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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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Pant B, Gumel AB. Mathematical assessment of the roles of age heterogeneity and vaccination on the dynamics and control of SARS-CoV-2. Infect Dis Model 2024; 9:828-874. [PMID: 38725431 PMCID: PMC11079469 DOI: 10.1016/j.idm.2024.04.007] [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: 09/29/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with m heterogeneous sub-populations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with m = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the sub-populations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the one-group model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneous mixing significantly reduces the level of vaccination coverage needed to achieve vaccine-derived herd immunity (specifically, for the heterogeneous model, herd-immunity can be attained during Wave C if a moderate proportion of susceptible individuals are fully vaccinated). The consequence of this result is that vaccination models for SARS-CoV-2 that do not explicitly account for age heterogeneity may be overestimating the level of vaccine-derived herd immunity threshold needed to eliminate the SARS-CoV-2 pandemic.
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Affiliation(s)
- Binod Pant
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Abba B. Gumel
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
- Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria, 0002, South Africa
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Khurana MP, Curran-Sebastian J, Scheidwasser N, Morgenstern C, Rasmussen M, Fonager J, Stegger M, Tang MHE, Juul JL, Escobar-Herrera LA, Møller FT, Albertsen M, Kraemer MUG, du Plessis L, Jokelainen P, Lehmann S, Krause TG, Ullum H, Duchêne DA, Mortensen LH, Bhatt S. High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark. Nat Commun 2024; 15:7123. [PMID: 39164246 PMCID: PMC11335946 DOI: 10.1038/s41467-024-51371-0] [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: 05/07/2024] [Accepted: 08/01/2024] [Indexed: 08/22/2024] Open
Abstract
Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged < 15 and > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.
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Affiliation(s)
- Mark P Khurana
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | - Jacob Curran-Sebastian
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Neil Scheidwasser
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Christian Morgenstern
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Morten Rasmussen
- Virus Research and Development Laboratory, Statens Serum Institut, Copenhagen, Denmark
| | - Jannik Fonager
- Virus Research and Development Laboratory, Statens Serum Institut, Copenhagen, Denmark
| | - Marc Stegger
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark
- Antimicrobial Resistance and Infectious Diseases Laboratory, Harry Butler Institute, Murdoch University, Murdoch, WA, Australia
| | - Man-Hung Eric Tang
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark
| | - Jonas L Juul
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | | | - Mads Albertsen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | | | - Louis du Plessis
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Pikka Jokelainen
- Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Tyra G Krause
- Epidemiological Infectious Disease Preparedness, Statens Serum Institut Copenhagen, Copenhagen, Denmark
| | | | - David A Duchêne
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Laust H Mortensen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- Statistics Denmark, Copenhagen, Denmark
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
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Demongeot J, Magal P. Data-driven mathematical modeling approaches for COVID-19: A survey. Phys Life Rev 2024; 50:166-208. [PMID: 39142261 DOI: 10.1016/j.plrev.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 08/02/2024] [Indexed: 08/16/2024]
Abstract
In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).
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Affiliation(s)
- Jacques Demongeot
- Université Grenoble Alpes, AGEIS EA7407, La Tronche, F-38700, France.
| | - Pierre Magal
- Department of Mathematics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China; Univ. Bordeaux, IMB, UMR 5251, Talence, F-33400, France; CNRS, IMB, UMR 5251, Talence, F-33400, France
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Chaturvedi M, Rodiah I, Kretzschmar M, Scholz S, Lange B, Karch A, Jaeger VK. Estimating the relative importance of epidemiological and behavioural parameters for epidemic mpox transmission: a modelling study. BMC Med 2024; 22:297. [PMID: 39020322 PMCID: PMC11256368 DOI: 10.1186/s12916-024-03515-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 07/02/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Many European countries experienced outbreaks of mpox in 2022, and there was an mpox outbreak in 2023 in the Democratic Republic of Congo. There were many apparent differences between these outbreaks and previous outbreaks of mpox; the recent outbreaks were observed in men who have sex with men after sexual encounters at common events, whereas earlier outbreaks were observed in a wider population with no identifiable link to sexual contacts. These apparent differences meant that data from previous outbreaks could not reliably be used to parametrise infectious disease models during the 2022 and 2023 mpox outbreaks, and modelling efforts were hampered by uncertainty around key transmission and immunity parameters. METHODS We developed a stochastic, discrete-time metapopulation model for mpox that allowed for sexual and non-sexual transmission and the implementation of non-pharmaceutical interventions, specifically contact tracing and pre- and post-exposure vaccinations. We calibrated the model to case data from Berlin and used Sobol sensitivity analysis to identify parameters that mpox transmission is especially sensitive to. We also briefly analysed the sensitivity of the effectiveness of non-pharmaceutical interventions to various efficacy parameters. RESULTS We found that variance in the transmission probabilities due to both sexual and non-sexual transmission had a large effect on mpox transmission in the model, as did the level of immunity to mpox conferred by a previous smallpox vaccination. Furthermore, variance in the number of pre-exposure vaccinations offered was the dominant contributor to variance in mpox dynamics in men who have sex with men. If pre-exposure vaccinations were not available, both the accuracy and timeliness of contact tracing had a large impact on mpox transmission in the model. CONCLUSIONS Our results are valuable for guiding epidemiological studies for parameter ascertainment and identifying key factors for success of non-pharmaceutical interventions.
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Affiliation(s)
- Madhav Chaturvedi
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Isti Rodiah
- Department of Epidemiology, Helmholtz Centre for Infection Research, Brunswick, Germany
| | - Mirjam Kretzschmar
- Department of Epidemiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stefan Scholz
- Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Berit Lange
- Department of Epidemiology, Helmholtz Centre for Infection Research, Brunswick, Germany
- German Center for Infection Research, Brunswick, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Veronika K Jaeger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
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Gorji H, Stauffer N, Lunati I. Emergence of the reproduction matrix in epidemic forecasting. J R Soc Interface 2024; 21:20240124. [PMID: 39081116 PMCID: PMC11289658 DOI: 10.1098/rsif.2024.0124] [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: 02/21/2024] [Accepted: 05/30/2024] [Indexed: 08/02/2024] Open
Abstract
During the recent COVID-19 pandemic, the instantaneous reproduction number, R(t), has surged as a widely used measure to target public health interventions aiming at curbing the infection rate. In analogy with the basic reproduction number that arises from the linear stability analysis, R(t) is typically interpreted as a threshold parameter that separates exponential growth (R(t) > 1) from exponential decay (R(t) < 1). In real epidemics, however, the finite number of susceptibles, the stratification of the population (e.g. by age or vaccination state), and heterogeneous mixing lead to more complex epidemic courses. In the context of the multidimensional renewal equation, we generalize the scalar R(t) to a reproduction matrix, [Formula: see text], which details the epidemic state of the stratified population, and offers a concise epidemic forecasting scheme. First, the reproduction matrix is computed from the available incidence data (subject to some a priori assumptions), then it is projected into the future by a transfer functional to predict the epidemic course. We demonstrate that this simple scheme allows realistic and accurate epidemic trajectories both in synthetic test cases and with reported incidence data from the COVID-19 pandemic. Accounting for the full heterogeneity and nonlinearity of the infection process, the reproduction matrix improves the prediction of the infection peak. In contrast, the scalar reproduction number overestimates the possibility of sustaining the initial infection rate and leads to an overshoot in the incidence peak. Besides its simplicity, the devised forecasting scheme offers rich flexibility to be generalized to time-dependent mitigation measures, contact rate, infectivity and vaccine protection.
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Affiliation(s)
- Hossein Gorji
- Laboratory for Computational Engineering, Empa, Dübendorf, Switzerland
| | - Noé Stauffer
- Laboratory for Computational Engineering, Empa, Dübendorf, Switzerland
- Chair of Computational Mathematics and Simulation Science, EPFL, Switzerland
| | - Ivan Lunati
- Laboratory for Computational Engineering, Empa, Dübendorf, Switzerland
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Jarvis CI, Coletti P, Backer JA, Munday JD, Faes C, Beutels P, Althaus CL, Low N, Wallinga J, Hens N, Edmunds WJ. Social contact patterns following the COVID-19 pandemic: a snapshot of post-pandemic behaviour from the CoMix study. Epidemics 2024; 48:100778. [PMID: 38964131 DOI: 10.1016/j.epidem.2024.100778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/27/2024] [Accepted: 06/14/2024] [Indexed: 07/06/2024] Open
Abstract
The COVID-19 pandemic led to unprecedented changes in behaviour. To estimate if these persisted, a final round of the CoMix social contact survey was conducted in four countries at a time when all societal restrictions had been lifted for several months. We conducted a survey on a nationally representative sample in the UK, Netherlands (NL), Belgium (BE), and Switzerland (CH). Participants were asked about their contacts and behaviours on the previous day. We calculated contact matrices and compared the contact levels to a pre-pandemic baseline to estimate R0. Data collection occurred from 17 November to 7 December 2022. 7477 participants were recruited. Some were asked to undertake the survey on behalf of their children. Only 14.4 % of all participants reported wearing a facemask on the previous day. Self-reported vaccination rates in adults were similar for each country at around 86 %. Trimmed mean recorded contacts were highest in NL with 9.9 (95 % confidence interval [CI] 9.0-10.8) contacts per person per day and lowest in CH at 6.0 (95 % CI 5.4-6.6). Contacts at work were lowest in the UK (1.4 contacts per person per day) and highest in NL at 2.8 contacts per person per day. Other contacts were also lower in the UK at 1.6 per person per day (95 % CI 1.4-1.9) and highest in NL at 3.4 recorded per person per day (95 % CI 43.0-4.0). The next-generation approach suggests that R0 for a close-contact disease would be roughly half pre-pandemic levels in the UK, 80 % in NL and intermediate in the other two countries. The pandemic appears to have resulted in lasting changes in contact patterns expected to have an impact on the epidemiology of many different pathogens. Further post-pandemic surveys are necessary to confirm this finding.
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Affiliation(s)
- Christopher I Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Pietro Coletti
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium.
| | - Jantien A Backer
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - James D Munday
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK; Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
| | - Christel Faes
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium
| | - Philippe Beutels
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk 2610, Belgium
| | - Christian L Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Niel Hens
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek 3590, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk 2610, Belgium
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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Chen J, Bhattacharya P, Hoops S, Machi D, Adiga A, Mortveit H, Venkatramanan S, Lewis B, Marathe M. Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub. Epidemics 2024; 48:100779. [PMID: 39024889 DOI: 10.1016/j.epidem.2024.100779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 05/20/2024] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity.
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Affiliation(s)
- Jiangzhuo Chen
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
| | | | - Stefan Hoops
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Dustin Machi
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | | | - Bryan Lewis
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA; Department of Computer Science, University of Virginia, Charlottesville, VA, USA.
