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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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Duong KN, Nguyen DT, Kategeaw W, Liang X, Khaing W, Visnovsky LD, Veettil SK, McFarland MM, Nelson RE, Jones BE, Pavia AT, Coates E, Khader K, Love J, Vega Yon GG, Zhang Y, Willson T, Dorsan E, Toth DJ, Jones MM, Samore MH, Chaiyakunapruk N. Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review. LANCET REGIONAL HEALTH. AMERICAS 2024; 35:100806. [PMID: 38948323 PMCID: PMC11214325 DOI: 10.1016/j.lana.2024.100806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 07/02/2024]
Abstract
During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding This research was funded by the Centers for Disease Control and Prevention (CDC).
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Affiliation(s)
- Khanh N.C. Duong
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Danielle T. Nguyen
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Warittakorn Kategeaw
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Xi Liang
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Win Khaing
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Lindsay D. Visnovsky
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Sajesh K. Veettil
- International Medical University, School of Pharmacy, Department of Pharmacy Practice, Kuala Lumpur, Malaysia
| | - Mary M. McFarland
- Spencer S. Eccles Health Sciences Library, University of Utah, Salt Lake City, UT, USA
| | - Richard E. Nelson
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Barbara E. Jones
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Pulmonary & Critical Care, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrew T. Pavia
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Pediatric Infectious Diseases, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Emma Coates
- Department of Mathematics & Statistics, McMaster University, Ontario, Canada
| | - Karim Khader
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Jay Love
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - George G. Vega Yon
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Yue Zhang
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Tina Willson
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Egenia Dorsan
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Damon J.A. Toth
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Makoto M. Jones
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Matthew H. Samore
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Nathorn Chaiyakunapruk
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
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3
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Rao IJ, Brandeau ML. Vaccination for communicable endemic diseases: optimal allocation of initial and booster vaccine doses. J Math Biol 2024; 89:21. [PMID: 38926228 DOI: 10.1007/s00285-024-02111-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: 10/04/2023] [Revised: 05/08/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024]
Abstract
For some communicable endemic diseases (e.g., influenza, COVID-19), vaccination is an effective means of preventing the spread of infection and reducing mortality, but must be augmented over time with vaccine booster doses. We consider the problem of optimally allocating a limited supply of vaccines over time between different subgroups of a population and between initial versus booster vaccine doses, allowing for multiple booster doses. We first consider an SIS model with interacting population groups and four different objectives: those of minimizing cumulative infections, deaths, life years lost, or quality-adjusted life years lost due to death. We solve the problem sequentially: for each time period, we approximate the system dynamics using Taylor series expansions, and reduce the problem to a piecewise linear convex optimization problem for which we derive intuitive closed-form solutions. We then extend the analysis to the case of an SEIS model. In both cases vaccines are allocated to groups based on their priority order until the vaccine supply is exhausted. Numerical simulations show that our analytical solutions achieve results that are close to optimal with objective function values significantly better than would be obtained using simple allocation rules such as allocation proportional to population group size. In addition to being accurate and interpretable, the solutions are easy to implement in practice. Interpretable models are particularly important in public health decision making.
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Affiliation(s)
- Isabelle J Rao
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada.
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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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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5
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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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Caulkins JP, Grass D, Feichtinger G, Hartl RF, Kort PM, Kuhn M, Prskawetz A, Sanchez-Romero M, Seidl A, Wrzaczek S. The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes? EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 311:233-250. [PMID: 37342758 PMCID: PMC10131897 DOI: 10.1016/j.ejor.2023.04.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 04/19/2023] [Indexed: 06/23/2023]
Abstract
The COVID-19 pandemic has devastated lives and economies around the world. Initially a primary response was locking down parts of the economy to reduce social interactions and, hence, the virus' spread. After vaccines have been developed and produced in sufficient quantity, they can largely replace broad lock downs. This paper explores how lockdown policies should be varied during the year or so gap between when a vaccine is approved and when all who wish have been vaccinated. Are vaccines and lockdowns substitutes during that crucial time, in the sense that lockdowns should be reduced as vaccination rates rise? Or might they be complementary with the prospect of imminent vaccination increasing the value of stricter lockdowns, since hospitalization and death averted then may be permanently prevented, not just delayed? We investigate this question with a simple dynamic optimization model that captures both epidemiological and economic considerations. In this model, increasing the rate of vaccine deployment may increase or reduce the optimal total lockdown intensity and duration, depending on the values of other model parameters. That vaccines and lockdowns can act as either substitutes or complements even in a relatively simple model casts doubt on whether in more complicated models or the real world one should expect them to always be just one or the other. Within our model, for parameter values reflecting conditions in developed countries, the typical finding is to ease lockdown intensity gradually after substantial shares of the population have been vaccinated, but other strategies can be optimal for other parameter values. Reserving vaccines for those who have not yet been infected barely outperforms simpler strategies that ignore prior infection status. For certain parameter combinations, there are instances in which two quite different policies can perform equally well, and sometimes very small increases in vaccine capacity can tip the optimal solution to one that involves much longer and more intense lockdowns.
