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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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dePillis L, Caffrey R, Chen G, Dela MD, Eldevik L, McConnell J, Shabahang S, Varvel SA. A mathematical model of the within-host kinetics of SARS-CoV-2 neutralizing antibodies following COVID-19 vaccination. J Theor Biol 2023; 556:111280. [PMID: 36202234 PMCID: PMC9529354 DOI: 10.1016/j.jtbi.2022.111280] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 08/22/2022] [Accepted: 09/14/2022] [Indexed: 10/25/2022]
Abstract
Compelling evidence continues to build to support the idea that SARS-CoV-2 Neutralizing Antibody (NAb) levels in an individual can serve as an important indicator of the strength of protective immunity against infection. It is not well understood why NAb levels in some individuals remain high over time, while in others levels decline rapidly. In this work, we present a two-state mathematical model of within-host NAb dynamics in response to vaccination. By fitting only four host-specific parameters, the model is able to capture individual-specific NAb levels over time as measured by the AditxtScore™ for NAbs. The model can serve as a foundation for predicting NAb levels in the long-term, understanding connections between NAb levels, protective immunity, and breakthrough infections, and potentially guiding decisions about whether and when a booster vaccination may be warranted.
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Affiliation(s)
- Lisette dePillis
- Department of Mathematics, Harvey Mudd College, 301 Platt Blvd., Claremont, CA 91711, United States.
| | - Rebecca Caffrey
- Aditxt, Inc. 737 N. Fifth Street, Suite 200, Richmond, VA 23219, United States
| | - Ge Chen
- Aditxt, Inc. 737 N. Fifth Street, Suite 200, Richmond, VA 23219, United States
| | - Mark D. Dela
- California State Polytechnic University, Pomona, CA, United States
| | - Leif Eldevik
- Aditxt, Inc. 737 N. Fifth Street, Suite 200, Richmond, VA 23219, United States
| | - Joseph McConnell
- Aditxt, Inc. 737 N. Fifth Street, Suite 200, Richmond, VA 23219, United States
| | - Shahrokh Shabahang
- Aditxt, Inc. 737 N. Fifth Street, Suite 200, Richmond, VA 23219, United States
| | - Stephen A. Varvel
- Aditxt, Inc. 737 N. Fifth Street, Suite 200, Richmond, VA 23219, United States
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Bracis C, Moore M, Swan DA, Matrajt L, Anderson L, Reeves DB, Burns E, Schiffer JT, Dimitrov D. Improving vaccination coverage and offering vaccine to all school-age children allowed uninterrupted in-person schooling in King County, WA: Modeling analysis. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5699-5716. [PMID: 35603374 PMCID: PMC9553324 DOI: 10.3934/mbe.2022266] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The rapid spread of highly transmissible SARS-CoV-2 variants combined with slowing pace of vaccination in Fall 2021 created uncertainty around the future trajectory of the epidemic in King County, Washington, USA. We analyzed the benefits of offering vaccination to children ages 5-11 and expanding the overall vaccination coverage using mathematical modeling. We adapted a mathematical model of SARS-CoV-2 transmission, calibrated to data from King County, Washington, to simulate scenarios of vaccinating children aged 5-11 with different starting dates and different proportions of physical interactions (PPI) in schools being restored. Dynamic social distancing was implemented in response to changes in weekly hospitalizations. Reduction of hospitalizations and estimated time under additional social distancing measures are reported over the 2021-2022 school year. In the scenario with 85% vaccination coverage of 12+ year-olds, offering early vaccination to children aged 5-11 with 75% PPI was predicted to prevent 756 (median, IQR 301-1434) hospitalizations cutting youth hospitalizations in half compared to no vaccination and largely reducing the need for additional social distancing measures over the school year. If, in addition, 90% overall vaccination coverage was reached, 60% of remaining hospitalizations would be averted and the need for increased social distancing would almost certainly be avoided. Our work suggests that uninterrupted in-person schooling in King County was partly possible because reasonable precaution measures were taken at schools to reduce infectious contacts. Rapid vaccination of all school-aged children provides meaningful reduction of the COVID-19 health burden over this school year but only if implemented early. It remains critical to vaccinate as many people as possible to limit the morbidity and mortality associated with future epidemic waves.
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Affiliation(s)
- Chloe Bracis
- Université Grenoble Alpes, TIMC-IMAG/MAGE, Grenoble 38000, France
| | - Mia Moore
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - David A. Swan
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Laura Matrajt
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Larissa Anderson
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Daniel B. Reeves
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Joshua T. Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center; Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Dobromir Dimitrov
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
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Modeling the Transmission of the SARS-CoV-2 Delta Variant in a Partially Vaccinated Population. Viruses 2022; 14:v14010158. [PMID: 35062363 PMCID: PMC8781299 DOI: 10.3390/v14010158] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/16/2021] [Accepted: 01/12/2022] [Indexed: 12/19/2022] Open
Abstract
In a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that considers both subpopulations and immunity parameters, including vaccination rates, vaccine effectiveness, and a gradual loss of protection. The model forecasted the spread of the SARS-CoV-2 delta variant in the US under varied transmission and vaccination rates. We further obtained the control reproduction number and conducted sensitivity analyses to determine how each parameter may affect virus transmission. Although our model has several limitations, the number of infected individuals was shown to be a magnitude greater (~10×) in the unvaccinated subpopulation compared to the vaccinated subpopulation. Our results show that a combination of strengthening vaccine-induced immunity and preventative behavioral measures like face mask-wearing and contact tracing will likely be required to deaccelerate the spread of infectious SARS-CoV-2 variants.
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Sun X, Wandelt S, Zhang A. Vaccination passports: Challenges for a future of air transportation. TRANSPORT POLICY 2021; 110:394-401. [PMID: 34608363 PMCID: PMC8481163 DOI: 10.1016/j.tranpol.2021.06.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/26/2021] [Accepted: 06/24/2021] [Indexed: 05/21/2023]
Abstract
COVID-19 has been a major setback for air transportation; many airlines had to request for bailouts and the international flights connectivity is only restarting slowly. Accordingly, many aviation stakeholders put hopes into the ongoing process of vaccination, with the expectation that a high degree of vaccination will push the envelope for a return to normalcy. One prerequisite for reviving international air connectivity is the introduction of verification documents, also called "vaccination passports". These passports, however, come with several challenges which need to be overcome in order to enable recovery. In this study, we propose a framework covering five important aspects and policy challenges concerning the introduction of vaccination passports for a return of aviation, covering the topics: Competition, Epidemiology, Technology, Ethics, and Politics. Neglecting to appropriately address these challenges will likely not only delay the recovery, but possibly miss an important opportunity before new disastrous events appear on the horizon.
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Affiliation(s)
- Xiaoqian Sun
- National Key Laboratory of CNS/ATM, School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China
- National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191, Beijing, China
| | - Sebastian Wandelt
- National Key Laboratory of CNS/ATM, School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China
- National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191, Beijing, China
| | - Anming Zhang
- Sauder School of Business, University of British Columbia, Vancouver, BC, Canada
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