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Costantino V, MacIntyre CR. Impact of vaccine coverage and disruption to health services on COVID-19 in Ukraine. Sci Rep 2024; 14:14729. [PMID: 38926448 PMCID: PMC11208616 DOI: 10.1038/s41598-024-57447-7] [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: 02/23/2023] [Accepted: 03/18/2024] [Indexed: 06/28/2024] Open
Abstract
COVID-19 surveillance in Ukraine ceased after the Russian invasion of the country in 2022, on a background of low vaccination rates of 34.5% for two doses at this time. We conducted a modelling study to estimate the epidemic trajectory of SARS-COV-2 in Ukraine after the start of the war. We use a COVID-19 deterministic Susceptible-Exposed-Infected-Recovered (SEIR) model for Ukraine to estimate the impact of increased vaccination coverage and masking as public health interventions. We fit the model output to case notification data between 6 January and 25 February 2022, then we forecast the COVID-19 epidemic trajectory in different scenarios of mask use and vaccine coverage. In the best-case scenario, 69% of the Ukrainian population would have been infected in the first half of 2022. Increasing mask use from 50 to 80% reduces cases and deaths by 17% and 30% respectively, while increasing vaccination rates to 60% and 9.6% for two and three doses respectively results in a 3% reduction in cases and 28% in deaths. However, if vaccination is increased to a higher coverage of 80% with two doses and 12.8% with three, or mask effectiveness is reduced to 40%, increasing vaccination coverage is more effective. The loss of health services, displacement, and destruction of infrastructure will amplify the risk of COVID-19 in Ukraine and make vaccine programs less feasible. Masks do not need the health infrastructure or cold-chain logistics required for vaccines and are more feasible for rapid epidemic control during war. However, increasing vaccine coverage will save more lives. Vaccination of refugees who have fled to other countries can be more feasibly achieved.
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Affiliation(s)
- Valentina Costantino
- The Biosecurity Program, The Kirby Institute, University of New South Wales, High street, Kensington, Sydney, Australia.
| | - Chandini R MacIntyre
- The Biosecurity Program, The Kirby Institute, University of New South Wales, High street, Kensington, Sydney, Australia
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
- Watts College of Public Affairs and Community Solutions, Arizona State University, Tempe, AZ, USA
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10
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Bouhali A, Aribi WB, Miled SB, Kebir A. Impact of immunity loss on the optimal vaccination strategy for an age-structured epidemiological model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6372-6392. [PMID: 39176430 DOI: 10.3934/mbe.2024278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
The pursuit of effective vaccination strategies against COVID-19 remains a critical endeavour in global public health, particularly amidst challenges posed by immunity loss and evolving epidemiological dynamics. This study investigated optimal vaccination strategies by considering age structure, immunity dynamics, and varying maximal vaccination rates. To this end, we formulated an SEIR model stratified into $ n $ age classes, with the vaccination rate as an age-dependent control variable in an optimal control problem. We developed an objective function aimed at minimising critical infections while optimising vaccination efforts and then conducted rigorous mathematical analyses to ensure the existence and characterization of the optimal control. Using data from three countries with diverse age distributions, in expansive, constrictive, and stationary pyramids, we performed numerical simulations to evaluate the optimal age-dependent vaccination strategy, number of critical infections, and vaccination frequency. Our findings highlight the significant influence of maximal vaccination rates on shaping optimal vaccination strategies. Under constant maximal vaccination rates, prioritising age groups based on population demographics proves effective, with higher rates resulting in fewer critically infected individuals across all age distributions. Conversely, adopting age-dependent maximal vaccination rates, akin to the WHO strategy, may not always lead to the lowest critical infection peaks but offers a viable alternative in resource-constrained settings.
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Affiliation(s)
- Amira Bouhali
- BioInformatics, bioMathematics and bioStatistics (BIMS-LR16IPT09), Institute Pasteur of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
- National Engineering School of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
| | - Walid Ben Aribi
- BioInformatics, bioMathematics and bioStatistics (BIMS-LR16IPT09), Institute Pasteur of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
- School of Business, Esprit School of Business, Ariana 2083, Tunisia
| | - Slimane Ben Miled
- BioInformatics, bioMathematics and bioStatistics (BIMS-LR16IPT09), Institute Pasteur of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
| | - Amira Kebir
- BioInformatics, bioMathematics and bioStatistics (BIMS-LR16IPT09), Institute Pasteur of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia
- Preparatory Institute for Engineering Studies in Tunis, Tunis University, Tunis 1089, Tunisia
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Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Giovanni Maldonado Briones H, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Augusto Joaquim O, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol. PLoS One 2024; 19:e0301638. [PMID: 38913670 PMCID: PMC11195963 DOI: 10.1371/journal.pone.0301638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/15/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. METHODS To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. DISCUSSION Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
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Affiliation(s)
- Obianuju Genevieve Aguolu
- Division of Epidemiology, College of Public Heath, The Ohio State University, Columbus, Ohio, United States of America
| | - Moses Chapa Kiti
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Kristin Nelson
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic–Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | | | | | | | - Gifta John
- Christian Medical College Vellore, Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Sara Kim
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Holin Chen
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | - Inci Yildirim
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
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12
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Avusuglo WS, Han Q, Woldegerima WA, Bragazzi N, Asgary A, Ahmadi A, Orbinski J, Wu J, Mellado B, Kong JD. Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework. BMC Public Health 2024; 24:1540. [PMID: 38849785 PMCID: PMC11157731 DOI: 10.1186/s12889-024-18984-y] [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: 04/19/2024] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
OBJECTIVE To assess the impact of self-medication on the transmission dynamics of COVID-19 across different age groups, examine the interplay of vaccination and self-medication in disease spread, and identify the age group most prone to self-medication. METHODS We developed an age-structured compartmentalized epidemiological model to track the early dynamics of COVID-19. Age-structured data from the Government of Gauteng, encompassing the reported cumulative number of cases and daily confirmed cases, were used to calibrate the model through a Markov Chain Monte Carlo (MCMC) framework. Subsequently, uncertainty and sensitivity analyses were conducted on the model parameters. RESULTS We found that self-medication is predominant among the age group 15-64 (74.52%), followed by the age group 0-14 (34.02%), and then the age group 65+ (11.41%). The mean values of the basic reproduction number, the size of the first epidemic peak (the highest magnitude of the disease), and the time of the first epidemic peak (when the first highest magnitude occurs) are 4.16499, 241,715 cases, and 190.376 days, respectively. Moreover, we observed that self-medication among individuals aged 15-64 results in the highest spreading rate of COVID-19 at the onset of the outbreak and has the greatest impact on the first epidemic peak and its timing. CONCLUSION Studies aiming to understand the dynamics of diseases in areas prone to self-medication should account for this practice. There is a need for a campaign against COVID-19-related self-medication, specifically targeting the active population (ages 15-64).
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Affiliation(s)
- Wisdom S Avusuglo
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
| | - Qing Han
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
| | | | - Nicola Bragazzi
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
| | - Ali Asgary
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
- The Advanced Disaster, Emergency and Rapid Response Program, York University, Toronto, Canada
| | - Ali Ahmadi
- K. N.Toosi University of Technology, Faculty of Computer Engineering, Tehran, Iran
| | - James Orbinski
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), the Dahdaleh Institute for Global Health Research, York University, Toronto, Canada
| | - Jianhong Wu
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada
| | - Bruce Mellado
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), University of the Witwatersrand, Johannesburg, South Africa
| | - Jude Dzevela Kong
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada.
- Artificial Intelligence & Mathematical Modeling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
- Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, Canada.
- Department of Mathematics, University of Toronto, Toronto, Canada.
- Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), University of Toronto, Toronto, Canada.
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13
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Lee H, Choi H, Lee H, Lee S, Kim C. Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network. Front Public Health 2024; 12:1362823. [PMID: 38887240 PMCID: PMC11180726 DOI: 10.3389/fpubh.2024.1362823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
Introduction This paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance. Methods To address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. Results The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities. Conclusion This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.
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Affiliation(s)
- Hyunwoo Lee
- Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
| | - Hayoung Choi
- Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
| | - Hyojung Lee
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Sunmi Lee
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Applied Mathematics, Kyunghee University, Yongin-si, Republic of Korea
| | - Changhoon Kim
- Department of Preventive Medicine, College of Medicine, Pusan National University, Busan, Republic of Korea
- Busan Center for Infectious Disease Control and Prevention, Pusan National University Hospital, Busan, Republic of Korea
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14
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Chinazzi M, Davis JT, Y Piontti AP, Mu K, Gozzi N, Ajelli M, Perra N, Vespignani A. A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US. Epidemics 2024; 47:100757. [PMID: 38493708 DOI: 10.1016/j.epidem.2024.100757] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/22/2024] [Accepted: 02/26/2024] [Indexed: 03/19/2024] Open
Abstract
The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.
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Affiliation(s)
- Matteo Chinazzi
- The Roux Institute, Northeastern University, Portland, ME, USA; Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Jessica T Davis
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA
| | - Nicolò Gozzi
- Institute for Scientific Interchange Foundation, Turin, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Nicola Perra
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA; School of Mathematical Sciences, Queen Mary University, London, UK
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA, USA; Institute for Scientific Interchange Foundation, Turin, Italy.
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15
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Lewnard JA, Charani E, Gleason A, Hsu LY, Khan WA, Karkey A, Chandler CIR, Mashe T, Khan EA, Bulabula ANH, Donado-Godoy P, Laxminarayan R. Burden of bacterial antimicrobial resistance in low-income and middle-income countries avertible by existing interventions: an evidence review and modelling analysis. Lancet 2024; 403:2439-2454. [PMID: 38797180 DOI: 10.1016/s0140-6736(24)00862-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/18/2024] [Accepted: 04/22/2024] [Indexed: 05/29/2024]
Abstract
National action plans enumerate many interventions as potential strategies to reduce the burden of bacterial antimicrobial resistance (AMR). However, knowledge of the benefits achievable by specific approaches is needed to inform policy making, especially in low-income and middle-income countries (LMICs) with substantial AMR burden and low health-care system capacity. In a modelling analysis, we estimated that improving infection prevention and control programmes in LMIC health-care settings could prevent at least 337 000 (95% CI 250 200-465 200) AMR-associated deaths annually. Ensuring universal access to high-quality water, sanitation, and hygiene services would prevent 247 800 (160 000-337 800) AMR-associated deaths and paediatric vaccines 181 500 (153 400-206 800) AMR-associated deaths, from both direct prevention of resistant infections and reductions in antibiotic consumption. These estimates translate to prevention of 7·8% (5·6-11·0) of all AMR-associated mortality in LMICs by infection prevention and control, 5·7% (3·7-8·0) by water, sanitation, and hygiene, and 4·2% (3·4-5·1) by vaccination interventions. Despite the continuing need for research and innovation to overcome limitations of existing approaches, our findings indicate that reducing global AMR burden by 10% by the year 2030 is achievable with existing interventions. Our results should guide investments in public health interventions with the greatest potential to reduce AMR burden.
