1
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Schmidt PW. Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany. Stat Med 2024; 43:1933-1954. [PMID: 38422989 DOI: 10.1002/sim.10046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 01/11/2024] [Accepted: 02/10/2024] [Indexed: 03/02/2024]
Abstract
Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.
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2
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D’Agostino McGowan L, Grantz KH, Murray E. Quantifying Uncertainty in Mechanistic Models of Infectious Disease. Am J Epidemiol 2021; 190:1377-1385. [PMID: 33475686 PMCID: PMC7929394 DOI: 10.1093/aje/kwab013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/23/2022] Open
Abstract
This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on SARS-CoV-2. We describe the statistical uncertainty as belonging to three categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, \documentclass[12pt]{minimal}
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}{}${R}_0$\end{document}, for SARS-CoV-2.
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Affiliation(s)
- Lucy D’Agostino McGowan
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC, United States
- Correspondence to Dr. Lucy D’Agostino McGowan, Department of Mathematics and Statistics, Wake Forest University, 127 Manchester Hall Box 7388, Winston-Salem, NC 27109, (e-mail: )
| | - Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Eleanor Murray
- Department of Epidemiology, Boston University, Boston MA, United States
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3
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Stocks T, Britton T, Höhle M. Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany. Biostatistics 2021; 21:400-416. [PMID: 30265310 PMCID: PMC7307980 DOI: 10.1093/biostatistics/kxy057] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 05/27/2018] [Accepted: 07/14/2018] [Indexed: 01/01/2023] Open
Abstract
Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software69, 1–43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number \documentclass[12pt]{minimal}
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}{}$R_0$\end{document} using these data.
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Affiliation(s)
- Theresa Stocks
- Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden
| | - Tom Britton
- Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden
| | - Michael Höhle
- Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden
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4
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Raimúndez E, Dudkin E, Vanhoefer J, Alamoudi E, Merkt S, Fuhrmann L, Bai F, Hasenauer J. COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling. Epidemics 2021; 34:100439. [PMID: 33556763 PMCID: PMC7845523 DOI: 10.1016/j.epidem.2021.100439] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 01/12/2023] Open
Abstract
Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.
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Affiliation(s)
- Elba Raimúndez
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany; Technische Universität München, Center for Mathematics, Garching, Germany
| | - Erika Dudkin
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Jakob Vanhoefer
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Emad Alamoudi
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Simon Merkt
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Lara Fuhrmann
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Fan Bai
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Jan Hasenauer
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany; Technische Universität München, Center for Mathematics, Garching, Germany; Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.
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5
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Bracher J, Held L. A marginal moment matching approach for fitting endemic-epidemic models to underreported disease surveillance counts. Biometrics 2020; 77:1202-1214. [PMID: 32920842 DOI: 10.1111/biom.13371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 09/01/2020] [Indexed: 11/30/2022]
Abstract
Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic-epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model-based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time-varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany.
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Affiliation(s)
- Johannes Bracher
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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6
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Tönsing C, Timmer J, Kreutz C. Profile likelihood-based analyses of infectious disease models. Stat Methods Med Res 2018; 27:1979-1998. [PMID: 29512437 DOI: 10.1177/0962280217746444] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Ordinary differential equation models are frequently applied to describe the temporal evolution of epidemics. However, ordinary differential equation models are also utilized in other scientific fields. We summarize and transfer state-of-the art approaches from other fields like Systems Biology to infectious disease models. For this purpose, we use a simple SIR model with data from an influenza outbreak at an English boarding school in 1978 and a more complex model of a vector-borne disease with data from the Zika virus outbreak in Colombia in 2015-2016. Besides parameter estimation using a deterministic multistart optimization approach, a multitude of analyses based on the profile likelihood are presented comprising identifiability analysis and model reduction. The analyses were performed using the freely available modeling framework Data2Dynamics (data2dynamics.org) which has been awarded as best performing within the DREAM6 parameter estimation challenge and in the DREAM7 network reconstruction challenge.
