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Nunes MC, Thommes E, Fröhlich H, Flahault A, Arino J, Baguelin M, Biggerstaff M, Bizel-Bizellot G, Borchering R, Cacciapaglia G, Cauchemez S, Barbier--Chebbah A, Claussen C, Choirat C, Cojocaru M, Commaille-Chapus C, Hon C, Kong J, Lambert N, Lauer KB, Lehr T, Mahe C, Marechal V, Mebarki A, Moghadas S, Niehus R, Opatowski L, Parino F, Pruvost G, Schuppert A, Thiébaut R, Thomas-Bachli A, Viboud C, Wu J, Crépey P, Coudeville L. Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. Infect Dis Model 2024; 9:501-518. [PMID: 38445252 PMCID: PMC10912817 DOI: 10.1016/j.idm.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/07/2024] Open
Abstract
In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.
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Affiliation(s)
- Marta C. Nunes
- Center of Excellence in Respiratory Pathogens (CERP), Hospices Civils de Lyon (HCL) and Centre International de Recherche en Infectiologie (CIRI), Équipe Santé Publique, Épidémiologie et Écologie Évolutive des Maladies Infectieuses (PHE3ID), Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard Lyon 1, Lyon, France
- South African Medical Research Council, Vaccines & Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Edward Thommes
- New Products and Innovation (NPI), Sanofi Vaccines (Global), Toronto, Ontario, Canada
- Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT (b-it), Bonn, Germany
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland and Swiss School of Public Health, Zürich, Switzerland
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew Biggerstaff
- National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Gaston Bizel-Bizellot
- Departement of Computational Biology, Departement of Global Health, Institut Pasteur, Paris, France
| | - Rebecca Borchering
- National Center for Immunization and Respiratory Diseases (NCIRD), US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Giacomo Cacciapaglia
- Institut de Physique des Deux Infinis de Lyon (IP2I), UMR5822, IN2P3/CNRS, Université Claude Bernard Lyon 1, Villeurbanne, France
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000 CNRS, Paris, France
| | - Alex Barbier--Chebbah
- Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR 3571, France
| | - Carsten Claussen
- Fraunhofer-Institute for Translational Medicine and Pharmacology, Hamburg, Germany
| | - Christine Choirat
- Institute of Global Health, Faculty of Medicine, University of Geneva, Switzerland
| | - Monica Cojocaru
- Mathematics & Statistics Department, College of Engineering and Physical Sciences, University of Guelph, Guelph, Ontario, Canada
| | | | - Chitin Hon
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China
| | - Jude Kong
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | | | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, Saarbrücken, Germany
| | | | - Vincent Marechal
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, Paris, France
| | | | - Seyed Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Lulla Opatowski
- UMR 1018, Team “Anti-infective Evasion and Pharmacoepidemiology”, Université Paris-Saclay, UVSQ, INSERM, France
- Epidemiology and Modelling of Antibiotic Evasion, Institut Pasteur, Université Paris Cité, Paris, France
| | - Francesco Parino
- Sorbonne Université, INSERM, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | | | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Rodolphe Thiébaut
- Bordeaux University, Department of Public Health, Inserm UMR 1219 Bordeaux Population Health Research Center, Inria SISTM, Bordeaux, France
| | | | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Jianhong Wu
- York Emergency Mitigation, Engagement, Response, and Governance Institute, Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
| | - Pascal Crépey
- EHESP, Université de Rennes, CNRS, IEP Rennes, Arènes - UMR 6051, RSMS – Inserm U 1309, Rennes, France
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Han K, Lee B, Lee D, Heo G, Oh J, Lee S, Apio C, Park T. Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data. Sci Rep 2024; 14:9962. [PMID: 38693172 PMCID: PMC11063074 DOI: 10.1038/s41598-024-58835-9] [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: 12/13/2023] [Accepted: 04/03/2024] [Indexed: 05/03/2024] Open
Abstract
The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (like new confirmed cases, new cases in intensive care unit, and new deaths for each day) helped prepare the appropriate response (e.g., creating additional intensive care unit beds, and implementing strict interventions). Various predictive models and predictor variables have been used to forecast these indicators. However, the impact of prediction models and predictor variables on forecasting performance has not been systematically well analyzed. Here, we compared the forecasting performance using a linear mixed model in terms of prediction models (mathematical, statistical, and AI/machine learning models) and predictor variables (vaccination rate, stringency index, and Omicron variant rate) for seven selected countries with the highest vaccination rates. We decided on our best models based on the Bayesian Information Criterion (BIC) and analyzed the significance of each predictor. Simple models were preferred. The selection of the best prediction models and the use of Omicron variant rate were considered essential in improving prediction accuracies. For the test data period before Omicron variant emergence, the selection of the best models was the most significant factor in improving prediction accuracy. For the test period after Omicron emergence, Omicron variant rate use was considered essential in deciding forecasting accuracy. For prediction models, ARIMA, lightGBM, and TSGLM generally performed well in both test periods. Linear mixed models with country as a random effect has proven that the choice of prediction models and the use of Omicron data was significant in determining forecasting accuracies for the highly vaccinated countries. Relatively simple models, fit with either prediction model or Omicron data, produced best results in enhancing forecasting accuracies with test data.
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Affiliation(s)
- Kyulhee Han
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Bogyeom Lee
- Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea
| | - Doeun Lee
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Gyujin Heo
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jooha Oh
- Ross School of Business, University of Michigan-Ann Arbor, Ann Arbor, MI, United States
| | - Seoyoung Lee
- College of Humanities, Seoul National University, Seoul, Republic of Korea
| | - Catherine Apio
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Taesung Park
- Ross School of Business, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.
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Willem L, Abrams S, Franco N, Coletti P, Libin PJK, Wambua J, Couvreur S, André E, Wenseleers T, Mao Z, Torneri A, Faes C, Beutels P, Hens N. The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022). BMC Public Health 2024; 24:1171. [PMID: 38671366 PMCID: PMC11047051 DOI: 10.1186/s12889-024-18576-w] [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: 10/27/2023] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers. METHODS We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake. RESULTS Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being. CONCLUSIONS When calculating the impact of measures to mitigate disease spread in terms of life years lost due to COVID-19 mortality, we highlight the impact of COVID-19 on the health-related quality of life of survivors. Our study underscores that disease-related morbidity could constitute a significant part of the overall health burden. Our quantitative findings depend on the specific setup of the interventions under review, which is open to debate or should be contextualised within future situations.
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Affiliation(s)
- Lander Willem
- Department of Family Medicine and Population Health, Antwerp, Belgium.
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
| | - Steven Abrams
- Department of Family Medicine and Population Health, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Pietro Coletti
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Pieter J K Libin
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - James Wambua
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Simon Couvreur
- Department of Epidemiology and public health, Sciensano, Brussel, Belgium
| | - Emmanuel André
- National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, University of Leuven, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, University of Leuven, Leuven, Belgium
| | - Zhuxin Mao
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Andrea Torneri
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
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Cori A. SIR… or MADAM? The impact of privilege on careers in epidemic modelling. Epidemics 2024:100769. [PMID: 38644157 DOI: 10.1016/j.epidem.2024.100769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/11/2024] [Accepted: 04/12/2024] [Indexed: 04/23/2024] Open
Abstract
As we emerge from what may be the largest global public health crises of our lives, our community of epidemic modellers is naturally reflecting. What role can modelling play in supporting decision making during epidemics? How could we more effectively interact with policy makers? How should we design future disease surveillance systems? All crucial questions. But who is going to be addressing them in 10 years' time? With high burnout and poor attrition rates in academia, both magnified in our field by our unprecedented efforts during the pandemic, and with low wages coinciding with inflation at its highest for decades, how do we retain talent? This is a multifaceted challenge, that I argue is underpinned by privilege. In this perspective, I introduce the notion of privilege and highlight how various aspects of privilege (namely gender, ethnicity, sexual orientation, language and caring responsibilities) may affect the ability of individuals to access to and progress within academic modelling careers. I propose actions that members of the epidemic modelling research community may take to mitigate these issues and ensure we have a more diverse and equitable workforce going forward.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, School of Public Health Building, Wood Lane, White City, London W12 0BZ, United Kingdom.
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Gardner BJ, Kilpatrick AM. Predicting Vaccine Effectiveness for Hospitalization and Symptomatic Disease for Novel SARS-CoV-2 Variants Using Neutralizing Antibody Titers. Viruses 2024; 16:479. [PMID: 38543844 PMCID: PMC10975673 DOI: 10.3390/v16030479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 05/23/2024] Open
Abstract
The emergence of new virus variants, including the Omicron variant (B.1.1.529) of SARS-CoV-2, can lead to reduced vaccine effectiveness (VE) and the need for new vaccines or vaccine doses if the extent of immune evasion is severe. Neutralizing antibody titers have been shown to be a correlate of protection for SARS-CoV-2 and other pathogens, and could be used to quickly estimate vaccine effectiveness for new variants. However, no model currently exists to provide precise VE estimates for a new variant against severe disease for SARS-CoV-2 using robust datasets from several populations. We developed predictive models for VE against COVID-19 symptomatic disease and hospitalization across a 54-fold range of mean neutralizing antibody titers. For two mRNA vaccines (mRNA-1273, BNT162b2), models fit without Omicron data predicted that infection with the BA.1 Omicron variant increased the risk of hospitalization 2.8-4.4-fold and increased the risk of symptomatic disease 1.7-4.2-fold compared to the Delta variant. Out-of-sample validation showed that model predictions were accurate; all predictions were within 10% of observed VE estimates and fell within the model prediction intervals. Predictive models using neutralizing antibody titers can provide rapid VE estimates, which can inform vaccine booster timing, vaccine design, and vaccine selection for new virus variants.
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Affiliation(s)
- Billy J. Gardner
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA
| | - A. Marm Kilpatrick
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA
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Duval D, Evans B, Sanders A, Hill J, Simbo A, Kavoi T, Lyell I, Simmons Z, Qureshi M, Pearce-Smith N, Arevalo CR, Beck CR, Bindra R, Oliver I. Non-pharmaceutical interventions to reduce COVID-19 transmission in the UK: a rapid mapping review and interactive evidence gap map. J Public Health (Oxf) 2024:fdae025. [PMID: 38426578 DOI: 10.1093/pubmed/fdae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/15/2024] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) were crucial in the response to the COVID-19 pandemic, although uncertainties about their effectiveness remain. This work aimed to better understand the evidence generated during the pandemic on the effectiveness of NPIs implemented in the UK. METHODS We conducted a rapid mapping review (search date: 1 March 2023) to identify primary studies reporting on the effectiveness of NPIs to reduce COVID-19 transmission. Included studies were displayed in an interactive evidence gap map. RESULTS After removal of duplicates, 11 752 records were screened. Of these, 151 were included, including 100 modelling studies but only 2 randomized controlled trials and 10 longitudinal observational studies.Most studies reported on NPIs to identify and isolate those who are or may become infectious, and on NPIs to reduce the number of contacts. There was an evidence gap for hand and respiratory hygiene, ventilation and cleaning. CONCLUSIONS Our findings show that despite the large number of studies published, there is still a lack of robust evaluations of the NPIs implemented in the UK. There is a need to build evaluation into the design and implementation of public health interventions and policies from the start of any future pandemic or other public health emergency.
