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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] [Grants] [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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Waseel F, Streftaris G, Rudrusamy B, Dass SC. Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach. Infect Dis Model 2024; 9:527-556. [PMID: 38525308 PMCID: PMC10958481 DOI: 10.1016/j.idm.2024.02.010] [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: 11/26/2023] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 03/26/2024] Open
Abstract
The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57-66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.
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Affiliation(s)
- Farhad Waseel
- School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia
- Faculty of Mathematics, Kabul University, Kabul, Afghanistan
| | - George Streftaris
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom
- Maxwell Institute for Mathematical Sciences, United Kingdom
| | - Bhuvendhraa Rudrusamy
- School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia
| | - Sarat C. Dass
- School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia
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5
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Servadio JL, Choisy M, Thai PQ, Boni MF. Influenza vaccination allocation in tropical settings under constrained resources. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.08.24302551. [PMID: 38370625 PMCID: PMC10871372 DOI: 10.1101/2024.02.08.24302551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Influenza virus seasonality, synchronicity, and vaccine supply differ substantially between temperate and tropical settings, and optimal vaccination strategy may differ on this basis. Most national vaccine recommendations focus on high-risk groups, elderly populations, and healthcare workers despite previous analyses demonstrating broad benefits to vaccinating younger high-contact age groups. Here, we parameterized an age-structured non-seasonal asynchronous epidemiological model of influenza virus transmission for a tropical low-income setting. We evaluated timing and age allocation of vaccines across vaccine supplies ranging from 10% to 90% using decade-based age groups. Year-round vaccination was beneficial when comparing to vaccination strategies focused on a particular time of year. When targeting a single age-group for vaccine prioritization, maximum vaccine allocation to the 10-19 high-contact age group minimized annual influenza mortality for all but one vaccine supply. When evaluating across all possible age allocations, optimal strategies always allocated a plurality of vaccines to school-age children (10-19). The converse however was not true as not all strategies allocating a plurality to children aged 10-19 minimized mortality. Allocating a high proportion of vaccine supply to the 10-19 age group is necessary but not sufficient to minimize annual mortality as distribution of remaining vaccine doses to other age groups also needs to be optimized. Strategies focusing on indirect benefits (vaccinating children) showed higher variance in mortality outcomes than strategies focusing on direct benefits (vaccinating the elderly). However, the indirect benefit approaches showed lower mean mortality and lower minimum mortality than vaccination focused on the elderly.
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Affiliation(s)
- Joseph L Servadio
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
| | - Marc Choisy
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
- School of Preventative Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | - Maciej F Boni
- Center for Infectious Disease Dynamics and Department of Biology, Pennsylvania State University, University Park, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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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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Yang F, Tran TNA, Howerton E, Boni MF, Servadio JL. Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States. BMC Med 2023; 21:321. [PMID: 37620926 PMCID: PMC10463609 DOI: 10.1186/s12916-023-03025-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND As we continue the fourth year of the COVID-19 epidemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. METHODS We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus and account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider three exponentially distributed waning rates, with means of 365 days (1 year), 548 days (1.5 years), and 730 days (2 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. RESULTS As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 81,000-164,600 annual reported deaths, depending on duration of immunity, are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with 52-80% annual vaccination coverage and below 10,000 annual deaths with 59-83% annual vaccination coverage, depending on duration of immunity. Universal treatment reduces hospitalizations by 88.6% and deaths by 93.1% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. CONCLUSIONS Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.
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Affiliation(s)
- Fuhan Yang
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, USA
| | - Thu Nguyen-Anh Tran
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, USA
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, USA
| | - Maciej F Boni
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, USA.
| | - Joseph L Servadio
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, USA.
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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.