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Affiliation(s)
- Joseph A Lewnard
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
| | - Esmita Charani
- Division of Infectious Diseases & HIV Medicine, Department of Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Alec Gleason
- One Health Trust, Bengaluru, India; High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Li Yang Hsu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Wasif Ali Khan
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Abhilasha Karkey
- Oxford University Clinical Research Unit, Patan Academy of Health Sciences, Lalitpur, Nepal
| | - Clare I R Chandler
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK; Antimicrobial Resistance Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Tapfumanei Mashe
- One Health Office, Ministry of Health and Child Care, Harare, Zimbabwe; Health System Strengthening Unit, WHO, Harare, Zimbabwe
| | - Ejaz Ahmed Khan
- Department of Pediatrics, Shifa Tameer-e-Millat University, Shifa International Hospital, Islamabad, Pakistan
| | - Andre N H Bulabula
- Division of Disease Control and Prevention, Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia
| | - Pilar Donado-Godoy
- AMR Global Health Research Unit, Colombian Integrated Program of Antimicrobial Resistance Surveillance, Corporación Colombiana de Investigación Agropecuaria, Cundinamarca, Colombia
| | - Ramanan Laxminarayan
- One Health Trust, Bengaluru, India; High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA.
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16
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Ryan GE, Shearer FM, McCaw JM, McVernon J, Golding N. Estimating measures to reduce the transmission of SARS-CoV-2 in Australia to guide a 'National Plan' to reopening. Epidemics 2024; 47:100763. [PMID: 38513465 DOI: 10.1016/j.epidem.2024.100763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
The availability of COVID-19 vaccines promised a reduction in the severity of disease and relief from the strict public health and social measures (PHSMs) imposed in many countries to limit spread and burden of COVID-19. We were asked to define vaccine coverage thresholds for Australia's transition to easing restrictions and reopening international borders. Using evidence of vaccine effectiveness against the then-circulating Delta variant, we used a mathematical model to determine coverage targets. The absence of any COVID-19 infections in many sub-national jurisdictions in Australia posed particular methodological challenges. We used a novel metric called Transmission Potential (TP) as a proxy measure of the population-level effective reproduction number. We estimated TP of the Delta variant under a range of PHSMs, test-trace-isolate-quarantine (TTIQ) efficiencies, vaccination coverage thresholds, and age-based vaccine allocation strategies. We found that high coverage across all ages (≥70%) combined with ongoing TTIQ and minimal PHSMs was sufficient to avoid lockdowns. At lesser coverage (≤60%) rapid case escalation risked overwhelming of the health sector or the need to reimpose stricter restrictions. Maintaining low case numbers was most beneficial for health and the economy, and at higher coverage levels (≥80%) further easing of restrictions was deemed possible. These results directly informed easing of COVID-19 restrictions in Australia.
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Affiliation(s)
- Gerard E Ryan
- Telethon Kids Institute, Nedlands, WA, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC, Australia.
| | - Freya M Shearer
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC, Australia
| | - James M McCaw
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC, Australia; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Jodie McVernon
- Department of Infectious Diseases, Melbourne Medical School, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia; Victorian Infectious Diseases Reference Laboratory, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Nick Golding
- Telethon Kids Institute, Nedlands, WA, Australia; Curtin University, Perth, Australia
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17
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Giannini F, Hogan AB, Sarna M, Glass K, Moore HC. Modelling respiratory syncytial virus age-specific risk of hospitalisation in term and preterm infants. BMC Infect Dis 2024; 24:510. [PMID: 38773455 PMCID: PMC11110433 DOI: 10.1186/s12879-024-09400-2] [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: 12/12/2023] [Accepted: 05/13/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory infections in children worldwide. The highest incidence of severe disease is in the first 6 months of life, with infants born preterm at greatest risk for severe RSV infections. The licensure of new RSV therapeutics (a long-acting monoclonal antibody and a maternal vaccine) in Europe, USA, UK and most recently in Australia, has driven the need for strategic decision making on the implementation of RSV immunisation programs. Data driven approaches, considering the local RSV epidemiology, are critical to advise on the optimal use of these therapeutics for effective RSV control. METHODS We developed a dynamic compartmental model of RSV transmission fitted to individually-linked population-based laboratory, perinatal and hospitalisation data for 2000-2012 from metropolitan Western Australia (WA), stratified by age and prior exposure. We account for the differential risk of RSV-hospitalisation in full-term and preterm infants (defined as < 37 weeks gestation). We formulated a function relating age, RSV exposure history, and preterm status to the risk of RSV-hospitalisation given infection. RESULTS The age-to-risk function shows that risk of hospitalisation, given RSV infection, declines quickly in the first 12 months of life for all infants and is 2.6 times higher in preterm compared with term infants. The hospitalisation risk, given infection, declines to < 10% of the risk at birth by age 7 months for term infants and by 9 months for preterm infants. CONCLUSIONS The dynamic model, using the age-to-risk function, characterises RSV epidemiology for metropolitan WA and can now be extended to predict the impact of prevention measures. The stratification of the model by preterm status will enable the comparative assessment of potential strategies in the extended model that target this RSV risk group relative to all-population approaches. Furthermore, the age-to-risk function developed in this work has wider relevance to the epidemiological characterisation of RSV.
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Affiliation(s)
- Fiona Giannini
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, 15 Hospital Ave, Nedlands, WA, 6009, Australia.
| | - Alexandra B Hogan
- School of Population Health, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Mohinder Sarna
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, 15 Hospital Ave, Nedlands, WA, 6009, Australia
- School of Population Health, Curtin University, Perth, WA, 6002, Australia
| | - Kathryn Glass
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, 15 Hospital Ave, Nedlands, WA, 6009, Australia
- National Centre for Epidemiology and Population Health, The Australian National University, 62 Mills Rd, Acton ACT, 2601, Australia
| | - Hannah C Moore
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, 15 Hospital Ave, Nedlands, WA, 6009, Australia
- School of Population Health, Curtin University, Perth, WA, 6002, Australia
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18
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Manna A, Koltai J, Karsai M. Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics. Nat Commun 2024; 15:4137. [PMID: 38755162 PMCID: PMC11099065 DOI: 10.1038/s41467-024-48332-y] [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: 09/05/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
Individuals' socio-demographic and economic characteristics crucially shape the spread of an epidemic by largely determining the exposure level to the virus and the severity of the disease for those who got infected. While the complex interplay between individual characteristics and epidemic dynamics is widely recognised, traditional mathematical models often overlook these factors. In this study, we examine two important aspects of human behaviour relevant to epidemics: contact patterns and vaccination uptake. Using data collected during the COVID-19 pandemic in Hungary, we first identify the dimensions along which individuals exhibit the greatest variation in their contact patterns and vaccination uptake. We find that generally higher socio-economic groups of the population have a higher number of contacts and a higher vaccination uptake with respect to disadvantaged groups. Subsequently, we propose a data-driven epidemiological model that incorporates these behavioural differences. Finally, we apply our model to analyse the fourth wave of COVID-19 in Hungary, providing valuable insights into real-world scenarios. By bridging the gap between individual characteristics and epidemic spread, our research contributes to a more comprehensive understanding of disease dynamics and informs effective public health strategies.
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Affiliation(s)
- Adriana Manna
- Department of Network and Data Science, Central European University, Quellenstraße 51, Vienna, 1100, Austria
| | - Júlia Koltai
- National Laboratory for Health Security, HUN-REN Centre for Social Sciences, Tóth Kálmán utca 4, Budapest, 1097, Hungary
- Department of Social Research Methodology, Faculty of Social Sciences, Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest, 1117, Hungary
| | - Márton Karsai
- Department of Network and Data Science, Central European University, Quellenstraße 51, Vienna, 1100, Austria.
- National Laboratory for Health Security, HUN-REN Rényi Institute of Mathematics, Reáltanoda utca 13-15, Budapest, 1053, Hungary.
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19
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Jitsuk NC, Chadsuthi S, Modchang C. Vaccination strategies impact the probability of outbreak extinction: A case study of COVID-19 transmission. Heliyon 2024; 10:e28042. [PMID: 38524580 PMCID: PMC10958689 DOI: 10.1016/j.heliyon.2024.e28042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 03/10/2024] [Accepted: 03/11/2024] [Indexed: 03/26/2024] Open
Abstract
Mass vaccination has proven to be an effective control measure for mitigating the transmission of infectious diseases. Throughout history, various vaccination strategies have been employed to control infections and terminate outbreaks. In this study, we utilized the transmission of COVID-19 as a case study and constructed a stochastic age-structured compartmental model to investigate the effectiveness of different vaccination strategies. Our analysis focused on estimating the outbreak extinction probability under different vaccination scenarios in both homogeneous and heterogeneous populations. Notably, we found that population heterogeneity can enhance the likelihood of outbreak extinction at varying levels of vaccine coverage. Prioritizing vaccinations for individuals with higher infection risk was found to maximize outbreak extinction probability and reduce overall infections, while allocating vaccines to those with higher mortality risk has been proven more effective in reducing deaths. Moreover, our study highlighted the significance of booster doses as the vaccine effectiveness wanes over time, showing that they can significantly enhance the extinction probability and mitigate disease transmission.
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Affiliation(s)
- Natcha C. Jitsuk
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Sudarat Chadsuthi
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Department of Physics, Research Center for Academic Excellence in Applied Physics, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Centre of Excellence in Mathematics, MHESI, Bangkok, 10400, Thailand
- Thailand Center of Excellence in Physics, Ministry of Higher Education, Science, Research and Innovation, 328 Si Ayutthaya Road, Bangkok, 10400, Thailand
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Dall’Amico L, Kleynhans J, Gauvin L, Tizzoni M, Ozella L, Makhasi M, Wolter N, Language B, Wagner RG, Cohen C, Tempia S, Cattuto C. Estimating household contact matrices structure from easily collectable metadata. PLoS One 2024; 19:e0296810. [PMID: 38483886 PMCID: PMC10939291 DOI: 10.1371/journal.pone.0296810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/18/2023] [Indexed: 03/17/2024] Open
Abstract
Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
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Affiliation(s)
| | - Jackie Kleynhans
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laetitia Gauvin
- ISI Foundation, Turin, Italy
- Institute for Research on sustainable Development, UMR215 PRODIG, Aubervilliers, France
| | - Michele Tizzoni
- ISI Foundation, Turin, Italy
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | | | - Mvuyo Makhasi
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
| | - Nicole Wolter
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Brigitte Language
- Unit for Environmental Science and Management, Climatology Research Group, North-West University, Potchefstroom, South Africa
| | - Ryan G. Wagner
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Agincourt, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stefano Tempia
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ciro Cattuto
- ISI Foundation, Turin, Italy
- Department of Informatics, University of Turin, Turin, Italy
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21
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Kanté DSI, Jebrane A, Boukamel A, Hakim A. Morocco's population contact matrices: A crowd dynamics-based approach using aggregated literature data. PLoS One 2024; 19:e0296740. [PMID: 38483954 PMCID: PMC10939283 DOI: 10.1371/journal.pone.0296740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/18/2023] [Indexed: 03/17/2024] Open
Abstract
Estimation of contact patterns is often based on questionnaires and time-use data. The results obtained using these methods have been used extensively over the years and recently to predict the spread of the COVID-19 pandemic. They have also been used to test the effectiveness of non-pharmaceutical measures such as social distance. The latter is integrated into epidemiological models by multiplying contact matrices by control functions. We present a novel method that allows the integration of social distancing and other scenarios such as panic. Our method is based on a modified social force model. The model is calibrated using data relating to the movements of individuals and their interactions such as desired walking velocities and interpersonal distances as well as demographic data. We used the framework to assess contact patterns in different social contexts in Morocco. The estimated matrices are extremely assortative and exhibit patterns similar to those observed in other studies including the POLYMOD project. Our findings suggest social distancing would reduce the numbers of contacts by 95%. Further, we estimated the effect of panic on contact patterns, which indicated an increase in the number of contacts of 11%. This approach could be an alternative to questionnaire-based methods in the study of non-pharmaceutical measures and other specific scenarios such as rush hours. It also provides a substitute for estimating children's contact patterns which are typically assessed through parental proxy reporting in surveys.