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Affiliation(s)
- D Duval
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - B Evans
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - A Sanders
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - J Hill
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - A Simbo
- Evaluation and Epidemiological Science Division, UKHSA, Colindale NW9 5EQ, UK
| | - T Kavoi
- Cheshire and Merseyside Health Protection Team, UKHSA, Liverpool L3 1DS, UK
| | - I Lyell
- Greater Manchester Health Protection Team, UKHSA, Manchester M1 3BN, UK
| | - Z Simmons
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - M Qureshi
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - N Pearce-Smith
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - C R Arevalo
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - C R Beck
- Evaluation and Epidemiological Science Division, UKHSA, Salisbury SP4 0JG, UK
| | - R Bindra
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - I Oliver
- Director General Science and Research and Chief Scientific Officer, UKHSA, London E14 5EA, UK
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Wu G, Zhang W, Wu W, Wang P, Huang Z, Wu Y, Li J, Zhang W, Du Z, Hao Y. Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States. Front Public Health 2024; 12:1343950. [PMID: 38450145 PMCID: PMC10915018 DOI: 10.3389/fpubh.2024.1343950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Introduction Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics. Methods Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic. Results We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022. Conclusion Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.
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Affiliation(s)
- Gonghua Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wanfang Zhang
- Guangzhou Liwan District Center for Disease Prevention and Control, Guangzhou, China
| | - Wenjing Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Pengyu Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zitong Huang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Yueqian Wu
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Junxi Li
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
- Guangzhou Joint Research Center for Disease Surveillance and Risk Assessment, Sun Yat-sen University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
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8
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Fu R, Liu W, Wang S, Zhao J, Cui Q, Hu Z, Zhang L, Wang F. Scenario analysis of COVID-19 dynamical variations by different social environmental factors: a case study in Xinjiang. Front Public Health 2024; 12:1297007. [PMID: 38435296 PMCID: PMC10906079 DOI: 10.3389/fpubh.2024.1297007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background With the rapid advancement of the One Health approach, the transmission of human infectious diseases is generally related to environmental and animal health. Coronavirus disease (COVID-19) has been largely impacted by environmental factors regionally and globally and has significantly disrupted human society, especially in low-income regions that border many countries. However, few research studies have explored the impact of environmental factors on disease transmission in these regions. Methods We used the Xinjiang Uygur Autonomous Region as the study area to investigate the impact of environmental factors on COVID-19 variation using a dynamic disease model. Given the special control and prevention strategies against COVID-19 in Xinjiang, the focus was on social and environmental factors, including population mobility, quarantine rates, and return rates. The model performance was evaluated using the statistical metrics of correlation coefficient (CC), normalized absolute error (NAE), root mean square error (RMSE), and distance between the simulation and observation (DISO) indices. Scenario analyses of COVID-19 in Xinjiang encompassed three aspects: different population mobilities, quarantine rates, and return rates. Results The results suggest that the established dynamic disease model can accurately simulate and predict COVID-19 variations with high accuracy. This model had a CC value of 0.96 and a DISO value of less than 0.35. According to the scenario analysis results, population mobilities have a large impact on COVID-19 variations, with quarantine rates having a stronger impact than return rates. Conclusion These results provide scientific insight into the control and prevention of COVID-19 in Xinjiang, considering the influence of social and environmental factors on COVID-19 variation. The control and prevention strategies for COVID-19 examined in this study may also be useful for the control of other infectious diseases, especially in low-income regions that are bordered by many countries.
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Affiliation(s)
- Ruonan Fu
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wanli Liu
- Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
| | - Senlu Wang
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
- Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
| | - Jun Zhao
- Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
| | - Qianqian Cui
- School of Mathematics and Statistics, Ningxia University, Yingchuan, Ningxia, China
| | - Zengyun Hu
- School of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, China
| | - Ling Zhang
- School of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Mallela A, Chen Y, Lin YT, Miller EF, Neumann J, He Z, Nelson KE, Posner RG, Hlavacek WS. Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States. Bull Math Biol 2024; 86:31. [PMID: 38353870 DOI: 10.1007/s11538-024-01258-4] [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: 05/11/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.
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Affiliation(s)
- Abhishek Mallela
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Ye Chen
- Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Yen Ting Lin
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
- Information Sciences Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Ely F Miller
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Jacob Neumann
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Zhili He
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Kathryn E Nelson
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Richard G Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - William S Hlavacek
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
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10
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Robert A, Chapman LAC, Grah R, Niehus R, Sandmann F, Prasse B, Funk S, Kucharski AJ. Predicting subnational incidence of COVID-19 cases and deaths in EU countries. BMC Infect Dis 2024; 24:204. [PMID: 38355414 DOI: 10.1186/s12879-024-08986-x] [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: 09/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. METHODS We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. RESULTS At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. DISCUSSION Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
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Affiliation(s)
- Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Rok Grah
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
- Current address: Robert Koch Institute, Berlin, Germany
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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11
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Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, Vandemaele K, Viboud C, Riley S, McCaw JM, Shearer FM. Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerg Infect Dis 2024; 30:e230768. [PMID: 38190760 PMCID: PMC10826770 DOI: 10.3201/eid3002.230768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.
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12
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Boldea O, Alipoor A, Pei S, Shaman J, Rozhnova G. Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic. PNAS NEXUS 2024; 3:pgae024. [PMID: 38312225 PMCID: PMC10837015 DOI: 10.1093/pnasnexus/pgae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.
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Affiliation(s)
- Otilia Boldea
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Amir Alipoor
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Sen Pei
- 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
- Columbia Climate School, Columbia University, New York, NY 10025, USA
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, The Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht 3584 CE, The Netherlands
- Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
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13
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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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14
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Cao H, Cao L. Differentiating behavioral impact with or without vaccination certification under mass vaccination and non-pharmaceutical interventions on mitigating COVID-19. Sci Rep 2024; 14:707. [PMID: 38184669 PMCID: PMC10771507 DOI: 10.1038/s41598-023-50421-9] [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: 10/21/2023] [Accepted: 12/19/2023] [Indexed: 01/08/2024] Open
Abstract
As COVID-19 vaccines became widely available worldwide, many countries implemented vaccination certification, also known as a "green pass", to promote and expedite vaccination on containing virus spread from the latter half of 2021. This policy allowed those vaccinated to have more freedom in public activities compared to more constraints on the unvaccinated in addition to existing non-pharmaceutical interventions (NPIs). Accordingly, the vaccination certification also induced heterogeneous behaviors of unvaccinated and vaccinated groups. This makes it essential yet challenging to model the behavioral impact of vaccination certification on the two groups and the transmission dynamics of COVID-19 within and between the groups. Very limited quantitative work is available for addressing these purposes. Here we propose an extended epidemiological model SEIQRD[Formula: see text] to effectively distinguish the behavioral impact of vaccination certification on unvaccinated and vaccinated groups through incorporating two contrastive transmission chains. SEIQRD[Formula: see text] also quantifies the impact of the green pass policy. With the resurgence of COVID-19 in Greece, Austria, and Israel in 2021, our simulation results indicate that their implementation of vaccination certification brought about more than a 14-fold decrease in the total number of infections and deaths as compared to a scenario with no such a policy. Additionally, a green pass policy may offer a reasonable practical solution to strike the balance between public health and individual's freedom during the pandemic.
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Affiliation(s)
- Hu Cao
- School of Computing, Macquarie University, Sydney, NSW, 2109, Australia
| | - Longbing Cao
- School of Computing, Macquarie University, Sydney, NSW, 2109, Australia.
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15
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Lunt R, Quinot C, Kirsebom F, Andrews N, Skarnes C, Letley L, Haskins D, Angel C, Firminger S, Ratcliffe K, Rajan S, Sherridan A, Ijaz S, Zambon M, Brown K, Ramsay M, Bernal JL. The impact of vaccination and SARS-CoV-2 variants on the virological response to SARS-CoV-2 infections during the Alpha, Delta, and Omicron waves in England. J Infect 2024; 88:21-29. [PMID: 37926118 DOI: 10.1016/j.jinf.2023.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/07/2023]
Abstract
Vaccination status and the SARS-CoV-2 variant individuals are infected with are known to independently impact viral dynamics; however, little is known about the interaction of these two factors and how this impacts viral dynamics. Here we investigated how monovalent vaccination modified the time course and viral load of infections from different variants. Regression analyses were used to investigate the impact of vaccination on cycle threshold values and disease severity, and interval-censored survival analyses were used to investigate the impact of vaccination on duration of positivity. A range of covariates were adjusted for as potential confounders and investigated for their own effects in exploratory analyses. All analyses were done combining all variants and stratified by variant. For those infected with Alpha or Delta, vaccinated individuals were more likely to report mild disease than moderate/severe disease and had significantly shorter duration of positivity and lower viral loads compared to unvaccinated individuals. Vaccination had no impact on self-reported disease severity, viral load, or duration if positivity for those infected with Omicron. Overall, individuals who were immunosuppressed and clinically extremely vulnerable had longer duration of positivity and higher viral loads. This study adds to the evidence base on disease dynamics following COVID-19, demonstrating that vaccination mitigates severity of disease, the amount of detectable virus within infected individuals and reduces the time individuals are positive for. However, these effects have been significantly attenuated since the emergence of Omicron. Therefore, our findings strengthen the argument for using modified or multivalent vaccines that target emerging variants.