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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
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Chen YT. Effect of vaccination patterns and vaccination rates on the spread and mortality of the COVID-19 pandemic. HEALTH POLICY AND TECHNOLOGY 2023; 12:100699. [PMID: 36415885 PMCID: PMC9673057 DOI: 10.1016/j.hlpt.2022.100699] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Objectives Acquiring herd immunity through vaccination is the best way to curb the COVID-19 infection. Many countries have attempted to reach the herd immunity threshold as early as possible since the commencement of vaccination at the end of 2020. The purpose of this study is to (1) examine whether the pattern of vaccination rates affects the spread of COVID-19 and the consequent mortality and (2) investigate the level of cumulative vaccination rates that can begin to have an impact on reducing the spread and mortality of the pandemic. Methods This study selected 33 countries with higher vaccination rates as its sample set, classifying them into three groups as per vaccination patterns. Results The results showed that vaccination patterns have a significant impact on reducing spread and mortality. The full-speed vaccination pattern showed greater improvement in the spread of the COVID-19 pandemic than the other two patterns, while the striving vaccination pattern improved the most in terms of mortality. Secondly, the spread and mortality of the COVID pandemic started to significantly decline when the average cumulative vaccination rate reached 29.06 doses per 100 people and 7.88 doses per 100 people, respectively. Conclusion The study highlights the important role of vaccination patterns and the VTMR in reducing the epidemic spread and mortality.
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Affiliation(s)
- Yi-Tui Chen
- Department of Health Care Management, National Taipei University of Nursing and Health Sciences, No.365, Ming-te Road, Peitou District, Taipei City, Taiwan.,Department of Education and Research, Taipei City Hospital, Taipei City, Taiwan
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Zhang Q, Liu S, Zhang X, Du C, Si S, Chen J. A high-frequency QCM biosensing platform for label-free detection of the SARS-CoV-2 spike receptor-binding domain: an aptasensor and an immunosensor. Analyst 2023; 148:719-723. [PMID: 36723047 DOI: 10.1039/d3an00008g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Herein, high-frequency quartz crystal microbalance biosensing platforms were constructed using an aptamer and antibody as bioreceptors for fast and label-free detection of the SARS-CoV-2 RBD.
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Affiliation(s)
- Qingqing Zhang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, P. R. China.
| | - Shuping Liu
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, P. R. China.
| | - Xiaohua Zhang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, P. R. China.
| | - Cuicui Du
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, P. R. China.
| | - Shihui Si
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, P. R. China.
| | - Jinhua Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, P. R. China.
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11
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Yang F, Tran TNA, Howerton E, Boni MF, Servadio JL. Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.08.23285658. [PMID: 36798204 PMCID: PMC9934803 DOI: 10.1101/2023.02.08.23285658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Background As we enter the fourth year of the COVID-19 pandemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. Methods We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus. We account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider two exponentially-distributed waning rates, with means of 365 days (one year) and 548 days (1.5 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. Results As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 89,000 annual deaths (548-day waning) and 120,000 annual deaths (365-day waning) are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with >81% annual vaccination coverage, and below 10,000 annual deaths with >84% annual vaccination coverage. Universal treatment reduces hospitalizations by 88% and deaths by 93% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. Conclusions Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.
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Affiliation(s)
- Fuhan Yang
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, United States
| | - Thu Nguyen-Anh Tran
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, United States
| | - Emily Howerton
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, United States
| | - Maciej F Boni
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, United States
| | - Joseph L Servadio
- Department of Biology and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, 16802, United States
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12
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Ben Fredj S, Ghammem R, Zammit N, Maatouk A, Haddad N, Haddad N, Kachroudi M, Rebai S, Laadhari H, Ghodhbani MM, Maatoug J, Ghannem H. Risk factors for severe Covid-19 breakthrough infections: an observational longitudinal study. BMC Infect Dis 2022; 22:894. [DOI: 10.1186/s12879-022-07859-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/08/2022] [Indexed: 11/29/2022] Open
Abstract
Abstract
Background
The drive to vaccinate large populations is nowadays the main instrument for combating the pandemic and preventing serious disease and death. However, breakthrough infection (post-vaccination infection) still happens after vaccination among fully vaccinated people. We aimed to assess the severity outcomes and to determine its associated factors among vaccinated COVID-19 cases in the governorate of Sousse, Tunisia.
Methods
We carried out a five-month observational longitudinal study including all the population of Sousse. Confirmed infections of SARS-CoV-2 and the vaccination status are recorded in the daily COVID- 19 database of the Regional Office of the Tunisian Ministry of Health. We included all post-vaccination COVID-19 cases for the analysis of the COVID-19 serious outcomes. Data were collected via 15-min telephonic call interviews conducted by trained interviewers. Descriptive analysis with calculating incidence rates of confirmed COVID-19 cases per 100,000 inhabitants was conducted. In binary logistic regression, adjusted odds ratios along with 95% intervals confidence were performed to determine factors related to severe or critical COVID-19.