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Affiliation(s)
- Dramane Sam Idris Kanté
- Complex Systems and Interactions Team, Ecole Centrale Casablanca, Bouskoura, Morocco
- LAMAI, Faculty of Sciences and Technology, Cadi Ayyad University, Marrakesh, Morocco
| | - Aissam Jebrane
- Complex Systems and Interactions Team, Ecole Centrale Casablanca, Bouskoura, Morocco
| | - Adnane Boukamel
- Complex Systems and Interactions Team, Ecole Centrale Casablanca, Bouskoura, Morocco
| | - Abdelilah Hakim
- LAMAI, Faculty of Sciences and Technology, Cadi Ayyad University, Marrakesh, Morocco
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22
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Gonzalez-Parra G, Mahmud MS, Kadelka C. Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303726. [PMID: 38496570 PMCID: PMC10942533 DOI: 10.1101/2024.03.04.24303726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
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Affiliation(s)
- Gilberto Gonzalez-Parra
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, València, Spain
- Department of Mathematics, New Mexico Tech, 801 Leroy Place, Socorro, 87801, NM, USA
| | - Md Shahriar Mahmud
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
| | - Claus Kadelka
- Department of Mathematics, Iowa State University, 411 Morrill Rd, Ames, 50011, IA, USA
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23
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Park HJ, Gonsalves GS, Tan ST, Kelly JD, Rutherford GW, Wachter RM, Schechter R, Paltiel AD, Lo NC. Comparing frequency of booster vaccination to prevent severe COVID-19 by risk group in the United States. Nat Commun 2024; 15:1883. [PMID: 38448400 PMCID: PMC10917753 DOI: 10.1038/s41467-024-45549-9] [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: 07/01/2023] [Accepted: 01/26/2024] [Indexed: 03/08/2024] Open
Abstract
There is a public health need to understand how different frequencies of COVID-19 booster vaccines may mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age and immune status. By analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively reduce the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit against severe disease. In persons 75+ years, the model estimated that annual boosters would reduce absolute annual risk of severe COVID-19 by 199 (uncertainty interval: 183-232) cases per 100,000 persons, compared to a one-time booster vaccination. In contrast, for persons 18-49 years, the model estimated that annual boosters would reduce this risk by 14 (10-19) cases per 100,000 persons. Those with prior infection had lower benefit of more frequent boosting, and immunocompromised persons had larger benefit. Scenarios with emerging variants with immune evasion increased the benefit of more frequent variant-targeted boosters. This study underscores the benefit of considering key risk factors to inform frequency of COVID-19 booster vaccines in public health guidance and ensuring at least annual boosters in high-risk populations.
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Affiliation(s)
- Hailey J Park
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Gregg S Gonsalves
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Sophia T Tan
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - J Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- F.I. Proctor Foundation, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Institute for Global Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - George W Rutherford
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Institute for Global Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robert M Wachter
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - A David Paltiel
- Department of Health Policy and Management and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Nathan C Lo
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
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24
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Bekker-Nielsen Dunbar M. Transmission matrices used in epidemiologic modelling. Infect Dis Model 2024; 9:185-194. [PMID: 38249428 PMCID: PMC10796975 DOI: 10.1016/j.idm.2023.11.009] [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: 09/19/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 01/23/2024] Open
Abstract
Mixing matrices are included in infectious disease models to reflect transmission opportunities between population strata. These matrices were originally constructed on the basis of theoretical considerations and most of the early work in this area originates from research on sexually transferred diseases in the 80s, in response to AIDS. Later work in the 90s populated these matrices on the basis of survey data gathered to capture transmission risks for respiratory diseases. We provide an overview of developments in the construction of matrices for capturing transmission opportunities in populations. Such transmission matrices are useful for epidemiologic modelling to capture within and between stratum transmission and can be informed from theoretical mixing assumptions, informed by empirical evidence gathered through investigation as well as generated on the basis of data. Links to summary measures and threshold conditions are also provided.
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Affiliation(s)
- M. Bekker-Nielsen Dunbar
- Centre for Research on Pandemics & Society, OsloMet – Oslo Metropolitan University, HG536, Holbergs gate 1, Oslo, 0166, Norway
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25
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Lamghari A, Kanté DSI, Jebrane A, Hakim A. Modeling the impact of distancing measures on infectious disease spread: a case study of COVID-19 in the Moroccan population. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4370-4396. [PMID: 38549332 DOI: 10.3934/mbe.2024193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
This paper explores the impact of various distancing measures on the spread of infectious diseases, focusing on the spread of COVID-19 in the Moroccan population as a case study. Contact matrices, generated through a social force model, capture population interactions within distinct activity locations and age groups. These matrices, tailored for each distancing scenario, have been incorporated into an SEIR model. The study models the region as a network of interconnected activity locations, enabling flexible analysis of the effects of different distancing measures within social contexts and between age groups. Additionally, the method assesses the influence of measures targeting potential superspreaders (i.e., agents with a very high contact rate) and explores the impact of inter-activity location flows, providing insights beyond scalar contact rates or survey-based contact matrices. The results suggest that implementing intra-activity location distancing measures significantly reduces in the number of infected individuals relative to the act of imposing restrictions on individuals with a high contact rate in each activity location. The combination of both measures proves more advantageous. On a regional scale, characterized as a network of interconnected activity locations, restrictions on the movement of individuals with high contact rates was found to result in a $ 2 \% $ reduction, while intra-activity location-based distancing measures was found to achieve a $ 44 \% $ reduction. The combination of these two measures yielded a $ 48\% $ reduction.
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Affiliation(s)
- Abdelkarim Lamghari
- LAMAI, Faculty of Sciences and Technics, Department of Mathematics, Cadi Ayyad University, Marrakesh 40140, Morocco
| | - Dramane Sam Idris Kanté
- LAMAI, Faculty of Sciences and Technics, Department of Mathematics, Cadi Ayyad University, Marrakesh 40140, Morocco
- Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura 27182, Morocco
| | - Aissam Jebrane
- Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura 27182, Morocco
| | - Abdelilah Hakim
- LAMAI, Faculty of Sciences and Technics, Department of Mathematics, Cadi Ayyad University, Marrakesh 40140, Morocco
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26
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Shimul SN, Hussain M, Faisel AJ, Hamid SA, Sultana N, Kuddus MA. Impact of alternative Non-Pharmaceutical Interventions strategies for controlling COVID-19 outbreak in Bangladesh: A modeling study. PLoS One 2024; 19:e0293863. [PMID: 38394237 PMCID: PMC10890722 DOI: 10.1371/journal.pone.0293863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/20/2023] [Indexed: 02/25/2024] Open
Abstract
The COVID-19 pandemic has been a major health concern in Bangladesh until very recently. Although the Bangladesh government has employed various infection control strategies, more targeted Non-Pharmaceutical interventions (NPIs), including school closure, mask-wearing, hand washing, and social distancing have gained special attention. Despite significant long-term adverse effects of school closures, authorities have opted to keep schools closed to curb the spread of COVID-19 infection. However, there is limited knowledge about the impact of reopening schools alongside other NPI measures on the course of the epidemic. In this study, we implemented a mathematical modeling framework developed by the CoMo Consortium to explore the impact of NPIs on the dynamics of the COVID-19 outbreak and deaths for Bangladesh. For robustness, the results of prediction models are then validated through model calibration with incidence and mortality data and using external sources. Hypothetical projections are made under alternative NPIs where we compare the impact of current NPIs with school closures versus enhanced NPIs with school openings. Results suggest that enhanced NPIs with schools opened may have lower COVID-19 related prevalence and deaths. This finding indicates that enhanced NPIs and school openings may mitigate the long-term negative impacts of COVID-19 in low- and middle-income countries. Potential shortcomings and ways to improve the research are also discussed.
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Affiliation(s)
- Shafiun Nahin Shimul
- Institute of Health Economics, University of Dhaka, Dhaka, Bangladesh
- CDC Foundation Postdoctoral Fellow, Georgia State University, Atlanta, Georgia
| | - Mofakhar Hussain
- Institute of Health Policy, Management and Evaluation University of Toronto, Toronto, ON, Canada
| | - Abul Jamil Faisel
- President-Elect, The Public Health Association of Bangladesh & Institute of Health Economics, University of Dhaka, Dhaka, Bangladesh
| | - Syed Abdul Hamid
- Institute of Health Economics, University of Dhaka, Dhaka, Bangladesh
| | - Nasrin Sultana
- Institute of Health Economics, University of Dhaka, Dhaka, Bangladesh
| | - Md Abdul Kuddus
- Department of Mathematics, University of Rajshahi, Rajshahi, Bangladesh
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27
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Nagpal S, Kumar R, Noronha RF, Kumar S, Gupta D, Amarchand R, Gosain M, Sharma H, Menon GI, Krishnan A. Seasonal variations in social contact patterns in a rural population in north India: Implications for pandemic control. PLoS One 2024; 19:e0296483. [PMID: 38386667 PMCID: PMC10883557 DOI: 10.1371/journal.pone.0296483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 12/11/2023] [Indexed: 02/24/2024] Open
Abstract
Social contact mixing patterns are critical to model the transmission of communicable diseases, and have been employed to model disease outbreaks including COVID-19. Nonetheless, there is a paucity of studies on contact mixing in low and middle-income countries such as India. Furthermore, mathematical models of disease outbreaks do not account for the temporal nature of social contacts. We conducted a longitudinal study of social contacts in rural north India across three seasons and analysed the temporal differences in contact patterns. A contact diary survey was performed across three seasons from October 2015-16, in which participants were queried on the number, duration, and characteristics of contacts that occurred on the previous day. A total of 8,421 responses from 3,052 respondents (49% females) recorded characteristics of 180,073 contacts. Respondents reported a significantly higher number and duration of contacts in the winter, followed by the summer and the monsoon season (Nemenyi post-hoc, p<0.001). Participants aged 0-9 years and 10-19 years of age reported the highest median number of contacts (16 (IQR 12-21), 17 (IQR 13-24) respectively) and were found to have the highest node centrality in the social network of the region (pageranks = 0.20, 0.17). A large proportion (>80%) of contacts that were reported in schools or on public transport involved physical contact. To the best of our knowledge, our study is the first from India to show that contact mixing patterns vary by the time of the year and provides useful implications for pandemic control. We compared the differences in the number, duration and location of contacts by age-group and gender, and studied the impact of the season, age-group, employment and day of the week on the number and duration of contacts using multivariate negative binomial regression. We created a social network to further understand the age and gender-specific contact patterns, and used the contact matrices in each season to parameterise a nine-compartment agent-based model for simulating a COVID-19 epidemic in each season. Our results can be used to parameterize more accurate mathematical models for prediction of epidemiological trends of infections in rural India.