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Affiliation(s)
- J P Caulkins
- Heinz College, Carnegie Mellon University, Pittsburgh, USA
| | - D Grass
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg 2361, Austria
| | - G Feichtinger
- Department for Operations Research and Control Systems, Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria
| | - R F Hartl
- Department of Business Decisions and Analytics, University of Vienna, Vienna, Austria
| | - P M Kort
- Tilburg School of Economics and Management, Tilburg University, Tilburg, Netherlands
| | - M Kuhn
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg 2361, Austria
- Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/OeAW, University of Vienna), Austria
| | - A Prskawetz
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg 2361, Austria
- Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/OeAW, University of Vienna), Austria
- Research Group Economics, Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria
| | - M Sanchez-Romero
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg 2361, Austria
- Research Group Economics, Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria
| | - A Seidl
- Department of Business Decisions and Analytics, University of Vienna, Vienna, Austria
| | - S Wrzaczek
- International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg 2361, Austria
- Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/OeAW, University of Vienna), Austria
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7
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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.
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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
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Ríos-Bracamontes EF, Iñiguez-Arias LE, Ochoa-Jiménez RJ, Guzmán-Esquivel J, Cárdenas-Rojas MI, Murillo-Zamora E. Risk of Testing Positive for COVID-19 among Healthcare and Healthcare-Related Workers. Vaccines (Basel) 2023; 11:1260. [PMID: 37515075 PMCID: PMC10385201 DOI: 10.3390/vaccines11071260] [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: 06/26/2023] [Revised: 07/14/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023] Open
Abstract
Understanding the risk factors associated with COVID-19 infection among healthcare workers is crucial for infection prevention and control. The aim of this study was to examine the risk of testing positive for COVID-19 among a multicenter cohort of workers, taking into account their occupational roles (medical professionals, staff in operational and administrative roles, or laboratory personnel) in healthcare settings. The data analyzed in this study included 2163 individuals with suggestive COVID-19 symptoms who underwent laboratory testing. The incidence rate in the study sample was calculated to be 15.3 cases per 10,000 person-days. The results from the multiple regression model indicated that job roles were not significantly associated with the risk of testing positive. However, age and the duration of the pandemic were identified as significant risk factors, with increasing age and longer pandemic duration being associated with a higher risk of testing positive. Additionally, vaccination was found to reduce the risk of testing positive. These findings provide valuable insights into COVID-19 transmission among indoor healthcare workers, highlighting the influence of age, pandemic duration, and vaccination on infection risk. Further research is needed to develop evidence-based strategies aimed at protecting healthcare workers and preventing virus spread in healthcare settings.
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Affiliation(s)
- Eder Fernando Ríos-Bracamontes
- Departamento de Medicina Interna, Hospital General de Zona No. 1, Instituto Mexicano del Seguro Social, Av. Lapislázuli 250, Col. El Haya, Villa de Álvarez 28984, Mexico
| | - Luz Elena Iñiguez-Arias
- Departamento de Medicina Interna, Hospital General de Zona No. 1, Instituto Mexicano del Seguro Social, Av. Lapislázuli 250, Col. El Haya, Villa de Álvarez 28984, Mexico
| | - Rodolfo José Ochoa-Jiménez
- Departamento de Medicina Interna, Hospital General de Zona No. 1, Instituto Mexicano del Seguro Social, Av. Lapislázuli 250, Col. El Haya, Villa de Álvarez 28984, Mexico
| | - José Guzmán-Esquivel
- Unidad de Investigación en Epidemiología Clínica, Instituto Mexicano del Seguro Social, Av. Lapislázuli 250, Col. El Haya, Villa de Álvarez 28984, Mexico
| | - Martha Irazema Cárdenas-Rojas
- Unidad de Investigación en Epidemiología Clínica, Instituto Mexicano del Seguro Social, Av. Lapislázuli 250, Col. El Haya, Villa de Álvarez 28984, Mexico
| | - Efrén Murillo-Zamora
- Unidad de Investigación en Epidemiología Clínica, Instituto Mexicano del Seguro Social, Av. Lapislázuli 250, Col. El Haya, Villa de Álvarez 28984, Mexico
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9
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Abell IR, McCaw JM, Baker CM. Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation. Infect Dis Model 2023; 8:539-550. [PMID: 37288288 PMCID: PMC10241858 DOI: 10.1016/j.idm.2023.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 06/09/2023] Open
Abstract
Vaccination is an important epidemic intervention strategy. However, it is generally unclear how the outcomes of different vaccine strategies change depending on population characteristics, vaccine mechanisms and allocation objective. In this paper we develop a conceptual mathematical model to simulate strategies for pre-epidemic vaccination. We extend the SEIR model to incorporate a range of vaccine mechanisms and disease characteristics. We then compare the outcomes of optimal and suboptimal vaccination strategies for three public health objectives (total infections, total symptomatic infections and total deaths) using numerical optimisation. Our comparison shows that the difference in outcomes between vaccinating optimally and suboptimally depends on vaccine mechanisms, disease characteristics, and objective considered. Our modelling finds vaccines that impact transmission produce better outcomes as transmission is reduced for all strategies. For vaccines that impact the likelihood of symptomatic disease or dying due to infection, the improvement in outcome as we decrease these variables is dependent on the strategy implemented. Through a principled model-based process, this work highlights the importance of designing effective vaccine allocation strategies. We conclude that efficient allocation of resources can be just as crucial to the success of a vaccination strategy as the vaccine effectiveness and/or amount of vaccines available.