Results
As of 31 July 2021, 107,545 persons over 19 years old have received at least one dose of COVID-19 vaccination. Among the vaccinated population, we traced and included 765 breakthrough infection cases, and the incidence rate was 711.3 per week. The majority were female (sex-ratio = 0.8), and the average age of the overall cases was 55.7 years. The prevalence of severe or critical cases in vaccinated COVID-19 patients occurs in 10.8% of cases. Patients with a medical history of cardiovascular diseases had more than two times increased odds to have a severe or critical disease. We also found the highest self-estimation of adherence to preventive measures was inversely correlated to serious cases and having an incomplete vaccination schema was strongly associated with complications.
Conclusions
We tried to provide evidence about the breakthrough infections to improve measures of prevention and control of COVID-19. Boosting immunity for vulnerable patients added to maintaining and promoting preventive measures are not only essential to prevent severe cases of breakthrough infections of COVID-19, but also other influenza-like diseases.
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13
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Lin XQ, Li AL, Zhang MX, Lv L, Chen Y, Chen HD, Tung TH, Zhu JS. Willingness of Older Adults with Chronic Diseases to Receive a Booster Dose of Inactivated Coronavirus Disease 2019 Vaccine: A Cross-Sectional Study in Taizhou, China. Vaccines (Basel) 2022; 10:vaccines10101665. [PMID: 36298530 PMCID: PMC9611491 DOI: 10.3390/vaccines10101665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 11/21/2022] Open
Abstract
Vaccination is an important measure to control the spread of COVID-19 among elderly high-risk groups; however, the propensity to receive COVID-19 vaccine boosters has not been evaluated in these populations. Here, we aimed to investigate the willingness to receive a COVID-19 vaccine booster among the elderly chronic disease population in Taizhou, China. A cross-sectional, hospital-based survey was conducted in the outpatient department of a tertiary care hospital between 6 July and 11 August 2021 in Taizhou, China, and the data were uploaded to Wen-Juan-Xing, one of the largest online platforms used to collect survey data in China. The targeted population was non-oncology chronic disease patients aged 60 years and above. The minimum sample size was 229, determined by the G*Power software (v3.1.9.2, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). A total of 254 patients with valid data were enrolled in this study, with a response rate of 82.5% (254/308). Chi-square tests and one-way binary regression were used to compare the proportions and the degree of influence of categorical factors. The magnitude of the effect for the comparisons was measured by Gramer’s V. A multivariate binary logistic regression model was used to correct for confounders and to identify factors. All data were analyzed using SPSS v24.0 (IBM Corporation, Armonk, NY, USA). A total of 198 respondents (77.9%) were willing to receive a COVID-19 vaccine booster dose, and 77.6% of respondents were willing to receive the primary dose. Age < 70 years (OR 2.82), stable disease control (OR 2.79), confidence in the effectiveness of the COVID-19 vaccine (OR 3.11), and vaccine recipient (OR 5.02) were significantly associated with the willingness to receive a COVID-19 vaccine booster dose. Promoting primary dose vaccination is essential for advancing booster vaccination, and it is important to focus on elderly patients’ confidence in the vaccine, in addition to strengthening health management and promoting disease stability. Follow-up studies should focus on elderly patients who belong to specific disease groups.
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Affiliation(s)
- Xiao-Qing Lin
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - A-Li Li
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - Mei-Xian Zhang
- Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - Li Lv
- Department of Infectious Diseases, Taizhou Hospital, Zhejiang University, Linhai 317000, China
| | - Yan Chen
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - He-Dan Chen
- Department of Infectious Diseases, Taizhou Hospital, Zhejiang University, Linhai 317000, China
| | - Tao-Hsin Tung
- Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, China
- Correspondence: (T.-H.T.); (J.-S.Z.)
| | - Jian-Sheng Zhu
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai 317000, China
- Correspondence: (T.-H.T.); (J.-S.Z.)