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Affiliation(s)
| | - Rakesh Kumar
- All India Institute of Medical Sciences, New Delhi, India
| | | | - Supriya Kumar
- Bill and Melinda Gates Foundation, Seattle, WA, United States of America
| | | | | | - Mudita Gosain
- All India Institute of Medical Sciences, New Delhi, India
| | | | | | - Anand Krishnan
- All India Institute of Medical Sciences, New Delhi, India
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28
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Hamilton MA, Knight J, Mishra S. Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models. Am J Epidemiol 2024; 193:339-347. [PMID: 37715459 PMCID: PMC10840077 DOI: 10.1093/aje/kwad185] [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: 12/02/2022] [Revised: 06/16/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023] Open
Abstract
Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and ≥15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models.
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Affiliation(s)
| | | | - Sharmistha Mishra
- Correspondence to Dr. Sharmistha Mishra, Department of Medicine, University of Toronto, Li Ka Shing Knowledge Institute, Unity Health Toronto, 209 Victoria Street, Toronto M5B 1T8, Canada (e-mail: )
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29
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Patterson J, Cleary S, Norman JM, Van Zyl H, Awine T, Mayet S, Kagina B, Muloiwa R, Hussey G, Silal SP. Modelling the Cost-Effectiveness of Hepatitis A in South Africa. Vaccines (Basel) 2024; 12:116. [PMID: 38400100 PMCID: PMC10893480 DOI: 10.3390/vaccines12020116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/14/2024] [Accepted: 01/22/2024] [Indexed: 02/25/2024] Open
Abstract
The World Health Organization (WHO) recommends the consideration of introducing routine hepatitis A vaccination into national immunization schedules for children ≥ 1 years old in countries with intermediate HAV endemicity. Recent data suggest that South Africa is transitioning from high to intermediate HAV endemicity, thus it is important to consider the impact and cost of potential routine hepatitis A vaccination strategies in the country. An age-structured compartmental model of hepatitis A transmission was calibrated with available data from South Africa, incorporating direct costs of hepatitis A treatment and vaccination. We used the calibrated model to evaluate the impact and costs of several childhood hepatitis A vaccination scenarios from 2023 to 2030. We assessed how each scenario impacted the burden of hepatitis A (symptomatic hepatitis A cases and mortality) as well as calculated the incremental cost per DALY averted as compared to the South African cost-effectiveness threshold. All costs and outcomes were discounted at 5%. For the modelled scenarios, the median estimated cost of the different vaccination strategies ranged from USD 1.71 billion to USD 2.85 billion over the period of 2023 to 2030, with the cost increasing for each successive scenario and approximately 39-52% of costs being due to vaccination. Scenario 1, which represented the administration of one dose of the hepatitis A vaccine in children < 2 years old, requires approximately 5.3 million vaccine doses over 2023-2030 and is projected to avert a total of 136,042 symptomatic cases [IQR: 88,842-221,483] and 31,106 [IQR: 22,975-36,742] deaths due to hepatitis A over the period of 2023 to 2030. The model projects that Scenario 1 would avert 8741 DALYs over the period of 2023 to 2030; however, it is not cost-effective against the South African cost-effectiveness threshold with an ICER per DALY averted of USD 21,006. While Scenario 3 and 4 included the administration of more vaccine doses and averted more symptomatic cases of hepatitis A, these scenarios were absolutely dominated owing to the population being infected before vaccination through the mass campaigns at older ages. The model was highly sensitive to variation of access to liver transplant in South Africa. When increasing the access to liver transplant to 100% for the baseline and Scenario 1, the ICER for Scenario 1 becomes cost-effective against the CET (ICER = USD 2425). Given these findings, we recommend further research is conducted to understand the access to liver transplants in South Africa and better estimate the cost of liver transplant care for hepatitis A patients. The modelling presented in this paper has been used to develop a user-friendly application for vaccine policy makers to further interrogate the model outcomes and consider the costs and benefits of introducing routine hepatitis A vaccination in South Africa.
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Affiliation(s)
- Jenna Patterson
- Vaccines for Africa Initiative, School of Public Health, University of Cape Town, Cape Town 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7925, South Africa
| | - Susan Cleary
- School of Public Health, University of Cape Town, Cape Town 7925, South Africa
| | - Jared Michael Norman
- Modelling and Simulation Hub, Africa (MASHA), Department of Statistical Sciences, University of Cape Town, Cape Town 7700, South Africa
| | - Heiletjé Van Zyl
- Modelling and Simulation Hub, Africa (MASHA), Department of Statistical Sciences, University of Cape Town, Cape Town 7700, South Africa
| | - Timothy Awine
- Modelling and Simulation Hub, Africa (MASHA), Department of Statistical Sciences, University of Cape Town, Cape Town 7700, South Africa
| | - Saadiyah Mayet
- Modelling and Simulation Hub, Africa (MASHA), Department of Statistical Sciences, University of Cape Town, Cape Town 7700, South Africa
| | - Benjamin Kagina
- Vaccines for Africa Initiative, School of Public Health, University of Cape Town, Cape Town 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7925, South Africa
| | - Rudzani Muloiwa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town 7700, South Africa
| | - Gregory Hussey
- Vaccines for Africa Initiative, School of Public Health, University of Cape Town, Cape Town 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7925, South Africa
| | - Sheetal Prakash Silal
- Modelling and Simulation Hub, Africa (MASHA), Department of Statistical Sciences, University of Cape Town, Cape Town 7700, South Africa
- Centre for Global Health, Nuffield Department of Medicine, Oxford University, Oxford OX3 7LG, UK
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30
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Pelton SI, Mould-Quevedo JF, Nguyen VH. Modelling the population-level benefits and cost-effectiveness of cell-based quadrivalent influenza vaccine for children and adolescents aged 6 months to 17 years in the US. Expert Rev Vaccines 2024; 23:82-87. [PMID: 38093415 DOI: 10.1080/14760584.2023.2295014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/11/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Cell-based quadrivalent influenza vaccines (QIVc) can increase effectiveness against seasonal influenza by avoiding mismatch from egg adaption of vaccine viruses. This study evaluates the population-level cost-effectiveness and impacts on health outcomes of QIVc versus an egg-based vaccine (QIVe) in children aged 6 months to 17 years in the US. RESEARCH DESIGN AND METHODS A dynamic age-structured susceptible-exposed-infected-recovered model was used to simulate influenza transmission in low and high incidence seasons for two scenarios: 1. QIVe for 6 months-17 year-olds, QIVc for 18-64 year-olds, and adjuvanted QIV (aQIV) for ≥ 65 year-olds, and 2. QIVc for 6 months-64 year-olds, and aQIV for ≥ 65 year-olds. Probabilistic sensitivity analysis was performed to account for uncertainty in parameter estimates. Cost-effectiveness was evaluated as incremental cost-effectiveness ratios (ICERs). RESULTS Extension of QIVc to children resulted in 3-4% reductions in cases (1,656,271), hospitalizations (16,688), and deaths (2,126) at a population level in a high incidence season, and 65% reductions (cases: 2,856,384; hospitalizations: 31667; deaths: 4,163) in a low incidence season. Use of QIVc would be cost-saving, with ICERs of -$16,427/QALY and -$8,100/QALY from a payer perspective and -$22,669/QALY and -$15,015/QALY from a societal perspective, for low and high incidence seasons respectively. Cost savings were estimated at approximately $468 million and $1.366 billion for high and low incidence seasons, respectively. CONCLUSION Use of QIVc instead of QIVe in children > 6 months of age in the US would reduce the disease burden and be cost-saving from both a payer and societal perspective.
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Affiliation(s)
- Stephen I Pelton
- Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA
| | | | - Van Hung Nguyen
- Global Health Economics and Epidemiology, VHN Consulting Inc, Montreal, Canada
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George A, Lacey P, Badrinath P, Gray A, Turner P, Harwood C, Gregson M. Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach. BMJ Open 2023; 13:e072975. [PMID: 38081661 PMCID: PMC10729138 DOI: 10.1136/bmjopen-2023-072975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES To develop, test, validate and implement a system dynamics model to simulate the pandemic progress and the impact of various interventions on viral spread, healthcare utilisation and demand in secondary care. DESIGN We adopted the system dynamics model incorporating susceptible, exposed, infection and recovery framework to simulate the progress of the pandemic and how the interventions for the COVID-19 response influence the outcomes with a focus on secondary care. SETTING This study was carried out covering all the local health systems in Southeast of England with a catchment population of six million with a specific focus on Kent and Medway system. PARTICIPANTS Six local health systems in Southeast of England using Kent and Medway as a case study. INTERVENTIONS Short to medium 'what if' scenarios incorporating human behaviour, non-pharmaceutical interventions and medical interventions were tested using the model with regular and continuous feedback of the model results to the local health system leaders for monitoring, planning and rapid response as needed. MAIN OUTCOME MEASURES Daily output from the model which included number infected in the population, hospital admissions needing COVID-19 care, occupied general beds, continuous positive airway pressure beds, intensive care beds, hospital discharge pathways and deaths. RESULTS We successfully implemented a regional series of models based on the local population needs which were used in healthcare planning as part of the pandemic response. CONCLUSIONS In this study, we have demonstrated the utility of system dynamics modelling incorporating local intelligence and collaborative working during the pandemic to respond rapidly and take decisions to protect the population. This led to strengthened cooperation among partners and ensured that the local population healthcare needs were met.
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Affiliation(s)
- Abraham George
- Kent County Council Adult Social Care and Health, Maidstone, UK
| | - Peter Lacey
- The Whole Systems Partnership, Knaresborough, UK
| | | | - Alex Gray
- The Whole Systems Partnership, Knaresborough, UK
| | - Paul Turner
- The Whole Systems Partnership, Knaresborough, UK
| | | | - Mark Gregson
- The Whole Systems Partnership, Knaresborough, UK
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Akanteva A, Dick DW, Amiraslani S, Heffernan JM. Canadian Covid-19 pandemic public health mitigation measures at the province level. Sci Data 2023; 10:882. [PMID: 38066033 PMCID: PMC10709578 DOI: 10.1038/s41597-023-02759-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
The Covid-19 pandemic has prompted governments across the world to enforce a range of public health interventions. We introduce the Covid-19 Policy Response Canadian tracker (CPRCT) database that tracks and records implemented public health measures in every province and territory in Canada. The implementations are recorded on a four-level ordinal scale (0-3) for three domains, (Schools, Work, and Other), capturing differences in degree of response. The data-set allows the exploration of the effects of public health mitigation on the spread of Covid-19, as well as provides a near-real-time record in an accessible format that is useful for a diverse range of modeling and research questions.
