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González-Parra G, Mahmud MS, Kadelka C. Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models. Infect Dis Model 2024; 9:1057-1080. [PMID: 38988830 PMCID: PMC11233876 DOI: 10.1016/j.idm.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 07/12/2024] Open
Abstract
As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
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Affiliation(s)
- Gilberto González-Parra
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain
- Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA
| | - Md Shahriar Mahmud
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
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2
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Soe P, Sadarangani M, Naus M, Muller MP, Vanderkooi OG, Kellner JD, Top KA, Wong H, Isenor JE, Marty K, De Serres G, Valiquette L, McGeer A, Bettinger JA. Impact of recruitment strategies on individual participation practices in the Canadian National Vaccine Safety Network: prospective cohort study. Front Public Health 2024; 12:1385426. [PMID: 39188790 PMCID: PMC11345188 DOI: 10.3389/fpubh.2024.1385426] [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: 03/12/2024] [Accepted: 07/29/2024] [Indexed: 08/28/2024] Open
Abstract
Background The Canadian National Vaccine Safety (CANVAS) network conducted a multi-center, prospective vaccine safety study to collect safety data after dose 1 and 2 of COVID-19 vaccines and follow up safety information 7 months after dose 1. Objective This study aimed to describe and evaluate the recruitment methods used by CANVAS and the retention of participants by each modality. Methods CANVAS deployed a multi-pronged recruitment approach to reach a larger sample, without in-person recruitment. Three primary recruitment strategies were used: passive recruitment, technology-assisted electronic invitation through the vaccine booking system (auto-invitation), or auto-registration through the vaccine registries (auto-enrollment). Results Between December 2020 and April 2022, approximately 1.3 million vaccinated adults either self-enrolled or were auto-enrolled in CANVAS, representing about 5% of the vaccinated adult Canadian population. Approximately 1 million participants were auto-enrolled, 300,000 were recruited by auto-invitation, and 5,000 via passive recruitment. Overall survey completion rates for dose 1, dose 2 and the 7-month follow-up surveys were 51.7% (681,198 of 1,318,838), 54.3% (369,552 of 681,198), and 66.4% (452,076 of 681,198), respectively. Completion rates were lower among auto-enrolled participants compared to passively recruited or auto-invited participants who self-enrolled. However, auto-enrolled samples were much larger, which offset the lower completion rates. Conclusion Our data suggest that auto-enrollment provided an opportunity to reach and retain a larger number of individuals in the study compared to other recruitment modalities.
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Affiliation(s)
- Phyumar Soe
- Vaccine Evaluation Center, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Manish Sadarangani
- Vaccine Evaluation Center, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Monika Naus
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- BC Center for Disease Control, Vancouver, BC, Canada
| | | | - Otto G. Vanderkooi
- Department of Pediatrics and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - James D. Kellner
- Department of Pediatrics and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Karina A. Top
- Department of Pediatrics, Canadian Center for Vaccinology, IWK Health, Dalhousie University, Halifax, NS, Canada
- Department of Pediatrics, Dalhousie University, Halifax, NS, Canada
| | - Hubert Wong
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Jennifer E. Isenor
- Department of Pediatrics, Canadian Center for Vaccinology, IWK Health, Dalhousie University, Halifax, NS, Canada
- Department of Pediatrics, Dalhousie University, Halifax, NS, Canada
| | - Kimberly Marty
- Vaccine Evaluation Center, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | | | - Louis Valiquette
- Department of Microbiology and Infectious Diseases, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Allison McGeer
- Department of Microbiology, Sinai Health System, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Julie A. Bettinger
- Vaccine Evaluation Center, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
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Sibanda B, Haryanto B. Assessing the Impact of COVID-19 Vaccination Programs on the Reduction of COVID-19 Cases: A Systematic Literature Review. Ann Glob Health 2024; 90:45. [PMID: 39070079 PMCID: PMC11276414 DOI: 10.5334/aogh.4484] [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: 05/24/2024] [Accepted: 06/29/2024] [Indexed: 07/30/2024] Open
Abstract
Background: Vaccination is the most effective way to prevent serious illness and death from COVID-19 among the various preventive interventions available. Objective: This review aimed to assess the actual effectiveness of COVID-19 vaccines in curbing the transmission and incidence of COVID-19 cases, to examine the role of different vaccine types in controlling the COVID-19 pandemic, as well as to identify the key factors influencing the efficacy of COVID-19 vaccines in containing the spread of the virus. Methods: The suggestions made by the PRISMA Framework were adhered to. To find the publications for the 2020-2023 timeframe, searches were performed through the PubMed databases, EMBASE, Scopus, and ProQuest. For the review, 17 reports satisfied the inclusion requirements. Ad26.CoV2.S or ChAdOx1-S, Gam-COVID-Vac(GAM), Sinovac Life Sciences Co., Oxford-AstraZeneca, Pfizer-BioNTech, and viral vector vaccines are among the vaccines that act on various variations. They dealt with the Delta, B.1.1.519, Omicron, and Alpha variations. Findings: Vaccinations against various Variants resulted in fewer COVID-19 infections, fewer deaths, and fewer hospitalizations. The emergency of the Delta variant, persons over 60, and vaccine hesitancy were the main issues affecting the effectiveness of COVID-19 vaccinations in containing the virus's spread. Conclusion: The collective evidence strongly supports the conclusion that COVID-19 vaccination plays a crucial role in mitigating the spread of the virus and reducing the severity of illness among those who contract the virus.
