
| | - Álvaro Barrera-Ocampo
- Grupo de Investigación Natura, Facultad de Ciencias Naturales, Universidad ICESI, Calle 18 No. 122-135, Cali 760035, Colombia;
| | - Jose Oñate-Garzón
- Grupo de Investigación en Química y Biotecnología (QUIBIO), Facultad de Ciencias Básicas, Universidad Santiago de Cali, Calle 5 No. 62-00, Cali, Colombia 760035, Colombia;
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Salmanov AG, Shchehlov DV, Shkorbotun VO, Bortnik IM, Svyrydiuk O, Gudym MS, Krylova AS. MOLECULAR EPIDEMIOLOGY OF THE TRANSMISSION OF METHICILLIN-RESISTANT STAPHYLOCOCCUS AUREUS IN KYIV ACUTE CARE HOSPITALS, UKRAINE. WIADOMOSCI LEKARSKIE (WARSAW, POLAND : 1960) 2022; 75:857-864. [PMID: 35633361 DOI: 10.36740/wlek202204120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE The aim: To evaluate the potential of transmission of methicillin-resistance Staphylococcus aureus (MRSA) in Ukrainian acute care hospitals. PATIENTS AND METHODS Materials and methods: We performed a multicenter cross-sectional study. Definitions of HAI were used from the CDC/ NHSN. The susceptibility to antibiotics was determined by disk diffusion method according to the EUCAST. The cefoxitin-resistant isolates S.aureus were analyzed for the presence of the mecA gene and femA endogenous control gene using PCR. The virulence factor encoding genes (lukS-PV and lukF-PV) were detected by PCR. RESULTS Results: Of 2,421 patients with HAIs caused S.aureus included in the study, 28.7% patients had MRSA. Prevalence of nasal carriage rate of MRSA among healthcare workers (HCWs) was 33.3%. MRSA contamination of hands and uniforms/gowns of HCW were 32.2% and 29.7%, respectively. MRSA contamination in the inanimate environment surfaces in near- and extended patients areas were 26.9%. The predominant MRSA contamination in hospital environment surfaces were: room inner door knob (32.8%), bed rails (28.9%), room light switch (28.9%), chair (27.9%), bedside table handle (20.6%), bedside table (20.5%), and tray table (13.7%). The PVL gene was present in 38.7% of MRSA strains, isolated from patients with HAIs and in 55.7% of MRSA, isolated from environment surfaces in patient area. In addition, the PVL genes were detected in over 56.3% of MRSA isolated from HCWs carrier. CONCLUSION Conclusions: The majority of MRSA is acquired during hospitalization. Environmental surfaces may serve as potential reservoirs for nosocomial MRSA and facilitate transmissions via contact.
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Affiliation(s)
- Aidyn G Salmanov
- SHUPYK NATIONAL HEALTHCARE UNIVERSITY OF UKRAINE, KYIV, UKRAINE; INSTITUTE OF PEDIATRICS, OBSTETRICS AND GYNECOLOGY OF THE NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE, KYIV, UKRAINE; SCIENTIFIC-PRACTICAL CENTER OF ENDOVASCULAR NEURORADIOLOGY OF NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE, KYIV, UKRAINE
| | - Dmytro V Shchehlov
- SCIENTIFIC-PRACTICAL CENTER OF ENDOVASCULAR NEURORADIOLOGY OF NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE, KYIV, UKRAINE
| | | | - Ihor M Bortnik
- SCIENTIFIC-PRACTICAL CENTER OF ENDOVASCULAR NEURORADIOLOGY OF NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE, KYIV, UKRAINE
| | - Oleh Svyrydiuk
- SCIENTIFIC-PRACTICAL CENTER OF ENDOVASCULAR NEURORADIOLOGY OF NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE, KYIV, UKRAINE
| | - Maxim S Gudym
- SCIENTIFIC-PRACTICAL CENTER OF ENDOVASCULAR NEURORADIOLOGY OF NATIONAL ACADEMY OF MEDICAL SCIENCES OF UKRAINE, KYIV, UKRAINE
| | - Anna S Krylova
- SHUPYK NATIONAL HEALTHCARE UNIVERSITY OF UKRAINE, KYIV, UKRAINE
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Pei S, Liljeros F, Shaman J. Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings. Proc Natl Acad Sci U S A 2021; 118:e2111190118. [PMID: 34493678 PMCID: PMC8449327 DOI: 10.1073/pnas.2111190118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 08/03/2021] [Indexed: 12/14/2022] Open
Abstract
Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10027;
| | - Fredrik Liljeros
- Department of Sociology, Stockholm University, 114 19 Stockholm, Sweden
- Department of Public Health Sciences, Karolinska Institutet, 171 77 Solna, Sweden
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10027;