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Affiliation(s)
- Anna Akanteva
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada
| | - David W Dick
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada.
| | - Shirin Amiraslani
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada
| | - Jane M Heffernan
- Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Ontario, Canada.
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Wang K, Wang P, Jiang Z, Wang L, Zhou L, Qi D, Yin W, Meng P. Data-driven assessment of immune evasion and dynamic Zero-COVID policy on fast-spreading Omicron in Changchun. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21692-21716. [PMID: 38124616 DOI: 10.3934/mbe.2023960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Due to its immune evasion capability, the SARS-CoV-2 Omicron variant was declared a variant of concern by the World Health Organization. The spread of Omicron in Changchun (i.e., the capital of Jilin province in northeast of China) during the spring of 2022 was successfully curbed under the strategy of a dynamic Zero-COVID policy. To evaluate the impact of immune evasion on vaccination and other measures, and to understand how the dynamic Zero-COVID measure stopped the epidemics in Changchun, we establish a compartmental model over different stages and parameterized the model with actual reported data. The model simulation firstly shows a reasonably good fit between our model prediction and the data. Second, we estimate the testing rate in the early stage of the outbreak to reveal the real infection size. Third, numerical simulations show that the coverage of vaccine immunization in Changchun and the regular nucleic acid testing could not stop the epidemic, while the 'non-pharmaceutical' intervention measures utilized in the dynamic Zero-COVID policy could play significant roles in the containment of Omicron. Based on the parameterized model, numerical analysis demonstrates that if one wants to achieve epidemic control by fully utilizing the effect of 'dynamic Zero-COVID' measures, therefore social activities are restricted to the minimum level, and then the economic development may come to a halt. The insight analysis in this work could provide reference for infectious disease prevention and control measures in the future.
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Affiliation(s)
- Kun Wang
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Peng Wang
- Jilin Provincial Joint Key Labortory of Big Data Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Zhengang Jiang
- Jilin Provincial Joint Key Labortory of Big Data Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Lu Wang
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Linhua Zhou
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Dequan Qi
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Weishi Yin
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Pinchao Meng
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
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Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Briones HGM, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Joaquim OA, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299472. [PMID: 38105989 PMCID: PMC10723497 DOI: 10.1101/2023.12.05.23299472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
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Affiliation(s)
| | | | - Kristin Nelson
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic – Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | | | | | | | - Gifta John
- Christian Medical College Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Georgia, USA
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Sara Kim
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Holin Chen
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | - Inci Yildirim
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
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35
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Bokányi E, Vizi Z, Koltai J, Röst G, Karsai M. Real-time estimation of the effective reproduction number of COVID-19 from behavioral data. Sci Rep 2023; 13:21452. [PMID: 38052841 PMCID: PMC10698193 DOI: 10.1038/s41598-023-46418-z] [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: 01/27/2023] [Accepted: 10/31/2023] [Indexed: 12/07/2023] Open
Abstract
Monitoring the effective reproduction number [Formula: see text] of a rapidly unfolding pandemic in real-time is key to successful mitigation and prevention strategies. However, existing methods based on case numbers, hospital admissions or fatalities suffer from multiple measurement biases and temporal lags due to high test positivity rates or delays in symptom development or administrative reporting. Alternative methods such as web search and social media tracking are less directly indicating epidemic prevalence over time. We instead record age-stratified anonymous contact matrices at a daily resolution using a longitudinal online-offline survey in Hungary during the first two waves of the COVID-19 pandemic. This approach is innovative, cheap, and provides information in near real-time for estimating [Formula: see text] at a daily resolution. Moreover, it allows to complement traditional surveillance systems by signaling periods when official monitoring infrastructures are unreliable due to observational biases.
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Affiliation(s)
- Eszter Bokányi
- Institute of Logic, Language and Computation, University of Amsterdam, 1090GE, Amsterdam, The Netherlands
| | - Zsolt Vizi
- National Laboratory for Health Security, University of Szeged, Szeged, 6720, Hungary
| | - Júlia Koltai
- National Laboratory for Health Security, Centre for Social Sciences, Budapest, 1097, Hungary
- Faculty of Social Sciences, Eötvös Loránd University, Budapest, 1117, Hungary
| | - Gergely Röst
- National Laboratory for Health Security, University of Szeged, Szeged, 6720, Hungary
| | - Márton Karsai
- Department of Network and Data Science, Central European University, 1100, Vienna, Austria.
- National Laboratory for Health Security, Alfréd Rényi Institute of Mathematics, Budapest, 1053, Hungary.
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Patón M, Acuña JM, Rodríguez J. Evaluation of vaccine rollout strategies for emerging infectious diseases: A model-based approach including protection attitudes. Infect Dis Model 2023; 8:1032-1049. [PMID: 37674584 PMCID: PMC10477745 DOI: 10.1016/j.idm.2023.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 07/28/2023] [Accepted: 07/30/2023] [Indexed: 09/08/2023] Open
Abstract
Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak. Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.
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Affiliation(s)
- Mauricio Patón
- Department of Chemical Engineering, College of Engineering, Khalifa University, SAN Campus PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Juan M. Acuña
- Department of Epidemiology and Public Health, College of Medicine. Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Jorge Rodríguez
- Department of Chemical Engineering, College of Engineering, Khalifa University, SAN Campus PO Box 127788, Abu Dhabi, United Arab Emirates
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Anupong S, Chantanasaro T, Wilasang C, Jitsuk NC, Sararat C, Sornbundit K, Pattanasiri B, Wannigama DL, Amarasiri M, Chadsuthi S, Modchang C. Modeling vaccination strategies with limited early COVID-19 vaccine access in low- and middle-income countries: A case study of Thailand. Infect Dis Model 2023; 8:1177-1189. [PMID: 38074078 PMCID: PMC10709621 DOI: 10.1016/j.idm.2023.11.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/04/2023] [Accepted: 11/10/2023] [Indexed: 08/17/2024] Open
Abstract
Low- and middle-income countries faced significant challenges in accessing COVID-19 vaccines during the early stages of the pandemic. In this study, we utilized an age-structured modeling approach to examine the implications of various vaccination strategies, vaccine prioritization, and vaccine rollout speeds in Thailand, an upper-middle-income country experiencing vaccine shortages during the early stages of the pandemic. The model directly compares the effectiveness of several vaccination strategies, including the heterologous vaccination where CoronaVac (CV) vaccine was administered as the first dose, followed by ChAdOx1 nCoV-19 (AZ) vaccine as the second dose, under varying disease transmission dynamics. We found that the traditional AZ homologous vaccination was more effective than the CV homologous vaccination, regardless of disease transmission dynamics. However, combining CV and AZ vaccines via either parallel homologous or heterologous vaccinations was more effective than relying solely on AZ homologous vaccination. Additionally, prioritizing vaccination for the elderly aged 60 years and above was the most effective way to reduce mortality when community transmission is well-controlled. On the other hand, prioritizing workers aged 20-59 was most effective in lowering COVID-19 cases, irrespective of the transmission dynamics. Lastly, despite the vaccine prioritization strategy, rapid vaccine rollout speeds were crucial in reducing COVID-19 infections and deaths. These findings suggested that in low- and middle-income countries where early access to high-efficacy vaccines might be limited, obtaining any accessible vaccines as early as possible and using them in parallel with other higher-efficacy vaccines might be a better strategy than waiting for and relying solely on higher-efficacy vaccines.
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Affiliation(s)
- Suparinthon Anupong
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Tanakorn Chantanasaro
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Chaiwat Wilasang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Natcha C. Jitsuk
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Chayanin Sararat
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Kan Sornbundit
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Ratchaburi Learning Park, King Mongkut’s University of Technology Thonburi, Ratchaburi, Thailand
| | - Busara Pattanasiri
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Department of Physics, Faculty of Liberal Arts and Science, Kasetsart University Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
| | - Dhammika Leshan Wannigama
- Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Center of Excellence in Antimicrobial Resistance and Stewardship, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- School of Medicine, Faculty of Health and Medical Sciences, The University of Western Australia, Nedlands, Western Australia, Australia
- Biofilms and Antimicrobial Resistance Consortium of ODA Receiving Countries, The University of Sheffield, Sheffield, United Kingdom
- Pathogen Hunter’s Research Collaborative Team, Department of Infectious Diseases and Infection Control, Yamagata Prefectural Central Hospital, Yamagata, Japan
| | - Mohan Amarasiri
- Laboratory of Environmental Hygiene, Department of Health Science, School of Allied Health Sciences/Graduate School of Medical Sciences, Kitasato University, Kitasato, Sagamihara-Minami, Kanagawa, 252-0373, Japan
| | - Sudarat Chadsuthi
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Department of Physics, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Center for Disease Modeling, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Centre of Excellence in Mathematics, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10400, Thailand
- Thailand Center of Excellence in Physics, Ministry of Higher Education, Science, Research and Innovation, 328 Si Ayutthaya Road, Bangkok, 10400, Thailand
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Bents SJ, Viboud C, Grenfell BT, Hogan AB, Tempia S, von Gottberg A, Moyes J, Walaza S, Hansen C, Cohen C, Baker RE. Modeling the impact of COVID-19 nonpharmaceutical interventions on respiratory syncytial virus transmission in South Africa. Influenza Other Respir Viruses 2023; 17:e13229. [PMID: 38090227 PMCID: PMC10710953 DOI: 10.1111/irv.13229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/25/2023] [Accepted: 11/11/2023] [Indexed: 12/18/2023] Open
Abstract
Background The South African government employed various nonpharmaceutical interventions (NPIs) to reduce the spread of SARS-CoV-2. Surveillance data from South Africa indicates reduced circulation of respiratory syncytial virus (RSV) throughout the 2020-2021 seasons. Here, we use a mechanistic transmission model to project the rebound of RSV in the two subsequent seasons. Methods We fit an age-structured epidemiological model to hospitalization data from national RSV surveillance in South Africa, allowing for time-varying reduction in RSV transmission during periods of COVID-19 circulation. We apply the model to project the rebound of RSV in the 2022 and 2023 seasons. Results We projected an early and intense outbreak of RSV in April 2022, with an age shift to older infants (6-23 months old) experiencing a larger portion of severe disease burden than typical. In March 2022, government alerts were issued to prepare the hospital system for this potentially intense outbreak. We then assess the 2022 predictions and project the 2023 season. Model predictions for 2023 indicate that RSV activity has not fully returned to normal, with a projected early and moderately intense wave. We estimate that NPIs reduced RSV transmission between 15% and 50% during periods of COVID-19 circulation. Conclusions A wide range of NPIs impacted the dynamics of the RSV outbreaks throughout 2020-2023 in regard to timing, magnitude, and age structure, with important implications in a low- and middle-income countries (LMICs) setting where RSV interventions remain limited. More efforts should focus on adapting RSV models to LMIC data to project the impact of upcoming medical interventions for this disease.
