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McClymont H, Hu W. The effect of public health interventions on COVID-19 incidence in Queensland, Australia: a spatial cluster analysis. Infect Dis (Lond) 2024; 56:460-475. [PMID: 38446488 DOI: 10.1080/23744235.2024.2324355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/23/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements. METHODS Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases. RESULTS Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001. CONCLUSIONS Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.
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Affiliation(s)
- Hannah McClymont
- School of Public Health and Social Work, Ecosystem Change, Population Health and Early Warning (ECAPH) Research Group, Queensland University of Technology (QUT), Brisbane, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Ecosystem Change, Population Health and Early Warning (ECAPH) Research Group, Queensland University of Technology (QUT), Brisbane, Australia
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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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Vattiato G, Lustig A, Maclaren O, Binny RN, Hendy SC, Harvey E, O'Neale D, Plank MJ. Modelling Aotearoa New Zealand's COVID-19 protection framework and the transition away from the elimination strategy. ROYAL SOCIETY OPEN SCIENCE 2023; 10:220766. [PMID: 36756071 PMCID: PMC9890088 DOI: 10.1098/rsos.220766] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 01/06/2023] [Indexed: 05/29/2023]
Abstract
For the first 18 months of the COVID-19 pandemic, New Zealand used an elimination strategy to suppress community transmission of SARS-CoV-2 to zero or very low levels. In late 2021, high vaccine coverage enabled the country to transition away from the elimination strategy to a mitigation strategy. However, given negligible levels of immunity from prior infection, this required careful planning and an effective public health response to avoid uncontrolled outbreaks and unmanageable health impacts. Here, we develop an age-structured model for the Delta variant of SARS-CoV-2 including the effects of vaccination, case isolation, contact tracing, border controls and population-wide control measures. We use this model to investigate how epidemic trajectories may respond to different control strategies, and to explore trade-offs between restrictions in the community and restrictions at the border. We find that a low case tolerance strategy, with a quick change to stricter public health measures in response to increasing cases, reduced the health burden by a factor of three relative to a high tolerance strategy, but almost tripled the time spent in national lockdowns. Increasing the number of border arrivals was found to have a negligible effect on health burden once high vaccination rates were achieved and community transmission was widespread.
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Affiliation(s)
- Giorgia Vattiato
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
- Te Pūnaha Matatini, Auckland, New Zealand
| | - Audrey Lustig
- Te Pūnaha Matatini, Auckland, New Zealand
- Manaaki Whenua, Lincoln, New Zealand
| | - Oliver Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Rachelle N. Binny
- Te Pūnaha Matatini, Auckland, New Zealand
- Manaaki Whenua, Lincoln, New Zealand
| | - Shaun C. Hendy
- Department of Physics, University of Auckland, Auckland, New Zealand
- Te Pūnaha Matatini, Auckland, New Zealand
| | - Emily Harvey
- Te Pūnaha Matatini, Auckland, New Zealand
- M.E. Research, Takapuna, Auckland, New Zealand
| | - Dion O'Neale
- Department of Physics, University of Auckland, Auckland, New Zealand
- Te Pūnaha Matatini, Auckland, New Zealand
| | - Michael J. Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Te Pūnaha Matatini, Auckland, New Zealand
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Beerman JT, Beaumont GG, Giabbanelli PJ. A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors. Vaccines (Basel) 2022; 10:vaccines10101716. [PMID: 36298581 PMCID: PMC9607873 DOI: 10.3390/vaccines10101716] [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: 09/15/2022] [Revised: 09/27/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022] Open
Abstract
The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.
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Affiliation(s)
- Jack T. Beerman
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| | - Gwendal G. Beaumont
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
- IMT Mines Ales, 6 Av. de Clavieres, 30100 Ales, France
| | - Philippe J. Giabbanelli
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
- Correspondence:
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Sanz-Leon P, Hamilton LHW, Raison SJ, Pan AJX, Stevenson NJ, Stuart RM, Abeysuriya RG, Kerr CC, Lambert SB, Roberts JA. Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210311. [PMID: 35965469 PMCID: PMC9376720 DOI: 10.1098/rsta.2021.0311] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/13/2022] [Indexed: 05/21/2023]
Abstract
Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Paula Sanz-Leon
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Lachlan H W Hamilton
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Sebastian J Raison
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Anna J X Pan
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Robyn M Stuart
- Department of Mathematical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | | | - Cliff C Kerr
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA 98109, USA
| | - Stephen B Lambert
- National Centre for Immunisation Research and Surveillance for Vaccine Preventable Diseases, Westmead, NSW 2145, Australia
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
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