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Li C, Zhu Y, Qi C, Liu L, Zhang D, Wang X, She K, Jia Y, Liu T, He D, Xiong M, Li X. Estimating the Prevalence of Asymptomatic COVID-19 Cases and Their Contribution in Transmission - Using Henan Province, China, as an Example. Front Med (Lausanne) 2021; 8:591372. [PMID: 34249953 PMCID: PMC8260942 DOI: 10.3389/fmed.2021.591372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 05/04/2021] [Indexed: 01/02/2023] Open
Abstract
Background: Novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), is now sweeping across the world. A substantial proportion of infections only lead to mild symptoms or are asymptomatic, but the proportion and infectivity of asymptomatic infections remains unknown. In this paper, we proposed a model to estimate the proportion and infectivity of asymptomatic cases, using COVID-19 in Henan Province, China, as an example. Methods: We extended the conventional susceptible-exposed-infectious-recovered model by including asymptomatic, unconfirmed symptomatic, and quarantined cases. Based on this model, we used daily reported COVID-19 cases from January 21 to February 26, 2020, in Henan Province to estimate the proportion and infectivity of asymptomatic cases, as well as the change of effective reproductive number, Rt. Results: The proportion of asymptomatic cases among COVID-19 infected individuals was 42% and the infectivity was 10% that of symptomatic ones. The basic reproductive number R0 = 2.73, and Rt dropped below 1 on January 31 under a series of measures. Conclusion: The spread of the COVID-19 epidemic was rapid in the early stage, with a large number of asymptomatic infected individuals having relatively low infectivity. However, it was quickly brought under control with national measures.
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Affiliation(s)
- Chunyu Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuchen Zhu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chang Qi
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lili Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dandan Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xu Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Kaili She
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yan Jia
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Tingxuan Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Momiao Xiong
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Xiujun Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
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Zebrowski A, Rundle A, Pei S, Yaman T, Yang W, Carr BG, Sims S, Doorley R, Schluger N, Quinn JW, Shaman J, Branas CC. A Spatiotemporal Tool to Project Hospital Critical Care Capacity and Mortality From COVID-19 in US Counties. Am J Public Health 2021; 111:1113-1122. [PMID: 33856876 DOI: 10.2105/ajph.2021.306220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Objectives. To create a tool to rapidly determine where pandemic demand for critical care overwhelms county-level surge capacity and to compare public health and medical responses.Methods. In March 2020, COVID-19 cases requiring critical care were estimated using an adaptive metapopulation SEIR (susceptible‒exposed‒infectious‒recovered) model for all 3142 US counties for future 21-day and 42-day periods from April 2, 2020, to May 13, 2020, in 4 reactive patterns of contact reduction-0%, 20%, 30%, and 40%-and 4 surge response scenarios-very low, low, medium, and high.Results. In areas with increased demand, surge response measures could avert 104 120 additional deaths-55% through high clearance of critical care beds and 45% through measures such as greater ventilator access. The percentages of lives saved from high levels of contact reduction were 1.9 to 4.2 times greater than high levels of hospital surge response. Differences in projected versus actual COVID-19 demands were reasonably small over time.Conclusions. Nonpharmaceutical public health interventions had greater impact in minimizing preventable deaths during the pandemic than did hospital critical care surge response. Ready-to-go spatiotemporal supply and demand data visualization and analytics tools should be advanced for future preparedness and all-hazards disaster response.