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Affiliation(s)
- Isobel R. Abell
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia
| | - James M. McCaw
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and the University of Melbourne, Melbourne, Australia
| | - Christopher M. Baker
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- Melbourne Centre for Data Science, The University of Melbourne, Melbourne, Australia
- Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Melbourne, Australia
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10
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Federico S, Ferrari G, Torrente ML. Optimal vaccination in a SIRS epidemic model. ECONOMIC THEORY 2022; 77:1-26. [PMID: 36573250 PMCID: PMC9770565 DOI: 10.1007/s00199-022-01475-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 11/16/2022] [Indexed: 05/25/2023]
Abstract
We propose and solve an optimal vaccination problem within a deterministic compartmental model of SIRS type: the immunized population can become susceptible again, e.g. because of a not complete immunization power of the vaccine. A social planner thus aims at reducing the number of susceptible individuals via a vaccination campaign, while minimizing the social and economic costs related to the infectious disease. As a theoretical contribution, we provide a technical non-smooth verification theorem, guaranteeing that a semiconcave viscosity solution to the Hamilton-Jacobi-Bellman equation identifies with the minimal cost function, provided that the closed-loop equation admits a solution. Conditions under which the closed-loop equation is well-posed are then derived by borrowing results from the theory of Regular Lagrangian Flows. From the applied point of view, we provide a numerical implementation of the model in a case study with quadratic instantaneous costs. Amongst other conclusions, we observe that in the long-run the optimal vaccination policy is able to keep the percentage of infected to zero, at least when the natural reproduction number and the reinfection rate are small.
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Affiliation(s)
- Salvatore Federico
- Dipartimento di Economia, Università di Genova, Via F. Vivaldi 5, 16126 Genoa, Italy
| | - Giorgio Ferrari
- Center for Mathematical Economics (IMW), Bielefeld University, Universitätsstrasse 25, 33615 Bielefeld, Germany
| | - Maria-Laura Torrente
- Dipartimento di Economia, Università di Genova, Via F. Vivaldi 5, 16126 Genoa, Italy
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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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Rao IJ, Brandeau ML. Sequential allocation of vaccine to control an infectious disease. Math Biosci 2022; 351:108879. [PMID: 35843382 PMCID: PMC9288241 DOI: 10.1016/j.mbs.2022.108879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022]
Abstract
The problem of optimally allocating a limited supply of vaccine to control a communicable disease has broad applications in public health and has received renewed attention during the COVID-19 pandemic. This allocation problem is highly complex and nonlinear. Decision makers need a practical, accurate, and interpretable method to guide vaccine allocation. In this paper we develop simple analytical conditions that can guide the allocation of vaccines over time. We consider four objectives: minimize new infections, minimize deaths, minimize life years lost, or minimize quality-adjusted life years lost due to death. We consider an SIR model with interacting population groups. We approximate the model using Taylor series expansions, and develop simple analytical conditions characterizing the optimal solution to the resulting problem for a single time period. We develop a solution approach in which we allocate vaccines using the analytical conditions in each time period based on the state of the epidemic at the start of the time period. We illustrate our method with an example of COVID-19 vaccination, calibrated to epidemic data from New York State. Using numerical simulations, we show that our method achieves near-optimal results over a wide range of vaccination scenarios. Our method provides a practical, intuitive, and accurate tool for decision makers as they allocate limited vaccines over time, and highlights the need for more interpretable models over complicated black box models to aid in decision making.
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Affiliation(s)
- Isabelle J Rao
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States of America.
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States of America.