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14
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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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15
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Coccia M. COVID-19 pandemic over 2020 (withlockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors. ENVIRONMENTAL RESEARCH 2022; 208:112711. [PMID: 35033552 PMCID: PMC8757643 DOI: 10.1016/j.envres.2022.112711] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 05/19/2023]
Abstract
How is the dynamics of Coronavirus Disease 2019 (COVID-19) in 2020 with an health policy of full lockdowns and in 2021 with a vast campaign of vaccinations? The present study confronts this question here by developing a comparative analysis of the effects of COVID-19 pandemic between April-September 2020 (based upon strong control measures) and April-September 2021 (focused on health policy of vaccinations) in Italy, which was one of the first European countries to experience in 2020 high numbers of COVID-19 related infected individuals and deaths and in 2021 Italy has a high share of people fully vaccinated against COVID-19 (>89% of population aged over 12 years in January 2022). Results suggest that over the period under study, the arithmetic mean of confirmed cases, hospitalizations of people and admissions to Intensive Care Units (ICUs) in 2020 and 2021 is significantly equal (p-value<0.01), except fatality rate. Results suggest in December 2021 lower hospitalizations, admissions to ICUs, and fatality rate of COVID-19 than December 2020, though confirmed cases and mortality rates are in 2021 higher than 2020, and likely converging trends in the first quarter of 2022. These findings reveal that COVID-19 pandemic is driven by seasonality and environmental factors that reduce the negative effects in summer period, regardless control measures and/or vaccination campaigns. These findings here can be of benefit to design health policy responses of crisis management considering the growth of COVID-19 pandemic in winter months having reduced temperatures and low solar radiations ( COVID-19 has a behaviour of influenza-like illness). Hence, findings here suggest that strategies of prevention and control of infectious diseases similar to COVID-19 should be set up in summer months and fully implemented during low-solar-irradiation periods (autumn and winter period).
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Affiliation(s)
- Mario Coccia
- CNR, National Research Council of Italy - Via Real Collegio, n. 30 (Collegio Carlo Alberto), 10024, Moncalieri (TO), Italy.
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16
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Tran TNA, Wikle NB, Yang F, Inam H, Leighow S, Gentilesco B, Chan P, Albert E, Strong ER, Pritchard JR, Hanage WP, Hanks EM, Crawford FW, Boni MF. SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021. JAMA Netw Open 2022; 5:e2214171. [PMID: 35616938 PMCID: PMC9136627 DOI: 10.1001/jamanetworkopen.2022.14171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/09/2022] [Indexed: 12/15/2022] Open
Abstract
Importance In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated. Objective To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Design, Setting, and Participants This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021. Exposures COVID-19-positive test result reported to state department of health. Main Outcomes and Measures The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated. Results At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive. Conclusions and Relevance These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.
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Affiliation(s)
- Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park
| | - Nathan B. Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park
| | - Scott Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park
| | | | - Philip Chan
- Department of Medicine, Brown University, Providence, Rhode Island
| | - Emmy Albert
- Department of Physics, Pennsylvania State University, University Park
| | - Emily R. Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park
| | - Justin R. Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park
| | - William P. Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Ephraim M. Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park
| | - Forrest W. Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Department of Statistics and Data Science, Yale University, New Haven, Connecticut
| | - Maciej F. Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park
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17
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Rasambainarivo F, Ramiadantsoa T, Raherinandrasana A, Randrianarisoa S, Rice BL, Evans MV, Roche B, Randriatsarafara FM, Wesolowski A, Metcalf JC. Prioritizing COVID-19 vaccination efforts and dose allocation within Madagascar. BMC Public Health 2022; 22:724. [PMID: 35413894 PMCID: PMC9002044 DOI: 10.1186/s12889-022-13150-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background While mass COVID-19 vaccination programs are underway in high-income countries, limited availability of doses has resulted in few vaccines administered in low and middle income countries (LMICs). The COVID-19 Vaccines Global Access (COVAX) is a WHO-led initiative to promote vaccine access equity to LMICs and is providing many of the doses available in these settings. However, initial doses are limited and countries, such as Madagascar, need to develop prioritization schemes to maximize the benefits of vaccination with very limited supplies. There is some consensus that dose deployment should initially target health care workers, and those who are more vulnerable including older individuals. However, questions of geographic deployment remain, in particular associated with limits around vaccine access and delivery capacity in underserved communities, for example in rural areas that may also include substantial proportions of the population. Methods To address these questions, we developed a mathematical model of SARS-CoV-2 transmission dynamics and simulated various vaccination allocation strategies for Madagascar. Simulated strategies were based on a number of possible geographical prioritization schemes, testing sensitivity to initial susceptibility in the population, and evaluating the potential of tests for previous infection. Results Using cumulative deaths due to COVID-19 as the main outcome of interest, our results indicate that distributing the number of vaccine doses according to the number of elderly living in the region or according to the population size results in a greater reduction of mortality compared to distributing doses based on the reported number of cases and deaths. The benefits of vaccination strategies are diminished if the burden (and thus accumulated immunity) has been greatest in the most populous regions, but the overall strategy ranking remains comparable. If rapid tests for prior immunity may be swiftly and effectively delivered, there is potential for considerable gain in mortality averted, but considering delivery limitations modulates this. Conclusion At a subnational scale, our results support the strategy adopted by the COVAX initiative at a global scale. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13150-8.