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Affiliation(s)
- Alexis Zebrowski
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Andrew Rundle
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Sen Pei
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Tonguc Yaman
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Wan Yang
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Brendan G Carr
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Sarah Sims
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Ronan Doorley
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Neil Schluger
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - James W Quinn
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Jeffrey Shaman
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
| | - Charles C Branas
- Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University
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Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study. PLoS Comput Biol 2021; 17:e1008600. [PMID: 33534784 PMCID: PMC7857595 DOI: 10.1371/journal.pcbi.1008600] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/02/2020] [Indexed: 11/23/2022] Open
Abstract
The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread. Pathogens including multidrug resistant enterobacteriacae (MDR-E) inside hospital settings are associated with higher morbidity, mortality, and healthcare costs. Better understanding of the transmission routes of these pathogens is required to develop targeted and efficient measures to contain the spread of MDR-E in a hospital. We analyzed datasets from five hospitals in different countries to explore patient movement patterns between departments of these hospitals (intra-hospital movements). We assessed whether movement patterns differ between patients at high or low risk of colonization. Our results show that in every intra-hospital network, there exist a few departments which are strongly connected and many peripheral departments which are loosely connected. High-risk patients stay on average longer in the hospital, and move more frequently between departments than low-risk patients. Targeting interventions to strongly connected departments may help to reduce pathogen spread inside the hospital. To explore this, we simulated the spread of MDR-E inside one hospital using an agent-based model that includes patient movements. In the simulations, we found positive correlations between departments’ prevalence and network characteristics such as degree and weighted degree, thus highlighting the importance of targeting interventions to departments of higher (weighted) degree to control the spread of MDR-E.
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Pei S, Teng X, Lewis P, Shaman J. Optimizing respiratory virus surveillance networks using uncertainty propagation. Nat Commun 2021; 12:222. [PMID: 33431854 PMCID: PMC7801666 DOI: 10.1038/s41467-020-20399-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/01/2020] [Indexed: 02/07/2023] Open
Abstract
Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
| | - Xian Teng
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Paul Lewis
- Integrated Biosurveillance Section, Armed Forces Health Surveillance Branch, Silver Spring, MD, 20904, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
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Suleiman A, Ademola O, Olalekan O. Constellation of methicillin-resistant genomic islands (SCCmec) among nasal meticillin-resistant Staphylococcus aureus isolates. ACTA FACULTATIS MEDICAE NAISSENSIS 2021. [DOI: 10.5937/afmnai38-25349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
The apprehensiveness for the knowledge vacuum on existential threat of nasal carriage of pvl+ healthcare-acquired meticillin-resistant Staphylococcus aureus (HA-MRSA) strains amongst subjects in hospitals have led us to pursue a grasp on the constellation of staphylococcal cassette chromosome mec (SCCmec) types and pvl gene among mecA positive MRSA nasal strains. This was accomplished by phenotypic (catalase, coagulase, Microgen staph ID, ORSAB) and genotypic (polymerase chain reaction) biotyping techniques. All the mecA+ strains harboured the SCCmec gene; SCCmec type I prevailed in 43.75% and pvl was found in 42.1% of the isolates. Dual carriage of mecA and pvl genes occurred in six (37.5%, n = 6/16) strains. Overall, majority of the mecA+ MRSA strains documented in this study carried SCCmec elements of the HA genotype with a hint of community-acquired (CA)genotype suggesting a possible coexistence of both HA-MRSA and community-acquiredhealthcare-acquired meticillin-resistant Staphylococcus aureus (CA-MRSA) strains. Consequently, the implementation of methodical surveillance is needed for the evaluation of potential shifts in directionality of (HA-MRSA/CA-MRSA) pvl+ MRSA clones in our hospitals for effective and prudent antimicrobial stewardship.