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Affiliation(s)
- Brightwell Sibanda
- Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia
| | - Budi Haryanto
- Department of Environmental Health, Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia
- Research Center for Climate Change, Universitas Indonesia, Depok, West Java, Indonesia
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Gonzalez-Parra G, Mahmud MS, Kadelka C. Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303726. [PMID: 38496570 PMCID: PMC10942533 DOI: 10.1101/2024.03.04.24303726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
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Affiliation(s)
- Gilberto Gonzalez-Parra
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain
- Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA
| | - Md Shahriar Mahmud
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
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Ganser I, Buckeridge DL, Heffernan J, Prague M, Thiébaut R. Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study. Epidemics 2024; 46:100744. [PMID: 38324970 DOI: 10.1016/j.epidem.2024.100744] [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: 08/07/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. METHODS To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. RESULTS The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. CONCLUSION Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.
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Affiliation(s)
- Iris Ganser
- Univ. Bordeaux, Inserm, BPH Research Center, SISTM Team, UMR 1219 Bordeaux, France; McGill Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - David L Buckeridge
- McGill Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - Jane Heffernan
- Mathematics & Statistics, Centre for Disease Modelling, York University, Toronto, Ontario, Canada
| | - Mélanie Prague
- Univ. Bordeaux, Inserm, BPH Research Center, SISTM Team, UMR 1219 Bordeaux, France; Inria, Inria Bordeaux - Sud-Ouest, Talence, France; Vaccine Research Institute, F-94010 Creteil, France
| | - Rodolphe Thiébaut
- Univ. Bordeaux, Inserm, BPH Research Center, SISTM Team, UMR 1219 Bordeaux, France; Inria, Inria Bordeaux - Sud-Ouest, Talence, France; Vaccine Research Institute, F-94010 Creteil, France; Bordeaux University Hospital, Medical Information Department, Bordeaux, France.
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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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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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Akanteva A, Dick DW, Amiraslani S, Heffernan JM. Canadian Covid-19 pandemic public health mitigation measures at the province level. Sci Data 2023; 10:882. [PMID: 38066033 PMCID: PMC10709578 DOI: 10.1038/s41597-023-02759-y] [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: 04/04/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
The Covid-19 pandemic has prompted governments across the world to enforce a range of public health interventions. We introduce the Covid-19 Policy Response Canadian tracker (CPRCT) database that tracks and records implemented public health measures in every province and territory in Canada. The implementations are recorded on a four-level ordinal scale (0-3) for three domains, (Schools, Work, and Other), capturing differences in degree of response. The data-set allows the exploration of the effects of public health mitigation on the spread of Covid-19, as well as provides a near-real-time record in an accessible format that is useful for a diverse range of modeling and research questions.
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Affiliation(s)
- Anna Akanteva
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada
| | - David W Dick
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada.
| | - Shirin Amiraslani
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada
| | - Jane M Heffernan
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada.