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Affiliation(s)
- Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. .,Mahaliana Labs SARL, Antananarivo, Madagascar.
| | - Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Antananarivo, Madagascar.,Department of Mathematics, University of Fianarantsoa, Antananarivo, Madagascar.,MIVEGEC, Université de Montpellier, CNRS, Montpellier, IRD, France
| | - Antso Raherinandrasana
- Surveillance Unit, Ministry of Health of Madagascar, Antananarivo, Madagascar.,Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Michelle V Evans
- MIVEGEC, Université de Montpellier, CNRS, Montpellier, IRD, France
| | - Benjamin Roche
- MIVEGEC, Université de Montpellier, CNRS, Montpellier, IRD, France
| | - Fidiniaina Mamy Randriatsarafara
- Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar.,Direction of Preventive Medicine, Ministry of Health of Madagascar, Antananarivo, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessica C Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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18
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Wikle NB, Tran TNA, Gentilesco B, Leighow SM, Albert E, Strong ER, Brinda K, Inam H, Yang F, Hossain S, Chan P, Hanage WP, Messick M, Pritchard JR, Hanks EM, Boni MF. SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics. SCIENCE ADVANCES 2022; 8:eabf9868. [PMID: 35080987 PMCID: PMC8791616 DOI: 10.1126/sciadv.abf9868] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/03/2021] [Indexed: 05/03/2023]
Abstract
State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states-Rhode Island, Massachusetts, and Pennsylvania-that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.
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Affiliation(s)
- Nathan B. Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
| | | | - Scott M. Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA
| | - Emmy Albert
- Department of Physics, Pennsylvania State University, University Park, PA, USA
| | - Emily R. Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Karel Brinda
- Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Sajid Hossain
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Philip Chan
- Department of Medicine, Brown University, Providence, RI, USA
| | - William P. Hanage
- Center for Communicable Disease Dynamic, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Maria Messick
- Rhode Island Office of the Governor and Rhode Island Department of Health, Providence, RI, USA
| | - Justin R. Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA, USA
| | - Ephraim M. Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Maciej F. Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
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19
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Tran TNA, Wikle NB, Yang F, Inam H, Leighow S, Gentilesco B, Chan P, Albert E, Strong ER, Pritchard JR, Hanage WP, Hanks EM, Crawford FW, Boni MF. SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.12.06.21267375. [PMID: 34909789 PMCID: PMC8669856 DOI: 10.1101/2021.12.06.21267375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Estimating an infectious disease attack rate requires inference on the number of reported symptomatic cases of a disease, the number of unreported symptomatic cases, and the number of asymptomatic infections. Population-level immunity can then be estimated as the attack rate plus the number of vaccine recipients who had not been previously infected; this requires an estimate of the fraction of vaccines that were distributed to seropositive individuals. To estimate attack rates and population immunity in southern New England, we fit a validated dynamic epidemiological model to case, clinical, and death data streams reported by Rhode Island, Massachusetts, and Connecticut for the first 15 months of the COVID-19 pandemic, from March 1 2020 to May 31 2021. This period includes the initial spring 2020 wave, the major winter wave of 2020-2021, and the lagging wave of lineage B.1.1.7(Alpha) infections during March-April 2021. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in southern New England was still below 15%, setting the stage for a large winter wave. After the roll-out of vaccines in early 2021, population immunity in many states was expected to approach 70% by spring 2021, with more than half of this immune population coming from vaccinations. Our population immunity estimates for May 31 2021 are 73.4% (95% CrI: 72.9% - 74.1%) for Rhode Island, 64.1% (95% CrI: 64.0% - 64.4%) for Connecticut, and 66.3% (95% CrI: 65.9% - 66.9%) for Massachusetts, indicating that >33% of southern Englanders were still susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned due to 34% (Rhode Island), 25% (Connecticut), and 28% (Massachusetts) of vaccine distribution going to seropositive individuals. Future emergency-setting vaccination planning will likely have to consider over-vaccination as a strategy to ensure that high levels of population immunity are reached during the course of an ongoing epidemic.