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Pei S, Kandula S, Shaman J. Differential effects of intervention timing on COVID-19 spread in the United States. SCIENCE ADVANCES 2020; 6:eabd6370. [PMID: 33158911 PMCID: PMC7821895 DOI: 10.1126/sciadv.abd6370] [Citation(s) in RCA: 161] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/20/2020] [Indexed: 05/19/2023]
Abstract
Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
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21
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Bomfim R, Pei S, Shaman J, Yamana T, Makse HA, Andrade JS, Lima Neto AS, Furtado V. Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas. J R Soc Interface 2020; 17:20200691. [PMID: 33109025 DOI: 10.1098/rsif.2020.0691] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Dengue is a vector-borne disease transmitted by the Aedes genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is to study the impact of human mobility in urban areas on the spread of the dengue virus, and to test whether human mobility data can be used to improve predictions of dengue virus transmission at the neighbourhood level. We present two distinct forecasting systems for dengue transmission in Fortaleza: the first using artificial neural network methods and the second developed using a mechanistic model of disease transmission. We then present enhanced versions of the two forecasting systems that incorporate bus transportation data cataloguing movement among 119 neighbourhoods in Fortaleza. Each forecasting system was used to perform retrospective forecasts for historical dengue outbreaks from 2007 to 2015. Results show that both artificial neural networks and mechanistic models can accurately forecast dengue cases, and that the inclusion of human mobility data substantially improves the performance of both forecasting systems. While the mechanistic models perform better in capturing seasons with large-scale outbreaks, the neural networks more accurately forecast outbreak peak timing, peak intensity and annual dengue time series. These results have two practical implications: they support the creation of public policies from the use of the models created here to combat the disease and help to understand the impact of urban mobility on the epidemic in large cities.
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Affiliation(s)
- Rafael Bomfim
- Programa de Pós Graduação em Informática Aplicada Universidade de Fortaleza, Fortaleza, Brazil
| | - Sen Pei
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
| | - Teresa Yamana
- Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
| | - Hernán A Makse
- Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA
| | - José S Andrade
- Departamento de Física, Universidade Federal do Ceará, Campus do Pici, 60451-970 Fortaleza, Ceará, Brazil
| | - Antonio S Lima Neto
- Secretaria Municipal de Saúde de Fortaleza (SMS-Fortaleza), Fortaleza, Ceará, Brazil.,Centro de Ciências da Saúde, Universidade de Fortaleza (UNIFOR), Fortaleza, Ceará, Brazil
| | - Vasco Furtado
- Programa de Pós Graduação em Informática Aplicada Universidade de Fortaleza, Fortaleza, Brazil
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22
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Pei S, Shaman J. Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness. PLoS Comput Biol 2020; 16:e1008301. [PMID: 33090997 PMCID: PMC7608986 DOI: 10.1371/journal.pcbi.1008301] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 11/03/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.
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Affiliation(s)
- Sen Pei
- 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
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23
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Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep 2020; 10:9336. [PMID: 32518310 PMCID: PMC7283340 DOI: 10.1038/s41598-020-66270-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 05/14/2020] [Indexed: 11/09/2022] Open
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.
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Affiliation(s)
- Luis E C Rocha
- Department of Economics, Ghent University, Ghent, Belgium. .,Department of Physics and Astronomy, Ghent University, Ghent, Belgium.
| | | | - Markus Esch
- Department of Engineering, Saarland University of Applied Sciences, Saarbrücken, Germany
| | - Tom Lenaerts
- MLG, Université Libre de Bruxelles, Brussels, Belgium.,AI-lab, Vrije Universteit Brussel, Brussels, Belgium.,Interuniversity Institute for Bioinformatics, Brussels, Belgium
| | - Fredrik Liljeros
- Department of Sociology, Stockholm University, Stockholm, Sweden
| | - Anna Thorson
- Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden.,World Health Organisation, Geneva, Switzerland
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24
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Pei S, Kandula S, Shaman J. Differential Effects of Intervention Timing on COVID-19 Spread in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32511526 PMCID: PMC7273294 DOI: 10.1101/2020.05.15.20103655] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Assessing the effects of early non-pharmaceutical interventions1–5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6–10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1–2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 56.5% [95% Cl: 48.1%−65.9%] of reported infections and 54.0% [95% Cl: 43.6%−63.8%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University
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25
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Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020. [PMID: 32179701 DOI: 10.1126/science.abb32214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82-90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46-62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.