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Molla J, Farhang-Sardroodi S, Moyles IR, Heffernan JM. Pharmaceutical and non-pharmaceutical interventions for controlling the COVID-19 pandemic. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230621. [PMID: 38126062 PMCID: PMC10731327 DOI: 10.1098/rsos.230621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Disease spread can be affected by pharmaceutical interventions (such as vaccination) and non-pharmaceutical interventions (such as physical distancing, mask-wearing and contact tracing). Understanding the relationship between disease dynamics and human behaviour is a significant factor to controlling infections. In this work, we propose a compartmental epidemiological model for studying how the infection dynamics of COVID-19 evolves for people with different levels of social distancing, natural immunity and vaccine-induced immunity. Our model recreates the transmission dynamics of COVID-19 in Ontario up to December 2021. Our results indicate that people change their behaviour based on the disease dynamics and mitigation measures. Specifically, they adopt more protective behaviour when mandated social distancing measures are in effect, typically concurrent with a high number of infections. They reduce protective behaviour when vaccination coverage is high or when mandated contact reduction measures are relaxed, typically concurrent with a reduction of infections. We demonstrate that waning of infection and vaccine-induced immunity are important for reproducing disease transmission in autumn 2021.
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Affiliation(s)
- Jeta Molla
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
| | - Suzan Farhang-Sardroodi
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Iain R. Moyles
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
| | - Jane M. Heffernan
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
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Aogo RA, Zambrana JV, Sanchez N, Ojeda S, Kuan G, Balmaseda A, Gordon A, Harris E, Katzelnick LC. Effects of boosting and waning in highly exposed populations on dengue epidemic dynamics. Sci Transl Med 2023; 15:eadi1734. [PMID: 37967199 PMCID: PMC11001200 DOI: 10.1126/scitranslmed.adi1734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/13/2023] [Indexed: 11/17/2023]
Abstract
Sequential infection with multiple dengue virus (DENV) serotypes is thought to induce enduring protection against dengue disease. However, long-term antibody waning has been observed after repeated DENV infection. Here, we provide evidence that highly immune Nicaraguan children and adults (n = 4478) experience boosting and waning of antibodies during and after major Zika and dengue epidemics. We develop a susceptible-infected-recovered-susceptible (SIRS-type) model that tracks immunity by titer rather than number of infections to show that boosts in highly immune individuals can contribute to herd immunity, delaying their susceptibility to transmissible infection. In contrast, our model of lifelong immunity in highly immune individuals, as previously assumed, results in complete disease eradication after introduction. Periodic epidemics under this scenario can only be sustained with a constant influx of infected individuals into the population or a high basic reproductive number. We also find that Zika virus infection can boost DENV immunity and produce delays and then surges in dengue epidemics, as observed with real epidemiological data. This work provides insight into factors shaping periodicity in dengue incidence and may inform vaccine efforts to maintain population immunity.
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Affiliation(s)
- Rosemary A. Aogo
- Viral Epidemiology and Immunity Unit, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-3203, USA
| | - Jose Victor Zambrana
- Sustainable Sciences Institute, Managua, 14007, Nicaragua
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Nery Sanchez
- Sustainable Sciences Institute, Managua, 14007, Nicaragua
| | - Sergio Ojeda
- Sustainable Sciences Institute, Managua, 14007, Nicaragua
| | - Guillermina Kuan
- Sustainable Sciences Institute, Managua, 14007, Nicaragua
- Centro de Salud Sócrates Flores Vivas, Ministry of Health, Managua, 12014, Nicaragua
| | - Angel Balmaseda
- Sustainable Sciences Institute, Managua, 14007, Nicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, 16064, Nicaragua
| | - Aubree Gordon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Eva Harris
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA, 94720-3370, USA
| | - Leah C. Katzelnick
- Viral Epidemiology and Immunity Unit, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-3203, USA
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11
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Calvetti D, Somersalo E. Post-pandemic modeling of COVID-19: Waning immunity determines recurrence frequency. Math Biosci 2023; 365:109067. [PMID: 37708989 DOI: 10.1016/j.mbs.2023.109067] [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: 06/09/2023] [Accepted: 08/16/2023] [Indexed: 09/16/2023]
Abstract
There are many factors in the current phase of the COVID-19 pandemic that signal the need for new modeling ideas. In fact, most traditional infectious disease models do not address adequately the waning immunity, in particular as new emerging variants have been able to break the immune shield acquired either by previous infection by a different strain of the virus, or by inoculation of vaccines not effective for the current variant. Furthermore, in a post-pandemic landscape in which reporting is no longer a default, it is impossible to have reliable quantitative data at the population level. Our contribution to COVID-19 post-pandemic modeling is a simple mathematical predictive model along the age-distributed population framework, that can take into account the waning immunity in a transparent and easily controllable manner. Numerical simulations show that under static conditions, the model produces periodic solutions that are qualitatively similar to the reported data, with the period determined by the immunity waning profile. Evidence from the mathematical model indicates that the immunity dynamics is the main factor in the recurrence of infection spikes, however, irregular perturbation of the transmission rate, due to either mutations of the pathogen or human behavior, may result in suppression of recurrent spikes, and irregular time intervals between consecutive peaks. The spike amplitudes are sensitive to the transmission rate and vaccination strategies, but also to the skewness of the profile describing the waning immunity, suggesting that these factors should be taken into consideration when making predictions about future outbreaks.