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Affiliation(s)
- Samantha J. Bents
- Fogarty International Center, National Institutes of HealthBethesdaMarylandUSA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of HealthBethesdaMarylandUSA
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary BiologyPrinceton UniversityPrincetonNew JerseyUSA
| | - Alexandra B. Hogan
- School of Population HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Stefano Tempia
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Anne von Gottberg
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Pathology, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
- Department of Pathology, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
| | - Jocelyn Moyes
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Sibongile Walaza
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Chelsea Hansen
- Fogarty International Center, National Institutes of HealthBethesdaMarylandUSA
- Brotman Baty InstituteUniversity of WashingtonSeattleWashingtonUSA
- PandemiX Center, Department of Science & EnvironmentRoskilde UniversityRoskildeDenmark
| | - Cheryl Cohen
- Centre for Respiratory Diseases and MeningitisNational Institute for Communicable Diseases of the National Health Laboratory ServiceJohannesburgSouth Africa
- School of Public Health, Faculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Rachel E. Baker
- School of Public HealthBrown UniversityProvidenceRhode IslandUSA
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Bolton KJ, McCaw JM, Dafilis MP, McVernon J, Heffernan JM. Seasonality as a driver of pH1N12009 influenza vaccination campaign impact. Epidemics 2023; 45:100730. [PMID: 38056164 DOI: 10.1016/j.epidem.2023.100730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/18/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023] Open
Abstract
Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns.
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Affiliation(s)
- Kirsty J Bolton
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
| | - James M McCaw
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Mathew P Dafilis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Jodie McVernon
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Parkville, Australia
| | - Jane M Heffernan
- Centre for Disease Modelling, Mathematics & Statistics, York University, Canada
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Kiti MC, Aguolu OG, Zelaya A, Chen HY, Ahmed N, Batross J, Liu CY, Nelson KN, Jenness SM, Melegaro A, Ahmed F, Malik F, Omer SB, Lopman BA. Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021. Epidemics 2023; 45:100727. [PMID: 37948925 PMCID: PMC10730080 DOI: 10.1016/j.epidem.2023.100727] [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: 06/26/2023] [Revised: 10/21/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April-June 2020), 2 (November 2020-January 2021), 3 (June-August 2021), and 4 (November-December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2-4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time.
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Affiliation(s)
- Moses C Kiti
- Rollins School of Public Health, Emory University, GA, USA.
| | - Obianuju G Aguolu
- Yale Institute for Global Health, Yale University, CT, USA; Yale School of Medicine, Yale University, CT, USA
| | - Alana Zelaya
- Rollins School of Public Health, Emory University, GA, USA
| | - Holin Y Chen
- Rollins School of Public Health, Emory University, GA, USA
| | - Noureen Ahmed
- Yale Institute for Global Health, Yale University, CT, USA
| | | | - Carol Y Liu
- Rollins School of Public Health, Emory University, GA, USA
| | | | | | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Italy
| | - Faruque Ahmed
- Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Fauzia Malik
- Yale Institute for Global Health, Yale University, CT, USA
| | - Saad B Omer
- Yale Institute for Global Health, Yale University, CT, USA; Yale School of Medicine, Yale University, CT, USA
| | - Ben A Lopman
- Rollins School of Public Health, Emory University, GA, USA
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41
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Walker J, Aylett-Bullock J, Shi D, Kahindo Maina AG, Samir Evers E, Harlass S, Krauss F. A mixed-method approach to determining contact matrices in the Cox's Bazar refugee settlement. ROYAL SOCIETY OPEN SCIENCE 2023; 10:231066. [PMID: 38126066 PMCID: PMC10731328 DOI: 10.1098/rsos.231066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023]
Abstract
Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between subgroups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements-locations under-served by epidemic models in general. In this paper, we present a novel, mixed-method approach for deriving contact matrices in populations, which combines a lightweight, rapidly deployable survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. To validate our approach, we apply it to the UK population and compare our derived matrices with well-known contact matrices collected using traditional methods. Our findings demonstrate that our mixed-method approach successfully addresses some of the challenges faced by traditional and agent-based approaches to deriving contact matrices. It also shows potential for implementation in resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and internally displaced person (IDP) settlements and better serving these vulnerable communities.
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Affiliation(s)
- Joseph Walker
- Institute for Data Science, Durham, UK
- Institute for Particle Physics Phenomenology, Durham, UK
| | - Joseph Aylett-Bullock
- Institute for Data Science, Durham, UK
- United Nations Global Pulse, New York, NY, USA
| | - Difu Shi
- Institute for Data Science, Durham, UK
- Institute for Computational Cosmology, Durham, UK
| | | | | | | | - Frank Krauss
- Institute for Data Science, Durham, UK
- Institute for Particle Physics Phenomenology, Durham, UK
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42
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Evans MV, Ramiadantsoa T, Kauffman K, Moody J, Nunn CL, Rabezara JY, Raharimalala P, Randriamoria TM, Soarimalala V, Titcomb G, Garchitorena A, Roche B. Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts. J Infect Dis 2023; 228:1189-1197. [PMID: 36961853 PMCID: PMC11007394 DOI: 10.1093/infdis/jiad076] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/08/2023] [Accepted: 03/22/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable. METHODS We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts. RESULTS Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests. CONCLUSIONS We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable.
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Affiliation(s)
- Michelle V Evans
- Maladies Infectieuses et Vecteurs : Écologie, Génétique, Évolution et Contrôle, Université Montpellier, CNRS, IRD, Montpellier, France
| | - Tanjona Ramiadantsoa
- Maladies Infectieuses et Vecteurs : Écologie, Génétique, Évolution et Contrôle, Université Montpellier, CNRS, IRD, Montpellier, France
| | - Kayla Kauffman
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, USA
- Duke Global Health Institute, Durham, North Carolina, USA
- Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California, USA
| | - James Moody
- Department of Sociology, Duke University, Durham, North Carolina, USA
| | - Charles L Nunn
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, USA
- Duke Global Health Institute, Durham, North Carolina, USA
| | - Jean Yves Rabezara
- Department of Science and Technology, University of Antsiranana, Antsiranana, Madagascar
| | | | - Toky M Randriamoria
- Association Vahatra, Antananarivo, Madagascar
- Zoologie et Biodiversité Animale, Domaine Sciences et Technologies, Université d’Antananarivo, Antananarivo, Madagascar
| | - Voahangy Soarimalala
- Association Vahatra, Antananarivo, Madagascar
- Institut des Sciences et Techniques de l’Environnement, Université de Fianarantsoa, Fianarantsoa, Madagascar
| | - Georgia Titcomb
- Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California, USA
- Marine Science Institute, University of California, Santa Barbara, California, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Andres Garchitorena
- Maladies Infectieuses et Vecteurs : Écologie, Génétique, Évolution et Contrôle, Université Montpellier, CNRS, IRD, Montpellier, France
- Pivot, Ifanadiana, Madagascar
| | - Benjamin Roche
- Maladies Infectieuses et Vecteurs : Écologie, Génétique, Évolution et Contrôle, Université Montpellier, CNRS, IRD, Montpellier, France
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Hickey J, Rancourt DG. Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities. PLoS One 2023; 18:e0293556. [PMID: 37903148 PMCID: PMC10615287 DOI: 10.1371/journal.pone.0293556] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/15/2023] [Indexed: 11/01/2023] Open
Abstract
OBJECTIVES Since the declaration of the COVID-19 pandemic, many governments have imposed policies to reduce contacts between people who are presumed to be particularly vulnerable to dying from respiratory illnesses and the rest of the population. These policies typically address vulnerable individuals concentrated in centralized care facilities and entail limiting social contacts with visitors, staff members, and other care home residents. We use a standard epidemiological model to investigate the impact of such circumstances on the predicted infectious disease attack rates, for interacting robust and vulnerable populations. METHODS We implement a general susceptible-infectious-recovered (SIR) compartmental model with two populations: robust and vulnerable. The key model parameters are the per-individual frequencies of within-group (robust-robust and vulnerable-vulnerable) and between-group (robust-vulnerable and vulnerable-robust) infectious-susceptible contacts and the recovery times of individuals in the two groups, which can be significantly longer for vulnerable people. RESULTS Across a large range of possible model parameters including degrees of segregation versus intermingling of vulnerable and robust individuals, we find that concentrating the most vulnerable into centralized care facilities virtually always increases the infectious disease attack rate in the vulnerable group, without significant benefit to the robust group. CONCLUSIONS Isolated care homes of vulnerable residents are predicted to be the worst possible mixing circumstances for reducing harm in epidemic or pandemic conditions.
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Affiliation(s)
- Joseph Hickey
- Correlation Research in the Public Interest, Ottawa, Ontario, Canada
| | - Denis G. Rancourt
- Correlation Research in the Public Interest, Ottawa, Ontario, Canada
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Adhikari K, Gautam R, Pokharel A, Uprety KN, Vaidya NK. Data-driven models for the risk of infection and hospitalization during a pandemic: Case study on COVID-19 in Nepal. J Theor Biol 2023; 574:111622. [PMID: 37734704 DOI: 10.1016/j.jtbi.2023.111622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 08/11/2023] [Accepted: 09/12/2023] [Indexed: 09/23/2023]
Abstract
The newly emerging pandemic disease often poses unexpected troubles and hazards to the global health system, particularly in low and middle-income countries like Nepal. In this study, we developed mathematical models to estimate the risk of infection and the risk of hospitalization during a pandemic which are critical for allocating resources and planning health policies. We used our models in Nepal's unique data set to explore national and provincial-level risks of infection and risk of hospitalization during the Delta and Omicron surges. Furthermore, we used our model to identify the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate COVID-19 in various groups of people in Nepal. Our analysis shows no significant difference in reproduction numbers in provinces between the Delta and Omicron surge periods, but noticeable inter-provincial disparities in the risk of infection (for example, during Delta (Omicron) surges, the risk of infection of Bagmati province is: ∼ 98.94 (89.62); Madhesh province: ∼ 12.16 (5.1); Karnali province ∼31.16 (3) per hundred thousands). Our estimates show a significantly low level of hospitalization risk during the Omicron surge compared to the Delta surge (hospitalization risk is: ∼10% in Delta and ∼2.5% in Omicron). We also found significant inter-provincial disparities in the hospitalization rate (for example, ∼ 6% in Madhesh province and ∼ 21% in Sudur Paschim) during the Delta surge. Moreover, our results show that closing only schools, colleges, and workplaces reduces the risk of infection by one-third, while a complete lockdown reduces the infections by two-thirds. Our study provides a framework for the computation of the risk of infection and the risk of hospitalization and offers helpful information for controlling the pandemic.
