Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol
IntroductionThe complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessf...
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| Published in | BMJ open Vol. 12; no. 3; p. e052681 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
British Medical Journal Publishing Group
10.03.2022
BMJ Publishing Group LTD BMJ Publishing Group |
| Series | Protocol |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2044-6055 2044-6055 |
| DOI | 10.1136/bmjopen-2021-052681 |
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| Abstract | IntroductionThe complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.Methods and analysisWe propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.Ethics and disseminationApproved by Carleton University’s Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication. |
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| AbstractList | IntroductionThe complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.Methods and analysisWe propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.Ethics and disseminationApproved by Carleton University’s Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication. Introduction The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.Methods and analysis We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.Ethics and dissemination Approved by Carleton University’s Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication. The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.INTRODUCTIONThe complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.METHODS AND ANALYSISWe propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication.ETHICS AND DISSEMINATIONApproved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication. The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication. |
| Author | Sandhu, Rimple Sarkar, Abhijit Kendzerska, Tetyana Poirel, Dominique Dolean, Victorita Taillon, Peter J Edwards, Jodi D Daly, John M Robinson, Brandon Walker, Thomas Pettit, Chris L Ammi, Mehdi Mills, Shirley Mulpuru, Sunita Khalil, Mohammad Percival, Valerie |
| AuthorAffiliation | 12 National Renewable Energy Laboratory , Golden , Colorado , USA 9 School of Public Policy and Administration , Carleton University , Ottawa , Ontario , Canada 1 Department of Civil and Environmental Engineering , Carleton University , Ottawa , Ontario , Canada 3 ICES , Ottawa , Ontario , Canada 5 Department of Medicine , Faculty of Medicine, Division of Respirology, University of Ottawa , Ottawa , Ontario , Canada 8 Independent Control Systems Engineer , Ottawa , Ontario , Canada 16 Laboratoire J.A. Dieudonné , CNRS, Université Côte d’Azur , Nice , France 10 Sandia National Laboratories , Livermore , California , USA 2 School of Epidemiology and Public Health , University of Ottawa and University of Ottawa Heart Institute , Ottawa , Ontario , Canada 11 Schaffen Research Inc , Ottawa , Ontario , Canada 7 Royal Military College of Canada, Department of Mechanical and Aerospace Engineering , Kingston , Ontario , Canada 13 School of Mathematics and Statistics , Carleton University , Ottawa , Ontari |
| AuthorAffiliation_xml | – name: 14 School of International Affairs , Carleton University , Ottawa , Ontario , Canada – name: 1 Department of Civil and Environmental Engineering , Carleton University , Ottawa , Ontario , Canada – name: 12 National Renewable Energy Laboratory , Golden , Colorado , USA – name: 5 Department of Medicine , Faculty of Medicine, Division of Respirology, University of Ottawa , Ottawa , Ontario , Canada – name: 7 Royal Military College of Canada, Department of Mechanical and Aerospace Engineering , Kingston , Ontario , Canada – name: 15 Department of Mathematics and Statistics , University of Strathclyde , Glasgow , Scotland – name: 4 The Ottawa Hospital Research Institute , Ottawa , Ontario , Canada – name: 9 School of Public Policy and Administration , Carleton University , Ottawa , Ontario , Canada – name: 13 School of Mathematics and Statistics , Carleton University , Ottawa , Ontario , Canada – name: 2 School of Epidemiology and Public Health , University of Ottawa and University of Ottawa Heart Institute , Ottawa , Ontario , Canada – name: 10 Sandia National Laboratories , Livermore , California , USA – name: 16 Laboratoire J.A. Dieudonné , CNRS, Université Côte d’Azur , Nice , France – name: 8 Independent Control Systems Engineer , Ottawa , Ontario , Canada – name: 6 US Naval Academy, Aerospace Engineering Department , Annapolis , Maryland , USA – name: 11 Schaffen Research Inc , Ottawa , Ontario , Canada – name: 3 ICES , Ottawa , Ontario , Canada |
| Author_xml | – sequence: 1 givenname: Brandon surname: Robinson fullname: Robinson, Brandon organization: Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada – sequence: 2 givenname: Jodi D surname: Edwards fullname: Edwards, Jodi D organization: ICES, Ottawa, Ontario, Canada – sequence: 3 givenname: Tetyana surname: Kendzerska fullname: Kendzerska, Tetyana organization: Department of Medicine, Faculty of Medicine, Division of Respirology, University of Ottawa, Ottawa, Ontario, Canada – sequence: 4 givenname: Chris L surname: Pettit fullname: Pettit, Chris L organization: US Naval Academy, Aerospace Engineering Department, Annapolis, Maryland, USA – sequence: 5 givenname: Dominique surname: Poirel fullname: Poirel, Dominique organization: Royal Military College of Canada, Department of Mechanical and Aerospace Engineering, Kingston, Ontario, Canada – sequence: 6 givenname: John M surname: Daly fullname: Daly, John M organization: Independent Control Systems Engineer, Ottawa, Ontario, Canada – sequence: 7 givenname: Mehdi surname: Ammi fullname: Ammi, Mehdi organization: School of Public Policy and Administration, Carleton University, Ottawa, Ontario, Canada – sequence: 8 givenname: Mohammad surname: Khalil fullname: Khalil, Mohammad organization: Sandia National Laboratories, Livermore, California, USA – sequence: 9 givenname: Peter J surname: Taillon fullname: Taillon, Peter J organization: Schaffen Research Inc, Ottawa, Ontario, Canada – sequence: 10 givenname: Rimple surname: Sandhu fullname: Sandhu, Rimple organization: National Renewable Energy Laboratory, Golden, Colorado, USA – sequence: 11 givenname: Shirley surname: Mills fullname: Mills, Shirley organization: School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, Canada – sequence: 12 givenname: Sunita surname: Mulpuru fullname: Mulpuru, Sunita organization: Department of Medicine, Faculty of Medicine, Division of Respirology, University of Ottawa, Ottawa, Ontario, Canada – sequence: 13 givenname: Thomas surname: Walker fullname: Walker, Thomas organization: Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada – sequence: 14 givenname: Valerie surname: Percival fullname: Percival, Valerie organization: School of International Affairs, Carleton University, Ottawa, Ontario, Canada – sequence: 15 givenname: Victorita surname: Dolean fullname: Dolean, Victorita organization: Laboratoire J.A. Dieudonné, CNRS, Université Côte d’Azur, Nice, France – sequence: 16 givenname: Abhijit orcidid: 0000-0002-8427-8901 surname: Sarkar fullname: Sarkar, Abhijit email: abhijit.sarkar@carleton.ca organization: Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35273043$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1856206$$D View this record in Osti.gov |
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| Keywords | COVID-19 epidemiology statistics & research methods |
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| Snippet | IntroductionThe complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression... The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult.... Introduction The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease... |
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| SubjectTerms | Algorithms Asymptomatic BASIC BIOLOGICAL SCIENCES Bayes Theorem Bayesian inference Calibration Comorbidity Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control COVID-19 vaccines Disease transmission Epidemiological Models Epidemiology Humans Immunization MATHEMATICS AND COMPUTING Pandemics Population Quarantine SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 sparse learning statistics & research methods |
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| Title | Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol |
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