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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Karr J, Malik-Sheriff RS, Osborne J, Gonzalez-Parra G, Forgoston E, Bowness R, Liu Y, Thompson R, Garira W, Barhak J, Rice J, Torres M, Dobrovolny HM, Tang T, Waites W, Glazier JA, Faeder JR, Kulesza A. Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility. Front Syst Biol 2022; 2:822606. [PMID: 36909847 PMCID: PMC10002468 DOI: 10.3389/fsysb.2022.822606] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.
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Affiliation(s)
- Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rahuman S. Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - James Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia
| | | | - Eric Forgoston
- Department of Applied Mathematics and Statistics, Montclair State University, Montclair, NJ, United States
| | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Robin Thompson
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Winston Garira
- Department of Mathematics and Applied Mathematics, Modelling Health and Environmental Linkages Research Group, University of Venda, Limpopo, South Africa
| | - Jacob Barhak
- Jacob Barhak Analytics, Austin, TX, United States
| | - John Rice
- Independent Retired Working Group Volunteer, Virginia Beach, VA, United States
| | - Marcella Torres
- Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, United States
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, United States
| | - Tingting Tang
- Department of Mathematics and Statistics in San Diego State University (SDSU) and SDSU Imperial Valley, Calexico, CA, United States
| | - William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
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Abstract
The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.
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Affiliation(s)
- Gilberto Gonzalez-Parra
- Department of Mathematics, New Mexico Tech, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
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Martínez-Rodríguez D, Gonzalez-Parra G, Villanueva RJ. Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach. Epidemiologia (Basel) 2021; 2:140-161. [PMID: 35141702 PMCID: PMC8824484 DOI: 10.3390/epidemiologia2020012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023] Open
Abstract
The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.
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Affiliation(s)
- David Martínez-Rodríguez
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
| | | | - Rafael-J. Villanueva
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
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Pinky L, Gonzalez-Parra G, Dobrovolny HM. Effect of stochasticity on coinfection dynamics of respiratory viruses. BMC Bioinformatics 2019; 20:191. [PMID: 30991939 PMCID: PMC6469119 DOI: 10.1186/s12859-019-2793-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 04/03/2019] [Indexed: 12/17/2022] Open
Abstract
Background Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality. Results In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection. Conclusions We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus. Electronic supplementary material The online version of this article (10.1186/s12859-019-2793-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lubna Pinky
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.,Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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Abdel-Hamid T, Ankel F, Battle-Fisher M, Gibson B, Gonzalez-Parra G, Jalali M, Kaipainen K, Kalupahana N, Karanfil O, Marathe A, Martinson B, McKelvey K, Sarbadhikari SN, Pintauro S, Poucheret P, Pronk N, Qian Y, Sazonov E, Van Oorschot K, Venkitasubramanian A, Murphy P. Public and health professionals' misconceptions about the dynamics of body weight gain/loss. Syst Dyn Rev 2014; 30:58-74. [PMID: 25620843 PMCID: PMC4300993 DOI: 10.1002/sdr.1517] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Human body energy storage operates as a stock-and-flow system with inflow (food intake) and outflow (energy expenditure). In spite of the ubiquity of stock-and-flow structures, evidence suggests that human beings fail to understand stock accumulation and rates of change, a difficulty called the stock-flow failure. This study examines the influence of health care training and cultural background in overcoming stock-flow failure. A standardized protocol assessed lay people's and health care professionals' ability to apply stock-and-flow reasoning to infer the dynamics of weight gain/loss during the holiday season (621 subjects from seven countries). Our results indicate that both types of subjects exhibited systematic errors indicative of use of erroneous heuristics. Stock-flow failure was found across cultures and was not improved by professional health training. The problem of stock-flow failure as a transcultural global issue with education and policy implications is discussed.
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Affiliation(s)
- Tarek Abdel-Hamid
- Department of Information Sciences, Naval Postgraduate School, Monterey, CA 93943, U.S.A
| | - Felix Ankel
- University of Minnesota Medical School Emergency Department, Saint Paul, United States
| | - Michele Battle-Fisher
- Wright State Boonshoft School of Medicine, Center for Global Health, Kettering, United States
| | - Bryan Gibson
- University of Utah, VA Salt Lake City Healthcare System, Salt Lake City, United States
| | | | - Mohammad Jalali
- Virginia Tech, Management Systems Engineering, Falls Church, United States
| | - Kirsikka Kaipainen
- VTT Technical Research Centre of Finland, VTT, ICT for Health, Tampere, Finland
| | - Nishan Kalupahana
- University of Tennessee, UT Obesity Research Center and Department of Animal Science, Knoxville, Tennessee, United States
| | | | - Achla Marathe
- Virginia Bioinformatics Institute, Department of Agricultural and Applied Economics, Blacksburg, United States
| | - Brian Martinson
- Health Partners Research Foundation, Minneapolis, United States
| | - Karma McKelvey
- Florida International University, Epidemiology, Miami, United States
| | | | - Stephen Pintauro
- University of Vermont, Nutrition and Food Sciences, Burlington, United States
| | - Patrick Poucheret
- Université Montpellier, Laboratoire de Pharmacologie et Physiopathologie Expérimentale, Montpellier, France
| | - Nicolaas Pronk
- Health Partners Research Foundation, Bloomington, United States
| | - Ying Qian
- Shanghai University, Information Management, Shanghai, China
| | - Edward Sazonov
- University of Alabama, Electrical and Computer Engineering, Tuscaloosa, United States
| | - Kim Van Oorschot
- BI Norwegian Business School, Department of Leadership and Organizational, Behaviour, Nydalsveien 37 0442 Oslo, Norway
| | | | - Philip Murphy
- Monterey Institute of International Studies, International Policy Studies, Monterey United States
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