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Andrade J, Duggan J. Anchoring the mean generation time in the SEIR to mitigate biases in ℜ 0 estimates due to uncertainty in the distribution of the epidemiological delays. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230515. [PMID: 37538746 PMCID: PMC10394422 DOI: 10.1098/rsos.230515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023]
Abstract
The basic reproduction number, ℜ 0 , is of paramount importance in the study of infectious disease dynamics. Primarily, ℜ 0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between ℜ 0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate ℜ 0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating ℜ 0 .
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Affiliation(s)
- Jair Andrade
- Data Science Institute and School of Computer Science, University of Galway, Galway, Republic of Ireland
| | - Jim Duggan
- Insight Centre for Data Analytics and School of Computer Science, University of Galway, Galway, Republic of Ireland
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2
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Treesatayapun C. Optimal interventional policy based on discrete-time fuzzy rules equivalent model utilizing with COVID-19 pandemic data. INT J MACH LEARN CYB 2023; 14:1-10. [PMID: 37360880 PMCID: PMC10098248 DOI: 10.1007/s13042-023-01829-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/29/2023] [Indexed: 06/28/2023]
Abstract
In this paper, a mathematical model of the COVID-19 pandemic is formulated by fitting it to actual data collected during the fifth wave of the COVID-19 pandemic in Coahuila, Mexico, from June 2022 to October 2022. The data sets used are recorded on a daily basis and presented in a discrete-time sequence. To obtain the equivalent data model, fuzzy rules emulated networks are utilized to derive a class of discrete-time systems based on the daily hospitalized individuals' data. The aim of this study is to investigate the optimal control problem to determine the most effective interventional policy including precautionary and awareness measures, the detection of asymptomatic and symptomatic individuals, and vaccination. A main theorem is developed to guarantee the closed-loop system performance by utilizing approximate functions of the equivalent model. The numerical results indicate that the proposed interventional policy can eradicate the pandemic within 1-8 weeks. Additionally, the results show that if the policy is implemented within the first 3 weeks, the number of hospitalized individuals remains below the hospital's capacity.
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Affiliation(s)
- C. Treesatayapun
- Department of Robotic and Advanced Manufacturing, CINVESTAV-IPN, No. 1062, Parque Industrial Ramos Arizpe, Ramos Arizpe, Coah., C.P. 25903 Mexico
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3
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Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland. ALGORITHMS 2022. [DOI: 10.3390/a15080270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Agent-based models can be used to better understand the impacts of lifting restrictions or implementing interventions during a pandemic. However, agent-based models are computationally expensive, and running a model of a large population can result in a simulation taking too long to run for the model to be a useful analysis tool during a public health crisis. To reduce computing time and power while running a detailed agent-based model for the spread of COVID-19 in the Republic of Ireland, we introduce a scaling factor that equates 1 agent to 100 people in the population. We present the results from model validation and show that the scaling factor increases the variability in the model output, but the average model results are similar in scaled and un-scaled models of the same population, and the scaled model is able to accurately simulate the number of cases per day in Ireland during the autumn of 2020. We then test the usability of the model by using the model to explore the likely impacts of increasing community mixing when schools reopen after summer holidays.
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4
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Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data. PLoS Comput Biol 2022; 18:e1010206. [PMID: 35759506 PMCID: PMC9269962 DOI: 10.1371/journal.pcbi.1010206] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 07/08/2022] [Accepted: 05/11/2022] [Indexed: 11/19/2022] Open
Abstract
The effective reproduction number (ℜt) is a theoretical indicator of the course of an infectious disease that allows policymakers to evaluate whether current or previous control efforts have been successful or whether additional interventions are necessary. This metric, however, cannot be directly observed and must be inferred from available data. One approach to obtaining such estimates is fitting compartmental models to incidence data. We can envision these dynamic models as the ensemble of structures that describe the disease’s natural history and individuals’ behavioural patterns. In the context of the response to the COVID-19 pandemic, the assumption of a constant transmission rate is rendered unrealistic, and it is critical to identify a mathematical formulation that accounts for changes in contact patterns. In this work, we leverage existing approaches to propose three complementary formulations that yield similar estimates for ℜt based on data from Ireland’s first COVID-19 wave. We describe these Data Generating Processes (DGP) in terms of State-Space models. Two (DGP1 and DGP2) correspond to stochastic process models whose transmission rate is modelled as Brownian motion processes (Geometric and Cox-Ingersoll-Ross). These DGPs share a measurement model that accounts for incidence and transmission rates, where mobility data is assumed as a proxy of the transmission rate. We perform inference on these structures using Iterated Filtering and the Particle Filter. The final DGP (DGP3) is built from a pool of deterministic models that describe the transmission rate as information delays. We calibrate this pool of models to incidence reports using Hamiltonian Monte Carlo. By following this complementary approach, we assess the tradeoffs associated with each formulation and reflect on the benefits/risks of incorporating proxy data into the inference process. We anticipate this work will help evaluate the implications of choosing a particular formulation for the dynamics and observation of the time-varying transmission rate.