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Affiliation(s)
- Thu Nguyen-Anh Tran
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
| | - Nathan B Wikle
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Fuhan Yang
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
| | - Haider Inam
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - Scott Leighow
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | | | - Philip Chan
- Department of Medicine, Brown University, Providence, RI
| | - Emmy Albert
- Department of Physics, Pennsylvania State University, University Park, PA
| | - Emily R Strong
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Justin R Pritchard
- Center for Infectious Disease Dynamics, Department of Bioengineering, Pennsylvania State University, University Park, PA
| | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Ephraim M Hanks
- Center for Infectious Disease Dynamics, Department of Statistics, Pennsylvania State University, University Park, PA
| | - Forrest W Crawford
- Department of Biostatistics, Yale Schools of Public Health, Yale University, New Haven, CT
- Department of Biostatistics, Yale Schools of Public Health, Yale University, New Haven, CT
| | - Maciej F Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA
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20
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Saadi N, Chi YL, Ghosh S, Eggo RM, McCarthy CV, Quaife M, Dawa J, Jit M, Vassall A. Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review. BMC Med 2021; 19:318. [PMID: 34847950 PMCID: PMC8632563 DOI: 10.1186/s12916-021-02190-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 11/17/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND How best to prioritise COVID-19 vaccination within and between countries has been a public health and an ethical challenge for decision-makers globally. We reviewed epidemiological and economic modelling evidence on population priority groups to minimise COVID-19 mortality, transmission, and morbidity outcomes. METHODS We searched the National Institute of Health iSearch COVID-19 Portfolio (a database of peer-reviewed and pre-print articles), Econlit, the Centre for Economic Policy Research, and the National Bureau of Economic Research for mathematical modelling studies evaluating the impact of prioritising COVID-19 vaccination to population target groups. The first search was conducted on March 3, 2021, and an updated search on the LMIC literature was conducted from March 3, 2021, to September 24, 2021. We narratively synthesised the main study conclusions on prioritisation and the conditions under which the conclusions changed. RESULTS The initial search identified 1820 studies and 36 studies met the inclusion criteria. The updated search on LMIC literature identified 7 more studies. 43 studies in total were narratively synthesised. 74% of studies described outcomes in high-income countries (single and multi-country). We found that for countries seeking to minimise deaths, prioritising vaccination of senior adults was the optimal strategy and for countries seeking to minimise cases the young were prioritised. There were several exceptions to the main conclusion, notably that reductions in deaths could be increased if groups at high risk of both transmission and death could be further identified. Findings were also sensitive to the level of vaccine coverage. CONCLUSION The evidence supports WHO SAGE recommendations on COVID-19 vaccine prioritisation. There is, however, an evidence gap on optimal prioritisation for low- and middle-income countries, studies that included an economic evaluation, and studies that explore prioritisation strategies if the aim is to reduce overall health burden including morbidity.
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Affiliation(s)
- Nuru Saadi
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
| | - Y-Ling Chi
- International Decision Support Initiative, Center for Global Development, London, UK
| | - Srobana Ghosh
- International Decision Support Initiative, Center for Global Development, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Ciara V McCarthy
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Matthew Quaife
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Jeanette Dawa
- Washington State University - Global Health Program, Nairobi, Kenya
- Center for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya
| | - Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Anna Vassall
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
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21
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Morozova O, Li ZR, Crawford FW. One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut. Sci Rep 2021; 11:20271. [PMID: 34642405 PMCID: PMC8511264 DOI: 10.1038/s41598-021-99590-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/29/2021] [Indexed: 12/16/2022] Open
Abstract
To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.
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Affiliation(s)
- Olga Morozova
- Program in Public Health, Department of Family, Population and Preventive Medicine, Stony Brook University (SUNY), Stony Brook, NY, 11794, USA.
| | - Zehang Richard Li
- Department of Statisitcs, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Forrest W Crawford
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06510, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06510, USA
- Yale School of Management, New Haven, CT, USA
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