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Affiliation(s)
- Christian Tönsing
- 1 Institute of Physics, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jens Timmer
- 1 Institute of Physics, University of Freiburg, Freiburg im Breisgau, Germany.,2 Center for Biosystems Analysis (ZBSA), University of Freiburg, Freiburg im Breisgau, Germany.,3 BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany
| | - Clemens Kreutz
- 1 Institute of Physics, University of Freiburg, Freiburg im Breisgau, Germany.,2 Center for Biosystems Analysis (ZBSA), University of Freiburg, Freiburg im Breisgau, Germany
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Yaesoubi R, Cohen T. Identifying cost-effective dynamic policies to control epidemics. Stat Med 2016; 35:5189-5209. [PMID: 27449759 PMCID: PMC5096998 DOI: 10.1002/sim.7047] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 06/08/2016] [Accepted: 06/22/2016] [Indexed: 11/07/2022]
Abstract
We describe a mathematical decision model for identifying dynamic health policies for controlling epidemics. These dynamic policies aim to select the best current intervention based on accumulating epidemic data and the availability of resources at each decision point. We propose an algorithm to approximate dynamic policies that optimize the population's net health benefit, a performance measure which accounts for both health and monetary outcomes. We further illustrate how dynamic policies can be defined and optimized for the control of a novel viral pathogen, where a policy maker must decide (i) when to employ or lift a transmission-reducing intervention (e.g. school closure) and (ii) how to prioritize population members for vaccination when a limited quantity of vaccines first become available. Within the context of this application, we demonstrate that dynamic policies can produce higher net health benefit than more commonly described static policies that specify a pre-determined sequence of interventions to employ throughout epidemics. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Reza Yaesoubi
- Health Policy and Management, Yale School of Public Health, 60 College Street, New Haven, 06520, CT, U.S.A..
| | - Ted Cohen
- Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, 06520, CT, U.S.A
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Goeyvaerts N, Willem L, Van Kerckhove K, Vandendijck Y, Hanquet G, Beutels P, Hens N. Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence. Epidemics 2015; 13:1-9. [PMID: 26616037 DOI: 10.1016/j.epidem.2015.04.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Revised: 04/10/2015] [Accepted: 04/24/2015] [Indexed: 12/20/2022] Open
Abstract
Dynamic transmission models are essential to design and evaluate control strategies for airborne infections. Our objective was to develop a dynamic transmission model for seasonal influenza allowing to evaluate the impact of vaccinating specific age groups on the incidence of infection, disease and mortality. Projections based on such models heavily rely on assumed 'input' parameter values. In previous seasonal influenza models, these parameter values were commonly chosen ad hoc, ignoring between-season variability and without formal model validation or sensitivity analyses. We propose to directly estimate the parameters by fitting the model to age-specific influenza-like illness (ILI) incidence data over multiple influenza seasons. We used a weighted least squares (WLS) criterion to assess model fit and applied our method to Belgian ILI data over six influenza seasons. After exploring parameter importance using symbolic regression, we evaluated a set of candidate models of differing complexity according to the number of season-specific parameters. The transmission parameters (average R0, seasonal amplitude and timing of the seasonal peak), waning rates and the scale factor used for WLS optimization, influenced the fit to the observed ILI incidence the most. Our results demonstrate the importance of between-season variability in influenza transmission and our estimates are in line with the classification of influenza seasons according to intensity and vaccine matching.