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Affiliation(s)
- Rachel Lunt
- UK Health Security Agency, London, United Kingdom.
| | | | | | - Nick Andrews
- UK Health Security Agency, London, United Kingdom; NIHR Health Protection Research Unit in Vaccines and Immunisation, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | | | | | | | | | | | | | - Samreen Ijaz
- UK Health Security Agency, London, United Kingdom
| | - Maria Zambon
- UK Health Security Agency, London, United Kingdom; NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, United Kingdom
| | - Kevin Brown
- UK Health Security Agency, London, United Kingdom
| | - Mary Ramsay
- UK Health Security Agency, London, United Kingdom; NIHR Health Protection Research Unit in Vaccines and Immunisation, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jamie Lopez Bernal
- UK Health Security Agency, London, United Kingdom; NIHR Health Protection Research Unit in Vaccines and Immunisation, London School of Hygiene and Tropical Medicine, London, United Kingdom; NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, United Kingdom
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16
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Sunagawa J, Park H, Kim KS, Komorizono R, Choi S, Ramirez Torres L, Woo J, Jeong YD, Hart WS, Thompson RN, Aihara K, Iwami S, Yamaguchi R. Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution. Nat Commun 2023; 14:7395. [PMID: 37989736 PMCID: PMC10663562 DOI: 10.1038/s41467-023-43043-2] [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: 08/19/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023] Open
Abstract
During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.
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Affiliation(s)
- Junya Sunagawa
- Department of Advanced Transdisciplinary Sciences, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Hyeongki Park
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Kwang Su Kim
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Ryo Komorizono
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Life and Medical Sciences (LiMe), Kyoto University, Kyoto, Japan
| | - Sooyoun Choi
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Lucia Ramirez Torres
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Joohyeon Woo
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yong Dam Jeong
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - William S Hart
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Robin N Thompson
- Mathematical Institute, University of Oxford, Oxford, UK
- Mathematics Institute, University of Warwick, Coventry, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
| | - Shingo Iwami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan.
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan.
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan.
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan.
- Science Groove Inc, Fukuoka, Japan.
| | - Ryo Yamaguchi
- Department of Advanced Transdisciplinary Sciences, Hokkaido University, Sapporo, Hokkaido, Japan.
- Department of Zoology & Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.
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17
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Chapman LAC, Aubry M, Maset N, Russell TW, Knock ES, Lees JA, Mallet HP, Cao-Lormeau VM, Kucharski AJ. Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia. Nat Commun 2023; 14:7330. [PMID: 37957160 PMCID: PMC10643399 DOI: 10.1038/s41467-023-43002-x] [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: 03/29/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Estimating the impact of vaccination and non-pharmaceutical interventions on COVID-19 incidence is complicated by several factors, including successive emergence of SARS-CoV-2 variants of concern and changing population immunity from vaccination and infection. We develop an age-structured multi-strain COVID-19 transmission model and inference framework to estimate vaccination and non-pharmaceutical intervention impact accounting for these factors. We apply this framework to COVID-19 waves in French Polynesia and estimate that the vaccination programme averted 34.8% (95% credible interval: 34.5-35.2%) of 223,000 symptomatic cases, 49.6% (48.7-50.5%) of 5830 hospitalisations and 64.2% (63.1-65.3%) of 1540 hospital deaths that would have occurred in a scenario without vaccination up to May 2022. We estimate the booster campaign contributed 4.5%, 1.9%, and 0.4% to overall reductions in cases, hospitalisations, and deaths. Our results suggest that removing lockdowns during the first two waves would have had non-linear effects on incidence by altering accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.
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Affiliation(s)
- Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Maite Aubry
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
| | - Noémie Maset
- Cellule Epi-surveillance Plateforme COVID-19, Tahiti, French Polynesia
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Edward S Knock
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - John A Lees
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Cambridgeshire, UK
| | | | - Van-Mai Cao-Lormeau
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
- Laboratoire de recherche sur les infections virales émergentes, Institut Louis Malardé, Tahiti, French Polynesia
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18
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Lythgoe KA, Golubchik T, Hall M, House T, Cahuantzi R, MacIntyre-Cockett G, Fryer H, Thomson L, Nurtay A, Ghafani M, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith D, Bashton M, Nelson A, Crown M, McCann C, Young GR, Andre Nunes dos Santos R, Richards Z, Tariq A, Fraser C, Diamond I, Barrett J, Walker AS, Bonsall D. Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey. Proc Biol Sci 2023; 290:20231284. [PMID: 37848057 PMCID: PMC10581763 DOI: 10.1098/rspb.2023.1284] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/08/2023] [Indexed: 10/19/2023] Open
Abstract
The Office for National Statistics Coronavirus (COVID-19) Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community, and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to their predecessors, although this was also accompanied by a gradual fall in average viral burdens from June 2021 to March 2023. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterized by steadily increasing divergence and diversity within lineages, but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within lineages, and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens.
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Affiliation(s)
- Katrina A. Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Roberto Cahuantzi
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Helen Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Mahan Ghafani
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Lorne J. Lonie
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | | | | | - Darren Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Andrew Nelson
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Clare McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Gregory R. Young
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Rui Andre Nunes dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | | | | | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | | | - Jeff Barrett
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
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19
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Lim MC, Singh S, Lai CH, Gill BS, Kamarudin MK, Md Zamri ASS, Tan CV, Zulkifli AA, Nadzri MNM, Mohd Ghazali N, Mohd Ghazali S, Md Iderus NH, Ahmad NARB, Suppiah J, Tee KK, Aris T, Ahmad LCRQ. Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models. Epidemiol Health 2023; 45:e2023093. [PMID: 37905314 PMCID: PMC10867513 DOI: 10.4178/epih.e2023093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/03/2023] [Indexed: 11/02/2023] Open
Abstract
OBJECTIVES This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak. METHODS SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison. RESULTS In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario). CONCLUSIONS Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.
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Affiliation(s)
- Mei Cheng Lim
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Sarbhan Singh
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Chee Herng Lai
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Balvinder Singh Gill
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Mohd Kamarulariffin Kamarudin
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Ahmed Syahmi Syafiq Md Zamri
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Cia Vei Tan
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Asrul Anuar Zulkifli
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Mohamad Nadzmi Md Nadzri
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Nur'ain Mohd Ghazali
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Sumarni Mohd Ghazali
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Nuur Hafizah Md Iderus
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Nur Ar Rabiah Binti Ahmad
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Jeyanthi Suppiah
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Kok Keng Tee
- Department of Medical Microbiology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Tahir Aris
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Lonny Chen Rong Qi Ahmad
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
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20
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Watanabe K, Nojima M, Nakase H, Sato T, Matsuura M, Aoyama N, Kobayashi T, Sakuraba H, Nishishita M, Yokoyama K, Esaki M, Hirai F, Nagahori M, Nanjo S, Omori T, Tanida S, Yokoyama Y, Moriya K, Maemoto A, Handa O, Ohmiya N, Tsuchiya K, Shinzaki S, Kato S, Uraoka T, Tanaka H, Takatsu N, Nishida A, Umeno J, Nakamura M, Mishima Y, Fujiya M, Tsuchida K, Hiraoka S, Okabe M, Toyonaga T, Matsuoka K, Andoh A, Hirota Y, Hisamatsu T. Trajectory analyses to identify persistently low responders to COVID-19 vaccination in patients with inflammatory bowel disease: a prospective multicentre controlled study, J-COMBAT. J Gastroenterol 2023; 58:1015-1029. [PMID: 37561155 DOI: 10.1007/s00535-023-02029-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND The degree of immune response to COVID-19 vaccination in inflammatory bowel disease (IBD) patients based on actual changes in anti-SARS-CoV-2 antibody titres over time is unknown. METHODS Data were prospectively acquired at four predetermined time points before and after two vaccine doses in a multicentre observational controlled study. The primary outcome was humoral immune response and vaccination safety in IBD patients. We performed trajectory analysis to identify the degree of immune response and associated factors in IBD patients compared with controls. RESULTS Overall, 645 IBD patients and 199 control participants were analysed. At 3 months after the second vaccination, the seronegative proportions were 20.3% (combination of anti-tumour necrosis factor [TNF]α and thiopurine) and 70.0% (triple combination including steroids), despite that 80.0% receiving the triple combination therapy were seropositive at 4 weeks after the second vaccination. Trajectory analyses indicated three degrees of change in immune response over time in IBD patients: high (57.7%), medium (35.6%), and persistently low (6.7%). In the control group, there was only one degree, which corresponded with IBD high responders. Older age, combined anti-TNFα and thiopurine (odds ratio [OR], 37.68; 95% confidence interval [CI], 5.64-251.54), steroids (OR, 21.47; 95%CI, 5.47-84.26), and tofacitinib (OR, 10.66; 95%CI, 1.49-76.31) were factors associated with persistently low response. Allergy history (OR, 0.17; 95%CI, 0.04-0.68) was a negatively associated factor. Adverse reactions after the second vaccination were significantly fewer in IBD than controls (31.0% vs 59.8%; p < 0.001). CONCLUSIONS Most IBD patients showed a sufficient immune response to COVID-19 vaccination regardless of clinical factors. Assessment of changes over time is essential to optimize COVID-19 vaccination, especially in persistently low responders.
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Affiliation(s)
- Kenji Watanabe
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Hyogo Medical University, 1-1, Mukogawa-cho, Nishinomiya, Hyogo, Japan.
- Department of Internal Medicine for Inflammatory Bowel Disease, University of Toyama, 2630, Sugitani, Toyama, 930-0194, Japan.