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Affiliation(s)
- D Calvetti
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, 30100 Euclid Avenue, Cleveland, OH 44106, United States of America
| | - E Somersalo
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, 30100 Euclid Avenue, Cleveland, OH 44106, United States of America.
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Moyles IR, Korosec CS, Heffernan JM. Determination of significant immunological timescales from mRNA-LNP-based vaccines in humans. J Math Biol 2023; 86:86. [PMID: 37121986 PMCID: PMC10149047 DOI: 10.1007/s00285-023-01919-3] [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: 07/25/2022] [Revised: 03/10/2023] [Accepted: 04/07/2023] [Indexed: 05/02/2023]
Abstract
A compartment model for an in-host liquid nanoparticle delivered mRNA vaccine is presented. Through non-dimensionalisation, five timescales are identified that dictate the lifetime of the vaccine in-host: decay of interferon gamma, antibody priming, autocatalytic growth, antibody peak and decay, and interleukin cessation. Through asymptotic analysis we are able to obtain semi-analytical solutions in each of the time regimes which allows us to predict maximal concentrations and better understand parameter dependence in the model. We compare our model to 22 data sets for the BNT162b2 and mRNA-1273 mRNA vaccines demonstrating good agreement. Using our analysis, we estimate the values for each of the five timescales in each data set and predict maximal concentrations of plasma B-cells, antibody, and interleukin. Through our comparison, we do not observe any discernible differences between vaccine candidates and sex. However, we do identify an age dependence, specifically that vaccine activation takes longer and that peak antibody occurs sooner in patients aged 55 and greater.
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Affiliation(s)
- Iain R Moyles
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada.
| | - Chapin S Korosec
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
| | - Jane M Heffernan
- Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, ON, M3J1P3, Canada
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13
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Wang X, Liang Y, Li J, Liu M. Modeling COVID-19 transmission dynamics incorporating media coverage and vaccination. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10392-10403. [PMID: 37322938 DOI: 10.3934/mbe.2023456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic has caused widespread concern around the world. In order to study the impact of media coverage and vaccination on the spread of COVID-19, we establish an SVEAIQR infectious disease model, and fit the important parameters such as transmission rate, isolation rate and vaccine efficiency based on the data from Shanghai Municipal Health Commission and the National Health Commission of the People's Republic of China. Meanwhile, the control reproduction number and the final size are derived. Moreover, through sensitivity analysis by PRCC (partial rank correlation coefficient), we discuss the effects of both the behavior change constant $ k $ according to media coverage and the vaccine efficiency $ \varepsilon $ on the transmission of COVID-19. Numerical explorations of the model suggest that during the outbreak of the epidemic, media coverage can reduce the final size by about 0.26 times. Besides that, comparing with $ 50\% $ vaccine efficiency, when the vaccine efficiency reaches $ 90\% $, the peak value of infected people decreases by about 0.07 times. In addition, we simulate the impact of media coverage on the number of infected people in the case of vaccination or non-vaccination. Accordingly, the management departments should pay attention to the impact of vaccination and media coverage.
