1
|
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.
Collapse
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
| |
Collapse
|
2
|
Salehi M, Alavi Darazam I, Nematollahi A, Alimohammadi M, Pouya S, Alimohammadi R, Khajavirad N, Porgoo M, Sedghi M, Mahdi Sepahi M, Azimi M, Hosseini H, Mahmoud Hashemi S, Dehghanizadeh S, Khoddami V. Safety and immunogenicity of COReNAPCIN, a SARS-CoV-2 mRNA vaccine, as a fourth heterologous booster in healthy Iranian adults: A double-blind, randomized, placebo-controlled, phase 1 clinical trial with a six-month follow-up. Int Immunopharmacol 2024; 134:112192. [PMID: 38761778 DOI: 10.1016/j.intimp.2024.112192] [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: 01/25/2024] [Revised: 04/17/2024] [Accepted: 04/29/2024] [Indexed: 05/20/2024]
Abstract
The recurrent COVID-19 infection, despite global vaccination, highlights the need for booster doses. A heterologous booster has been suggested to enhance immunity and protection against emerging variants of concern of the SARS-CoV-2 virus. In this report, we aimed to assess the safety, and immunogenicity of COReNAPCIN, as a fourth booster dose after three doses of inactivated vaccines. METHODS The study was conducted as a double-blind, randomized, placebo-controlled phase 1 clinical trial of the mRNA-based vaccine candidate, COReNAPCIN. The vaccine was injected as a heterologous booster in healthy Iranian adults aged 18-50 who had previously received three doses of inactivated SARS-CoV-2 vaccines. In the study, 30 participants were randomly assigned to receive either COReNAPCIN in two different doses (25 µg and 50 µg) or placebo. The vaccine candidate contained mRNA encoding the complete sequence of the pre-fusion stabilized Spike protein of SARS-CoV-2, formulated within lipid nanoparticles. The primary endpoint was safety and the secondary objective was humoral immunogenicity until 6 months post-vaccination. The cellular immunogenicity was pursued as an exploratory outcome. RESULTS COReNAPCIN was well tolerated in vaccinated individuals in both doses with no life-threatening or other serious adverse events. The most noticeable solicited adverse events were pain at the site of injection, fatigue and myalgia. Regarding the immunogenicity, despite the seroprevalence of SARS-CoV-2 antibodies due to the vaccination history for all and previous SARS-CoV-2 infection for some participants, the recipients of 25 and 50 µg COReNAPCIN, two weeks post-vaccination, showed 6·6 and 8·1 fold increase in the level of anti-RBD, and 11·5 and 21·7 fold increase in the level of anti-spike antibody, respectively. The geometric mean virus neutralizing titers reached 10.2 fold in the 25 µg group and 8.4 fold in 50 µg group of pre-boost levels. After 6 months, the measured anti-spike antibody concentration still maintains a geometric mean fold rise of 2.8 and 6.3, comparing the baseline levels in 25 and 50 µg groups, respectively. Additionally, the significant increase in the spike-specific IFN-ϒ T-cell response upon vaccination underscores the activation of cellular immunity. CONCLUSION COReNAPCIN booster showed favorable safety, tolerability, and immunogenicity profile, supporting its further clinical development (Trial registration: IRCT20230131057293N1).