| | - Masanori Nojima
- Center for Translational Research, The Institute of Medical Science Hospital, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, Japan
| | - Hiroshi Nakase
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, S-1, W-16, Chuo-ku, Sapporo, Japan
| | - Toshiyuki Sato
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Hyogo Medical University, 1-1, Mukogawa-cho, Nishinomiya, Hyogo, Japan
| | - Minoru Matsuura
- Department of Gastroenterology and Hepatology, Kyorin University School of Medicine, Shinkawa 6-20-2, Mitaka-shi, Mitaka, Tokyo, Japan
| | | | - Taku Kobayashi
- Center for Advanced IBD Research and Treatment, Department of Gastroenterology, Kitasato University Kitasato Institute Hospital, 5-9-1 Shirokane, Minato-ku, Tokyo, Japan
| | - Hirotake Sakuraba
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, Aomori, Japan
| | - Masakazu Nishishita
- Nishishita Gastrointestinal Hospital, 4-15, Kitakawahori-cho, Tennoji-ku, Osaka, Japan
| | - Kaoru Yokoyama
- Department of Gastroenterology, Kitasato University School of Medicine, 1-15-1, Kitasato, Minami-ku, Sagamihara, Kanagawa, Japan
| | - Motohiro Esaki
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, 1-1,5-Chome, Nabeshima, Saga, Japan
| | - Fumihito Hirai
- Department of Gastroenterology and Medicine, Faculty of Medicine, Fukuoka University, 7-45-1 Nanakuma, Jonan-ku, Fukuoka, Japan
| | - Masakazu Nagahori
- Clinical Research Center, Tokyo Medical and Dental University Hospital, 1-5-45 Yushima Bunkyo-ku, Tokyo, Japan
| | - Sohachi Nanjo
- Third Department of Internal Medicine, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama, Japan
| | - Teppei Omori
- Institute of Gastroenterology, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, Japan
| | - Satoshi Tanida
- Education and Research Center for Community Medicine, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Japan
| | - Yoshihiro Yokoyama
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, S-1, W-16, Chuo-ku, Sapporo, Japan
| | - Kei Moriya
- Department of Gastroenterology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara, Japan
| | - Atsuo Maemoto
- IBD Center, Sapporo Higashi Tokushukai Hospital, 3-1, Kita 33-Jo Higashi 14-Chome, Higashi-ku, Sapporo, Japan
| | - Osamu Handa
- Department of Internal Medicine, Division of Gastroenterology, Kawasaki Medical School, 577 Matsushima, Kurashiki, Okayama, Japan
| | - Naoki Ohmiya
- Department of Advanced Endoscopy, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi, Japan
| | - Kiichiro Tsuchiya
- Department of Gastroenterology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki, Japan
| | - Shinichiro Shinzaki
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Hyogo Medical University, 1-1, Mukogawa-cho, Nishinomiya, Hyogo, Japan
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, Japan
| | - Shingo Kato
- Department of Gastroenterology and Hepatology, Saitama Medical Center, Saitama Medical University, 1981 Kamoda, Kawagoe, Saitama, Japan
| | - Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, 3-39-22, Showa-machi, Maebashi, Gunma, Japan
| | - Hiroki Tanaka
- Sapporo IBD Clinic, 1-18, Minami-19, Nishi-8, Chuo-ku, Sapporo, Hokkaido, Japan
| | - Noritaka Takatsu
- Inflammatory Bowel Disease Center, Fukuoka University Chikushi Hospital, 1-1-1, Zokumyoin, Chikushino, Fukuoka, Japan
| | - Atsushi Nishida
- Department of Medicine, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga, Japan
| | - Junji Umeno
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, Japan
| | - Masanao Nakamura
- Department of Endoscopy, Nagoya University Hospital, 65 Tsurumai-cho, Showa-ku, Nagoya, Japan
| | - Yoshiyuki Mishima
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, 1060 Nishikawatsu-cho, Matsue,, Shimane, Japan
| | - Mikihiro Fujiya
- Gastroenterology and Endoscopy, Division of Metabolism and Biosystemic Science, Gastroenterology, and Hematology/Oncology, Department of Medicine, Asahikawa Medical University, 2-1-1-1 Midorigaoka Higashi, Asahikawa, Hokkaido, Japan
| | - Kenji Tsuchida
- Gastroenterology, Nagoya City University West Medical Center, 1-1-1, Hirate-cho, Kita-ku, Nagoya, Aichi, Japan
| | - Sakiko Hiraoka
- Department of Gastroenterology and Hepatology, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, 2-5-1 Shikata-cho, Kita-ku, Okayama, Japan
| | - Makoto Okabe
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, Japan
| | - Takahiko Toyonaga
- Division of Internal Medicine, Department of Gastroenterology and Hepatology, The Jikei University School of Medicine, 3-19-18 Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Katsuyoshi Matsuoka
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Sakura Medical Center, 564-1, Shimoshidu, Sakura, Chiba, Japan
| | - Akira Andoh
- Department of Medicine, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga, Japan
| | - Yoshio Hirota
- Clinical Epidemiology Research Center, SOUSEIKAI Medical Group (Medical Co. LTA), 3-6-1, Kashii-Teriha, Higashi-ku, Fukuoka, Japan
| | - Tadakazu Hisamatsu
- Department of Gastroenterology and Hepatology, Kyorin University School of Medicine, Shinkawa 6-20-2, Mitaka-shi, Mitaka, Tokyo, Japan
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21
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Feltham E, Forastiere L, Alexander M, Christakis NA. Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021. Nat Hum Behav 2023; 7:1708-1728. [PMID: 37524931 DOI: 10.1038/s41562-023-01654-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 06/14/2023] [Indexed: 08/02/2023]
Abstract
Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump's political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162) and 8.275 cases (95% CI = -1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.
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Affiliation(s)
- Eric Feltham
- Yale Institute for Network Science, Yale University, New Haven, CT, USA.
- Department of Sociology, Yale University, New Haven, CT, USA.
| | - Laura Forastiere
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Sociology, Yale University, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
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22
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Cho G, Kim YJ, Seo SH, Jang G, Lee H. Cost-effectiveness analysis of COVID-19 variants effects in an age-structured model. Sci Rep 2023; 13:15844. [PMID: 37739967 PMCID: PMC10516971 DOI: 10.1038/s41598-023-41876-x] [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: 04/29/2023] [Accepted: 09/01/2023] [Indexed: 09/24/2023] Open
Abstract
This study analyzes the impact of COVID-19 variants on cost-effectiveness across age groups, considering vaccination efforts and nonpharmaceutical interventions in Republic of Korea. We aim to assess the costs needed to reduce COVID-19 cases and deaths using age-structured model. The proposed age-structured model analyzes COVID-19 transmission dynamics, evaluates vaccination effectiveness, and assesses the impact of the Delta and Omicron variants. The model is fitted using data from the Republic of Korea between February 2021 and November 2022. The cost-effectiveness of interventions, medical costs, and the cost of death for different age groups are evaluated through analysis. The impact of different variants on cases and deaths is also analyzed, with the Omicron variant increasing transmission rates and decreasing case-fatality rates compared to the Delta variant. The cost of interventions and deaths is higher for older age groups during both outbreaks, with the Omicron outbreak resulting in a higher overall cost due to increased medical costs and interventions. This analysis shows that the daily cost per person for both the Delta and Omicron variants falls within a similar range of approximately $10-$35. This highlights the importance of conducting cost-effect analyses when evaluating the impact of COVID-19 variants.
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Affiliation(s)
- Giphil Cho
- Department of Artificial Intelligence and Software, Kangwon National University, Chuncheon, Gangwon, 25913, Republic of Korea
| | - Young Jin Kim
- Division of Data Analysis, Center for Global R&D Data Analysis, Korea Institute of Science and Technology Information (KISTI), Seoul, 02456, Republic of Korea
| | - Sang-Hyup Seo
- National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Geunsoo Jang
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, 41566, Republic of Korea.
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23
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Li JX, Liao PL, Wei JCC, Hsu SB, Yeh CJ. A chronological review of COVID-19 case fatality rate and its secular trend and investigation of all-cause mortality and hospitalization during the Delta and Omicron waves in the United States: a retrospective cohort study. Front Public Health 2023; 11:1143650. [PMID: 37799149 PMCID: PMC10548482 DOI: 10.3389/fpubh.2023.1143650] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 08/14/2023] [Indexed: 10/07/2023] Open
Abstract
Introduction Coronavirus disease 2019 (COVID-19) has caused more than 690 million deaths worldwide. Different results concerning the death rates of the Delta and Omicron variants have been recorded. We aimed to assess the secular trend of case fatality rate (CFR), identify risk factors associated with mortality following COVID-19 diagnosis, and investigate the risks of mortality and hospitalization during Delta and Omicron waves in the United States. Methods This study assessed 2,857,925 individuals diagnosed with COVID-19 in the United States from January 2020, to June 2022. The inclusion criterion was the presence of COVID-19 diagnostic codes in electronic medical record or a positive laboratory test of the SARS-CoV-2. Statistical analysis was bifurcated into two components, longitudinal analysis and comparative analysis. To assess the discrepancies in hospitalization and mortality rates for COVID-19, we identified the prevailing periods for the Delta and Omicron variants. Results Longitudinal analysis demonstrated four sharp surges in the number of deaths and CFR. The CFR was persistently higher in males and older age. The CFR of Black and White remained higher than Asians since January 2022. In comparative analysis, the adjusted hazard ratios for all-cause mortality and hospitalization were higher in Delta wave compared to the Omicron wave. Risk of all-cause mortality was found to be greater 14-30 days after a COVID-19 diagnosis, while the likelihood of hospitalization was higher in the first 14 days following a COVID-19 diagnosis in Delta wave compared with Omicron wave. Kaplan-Meier analysis revealed the cumulative probability of mortality was approximately 2-fold on day 30 in Delta than in Omicron cases (log-rank p < 0.001). The mortality risk ratio between the Delta and Omicron variants was 1.671 (95% Cl 1.615-1.729, log-rank p < 0.001). Delta also had a significantly increased mortality risk over Omicron in all age groups. The CFR of people aged above 80 years was extremely high as 17.33%. Conclusion Male sex and age seemed to be strong and independent risk factors of mortality in COVID-19. The Delta variant appears to cause more hospitalization and death than the Omicron variant.
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Affiliation(s)
- Jing-Xing Li
- Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
- Graduate Institute of Clinical Laboratory Sciences and Medical Biotechnology, National Taiwan University, Taipei, Taiwan
| | - Pei-Lun Liao
- Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - James Cheng-Chung Wei
- Department of Nursing, Chung Shan Medical University, Taichung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Allergy, Immunology & Rheumatology, Chung Shan Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan
| | - Shu-Bai Hsu
- College of Medicine, China Medical University, Taichung, Taiwan
- Department of Nursing, China Medical University Hospital, Taichung, Taiwan
| | - Chih-Jung Yeh
- Department of Public Health, Chung Shan Medical University, Taichung, Taiwan
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Walkowiak MP, Walkowiak D, Walkowiak J. To vaccinate or to isolate? Establishing which intervention leads to measurable mortality reduction during the COVID-19 Delta wave in Poland. Front Public Health 2023; 11:1221964. [PMID: 37744498 PMCID: PMC10513426 DOI: 10.3389/fpubh.2023.1221964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Background During the Delta variant COVID-19 wave in Poland there were serious regional differences in vaccination rates and discrepancies in the enforcement of pandemic preventive measures, which allowed us to assess the relative effectiveness of the policies implemented. Methods Creating a model that would predict mortality based on vaccination rates among the most vulnerable groups and the timing of the wave peak enabled us to calculate to what extent flattening the curve reduced mortality. Subsequently, a model was created to assess which preventive measures delayed the peak of infection waves. Combining those two models allowed us to estimate the relative effectiveness of those measures. Results Flattening the infection curve worked: according to our model, each week of postponing the peak of the wave reduced excess deaths by 1.79%. Saving a single life during the Delta wave required one of the following: either the vaccination of 57 high-risk people, or 1,258 low-risk people to build herd immunity, or the isolation of 334 infected individuals for a cumulative period of 10.1 years, or finally quarantining 782 contacts for a cumulative period of 19.3 years. Conclusions Except for the most disciplined societies, vaccination of high-risk individuals followed by vaccinating low-risk groups should have been the top priority instead of relying on isolation and quarantine measures which can incur disproportionately higher social costs. Our study demonstrates that even in a country with uniform policies, implementation outcomes varied, highlighting the importance of fine-tuning policies to regional specificity.