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Affiliation(s)
- Xiaojing Wang
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Yu Liang
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Jiahui Li
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Maoxing Liu
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
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14
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Are EB, Song Y, Stockdale JE, Tupper P, Colijn C. COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations. J Theor Biol 2023; 559:111368. [PMID: 36436733 PMCID: PMC9686052 DOI: 10.1016/j.jtbi.2022.111368] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence of new variants will shape the long-term burden and dynamics of COVID-19. We explore the transition to the endemic state, and the endemic incidence in British Columbia (BC), Canada and South Africa (SA), to compare low and high vaccination coverage settings with differing public health policies, using a combination of modelling approaches. We compare reopening (relaxation of public health measures) gradually and rapidly as well as at different vaccination levels. We examine how the eventual endemic state depends on the duration of immunity, the rate of importations, the efficacy of vaccines and the transmissibility. These depend on the evolution of the virus, which continues to undergo selection. Slower reopening leads to a lower peak level of incidence and fewer overall infections in the wave following reopening: as much as a 60% lower peak and a 10% lower total in some illustrative simulations; under realistic parameters, reopening when 70% of the population is vaccinated leads to a large resurgence in cases. The long-term endemic behaviour may stabilize as late as January 2023, with further waves of high incidence occurring depending on the transmissibility of the prevalent variant, duration of immunity, and antigenic drift. We find that long term endemic levels are not necessarily lower than current pandemic levels: in a population of 100,000 with representative parameter settings (Reproduction number 5, 1-year duration of immunity, vaccine efficacy at 80% and importations at 3 cases per 100K per day) there are over 100 daily incident cases in the model. Predicted prevalence at endemicity has increased more than twofold after the emergence and spread of Omicron. The consequent burden on health care systems depends on the severity of infection in immunized or previously infected individuals.
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Affiliation(s)
- Elisha B Are
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
| | - Yexuan Song
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Jessica E Stockdale
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Paul Tupper
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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15
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Safdar S, Ngonghala CN, Gumel AB. Mathematical assessment of the role of waning and boosting immunity against the BA.1 Omicron variant in the United States. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:179-212. [PMID: 36650762 DOI: 10.3934/mbe.2023009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Three safe and effective vaccines against SARS-CoV-2 have played a major role in combating COVID-19 in the United States. However, the effectiveness of these vaccines and vaccination programs has been challenged by the emergence of new SARS-CoV-2 variants of concern. A new mathematical model is formulated to assess the impact of waning and boosting of immunity against the Omicron variant in the United States. To account for gradual waning of vaccine-derived immunity, we considered three vaccination classes that represent high, moderate and low levels of immunity. We showed that the disease-free equilibrium of the model is globally-asymptotically, for two special cases, if the associated reproduction number is less than unity. Simulations of the model showed that vaccine-derived herd immunity can be achieved in the United States via a vaccination-boosting strategy which entails fully vaccinating at least 59% of the susceptible populace followed by the boosting of about 72% of the fully-vaccinated individuals whose vaccine-derived immunity has waned to moderate or low level. In the absence of boosting, waning of immunity only causes a marginal increase in the average number of new cases at the peak of the pandemic, while boosting at baseline could result in a dramatic reduction in the average number of new daily cases at the peak. Specifically, for the fast immunity waning scenario (where both vaccine-derived and natural immunity are assumed to wane within three months), boosting vaccine-derived immunity at baseline reduces the average number of daily cases at the peak by about 90% (in comparison to the corresponding scenario without boosting of the vaccine-derived immunity), whereas boosting of natural immunity (at baseline) only reduced the corresponding peak daily cases (in comparison to the corresponding scenario without boosting of natural immunity) by approximately 62%. Furthermore, boosting of vaccine-derived immunity is more beneficial (in reducing the burden of the pandemic) than boosting of natural immunity. Finally, boosting vaccine-derived immunity increased the prospects of altering the trajectory of COVID-19 from persistence to possible elimination.
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Affiliation(s)
- Salman Safdar
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Calistus N Ngonghala
- Department of Mathematics, University of Florida, Gainesville, FL 32611, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA
| | - Abba B Gumel
- Department of Mathematics, University of Maryland College Park, MD 20742, USA
- Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0002, South Africa
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Korosec CS, Farhang-Sardroodi S, Dick DW, Gholami S, Ghaemi MS, Moyles IR, Craig M, Ooi HK, Heffernan JM. Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex. Sci Rep 2022; 12:21232. [PMID: 36481777 PMCID: PMC9732004 DOI: 10.1038/s41598-022-25134-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
The lipid nanoparticle (LNP)-formulated mRNA vaccines BNT162b2 and mRNA-1273 are a widely adopted multi vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogenicity of the vaccine, albeit within a limited study time frame. Here, we use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data from 2020 to September 2021, to project a longer term understanding of immunity as a function of vaccine type, dosage amount, age, and sex. We estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals losing more than 99% humoral immunity relative to peak immunity by 8 months following the second dose. We predict that within an 8 month period following dose two (corresponding to the original CDC time-frame for administration of a third dose), there exists a period of time longer than 1 month where an individual has lost more than 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by 8 months following dose two we predict that individuals aged 18-55 have a four-fold humoral advantage compared to aged 56-70 and 70+ individuals. We find that sex has little effect on the immune response and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays at a substantially slower rate relative to peak immunity gained compared to two standard doses of either mRNA-1273 or BNT162b2. Our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies.