Collapse
Affiliation(s)
- Mohammadreza Salehi
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ilad Alavi Darazam
- Department of Infectious Diseases, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Infectious Diseases and Tropical Medicine Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | | | | | | | | | - Nasim Khajavirad
- Department of Internal Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | | | - Maryam Azimi
- Department of Medical Affairs, Pharmed Pajoohan Viera, Tehran, Iran
| | - Hamed Hosseini
- Clinical Trial Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mahmoud Hashemi
- Department of Immunology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | | |
Collapse
|
3
|
Mendes D, Machira Krishnan S, O'Brien E, Padgett T, Harrison C, Strain WD, Manca A, Ustianowski A, Butfield R, Hamson E, Reynard C, Yang J. Modelling COVID-19 Vaccination in the UK: Impact of the Autumn 2022 and Spring 2023 Booster Campaigns. Infect Dis Ther 2024; 13:1127-1146. [PMID: 38662331 PMCID: PMC11098993 DOI: 10.1007/s40121-024-00965-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 03/21/2024] [Indexed: 04/26/2024] Open
Abstract
INTRODUCTION The delivery of COVID-19 vaccines was successful in reducing hospitalizations and mortality. However, emergence of the Omicron variant resulted in increased virus transmissibility. Consequently, booster vaccination programs were initiated to decrease the risk of severe disease and death among vulnerable members of the population. This study aimed to estimate the effects of the booster program and alternative vaccination strategies on morbidity and mortality due to COVID-19 in the UK. METHOD A Susceptible-Exposed-Infectious-Recovered (SEIR) model was used to assess the impact of several vaccination strategies on severe outcomes associated with COVID-19, including hospitalizations, mortality, National Health Service (NHS) capacity quantified by hospital general ward and intensive care unit (ICU) bed days, and patient productivity. The model accounted for age-, risk- and immunity-based stratification of the UK population. Outcomes were evaluated over a 48-week time horizon from September 2022 to August 2023 considering the actual UK autumn 2022/spring 2023 booster campaigns and six counterfactual strategies. RESULTS The model estimated that the autumn 2022/spring 2023 booster campaign resulted in a reduction of 18,921 hospitalizations and 1463 deaths, compared with a no booster scenario. Utilization of hospital bed days due to COVID-19 decreased after the autumn 2022/spring 2023 booster campaign. Expanding the booster eligibility criteria and improving uptake improved all outcomes, including averting twice as many ICU admissions, preventing more than 20% additional deaths, and a sevenfold reduction in long COVID, compared with the autumn 2022/spring 2023 booster campaign. The number of productive days lost was reduced by fivefold indicating that vaccinating a wider population has a beneficial impact on the morbidities associated with COVID-19. CONCLUSION Our modelling demonstrates that the autumn 2022/spring 2023 booster campaign reduced COVID-19-associated morbidity and mortality. Booster campaigns with alternative eligibility criteria warrant consideration in the UK, given their potential to further reduce morbidity and mortality as future variants emerge.
Collapse
Affiliation(s)
| | | | - Esmé O'Brien
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | | | - Cale Harrison
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | | | | | - Andrew Ustianowski
- Manchester University Foundation Trust, University of Manchester, Manchester, UK
| | | | | | | | - Jingyan Yang
- Pfizer Inc, New York, USA
- Institute for Social and Economic Research and Policy, Columbia University, New York, USA
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Zavrakli E, Parnell A, Malone D, Duffy K, Dey S. Optimal age-specific vaccination control for COVID-19: An Irish case study. PLoS One 2023; 18:e0290974. [PMID: 37669287 PMCID: PMC10479919 DOI: 10.1371/journal.pone.0290974] [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: 11/04/2022] [Accepted: 08/21/2023] [Indexed: 09/07/2023] Open
Abstract
The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control. The model accounts for the various stages of the vaccination, and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. As a case study, we selected the Republic of Ireland. We use data provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups. We compare our simulated results with those of the vaccination policy taken by the Irish government to explore the advantages of our optimisation method. Our comparison suggests that a similar reduction in cases may have been possible even with a reduced set of vaccinations available for use.
Collapse
Affiliation(s)
- Eleni Zavrakli
- Hamilton Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
- Department of Mathematics and Statistics, Maynooth University, Maynooth, Co. Kildare, Ireland
- I-Form, Advanced Manufacturing Research Centre, Maynooth, Ireland
| | - Andrew Parnell
- Hamilton Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
- Department of Mathematics and Statistics, Maynooth University, Maynooth, Co. Kildare, Ireland
- I-Form, Advanced Manufacturing Research Centre, Maynooth, Ireland
| | - David Malone
- Hamilton Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
- Department of Mathematics and Statistics, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Ken Duffy
- Hamilton Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Subhrakanti Dey
- Department of Electrical Engineering, Uppsala University, Uppsala, Sweden
| |
Collapse
|
7
|
Luebben G, González-Parra G, Cervantes B. Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10828-10865. [PMID: 37322963 PMCID: PMC11216547 DOI: 10.3934/mbe.2023481] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.
Collapse
Affiliation(s)
- Giulia Luebben
- Department of Mathematics, New Mexico Tech, New Mexico, 87801, USA
| | | | - Bishop Cervantes
- Department of Mathematics, New Mexico Tech, New Mexico, 87801, USA
| |
Collapse
|