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Affiliation(s)
- Marcin Piotr Walkowiak
- Department of Preventive Medicine, Poznan University of Medical Sciences, Poznań, Poland
| | - Dariusz Walkowiak
- Department of Organization and Management in Health Care, Poznan University of Medical Sciences, Poznań, Poland
| | - Jarosław Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznań, Poland
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25
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Ochida N, Dupont-Rouzeyrol M, Moury PH, Demaneuf T, Gourinat AC, Mabon S, Jouan M, Cauchemez S, Mangeas M. Evaluating the strategies to control SARS-CoV-2 Delta variant spread in New Caledonia, a zero-COVID country until September 2021. IJID REGIONS 2023; 8:64-70. [PMID: 37583482 PMCID: PMC10423666 DOI: 10.1016/j.ijregi.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/21/2023] [Accepted: 06/26/2023] [Indexed: 08/17/2023]
Abstract
Objectives New Caledonia, a former zero-COVID country, was confronted with a SARS-CoV-2 Delta variant outbreak in September 2021. We evaluate the relative contribution of vaccination, lockdown, and timing of interventions on healthcare burden. Methods We developed an age-stratified mathematical model of SARS-CoV-2 transmission and vaccination calibrated for New Caledonia and evaluated three alternative scenarios. Results High virus transmission early on was estimated, with R0 equal to 6.6 (95% confidence interval [6.4-6.7]). Lockdown reduced R0 by 73% (95% confidence interval [70-76%]). Easing the lockdown increased transmission (39% reduction of the initial R0); but we did not observe an epidemic rebound. This contrasts with the rebound in hospital admissions (+116% total hospital admissions) that would have been expected in the absence of an intensified vaccination campaign (76,220 people or 34% of the eligible population were first-dose vaccinated during 1 month of lockdown). A 15-day earlier lockdown would have led to a significant reduction in the magnitude of the epidemic (-53% total hospital admissions). Conclusion The success of the response against the Delta variant epidemic in New Caledonia was due to an effective lockdown that provided additional time for people to vaccinate. Earlier lockdown would have greatly mitigated the magnitude of the epidemic.
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Affiliation(s)
- Noé Ochida
- UMR ENTROPIE, IRD, Université de La Réunion, IFREMER, Université de Nouvelle-Calédonie, CNRS, Noumea, New Caledonia
- Research and Expertise Unit on Dengue and Arboviruses, Institut Pasteur of New Caledonia, Pasteur Network, Noumea, New Caledonia
| | - Myrielle Dupont-Rouzeyrol
- Research and Expertise Unit on Dengue and Arboviruses, Institut Pasteur of New Caledonia, Pasteur Network, Noumea, New Caledonia
| | - Pierre-Henri Moury
- Department of Anesthesia and Intensive Care Medicine, Grenoble University Hospital, Grenoble, France
- Research and Expertise Unit of Epidemiology, Institut Pasteur of New Caledonia, Pasteur Network, Noumea, New Caledonia
- Intensive Care Unit, Gaston-Bourret Territorial Hospital Center, Dumbea-Sur-Mer, New Caledonia
| | | | - Ann-Clair Gourinat
- Microbiology Laboratory, Gaston-Bourret Territorial Hospital Center, Dumbea-Sur-Mer, New Caledonia
| | - Sébastien Mabon
- Directorate of Health and Social Affairs, Noumea, New Caledonia
| | - Marc Jouan
- Research and Expertise Unit on Dengue and Arboviruses, Institut Pasteur of New Caledonia, Pasteur Network, Noumea, New Caledonia
- Research and Expertise Unit of Epidemiology, Institut Pasteur of New Caledonia, Pasteur Network, Noumea, New Caledonia
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000, CNRS, Paris, France
| | - Morgan Mangeas
- UMR ENTROPIE, IRD, Université de La Réunion, IFREMER, Université de Nouvelle-Calédonie, CNRS, Noumea, New Caledonia
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26
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Zhou Z, Li D, Zhao Z, Shi S, Wu J, Li J, Zhang J, Gui K, Zhang Y, Ouyang Q, Mei H, Hu Y, Li F. Dynamical modelling of viral infection and cooperative immune protection in COVID-19 patients. PLoS Comput Biol 2023; 19:e1011383. [PMID: 37656752 PMCID: PMC10501599 DOI: 10.1371/journal.pcbi.1011383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/14/2023] [Accepted: 07/24/2023] [Indexed: 09/03/2023] Open
Abstract
Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.
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Affiliation(s)
- Zhengqing Zhou
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Dianjie Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Ziheng Zhao
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Shuyu Shi
- Peking University Third Hospital, Peking University, Beijing, China
| | - Jianghua Wu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianwei Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Jingpeng Zhang
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Ke Gui
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Yu Zhang
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Qi Ouyang
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Heng Mei
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fangting Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
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27
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Bhatia S, Wardle J, Nash RK, Nouvellet P, Cori A. Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study. Epidemics 2023; 44:100692. [PMID: 37399634 PMCID: PMC10284428 DOI: 10.1016/j.epidem.2023.100692] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/20/2023] [Accepted: 05/29/2023] [Indexed: 07/05/2023] Open
Abstract
The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat of new variants is critical for timely optimisation of control strategies. We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal use and interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users to rapidly explore spatial and temporal variations in the estimated transmission advantage. We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29 (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Delta is 1.77 (95% CrI 1.69-1.85) times more transmissible than Alpha (England data). Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, UK
| | - Jack Wardle
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Rebecca K Nash
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
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28
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Hu S, Xiong C, Zhao Y, Yuan X, Wang X. Vaccination, human mobility, and COVID-19 health outcomes: Empirical comparison before and during the outbreak of SARS-Cov-2 B.1.1.529 (Omicron) variant. Vaccine 2023; 41:5097-5112. [PMID: 37270367 PMCID: PMC10234469 DOI: 10.1016/j.vaccine.2023.05.056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/27/2023] [Accepted: 05/22/2023] [Indexed: 06/05/2023]
Abstract
The B.1.1.529 (Omicron) variant surge has raised concerns about the effectiveness of vaccines and the impact of imprudent reopening. Leveraging over two years of county-level COVID-19 data in the US, this study aims to investigate relationships among vaccination, human mobility, and COVID-19 health outcomes (assessed via case rate and case-fatality rate), controlling for socioeconomic, demographic, racial/ethnic, and partisan factors. A set of cross-sectional models was first fitted to empirically compare disparities in COVID-19 health outcomes before and during the Omicron surge. Then, time-varying mediation analyses were employed to delineate how the effects of vaccine and mobility on COVID-19 health outcomes vary over time. Results showed that vaccine effectiveness against case rate lost significance during the Omicron surge, while its effectiveness against case-fatality rate remained significant throughout the pandemic. We also documented salient structural inequalities in COVID-19-related outcomes, with disadvantaged populations consistently bearing a larger brunt of case and death tolls, regardless of high vaccination rates. Last, findings revealed that mobility presented a significantly positive relationship with case rates during each wave of variant outbreak. Mobility substantially mediated the direct effect from vaccination to case rate, leading to a 10.276 % (95 % CI: 6.257, 14.294) decrease in vaccine effectiveness on average. Altogether, our study implies that sole reliance on vaccination to halt COVID-19 needs to be re-examined. Well-resourced and coordinated efforts to enhance vaccine effectiveness, mitigate health disparity and selectively loosen non-pharmaceutical interventions are essential to bringing the pandemic to an end.
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Affiliation(s)
- Songhua Hu
- Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, United States.
| | - Chenfeng Xiong
- Department of Civil and Environmental Engineering, Villanova University, PA 19085, United States.
| | - Yingrui Zhao
- Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
| | - Xin Yuan
- Department of Civil and Environmental Engineering, Villanova University, PA 19085, United States
| | - Xuqiu Wang
- Department of Civil and Environmental Engineering, Villanova University, PA 19085, United States
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29
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Perez-Guzman PN, Knock E, Imai N, Rawson T, Elmaci Y, Alcada J, Whittles LK, Thekke Kanapram D, Sonabend R, Gaythorpe KAM, Hinsley W, FitzJohn RG, Volz E, Verity R, Ferguson NM, Cori A, Baguelin M. Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England. Nat Commun 2023; 14:4279. [PMID: 37460537 PMCID: PMC10352350 DOI: 10.1038/s41467-023-39661-5] [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: 02/08/2023] [Accepted: 06/23/2023] [Indexed: 07/20/2023] Open
Abstract
As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0-2.4), Wildtype (1.2%, 95% CrI 1.1-1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.
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Affiliation(s)
- Pablo N Perez-Guzman
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Thomas Rawson
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Yasin Elmaci
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Joana Alcada
- Adult Intensive Care Unit, Royal Brompton Hospital, London, UK
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Divya Thekke Kanapram
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
- Department of Engineering, Division of Electrical Engineering, University of Cambridge, Cambridge, UK
| | - Raphael Sonabend
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Richard G FitzJohn
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK.
- National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
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30
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Salmen A, Marti S, Hoepner AGF, Chan A, Hoepner R. The impact of immunotherapies on COVID-19 case fatality rates during the US vaccination campaign: a multidisciplinary open data analysis using FDA Adverse Event Reporting System and Our World in Data. Front Pharmacol 2023; 14:1186404. [PMID: 37397473 PMCID: PMC10308012 DOI: 10.3389/fphar.2023.1186404] [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: 03/16/2023] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
Introduction: Patients under immunotherapies were excluded from the pivotal trials of vaccinations against the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), and no population-level data on disease outcomes such as case fatality rates in relation to vaccination coverage exist. Our study aims to fill this gap by investigating whether CFRs in patients with immunotherapies decrease with increasing vaccination coverage in the total population. Methods: We combined aggregated open source data on COVID-19 vaccination coverage from "Our World in Data" with publicly available anonymized COVID-19 case reports from the FDA Adverse Event Reporting System to compute COVID-19 CFRs for patients under immunotherapy at different vaccination coverage levels in the total population. CFRs at different vaccination coverage levels were then compared to CFRs before vaccination campaign start. Results: While we found an overall decrease in CFRs on population level with increasing vaccination coverage, we found no decrease in people using anti-CD20 or glucocorticoids. Discussion: Risk-mitigation strategies on an individual- and population-level are thus still needed to lower the probability of fatal SARS-CoV2 infection for these vulnerable populations.