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Affiliation(s)
- Ruiyun Li
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
| | - Bin Chen
- Department of Land, Air and Water Resources, University of California, Davis, Davis, CA 95616, USA
| | - Yimeng Song
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 10084, P. R. China
| | - Wan Yang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
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26
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Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020; 368:489-493. [PMID: 32179701 PMCID: PMC7164387 DOI: 10.1126/science.abb3221] [Citation(s) in RCA: 2026] [Impact Index Per Article: 506.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 03/12/2020] [Indexed: 12/11/2022]
Abstract
Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82-90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46-62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.
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Affiliation(s)
- Ruiyun Li
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
| | - Bin Chen
- Department of Land, Air and Water Resources, University of California, Davis, Davis, CA 95616, USA
| | - Yimeng Song
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 10084, P. R. China
| | - Wan Yang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
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27
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Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020. [PMID: 32179701 DOI: 10.1126/science.abb3221/suppl_file/papv2.pdf] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82-90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46-62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.
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Affiliation(s)
- Ruiyun Li
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
| | - Bin Chen
- Department of Land, Air and Water Resources, University of California, Davis, Davis, CA 95616, USA
| | - Yimeng Song
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 10084, P. R. China
| | - Wan Yang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
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28
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Pei S. Influencer identification in dynamical complex systems. JOURNAL OF COMPLEX NETWORKS 2020; 8:cnz029. [PMID: 32774857 PMCID: PMC7391989 DOI: 10.1093/comnet/cnz029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 07/13/2019] [Indexed: 06/11/2023]
Abstract
The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e. the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g. independent cascading models) or discontinuous phase transitions (e.g. threshold models). We conclude the review with a summary and an outlook.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, USA
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29
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Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19). MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.02.14.20023127. [PMID: 32511439 PMCID: PMC7239048 DOI: 10.1101/2020.02.14.20023127] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Estimation of the fraction and contagiousness of undocumented novel coronavirus (COVID-19) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Many mild infections are typically not reported and, depending on their contagiousness, may support stealth transmission and the spread of documented infection. Methods Here we use observations of reported infection and spread within China in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with the emerging coronavirus, including the fraction of undocumented infections and their contagiousness. Results We estimate 86% of all infections were undocumented (95% CI: [82%-90%]) prior to the Wuhan travel shutdown (January 23, 2020). Per person, these undocumented infections were 52% as contagious as documented infections ([44%-69%]) and were the source of infection for two-thirds of documented cases. Our estimate of the reproductive number (2.23; [1.77-3.00]) aligns with earlier findings; however, after travel restrictions and control measures were imposed this number falls considerably. Conclusions A majority of COVID-19 infections were undocumented prior to implementation of control measures on January 23, and these undocumented infections substantially contributed to virus transmission. These findings explain the rapid geographic spread of COVID-19 and indicate containment of this virus will be particularly challenging. Our findings also indicate that heightened awareness of the outbreak, increased use of personal protective measures, and travel restriction have been associated with reductions of the overall force of infection; however, it is unclear whether this reduction will be sufficient to stem the virus spread.
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Affiliation(s)
- Ruiyun Li
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, United Kingdom
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Bin Chen
- Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA
| | - Yimeng Song
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 10084, P. R. China
| | - Wan Yang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
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30
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Pei S, Cane MA, Shaman J. Predictability in process-based ensemble forecast of influenza. PLoS Comput Biol 2019; 15:e1006783. [PMID: 30817754 PMCID: PMC6394909 DOI: 10.1371/journal.pcbi.1006783] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 01/12/2019] [Indexed: 11/18/2022] Open
Abstract
Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America
| | - Mark A. Cane
- Lamont-Doherty Earth Observatory, 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
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