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Affiliation(s)
| | - Ramesh Gautam
- Ratna Rajya Laxmi Campus, Tribhuvan University, Kathmandu, Nepal
| | - Anjana Pokharel
- Padma Kanya Multiple Campus, Tribhuvan University, Kathmandu, Nepal
| | - Kedar Nath Uprety
- Central Department of Mathematics, Tribhuvan University, Kathmandu, Nepal
| | - Naveen K Vaidya
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA; Computational Science Research Center, San Diego State University, San Diego, CA, USA; Viral Information Institute, San Diego State University, San Diego, CA, USA.
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45
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Schnyder SK, Molina JJ, Yamamoto R, Turner MS. Rational social distancing policy during epidemics with limited healthcare capacity. PLoS Comput Biol 2023; 19:e1011533. [PMID: 37844111 PMCID: PMC10602387 DOI: 10.1371/journal.pcbi.1011533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 10/26/2023] [Accepted: 09/20/2023] [Indexed: 10/18/2023] Open
Abstract
Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be 'overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.
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Affiliation(s)
- Simon K. Schnyder
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, Japan
| | - John J. Molina
- Department of Chemical Engineering, Kyoto University, Kyoto, Japan
| | - Ryoichi Yamamoto
- Department of Chemical Engineering, Kyoto University, Kyoto, Japan
| | - Matthew S. Turner
- Department of Physics, University of Warwick, Coventry, United Kingdom
- Institute for Global Pandemic Planning, University of Warwick, Coventry, United Kingdom
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46
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Gurbaxani BM, Hill AN, Patel P. Unpacking Cochrane's Update on Masks and COVID-19. Am J Public Health 2023; 113:1074-1078. [PMID: 37672741 PMCID: PMC10484132 DOI: 10.2105/ajph.2023.307377] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2023] [Indexed: 09/08/2023]
Affiliation(s)
- Brian M Gurbaxani
- Brian M. Gurbaxani and Pragna Patel are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, GA. Brian M. Gurbaxani is also with the Departments of Electrical and Computer Engineering and Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta. Andrew N. Hill is with the Global Health Center, Division of Parasitic Diseases and Malaria, CDC, and the Department of Biostatistics and Bioinformatics Department, Rollins School of Public Health, Emory University, Atlanta, GA. Pragna Patel is also with the Department of Medicine, Emory University
| | - Andrew N Hill
- Brian M. Gurbaxani and Pragna Patel are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, GA. Brian M. Gurbaxani is also with the Departments of Electrical and Computer Engineering and Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta. Andrew N. Hill is with the Global Health Center, Division of Parasitic Diseases and Malaria, CDC, and the Department of Biostatistics and Bioinformatics Department, Rollins School of Public Health, Emory University, Atlanta, GA. Pragna Patel is also with the Department of Medicine, Emory University
| | - Pragna Patel
- Brian M. Gurbaxani and Pragna Patel are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, GA. Brian M. Gurbaxani is also with the Departments of Electrical and Computer Engineering and Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta. Andrew N. Hill is with the Global Health Center, Division of Parasitic Diseases and Malaria, CDC, and the Department of Biostatistics and Bioinformatics Department, Rollins School of Public Health, Emory University, Atlanta, GA. Pragna Patel is also with the Department of Medicine, Emory University
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47
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Cho G, Kim YJ, Seo SH, Jang G, Lee H. Cost-effectiveness analysis of COVID-19 variants effects in an age-structured model. Sci Rep 2023; 13:15844. [PMID: 37739967 PMCID: PMC10516971 DOI: 10.1038/s41598-023-41876-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: 04/29/2023] [Accepted: 09/01/2023] [Indexed: 09/24/2023] Open
Abstract
This study analyzes the impact of COVID-19 variants on cost-effectiveness across age groups, considering vaccination efforts and nonpharmaceutical interventions in Republic of Korea. We aim to assess the costs needed to reduce COVID-19 cases and deaths using age-structured model. The proposed age-structured model analyzes COVID-19 transmission dynamics, evaluates vaccination effectiveness, and assesses the impact of the Delta and Omicron variants. The model is fitted using data from the Republic of Korea between February 2021 and November 2022. The cost-effectiveness of interventions, medical costs, and the cost of death for different age groups are evaluated through analysis. The impact of different variants on cases and deaths is also analyzed, with the Omicron variant increasing transmission rates and decreasing case-fatality rates compared to the Delta variant. The cost of interventions and deaths is higher for older age groups during both outbreaks, with the Omicron outbreak resulting in a higher overall cost due to increased medical costs and interventions. This analysis shows that the daily cost per person for both the Delta and Omicron variants falls within a similar range of approximately $10-$35. This highlights the importance of conducting cost-effect analyses when evaluating the impact of COVID-19 variants.
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Affiliation(s)
- Giphil Cho
- Department of Artificial Intelligence and Software, Kangwon National University, Chuncheon, Gangwon, 25913, Republic of Korea
| | - Young Jin Kim
- Division of Data Analysis, Center for Global R&D Data Analysis, Korea Institute of Science and Technology Information (KISTI), Seoul, 02456, Republic of Korea
| | - Sang-Hyup Seo
- National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Geunsoo Jang
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, 41566, Republic of Korea.
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Escosio RAS, Cawiding OR, Hernandez BS, Mendoza RG, Mendoza VMP, Mohammad RZ, Pilar-Arceo CPC, Salonga PKN, Suarez FLE, Sy PW, Vergara THM, de Los Reyes AA. A model-based strategy for the COVID-19 vaccine roll-out in the Philippines. J Theor Biol 2023; 573:111596. [PMID: 37597691 DOI: 10.1016/j.jtbi.2023.111596] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 08/21/2023]
Abstract
COVID-19 has affected millions of people worldwide, causing illness and death, and disrupting daily life while imposing a significant social and economic burden. Vaccination is an important control measure that significantly reduces mortality if properly and efficiently distributed. In this work, an age-structured model of COVID-19 transmission, incorporating an unreported infectious compartment, is developed. Three age groups are considered: young (0-19 years), adult (20-64 years), and elderly (65+ years). The transmission rate and reporting rate are determined for each group by utilizing the number of COVID-19 cases in the National Capital Region in the Philippines. Optimal control theory is employed to identify the best vaccine allocation to different age groups. Further, three different vaccination periods are considered to reflect phases of vaccination priority groups: the first, second, and third account for the inoculation of the elderly, adult and elderly, and all three age groups, respectively. This study could guide in making informed decisions in mitigating a population-structured disease transmission under limited resources.
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Affiliation(s)
- Rey Audie S Escosio
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines; Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Olive R Cawiding
- Department of Mathematical Sciences, KAIST, Daejeon 34141, Republic of Korea
| | - Bryan S Hernandez
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Renier G Mendoza
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Victoria May P Mendoza
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines; University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Rhudaina Z Mohammad
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Carlene P C Pilar-Arceo
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines; University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Pamela Kim N Salonga
- Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Fatima Lois E Suarez
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Polly W Sy
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Thomas Herald M Vergara
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines
| | - Aurelio A de Los Reyes
- Institute of Mathematics, University of the Philippines Diliman, Quezon City 1101, Philippines; University of the Philippines Resilience Institute, University of the Philippines Diliman, Quezon City 1101, Philippines; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea.
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49
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Vlad AI, Romanyukha AA, Sannikova TE. Circulation of Respiratory Viruses in the City: Towards an Agent-Based Ecosystem model. Bull Math Biol 2023; 85:100. [PMID: 37690100 DOI: 10.1007/s11538-023-01203-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 08/23/2023] [Indexed: 09/12/2023]
Abstract
Mathematical models play an important role in management of outbreaks of acute respiratory infections (ARI). While such models are generally used to study the spread of a solitary virus, in reality multiple viruses co-circulate in the population. These viruses have been studied in detail, including the course of infection and immune defense mechanisms. We developed an agent-based model, called ABM-ARI, assimilating heterogeneous data and theoretical knowledge into a biologically motivated system, that allows to reproduce the seasonal patterns of ARI incidence and simulate interventions. ABM-ARI uses city-specific data to create a synthetic population and to construct realistic contact networks in different activity settings. Characteristics of infection, immune protection and non-specific resistance were varied between individuals to account for the population heterogeneity. For the calibration, we minimised the normalised mean absolute error between simulated and observed epidemic curves. ABM-ARI was built based on the quantitative assessment of features of predominant respiratory viruses and epidemiological characteristics of the population. It provides a good fit to the observed epidemic curves for different age groups and viruses. We also simulated one-week school closures when student absences were at or above 10%, 20% or 30% and found that only 10% and 20% thresholds resulted in a reduction of the incidence. ABM-ARI has a great potential in tackling the challenge of emerging infections by simulating and evaluating the effectiveness of various interventions.
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Affiliation(s)
- A I Vlad
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia.
| | - A A Romanyukha
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia
| | - T E Sannikova
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow, Russia
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50
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Ko Y, Peck KR, Kim YJ, Kim DH, Jung E. Effective vaccination strategies to control COVID-19 in Korea: a modeling study. Epidemiol Health 2023; 45:e2023084. [PMID: 37723841 PMCID: PMC10867522 DOI: 10.4178/epih.e2023084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 08/07/2023] [Indexed: 09/20/2023] Open
Abstract
OBJECTIVES In Korea, as immunity levels of the coronavirus disease 2019 (COVID-19) in the population acquired through previous infections and vaccinations have decreased, booster vaccinations have emerged as a necessary measure to control new outbreaks. The objective of this study was to identify the most suitable vaccination strategy for controlling the surge in COVID-19 cases. METHODS A mathematical model was developed to concurrently evaluate the immunity levels induced by vaccines and infections. This model was then employed to investigate the potential for future resurgence and the possibility of control through the use of vaccines and antivirals. RESULTS As of May 11, 2023, if the current epidemic trend persists without further vaccination efforts, a peak in resurgence is anticipated to occur around mid-October of the same year. Under the most favorable circumstances, the peak number of severely hospitalized patients could be reduced by 43% (n=480) compared to the scenario without vaccine intervention (n=849). Depending on outbreak trends and vaccination strategies, the best timing for vaccination in terms of minimizing this peak varies from May 2023 to August 2023. CONCLUSIONS Our findings suggest that if the epidemic persist, the best timing for administering vaccinations would need to be earlier than currently outlined in the Korean plan. It is imperative to continue monitoring outbreak trends, as this is key to determining the best vaccination timing in order to manage potential future surges.
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Affiliation(s)
- Youngsuk Ko
- Department of Mathematics, Konkuk University, Seoul, Korea
| | - Kyong Ran Peck
- Division of Infectious Diseases, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yae-Jean Kim
- Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, Hallym University College of Medicine, Chuncheon, Korea
| | - Eunok Jung
- Department of Mathematics, Konkuk University, Seoul, Korea
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