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Affiliation(s)
- Anke Salmen
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Stefanie Marti
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Andreas G. F. Hoepner
- Department of Banking and Finance, Michael Smurfit Graduate Business School, University College Dublin, Dublin, Ireland
- Department for Financial Stability and Capital Markets (DG FISMA), Platform for Sustainable Finance, European Commission, Brussels, Belgium
| | - Andrew Chan
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Robert Hoepner
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
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31
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González RI, Moya PS, Bringa EM, Bacigalupe G, Ramírez-Santana M, Kiwi M. Model based on COVID-19 evidence to predict and improve pandemic control. PLoS One 2023; 18:e0286747. [PMID: 37319168 PMCID: PMC10270358 DOI: 10.1371/journal.pone.0286747] [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: 08/17/2022] [Accepted: 05/22/2023] [Indexed: 06/17/2023] Open
Abstract
Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.
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Affiliation(s)
- Rafael I. González
- Centro de Nanotecnología Aplicada, Universidad Mayor, Santiago, Chile
- Center for the Development of Nanoscience and Nanotechnology, CEDENNA, Santiago, Chile
| | - Pablo S. Moya
- Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiago, Chile
| | - Eduardo M. Bringa
- Centro de Nanotecnología Aplicada, Universidad Mayor, Santiago, Chile
- CONICET, Facultad de Ingeniería, Universidad de Mendoza, Mendoza, Argentina
| | - Gonzalo Bacigalupe
- School of Education and Human Development, University of Massachusetts Boston, Boston, MA, United States of America
- CreaSur, Universidad de Concepción, Concepción, Chile
| | - Muriel Ramírez-Santana
- Departamento de Salud Pública, Facultad de Medicina, Universidad Católica del Norte, Coquimbo, Chile
| | - Miguel Kiwi
- Center for the Development of Nanoscience and Nanotechnology, CEDENNA, Santiago, Chile
- Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiago, Chile
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Gaythorpe KAM, Fitzjohn RG, Hinsley W, Imai N, Knock ES, Perez Guzman PN, Djaafara B, Fraser K, Baguelin M, Ferguson NM. Data pipelines in a public health emergency: The human in the machine. Epidemics 2023; 43:100676. [PMID: 36913804 DOI: 10.1016/j.epidem.2023.100676] [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: 03/30/2022] [Revised: 01/31/2023] [Accepted: 03/06/2023] [Indexed: 03/10/2023] Open
Abstract
In an emergency epidemic response, data providers supply data on a best-faith effort to modellers and analysts who are typically the end user of data collected for other primary purposes such as to inform patient care. Thus, modellers who analyse secondary data have limited ability to influence what is captured. During an emergency response, models themselves are often under constant development and require both stability in their data inputs and flexibility to incorporate new inputs as novel data sources become available. This dynamic landscape is challenging to work with. Here we outline a data pipeline used in the ongoing COVID-19 response in the UK that aims to address these issues. A data pipeline is a sequence of steps to carry the raw data through to a processed and useable model input, along with the appropriate metadata and context. In ours, each data type had an individual processing report, designed to produce outputs that could be easily combined and used downstream. Automated checks were in-built and added as new pathologies emerged. These cleaned outputs were collated at different geographic levels to provide standardised datasets. Finally, a human validation step was an essential component of the analysis pathway and permitted more nuanced issues to be captured. This framework allowed the pipeline to grow in complexity and volume and facilitated the diverse range of modelling approaches employed by researchers. Additionally, every report or modelling output could be traced back to the specific data version that informed it ensuring reproducibility of results. Our approach has been used to facilitate fast-paced analysis and has evolved over time. Our framework and its aspirations are applicable to many settings beyond COVID-19 data, for example for other outbreaks such as Ebola, or where routine and regular analyses are required.
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Affiliation(s)
- Katy A M Gaythorpe
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom.
| | - Rich G Fitzjohn
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Wes Hinsley
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Natsuko Imai
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Edward S Knock
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Pablo N Perez Guzman
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Bimandra Djaafara
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Keith Fraser
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Marc Baguelin
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Neil M Ferguson
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
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Brett TS, Bansal S, Rohani P. Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state. PLoS Comput Biol 2023; 19:e1011263. [PMID: 37379328 PMCID: PMC10335681 DOI: 10.1371/journal.pcbi.1011263] [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: 08/24/2022] [Revised: 07/11/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023] Open
Abstract
The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.
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Affiliation(s)
- Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, D.C., United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease & Emergence Research (CIDER), Athens, Georgia, United States of America
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Kim Y, Donnelly CA, Nouvellet P. Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England. Nat Commun 2023; 14:2148. [PMID: 37059861 PMCID: PMC10103662 DOI: 10.1038/s41467-023-37813-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 03/30/2023] [Indexed: 04/16/2023] Open
Abstract
During the COVID-19 pandemic, national testing programmes were conducted worldwide on unprecedented scales. While testing behaviour is generally recognised as dynamic and complex, current literature demonstrating and quantifying such relationships is scarce, despite its importance for infectious disease surveillance and control. Here, we characterise the impacts of SARS-CoV-2 transmission, disease susceptibility/severity, risk perception, and public health measures on SARS-CoV-2 PCR testing behaviour in England over 20 months of the pandemic, by linking testing trends to underlying epidemic trends and contextual meta-data within a systematic conceptual framework. The best-fitting model describing SARS-CoV-2 PCR testing behaviour explained close to 80% of the total deviance in NHS test data. Testing behaviour showed complex associations with factors reflecting transmission level, disease susceptibility/severity (e.g. age, dominant variant, and vaccination), public health measures (e.g. testing strategies and lockdown), and associated changes in risk perception, varying throughout the pandemic and differing between infected and non-infected people.
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Affiliation(s)
- Younjung Kim
- Department of Evolution, Behaviour, and Environment, School of Life Sciences, University of Sussex, Brighton, UK
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Pierre Nouvellet
- Department of Evolution, Behaviour, and Environment, School of Life Sciences, University of Sussex, Brighton, UK.
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
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Cori A, Lassmann B, Nouvellet P. Data needs for better surveillance and response to infectious disease threats. Epidemics 2023:100685. [PMID: 37076350 PMCID: PMC10101508 DOI: 10.1016/j.epidem.2023.100685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023] Open
Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.
| | - Britta Lassmann
- Emerging Infections Task Force, European Society of Clinical Microbiology and Infectious Diseases
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK; School of Life Sciences, University of Sussex, Brighton, UK
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The added effect of non-pharmaceutical interventions and lifestyle behaviors on vaccine effectiveness against severe COVID-19 in Chile: a matched case-double control study. Vaccine 2023; 41:2947-2955. [PMID: 37024408 PMCID: PMC10067460 DOI: 10.1016/j.vaccine.2023.03.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023]
Abstract
Background All World Health Organization approved vaccines have demonstrated relatively high protection against moderate to severe COVID-19. Prospective vaccine effectiveness (VE) designs with first-hand data and population-based controls are nevertheless rare. Neighborhood compared to hospitalized controls, may differ in non-pharmaceutical interventions (NPI) compliance, which may influence VE results in real-world settings. We aimed to determine VE against COVID-19 intensive-care-unit (ICU) admission using hospital and community-matched controls in a prospective design. Methods We conducted a multicenter, observational study of matched cases and controls (1:3) in adults ≧18 from May to July 2021. For each case, a hospital control and two community controls were matched by age, gender, and hospital admission date or neighborhood of residence. Conditional logistic regression models were built, including interaction terms between NPIs, lifestyle behaviors, and vaccination status; the model’s β coefficients represent the added effect these terms had on COVID-19 VE. Results Cases and controls differed in several factors including education level, obesity prevalence, and behaviors such as compliance with routine vaccinations, use of facemasks, and routine handwashing. VE was 98·2% for full primary vaccination and 85·6% for partial vaccination when compared to community controls. VE tended to be higher when compared to community versus hospital controls, but the difference was not significant. A significant added effect to vaccination in reducing COVID-19 ICU admission was regular facemask use and VE was higher among individuals non-compliant with the national vaccine program, nor routine medical controls during the prior year. Conclusion VE against COVID-19 ICU admission in this stringent prospective case-double control study reached 98% two weeks after full primary vaccination, confirming the high effectiveness provided by earlier studies. Face mask use and hand washing were independent protective factors, the former adding additional benefit to VE. VE was significantly higher in subjects with increased risk behaviors.
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Rakhshan SA, Nejad MS, Zaj M, Ghane FH. Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19. Comput Biol Med 2023; 158:106817. [PMID: 36989749 PMCID: PMC10035804 DOI: 10.1016/j.compbiomed.2023.106817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 03/25/2023]
Abstract
It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system’s parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.
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Affiliation(s)
| | - Mahdi Soltani Nejad
- Department of Railway Engineering, Iran University of Science and Technology, Iran
| | - Marzie Zaj
- Department of Mathematics, Ferdowsi University of Mashhad, Iran
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Gao S, Shen M, Wang X, Wang J, Martcheva M, Rong L. A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US. J Theor Biol 2023; 565:111468. [PMID: 36940811 PMCID: PMC10027298 DOI: 10.1016/j.jtbi.2023.111468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 02/08/2023] [Accepted: 03/16/2023] [Indexed: 03/23/2023]
Abstract
COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.
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Affiliation(s)
- Shasha Gao
- School of Mathematics and Statistics, Jiangxi Normal University, Nanchang, 330000, China; Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America
| | - Mingwang Shen
- China-Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Xueying Wang
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99163, United States of America
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, United States of America
| | - Maia Martcheva
- Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America
| | - Libin Rong
- Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America.
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39
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Imai N, Rawson T, Knock ES, Sonabend R, Elmaci Y, Perez-Guzman PN, Whittles LK, Kanapram DT, Gaythorpe KAM, Hinsley W, Djaafara BA, Wang H, Fraser K, FitzJohn RG, Hogan AB, Doohan P, Ghani AC, Ferguson NM, Baguelin M, Cori A. Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study. Lancet Public Health 2023; 8:e174-e183. [PMID: 36774945 PMCID: PMC9910835 DOI: 10.1016/s2468-2667(22)00337-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 02/11/2023]
Abstract
BACKGROUND The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England. METHODS We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions. FINDINGS In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021. INTERPRETATION England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall. FUNDING UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU.