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Affiliation(s)
- Nele Goeyvaerts
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Agoralaan Gebouw D, B3590 Diepenbeek, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, B2610 Wilrijk, Belgium.
| | - Lander Willem
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Agoralaan Gebouw D, B3590 Diepenbeek, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, B2610 Wilrijk, Belgium; Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, B2020 Antwerp, Belgium
| | - Kim Van Kerckhove
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Agoralaan Gebouw D, B3590 Diepenbeek, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, B2610 Wilrijk, Belgium
| | - Yannick Vandendijck
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Agoralaan Gebouw D, B3590 Diepenbeek, Belgium
| | - Germaine Hanquet
- KCE - Belgian Health Care Knowledge Centre, Boulevard du Jardin Botanique 55, B1000 Brussels, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, B2610 Wilrijk, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Agoralaan Gebouw D, B3590 Diepenbeek, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, B2610 Wilrijk, Belgium
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9
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Bilcke J, Chapman R, Atchison C, Cromer D, Johnson H, Willem L, Cox M, Edmunds WJ, Jit M. Quantifying Parameter and Structural Uncertainty of Dynamic Disease Transmission Models Using MCMC: An Application to Rotavirus Vaccination in England and Wales. Med Decis Making 2015; 35:633-47. [PMID: 25623063 DOI: 10.1177/0272989x14566013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Accepted: 11/23/2014] [Indexed: 11/15/2022]
Abstract
BACKGROUND Two vaccines (Rotarix and RotaTeq) are highly effective at preventing severe rotavirus disease. Rotavirus vaccination has been introduced in the United Kingdom and other countries partly based on modeling and cost-effectiveness results. However, most of these models fail to account for the uncertainty about several vaccine characteristics and the mechanism of vaccine action. METHODS A deterministic dynamic transmission model of rotavirus vaccination in the United Kingdom was developed. This improves on previous models by 1) allowing for 2 different mechanisms of action for Rotarix and RotaTeq, 2) using clinical trial data to understand these mechanisms, and 3) accounting for uncertainty by using Markov Chain Monte Carlo. RESULTS In the long run, Rotarix and RotaTeq are predicted to reduce the overall rotavirus incidence by 50% (39%-63%) and 44% (30%-62%), respectively but with an increase in incidence in primary school children and adults up to 25 y of age. The vaccines are estimated to give more protection than 1 or 2 natural infections. The duration of protection is highly uncertain but has only impact on the predicted reduction in rotavirus burden for values lower than 10 y. The 2 vaccine mechanism structures fit equally well with the clinical trial data. Long-term postvaccination dynamics cannot be predicted reliably with the data available. CONCLUSION Accounting for the joint uncertainty of several vaccine characteristics resulted in more insight into which of these are crucial for determining the impact of rotavirus vaccination. Data for up to at least 10 y postvaccination and covering older children and adults are crucial to address remaining questions on the impact of widespread rotavirus vaccination.
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Affiliation(s)
- Joke Bilcke
- Centre for Health Economics Research & Modeling of Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium (JB, LW),Department of Infectious Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK (JB, HJ, WJE, MJ)
| | - Ruth Chapman
- Modelling and Economics Unit, Public Health England, London, UK (RC, MJ)
| | - Christina Atchison
- Department of Primary Care and Public Health, School of Public Health, Imperial College, London, UK (CA)
| | - Deborah Cromer
- Complex Systems in Biology Group, Centre for Vascular Research, UNSW Australia, Sydney, New South Wales, Australia (DC)
| | - Helen Johnson
- Department of Infectious Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK (JB, HJ, WJE, MJ)
| | - Lander Willem
- Centre for Health Economics Research & Modeling of Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium (JB, LW),Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium (LW),Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium (LW)
| | - Martin Cox
- Department of Applied Sciences and Health, Coventry University, Coventry, UK (MC)
| | - William John Edmunds
- Department of Infectious Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK (JB, HJ, WJE, MJ)
| | - Mark Jit
- Department of Infectious Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK (JB, HJ, WJE, MJ),Modelling and Economics Unit, Public Health England, London, UK (RC, MJ)
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10
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Weidemann F, Dehnert M, Koch J, Wichmann O, Höhle M. Modelling the epidemiological impact of rotavirus vaccination in Germany – A Bayesian approach. Vaccine 2014; 32:5250-7. [DOI: 10.1016/j.vaccine.2014.06.090] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 05/23/2014] [Accepted: 06/11/2014] [Indexed: 01/17/2023]
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