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Gleeson JP, Brendan Murphy T, O’Brien JD, Friel N, Bargary N, O'Sullivan DJP. Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210120. [PMID: 34802273 PMCID: PMC8607149 DOI: 10.1098/rsta.2021.0120] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Affiliation(s)
- James P. Gleeson
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
- Insight Centre for Data Analytics, Ireland
- Confirm Centre for Smart Manufacturing, Ireland
- Irish Epidemiological Modelling Advisory Group (IEMAG), Ireland
| | - Thomas Brendan Murphy
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland
- Insight Centre for Data Analytics, Ireland
- Irish Epidemiological Modelling Advisory Group (IEMAG), Ireland
| | - Joseph D. O’Brien
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
| | - Nial Friel
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland
- Insight Centre for Data Analytics, Ireland
| | - Norma Bargary
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
- Insight Centre for Data Analytics, Ireland
- Confirm Centre for Smart Manufacturing, Ireland
| | - David J. P. O'Sullivan
- MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
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McAloon CG, Wall P, Butler F, Codd M, Gormley E, Walsh C, Duggan J, Murphy TB, Nolan P, Smyth B, O'Brien K, Teljeur C, Green MJ, O'Grady L, Culhane K, Buckley C, Carroll C, Doyle S, Martin J, More SJ. Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies. BMC Public Health 2021; 21:2238. [PMID: 34886842 PMCID: PMC8655330 DOI: 10.1186/s12889-021-12318-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 11/22/2021] [Indexed: 12/23/2022] Open
Abstract
Background Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020. Results Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. Conclusions We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-12318-y.
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Affiliation(s)
- Conor G McAloon
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Patrick Wall
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Francis Butler
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Mary Codd
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Eamonn Gormley
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Cathal Walsh
- Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland
| | - Jim Duggan
- School of Computer Science, National University of Ireland Galway, Galway, Ireland
| | - T Brendan Murphy
- School of Mathematics and Statistics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Philip Nolan
- National University of Ireland Maynooth, Kildare, Ireland
| | - Breda Smyth
- Department of Public Health, Health Service Executive West, Galway, Ireland
| | | | - Conor Teljeur
- Health Information and Quality Authority, George's Court, Dublin 7, Ireland
| | - Martin J Green
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - Luke O'Grady
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - Kieran Culhane
- Central Statistics Office, Ardee road, Rathmines, Dublin, Ireland
| | - Claire Buckley
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Ciara Carroll
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Sarah Doyle
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Jennifer Martin
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Simon J More
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,Centre for Veterinary Epidemiology and Risk Analysis, School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland
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FitzJohn RG, Knock ES, Whittles LK, Perez-Guzman PN, Bhatia S, Guntoro F, Watson OJ, Whittaker C, Ferguson NM, Cori A, Baguelin M, Lees JA. Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate. Wellcome Open Res 2021; 5:288. [PMID: 34761122 PMCID: PMC8552050 DOI: 10.12688/wellcomeopenres.16466.2] [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] [Accepted: 06/07/2021] [Indexed: 11/20/2022] Open
Abstract
State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
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Affiliation(s)
- Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK.,Modelling and Economics Unit, National Infection Service, Public Health England, London, UK
| | - Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Fernando Guntoro
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK.,Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 8HT, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, UK
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Treesatayapun C. Epidemic model dynamics and fuzzy neural-network optimal control with impulsive traveling and migrating: Case study of COVID-19 vaccination. Biomed Signal Process Control 2021; 71:103227. [PMID: 34630624 PMCID: PMC8492748 DOI: 10.1016/j.bspc.2021.103227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/17/2021] [Accepted: 09/30/2021] [Indexed: 12/23/2022]
Abstract
To suppress the epidemics caused by a virus such as COVID-19, three effective strategies listing vaccination, quarantine and medical treatments, are employed under suitable policies. Quarantine motions may affect the economic systems and pharmaceutical medications may be recently in the developing phase. Thus, vaccination seems the best hope of the current situation to control COVID-19 epidemics. In this work, the dynamic model of COVID-19 epidemic is developed when the quarantine factor and the antiviral factor are established as free variables. Moreover, the impulsive populations are comprehended for traveling and migrating of individuals. The proposed dynamics with impulsive distractions are employed to generate the online data. Thereafter, the equivalent model is developed by using only the daily data of symptomatic infectious individuals and the optimal vaccination policy is derived by utilizing the closed-loop control topology. The theoretical framework of the proposed schemes validates the reduction of symptomatic infectious individuals by using fewer doses of vaccines comparing with the scheduling vaccination.
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Affiliation(s)
- C Treesatayapun
- Department of Robotic and Advanced Manufacturing, CINVESTAV-IPN, Mexico
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9
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Knock ES, Whittles LK, Perez-Guzman PN, Bhatia S, Guntoro F, Watson OJ, Whittaker C, Ferguson NM, Cori A, Baguelin M, FitzJohn RG, Lees JA. Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate. Wellcome Open Res 2020; 5:288. [DOI: 10.12688/wellcomeopenres.16466.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2020] [Indexed: 11/20/2022] Open
Abstract
State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
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