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Affiliation(s)
- Natsuko Imai
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Thomas Rawson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Edward S Knock
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK
| | - Raphael Sonabend
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Department of Computer Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany; Engineering Department, University of Cambridge, Cambridge, UK
| | - Yasin Elmaci
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Pablo N Perez-Guzman
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Lilith K Whittles
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Divya Thekke Kanapram
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Wes Hinsley
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Bimandra A Djaafara
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Haowei Wang
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Keith Fraser
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Richard G FitzJohn
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Alexandra B Hogan
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Patrick Doohan
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Azra C Ghani
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Neil M Ferguson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK
| | - Marc Baguelin
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anne Cori
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; National Institute for Health Research Health Protection Research Unit in Modelling Methodology, Imperial College London, UK Health Security Agency, London School of Hygiene & Tropical Medicine, London, UK.
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Cauchemez S, Bosetti P, Cowling BJ. Managing sources of error during pandemics. Science 2023; 379:437-439. [PMID: 36730404 DOI: 10.1126/science.add3173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic has highlighted important considerations for modeling future pandemics.
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Affiliation(s)
- Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Paolo Bosetti
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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41
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Lau JJ, Cheng SMS, Leung K, Lee CK, Hachim A, Tsang LCH, Yam KWH, Chaothai S, Kwan KKH, Chai ZYH, Lo THK, Mori M, Wu C, Valkenburg SA, Amarasinghe GK, Lau EHY, Hui DSC, Leung GM, Peiris M, Wu JT. Real-world COVID-19 vaccine effectiveness against the Omicron BA.2 variant in a SARS-CoV-2 infection-naive population. Nat Med 2023; 29:348-357. [PMID: 36652990 PMCID: PMC9941049 DOI: 10.1038/s41591-023-02219-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023]
Abstract
The SARS-CoV-2 Omicron variant has demonstrated enhanced transmissibility and escape of vaccine-derived immunity. Although first-generation vaccines remain effective against severe disease and death, robust evidence on vaccine effectiveness (VE) against all Omicron infections, irrespective of symptoms, remains sparse. We used a community-wide serosurvey with 5,310 subjects to estimate how vaccination histories modulated risk of infection in infection-naive Hong Kong during a large wave of Omicron BA.2 epidemic in January-July 2022. We estimated that Omicron infected 45% (41-48%) of the local population. Three and four doses of BNT162b2 or CoronaVac were effective against Omicron infection 7 days after vaccination (VE of 48% (95% credible interval 34-64%) and 69% (46-98%) for three and four doses of BNT162b2, respectively; VE of 30% (1-66%) and 56% (6-97%) for three and four doses of CoronaVac, respectively). At 100 days after immunization, VE waned to 26% (7-41%) and 35% (10-71%) for three and four doses of BNT162b2, and to 6% (0-29%) and 11% (0-54%) for three and four doses of CoronaVac. The rapid waning of VE against infection conferred by first-generation vaccines and an increasingly complex viral evolutionary landscape highlight the necessity for rapidly deploying updated vaccines followed by vigilant monitoring of VE.
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Affiliation(s)
- Jonathan J Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Samuel M S Cheng
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
| | - Cheuk Kwong Lee
- Hong Kong Red Cross Blood Transfusion Service, Hong Kong SAR, People's Republic of China
| | - Asmaa Hachim
- HKU-Pasteur Research Pole, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Leo C H Tsang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kenny W H Yam
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Sara Chaothai
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kelvin K H Kwan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Zacary Y H Chai
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tiffany H K Lo
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Masashi Mori
- Research Institute for Bioresources and Biotechnology, Ishikawa Prefectural University, Nonoichi, Japan
| | - Chao Wu
- Department of Pathology and Immunology, Washington University School of Medicine at St. Louis, St. Louis, MO, USA
| | - Sophie A Valkenburg
- HKU-Pasteur Research Pole, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
| | - Gaya K Amarasinghe
- Department of Pathology and Immunology, Washington University School of Medicine at St. Louis, St. Louis, MO, USA
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - David S C Hui
- Department of Medicine and Therapeutics and Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology and Infection, Hong Kong SAR, China
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China.
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- The University of Hong Kong - Shenzhen Hospital, Shenzhen, China.
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Wang A, Zhang X, Yan R, Bai D, He J. Evaluating the impact of multiple factors on the control of COVID-19 epidemic: A modelling analysis using India as a case study. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:6237-6272. [PMID: 37161105 DOI: 10.3934/mbe.2023269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The currently ongoing COVID-19 outbreak remains a global health concern. Understanding the transmission modes of COVID-19 can help develop more effective prevention and control strategies. In this study, we devise a two-strain nonlinear dynamical model with the purpose to shed light on the effect of multiple factors on the outbreak of the epidemic. Our targeted model incorporates the simultaneous transmission of the mutant strain and wild strain, environmental transmission and the implementation of vaccination, in the context of shortage of essential medical resources. By using the nonlinear least-square method, the model is validated based on the daily case data of the second COVID-19 wave in India, which has triggered a heavy load of confirmed cases. We present the formula for the effective reproduction number and give an estimate of it over the time. By conducting Latin Hyperbolic Sampling (LHS), evaluating the partial rank correlation coefficients (PRCCs) and other sensitivity analysis, we have found that increasing the transmission probability in contact with the mutant strain, the proportion of infecteds with mutant strain, the ratio of probability of the vaccinated individuals being infected, or the indirect transmission rate, all could aggravate the outbreak by raising the total number of deaths. We also found that increasing the recovery rate of those infecteds with mutant strain while decreasing their disease-induced death rate, or raising the vaccination rate, both could alleviate the outbreak by reducing the deaths. Our results demonstrate that reducing the prevalence of the mutant strain, improving the clearance of the virus in the environment, and strengthening the ability to treat infected individuals are critical to mitigate and control the spread of COVID-19, especially in the resource-constrained regions.
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Affiliation(s)
- Aili Wang
- School of Science, Xi'an University of Technology, Xi'an 710054, China
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Xueying Zhang
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Rong Yan
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Duo Bai
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
| | - Jingmin He
- School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Hill AL, Davis JT, Mu K, Xiong X, Pastore y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, España G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, Lessler J. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study. LANCET REGIONAL HEALTH. AMERICAS 2023; 17:100398. [PMID: 36437905 PMCID: PMC9679449 DOI: 10.1016/j.lana.2022.100398] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
Background The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding Various (see acknowledgments).
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Affiliation(s)
| | - Luke C. Mullany
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Emily Howerton
- The Pennsylvania State University, University Park, PA, USA
| | | | | | | | | | | | | | | | - J. Espino
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kaitlin Lovett
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Matt Kinsey
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Kate Tallaksen
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Shelby Wilson
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | - Lauren Shin
- Johns Hopkins University Applied Physics Laboratories Laurel, MD, USA
| | | | | | | | | | | | | | - Kunpeng Mu
- Northeastern University, Boston, MA, USA
| | | | | | | | | | | | | | | | - Bryan Lewis
- University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | | | | | - Stefan Hoops
- University of Virginia, Charlottesville, VA, USA
| | | | - Dustin Machi
- University of Virginia, Charlottesville, VA, USA
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Rajib Paul
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Daniel Janies
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | | | | | | | - Sen Pei
- Columbia University, New York, NY, USA
| | | | | | - Sean Cavany
- University of Notre Dame, Notre Dame, IN, USA
| | - Sean Moore
- University of Notre Dame, Notre Dame, IN, USA
| | | | - Jessica M. Healy
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B. Slayton
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael A. Johansson
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- CDC COVID-19 Response Team, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Katriona Shea
- The Pennsylvania State University, University Park, PA, USA
| | | | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Justin Lessler
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Zhu J, Wang Q, Huang M. Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19. Front Public Health 2023; 11:1129183. [PMID: 37168073 PMCID: PMC10166111 DOI: 10.3389/fpubh.2023.1129183] [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: 12/21/2022] [Accepted: 03/17/2023] [Indexed: 05/13/2023] Open
Abstract
The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.
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Nashebi R, Sari M, Kotil S. Using a real-world network to model the trade-off between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics. PeerJ 2022; 10:e14353. [PMID: 36540805 PMCID: PMC9760027 DOI: 10.7717/peerj.14353] [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: 04/18/2022] [Accepted: 10/17/2022] [Indexed: 12/23/2022] Open
Abstract
Background Human behaviour, economic activity, vaccination, and social distancing are inseparably entangled in epidemic management. This study aims to investigate the effects of various parameters such as stay-at-home restrictions, work hours, vaccination, and social distance on the containment of pandemics such as COVID-19. Methods To achieve this, we have developed an agent based model based on a time-dynamic graph with stochastic transmission events. The graph is constructed from a real-world social network. The edges of graph have been categorized into three categories: home, workplaces, and social environment. The conditions needed to mitigate the spread of wild-type COVID-19 and the delta variant have been analyzed. Our purposeful agent based model has carefully executed tens of thousands of individual-based simulations. We propose simple relationships for the trade-offs between effective reproduction number (R e), transmission rate, working hours, vaccination, and stay-at-home restrictions. Results We have found that the effect of a 13.6% increase in vaccination for wild-type (WT) COVID-19 is equivalent to reducing four hours of work or a one-day stay-at-home restriction. For the delta, 20.2% vaccination has the same effect. Also, since we can keep track of household and non-household infections, we observed that the change in household transmission rate does not significantly alter the R e. Household infections are not limited by transmission rate due to the high frequency of connections. For the specifications of COVID-19, the R e depends on the non-household transmissions rate. Conclusions Our findings highlight that decreasing working hours is the least effective among the non-pharmaceutical interventions. Our results suggest that policymakers decrease work-related activities as a last resort and should probably not do so when the effects are minimal, as shown. Furthermore, the enforcement of stay-at-home restrictions is moderately effective and can be used in conjunction with other measures if absolutely necessary.