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Affiliation(s)
- Chapin S Korosec
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada.
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada.
| | - Suzan Farhang-Sardroodi
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
- Department of Mathematics, University of Manitoba, 186 Dysart Road, Winnipeg, MB, R3T 2N2, Canada
| | - David W Dick
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
| | - Sameneh Gholami
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
| | - Mohammad Sajjad Ghaemi
- Digital Technologies Research Centre, National Research Council Canada, 222 College Street, Toronto, ON, M5T 3J1, Canada
| | - Iain R Moyles
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal & Sainte-Justine University Hospital Research Centre, 3175, ch. Côte Sainte-Catherine, Montréal, QC, H3T 1C5, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, 222 College Street, Toronto, ON, M5T 3J1, Canada
| | - Jane M Heffernan
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada.
- Centre for Disease Modelling, Mathematics and Statistics, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada.
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Caetano C, Morgado ML, Patrício P, Leite A, Machado A, Torres A, Pereira JF, Namorado S, Sottomayor A, Peralta-Santos A, Nunes B. Measuring the impact of COVID-19 vaccination and immunity waning: A modelling study for Portugal. Vaccine 2022; 40:7115-7121. [PMID: 36404429 PMCID: PMC9576223 DOI: 10.1016/j.vaccine.2022.10.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/30/2022] [Accepted: 10/07/2022] [Indexed: 11/05/2022]
Abstract
Vaccination strategies to control COVID-19 have been ongoing worldwide since the end of 2020. Understanding their possible effect is key to prevent future disease spread. Using a modelling approach, this study intends to measure the impact of the COVID-19 Portuguese vaccination strategy on the effective reproduction number and explore three scenarios for vaccine effectiveness waning. Namely, the no-immunity-loss, 1-year and 3-years of immunity duration scenarios. We adapted an age-structured SEIR deterministic model and used Portuguese hospitalisation data for the model calibration. Results show that, although the Portuguese vaccination plan had a substantial impact in reducing overall transmission, it might not be sufficient to control disease spread. A significant vaccination coverage of those above 5 years old, a vaccine effectiveness against disease of at least 80% and softer non-pharmaceutical interventions (NPIs), such as mask usage and social distancing, would be necessary to control disease spread in the worst scenario considered. The immunity duration scenario of 1-year displays a resurgence of COVID-19 hospitalisations by the end of 2021, the same is observed in 3-year scenario although with a lower magnitude. The no-immunity-loss scenario presents a low increase in hospitalisations. In both the 1-year and 3-year scenarios, a vaccination boost of those above 65 years old would result in a 53% and 38% peak reduction of non-ICU hospitalisations, respectively. These results suggest that NPIs should not be fully phased-out but instead be combined with a fast booster vaccination strategy to reduce healthcare burden.
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Affiliation(s)
- Constantino Caetano
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Portugal.
| | - Maria Luísa Morgado
- Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, Portugal; Department of Mathematics, University of Trás-os-Montes e Alto Doutor (UTAD), Portugal
| | - Paula Patrício
- Center for Mathematics and Applications (NovaMath), FCT NOVA and Department of Mathematics, Nova School of Science and Technology, Universidade NOVA de Lisboa, Quinta da Torre, Caparica, Portugal
| | - Andreia Leite
- NOVA National School of Public Health, Public Health Research Center, Universidade NOVA de Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Portugal
| | - Ausenda Machado
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; NOVA National School of Public Health, Public Health Research Center, Universidade NOVA de Lisboa, Portugal
| | - André Torres
- NOVA National School of Public Health, Public Health Research Center, Universidade NOVA de Lisboa, Portugal
| | - João Freitas Pereira
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; Department of Mathematics, University of Trás-os-Montes e Alto Doutor (UTAD), Portugal
| | - Sónia Namorado
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; NOVA National School of Public Health, Public Health Research Center, Universidade NOVA de Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Portugal
| | - Ana Sottomayor
- Direção de Serviços de Informação e Análise, Direção Geral da Saúde, Lisboa, Portugal
| | - André Peralta-Santos
- Direção de Serviços de Informação e Análise, Direção Geral da Saúde, Lisboa, Portugal
| | - Baltazar Nunes
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; NOVA National School of Public Health, Public Health Research Center, Universidade NOVA de Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Portugal
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