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Fadaki M, Abareshi A, Far SM, Lee PTW. Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia. TRANSPORTATION RESEARCH. PART E, LOGISTICS AND TRANSPORTATION REVIEW 2022; 161:102689. [PMID: 35431604 PMCID: PMC8995313 DOI: 10.1016/j.tre.2022.102689] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 01/21/2022] [Accepted: 03/24/2022] [Indexed: 05/26/2023]
Abstract
While the swift development and production of a COVID-19 vaccine has been a remarkable success, it is equally crucial to ensure that the vaccine is allocated and distributed in a timely and efficient manner. Prior research on pandemic supply chain has not fully incorporated the underlying factors and constraints in designing a vaccine allocation model. This study proposes an innovative vaccine allocation model to contain the spread of infectious diseases incorporating key contributing factors to the risk of uninoculated people including susceptibility rate and exposure risk. Analyses of the data collected from the state of Victoria in Australia show that a vaccine allocation model can deliver a superior performance in minimizing the risk of unvaccinated people when a multi-period approach is employed and augmenting operational mechanisms including transshipment between medical centers, capacity sharing, and mobile units being integrated into the vaccine allocation model.
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Affiliation(s)
- Masih Fadaki
- Department of Supply Chain and Logistics Management, RMIT University, Melbourne, VIC 3000, Australia
| | - Ahmad Abareshi
- Department of Supply Chain and Logistics Management, RMIT University, Melbourne, VIC 3000, Australia
| | - Shaghayegh Maleki Far
- Department of Supply Chain and Logistics Management, RMIT University, Melbourne, VIC 3000, Australia
| | - Paul Tae-Woo Lee
- Maritime Logistics and Free Trade Islands Research Center, Ocean College, Zhejiang University, Zhoushan, China
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Nganmeni Z, Pongou R, Tchantcho B, Tondji J. Vaccine and inclusion. JOURNAL OF PUBLIC ECONOMIC THEORY 2022; 24:JPET12590. [PMID: 35600414 PMCID: PMC9115285 DOI: 10.1111/jpet.12590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 05/12/2023]
Abstract
In majoritarian democracies, popular policies may not be inclusive, and inclusive policies may not be popular. This dilemma raises the crucial question of when it is possible to design a policy that is both inclusive and popular. We address this question in the context of vaccine allocation in a polarized economy facing a pandemic. In such an economy, individuals are organized around distinct networks and groups and have in-group preferences. We provide a complete characterization of the set of inclusive and popular vaccine allocations. The findings imply that the number of vaccine doses necessary to generate an inclusive and popular vaccine allocation is greater than the one necessary to obtain an allocation that is only popular. The analysis further reveals that it is always possible to design the decision-making rule of the economy to implement an inclusive and popular vaccine allocation. Under such a rule, the composition of any group endowed with the veto power should necessarily reflect the diversity of the society.
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Affiliation(s)
- Zéphirin Nganmeni
- UFR AES ‐ Economics and Management, Dionysian Economics Laboratory (L.E.D.)Université Paris 8Saint‐DenisFrance
| | - Roland Pongou
- Department of EconomicsUniversity of OttawaOttawaOntarioCanada
- Harvard Center for African StudiesHarvard UniversityCambridgeMassachusettsUnited States
| | - Bertrand Tchantcho
- Department of MathematicsAdvanced Teachers' Training College, University of Yaounde IYaoundeCameroon
| | - Jean‐Baptiste Tondji
- Department of EconomicsThe University of Texas Rio Grande Valley, Robert C. Vackar College of Business and EntrepreneurshipEdinburgTexasUSA
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Cristancho-Fajardo L, Ezanno P, Vergu E. Dynamic resource allocation for controlling pathogen spread on a large metapopulation network. J R Soc Interface 2022; 19:20210744. [PMID: 35259957 PMCID: PMC8905161 DOI: 10.1098/rsif.2021.0744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
To control the spread of an infectious disease over a large network, the optimal allocation by a social planner of a limited resource is a fundamental and difficult problem. We address this problem for a livestock disease that propagates on an animal trade network according to an epidemiological–demographic model based on animal demographics and trade data. We assume that the resource is dynamically allocated following a certain score, up to the limit of resource availability. We adapt a greedy approach to the metapopulation framework, obtaining new scores that minimize approximations of two different objective functions, for two control measures: vaccination and treatment. Through intensive simulations, we compare the greedy scores with several heuristics. Although topology-based scores can limit the spread of the disease, information on herd health status seems crucial to eradicating the disease. In particular, greedy scores are among the most effective in reducing disease prevalence, even though they do not always perform the best. However, some scores may be preferred in real life because they are easier to calculate or because they use a smaller amount of resources. The developed approach could be adapted to other epidemiological models or to other control measures in the metapopulation setting.
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Affiliation(s)
- Lina Cristancho-Fajardo
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas 78350, France.,INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, Nantes 44307, France
| | - Pauline Ezanno
- INRAE, Oniris, BIOEPAR, Site de la Chantrerie, CS40706, Nantes 44307, France
| | - Elisabeta Vergu
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas 78350, France
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