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Affiliation(s)
- Ramin Nashebi
- Department of Mathematics, Yildiz Technical University, Istanbul, Turkey
| | - Murat Sari
- Department of Mathematics, Yildiz Technical University, Istanbul, Turkey,Department of Mathematics Engineering, Faculty of Science and Letters, Istanbul Technical University, Istanbul, Turkey
| | - Seyfullah Kotil
- Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey
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Ojo MM, Benson TO, Peter OJ, Goufo EFD. Nonlinear optimal control strategies for a mathematical model of COVID-19 and influenza co-infection. PHYSICA A 2022; 607:128173. [PMID: 36106051 PMCID: PMC9461290 DOI: 10.1016/j.physa.2022.128173] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 08/29/2022] [Indexed: 05/29/2023]
Abstract
Infectious diseases have remained one of humanity's biggest problems for decades. Multiple disease infections, in particular, have been shown to increase the difficulty of diagnosing and treating infected people, resulting in worsening human health. For example, the presence of influenza in the population is exacerbating the ongoing COVID-19 pandemic. We formulate and analyze a deterministic mathematical model that incorporates the biological dynamics of COVID-19 and influenza to effectively investigate the co-dynamics of the two diseases in the public. The existence and stability of the disease-free equilibrium of COVID-19-only and influenza-only sub-models are established by using their respective threshold quantities. The result shows that the COVID-19 free equilibrium is locally asymptotically stable when R C < 1 , whereas the influenza-only model, is locally asymptotically stable when R F < 1 . Furthermore, the existence of the endemic equilibria of the sub-models is examined while the conditions for the phenomenon of backward bifurcation are presented. A generalized analytical result of the COVID-19-influenza co-infection model is presented. We run a numerical simulation on the model without optimal control to see how competitive outcomes between-hosts and within-hosts affect disease co-dynamics. The findings established that disease competitive dynamics in the population are determined by transmission probabilities and threshold quantities. To obtain the optimal control problem, we extend the formulated model by including three time-dependent control functions. The maximum principle of Pontryagin was used to prove the existence of the optimal control problem and to derive the necessary conditions for optimum disease control. A numerical simulation was performed to demonstrate the impact of different combinations of control strategies on the infected population. The findings show that, while single and twofold control interventions can be used to reduce disease, the threefold control intervention, which incorporates all three controls, will be the most effective in reducing COVID-19 and influenza in the population.
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Affiliation(s)
- Mayowa M Ojo
- Thermo Fisher Scientific, Microbiology Division, Lenexa, KS, USA
- Department of Mathematical Sciences, University of South Africa, Florida, South Africa
| | - Temitope O Benson
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York, USA
| | - Olumuyiwa James Peter
- Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City, Ondo State, Nigeria
- Department of Epidemiology and Biostatistics, School of Public Health, University of Medical Sciences, Ondo City, Ondo State, Nigeria
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Huang J, Qian Y, Shen W, Chen Y, Zhao L, Cao S, Rich E, Pastor Ansah J, Wu F. Optimizing national border reopening policies in the COVID-19 pandemic: A modeling study. Front Public Health 2022; 10:979156. [PMID: 36530669 PMCID: PMC9749815 DOI: 10.3389/fpubh.2022.979156] [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: 06/27/2022] [Accepted: 10/18/2022] [Indexed: 12/05/2022] Open
Abstract
Objective After emergence of the COVID-19 pandemic and subsequent restrictions, countries worldwide have sought to reopen as quickly as possible. However, reopening involves the risk of epidemic rebound. In this study, we investigated the effective policy combination to ensure safe reopen. Methods On the basis of the classical SEIR epidemic model, we constructed a COVID-19 system dynamics model, incorporating vaccination, border screening, and fever clinic unit monitoring policies. The case of China was used to validate the model and then to test policy combinations for safe reopening. Findings Vaccination was found to be crucial for safe reopening. When the vaccination rate reached 60%, the daily number of newly confirmed COVID-19 cases began to drop significantly and stabilized around 1,400 [1/1,000,000]. The border screening policy alone only delayed epidemic spread for 8 days but did not reduce the number of infections. Fever clinic unit monitoring alone could reduce the peak of new confirmed cases by 44% when the case identification rate rose from 20 to 80%. When combining polices, once the vaccination rate reached 70%, daily new confirmed cases stabilized at 90 [0.64/1,000,000] with an 80% case identification rate at fever clinic units and border screening. For new variants, newly confirmed cases did not stabilize until the vaccination rate reached 90%. Conclusion High vaccination rate is the base for reopening. Vaccination passport is less effective compared with a strong primary care monitoring system for early detection and isolation of the infected cases.
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Affiliation(s)
- Jiaoling Huang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Qian
- Business School, University of Shanghai for Science and Technology, Shanghai, China,*Correspondence: Ying Qian
| | - Wuzhi Shen
- Faculty of Social Sciences, University of Bergen, Bergen, Norway
| | - Yong Chen
- Department of Profession Management, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Laijun Zhao
- Business School, University of Shanghai for Science and Technology, Shanghai, China
| | - Siqi Cao
- Business School, University of Shanghai for Science and Technology, Shanghai, China
| | - Eliot Rich
- School of Business, University at Albany, State University of New York, New York, NY, United States
| | - John Pastor Ansah
- Case Western Reserve University, Center for Community Health Integration, Duke-NUS Medical School, Singapore, Singapore
| | - Fan Wu
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China,Fan Wu
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Wang Y, Sun K, Feng Z, Yi L, Wu Y, Liu H, Wang Q, Ajelli M, Viboud C, Yu H. Assessing the feasibility of sustaining SARS-CoV-2 local containment in China in the era of highly transmissible variants. BMC Med 2022; 20:442. [PMID: 36380354 PMCID: PMC9666984 DOI: 10.1186/s12916-022-02640-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 10/26/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The SARS-CoV-2 containment strategy has been successful in mainland China prior to the emergence of Omicron. However, in the era of highly transmissible variants, whether it is possible for China to sustain a local containment policy and under what conditions China could transition away from it are of paramount importance at the current stage of the pandemic. METHODS We developed a spatially structured, fully stochastic, individual-based SARS-CoV-2 transmission model to evaluate the feasibility of sustaining SARS-CoV-2 local containment in mainland China considering the Omicron variants, China's current immunization level, and nonpharmaceutical interventions (NPIs). We also built a statistical model to estimate the overall disease burden under various hypothetical mitigation scenarios. RESULTS We found that due to high transmissibility, neither Omicron BA.1 nor BA.2 could be contained by China's pre-Omicron NPI strategies which were successful prior to the emergence of the Omicron variants. However, increased intervention intensity, such as enhanced population mobility restrictions and multi-round mass testing, could lead to containment success. We estimated that an acute Omicron epidemic wave in mainland China would result in significant number of deaths if China were to reopen under current vaccine coverage with no antiviral uptake, while increasing vaccination coverage and antiviral uptake could substantially reduce the disease burden. CONCLUSIONS As China's current vaccination has yet to reach high coverage in older populations, NPIs remain essential tools to maintain low levels of infection while building up protective population immunity, ensuring a smooth transition out of the pandemic phase while minimizing the overall disease burden.
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Affiliation(s)
- Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Zhaomin Feng
- Beijing Center for Disease Prevention and Control (CDC), Beijing, China
| | - Lan Yi
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Yanpeng Wu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Quanyi Wang
- Beijing Center for Disease Prevention and Control (CDC), Beijing, China
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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Eales O, Wang H, Haw D, Ainslie KEC, Walters CE, Atchison C, Cooke G, Barclay W, Ward H, Darzi A, Ashby D, Donnelly CA, Elliott P, Riley S. Trends in SARS-CoV-2 infection prevalence during England's roadmap out of lockdown, January to July 2021. PLoS Comput Biol 2022; 18:e1010724. [PMID: 36417468 PMCID: PMC9728904 DOI: 10.1371/journal.pcbi.1010724] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/07/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Following rapidly rising COVID-19 case numbers, England entered a national lockdown on 6 January 2021, with staged relaxations of restrictions from 8 March 2021 onwards. AIM We characterise how the lockdown and subsequent easing of restrictions affected trends in SARS-CoV-2 infection prevalence. METHODS On average, risk of infection is proportional to infection prevalence. The REal-time Assessment of Community Transmission-1 (REACT-1) study is a repeat cross-sectional study of over 98,000 people every round (rounds approximately monthly) that estimates infection prevalence in England. We used Bayesian P-splines to estimate prevalence and the time-varying reproduction number (Rt) nationally, regionally and by age group from round 8 (beginning 6 January 2021) to round 13 (ending 12 July 2021) of REACT-1. As a comparator, a separate segmented-exponential model was used to quantify the impact on Rt of each relaxation of restrictions. RESULTS Following an initial plateau of 1.54% until mid-January, infection prevalence decreased until 13 May when it reached a minimum of 0.09%, before increasing until the end of the study to 0.76%. Following the first easing of restrictions, which included schools reopening, the reproduction number Rt increased by 82% (55%, 108%), but then decreased by 61% (82%, 53%) at the second easing of restrictions, which was timed to match the Easter school holidays. Following further relaxations of restrictions, the observed Rt increased steadily, though the increase due to these restrictions being relaxed was offset by the effects of vaccination and also affected by the rapid rise of Delta. There was a high degree of synchrony in the temporal patterns of prevalence between regions and age groups. CONCLUSION High-resolution prevalence data fitted to P-splines allowed us to show that the lockdown was effective at reducing risk of infection with school holidays/closures playing a significant part.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Haowei Wang
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - David Haw
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Kylie E. C. Ainslie
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Caroline E. Walters
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Christina Atchison
- School of Public Health, Imperial College London, London, United Kingdom
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Helen Ward
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
- Institute of Global Health Innovation at Imperial College London, London, United Kingdom
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Health Data Research (HDR) UK London at Imperial College, London, United Kingdom
- UK Dementia Research Institute at Imperial College, London, United Kingdom
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
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50
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Panovska-Griffiths J, Swallow B, Hinch R, Cohen J, Rosenfeld K, Stuart RM, Ferretti L, Di Lauro F, Wymant C, Izzo A, Waites W, Viner R, Bonell C, Fraser C, Klein D, Kerr CC. Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35965458 DOI: 10.6084/m9.figshare.c.6070427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The English SARS-CoV-2 epidemic has been affected by the emergence of new viral variants such as B.1.177, Alpha and Delta, and changing restrictions. We used statistical models and the agent-based model Covasim, in June 2021, to estimate B.1.177 to be 20% more transmissible than the wild type, Alpha to be 50-80% more transmissible than B.1.177 and Delta to be 65-90% more transmissible than Alpha. Using these estimates in Covasim (calibrated 1 September 2020 to 20 June 2021), in June 2021, we found that due to the high transmissibility of Delta, resurgence in infections driven by the Delta variant would not be prevented, but would be strongly reduced by delaying the relaxation of restrictions by one month and with continued vaccination. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- J Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
- The Queen's College, University of Oxford, Oxford
| | - B Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - R Hinch
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - J Cohen
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - K Rosenfeld
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - R M Stuart
- University of Copenhagen, Copenhagen, Denmark
| | - L Ferretti
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - F Di Lauro
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - C Wymant
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - A Izzo
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - W Waites
- Department of Public Health, Environments & Society, London School of Hygiene and Tropical Medicine, London, UK
- Department of Computer and Information Sciences, University of Strathclyde, G1 1XH Glasgow, UK
| | - R Viner
- UCL Great Ormond St. Institute of Child Health, University College London, London, UK
| | - C Bonell
- Department of Public Health, Environments & Society, London School of Hygiene and Tropical Medicine, London, UK
| | - C Fraser
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - D Klein
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - C C Kerr
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
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