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McAndrew F, Sacks-Davis R, Abeysuriya RG, Delport D, West D, Parta I, Majumdar S, Hellard M, Scott N. COVID-19 outbreaks in residential aged care facilities: an agent-based modeling study. Front Public Health 2024; 12:1344916. [PMID: 38835609 PMCID: PMC11148262 DOI: 10.3389/fpubh.2024.1344916] [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: 11/27/2023] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia. Methods The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%). Results Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs. Conclusion Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths.
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Affiliation(s)
| | - Rachel Sacks-Davis
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Romesh G Abeysuriya
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Dominic Delport
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Daniel West
- Victorian Government Department of Health, Melbourne, VIC, Australia
| | - Indra Parta
- Victorian Government Department of Health, Melbourne, VIC, Australia
| | - Suman Majumdar
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, VIC, Australia
| | - Margaret Hellard
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, VIC, Australia
- Department of Infectious Diseases, The University of Melbourne and Victorian Infectious Diseases Reference Laboratory, Parkville, VIC, Australia
| | - Nick Scott
- Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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Pedrana A, Bowring A, Heath K, Thomas AJ, Wilkinson A, Fletcher-Lartey S, Saich F, Munari S, Oliver J, Merner B, Altermatt A, Nguyen T, Nguyen L, Young K, Kerr P, Osborne D, Kwong EJL, Corona MV, Ke T, Zhang Y, Eisa L, Al-Qassas A, Malith D, Davis A, Gibbs L, Block K, Horyniak D, Wallace J, Power R, Vadasz D, Ryan R, Shearer F, Homer C, Collie A, Meagher N, Danchin M, Kaufman J, Wang P, Hassani A, Sadewo GRP, Robins G, Gallagher C, Matous P, Roden B, Karkavandi MA, Coutinho J, Broccatelli C, Koskinen J, Curtis S, Doyle JS, Geard N, Hill S, Coelho A, Scott N, Lusher D, Stoové MA, Gibney KB, Hellard M. Priority populations' experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study). BMJ Open 2024; 14:e076907. [PMID: 38216183 PMCID: PMC10806709 DOI: 10.1136/bmjopen-2023-076907] [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: 06/20/2023] [Accepted: 12/04/2023] [Indexed: 01/14/2024] Open
Abstract
INTRODUCTION Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy. METHODS AND ANALYSIS This protocol paper describes the data collection procedures for the Optimise Study, an ongoing longitudinal cohort of ~1000 Victorian adults and their social networks. Participants are recruited using snowball sampling with a set of seeds and two waves of snowball recruitment. Seeds are purposively selected from priority groups, including recent COVID-19 cases and close contacts and people at heightened risk of infection and/or adverse outcomes of COVID-19 infection and/or public health measures. Participants complete a schedule of monthly quantitative surveys and daily diaries for up to 24 months, plus additional surveys annually for up to 48 months. Cohort participants are recruited for qualitative interviews at key time points to enable in-depth exploration of people's lived experiences. Separately, community representatives are invited to participate in community engagement groups, which review and interpret research findings to inform policy and practice recommendations. ETHICS AND DISSEMINATION The Optimise longitudinal cohort and qualitative interviews are approved by the Alfred Hospital Human Research Ethics Committee (# 333/20). The Optimise Study CEG is approved by the La Trobe University Human Ethics Committee (# HEC20532). All participants provide informed verbal consent to enter the cohort, with additional consent provided prior to any of the sub studies. Study findings will be disseminated through public website (https://optimisecovid.com.au/study-findings/) and through peer-reviewed publications. TRIAL REGISTRATION NUMBER NCT05323799.
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Affiliation(s)
- Alisa Pedrana
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Anna Bowring
- Burnet Institute, Melbourne, Victoria, Australia
| | | | | | - Anna Wilkinson
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | | | - Freya Saich
- Burnet Institute, Melbourne, Victoria, Australia
| | | | - Jane Oliver
- Department of Infectious Diseases, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Bronwen Merner
- Centre for Health Communication and Participation, La Trobe University, Melbourne, Victoria, Australia
| | | | - Thi Nguyen
- Burnet Institute, Melbourne, Victoria, Australia
| | - Long Nguyen
- Burnet Institute, Melbourne, Victoria, Australia
| | | | - Phoebe Kerr
- Burnet Institute, Melbourne, Victoria, Australia
| | | | | | - Martha Vazquez Corona
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Tianhui Ke
- Burnet Institute, Melbourne, Victoria, Australia
| | - Yanqin Zhang
- Burnet Institute, Melbourne, Victoria, Australia
| | - Limya Eisa
- Burnet Institute, Melbourne, Victoria, Australia
| | | | - Deng Malith
- Burnet Institute, Melbourne, Victoria, Australia
| | - Angela Davis
- Burnet Institute, Melbourne, Victoria, Australia
| | - Lisa Gibbs
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Centre for Disaster Management and Public Safety, The University of Melbourne, Melbourne, Victoria, Australia
| | - Karen Block
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Danielle Horyniak
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jack Wallace
- Burnet Institute, Melbourne, Victoria, Australia
| | - Robert Power
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Danny Vadasz
- Health Issues Centre, Melbourne, Victoria, Australia
| | - Rebecca Ryan
- Centre for Health Communication and Participation, La Trobe University, Melbourne, Victoria, Australia
| | - Freya Shearer
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Alex Collie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Niamh Meagher
- Department of Infectious Diseases, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Margaret Danchin
- Murdoch Childrens Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica Kaufman
- Murdoch Childrens Research Institute, Parkville, Victoria, Australia
| | - Peng Wang
- School of Business, Law and Entrepreneurship, Swinburne University of Technology, Hawthorn, Victoria, Australia
- SNA Toolbox, Melbourne, Victoria, Australia
| | | | | | - Garry Robins
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Colin Gallagher
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Petr Matous
- The University of Sydney Faculty of Engineering and Information Technologies, Sydney, New South Wales, Australia
| | - Bopha Roden
- School of Business, Law and Entrepreneurship, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | | | - James Coutinho
- School of Business, Law and Entrepreneurship, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Chiara Broccatelli
- Institute for Social Science Research, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Johan Koskinen
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Statistics, Stockholm University, Stockholm, Sweden
| | - Stephanie Curtis
- Burnet Institute, Melbourne, Victoria, Australia
- Department of Infectious Diseases, Monash University, Clayton, Victoria, Australia
| | - Joseph S Doyle
- Burnet Institute, Melbourne, Victoria, Australia
- Department of Infectious Diseases, Monash University, Clayton, Victoria, Australia
| | - Nicholas Geard
- School of Computing & Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sophie Hill
- Centre for Health Communication and Participation, La Trobe University, Melbourne, Victoria, Australia
| | | | - Nick Scott
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dean Lusher
- School of Business, Law and Entrepreneurship, Swinburne University of Technology, Hawthorn, Victoria, Australia
- SNA Toolbox, Melbourne, Victoria, Australia
| | - Mark A Stoové
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Katherine B Gibney
- Department of Infectious Diseases, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Margaret Hellard
- Burnet Institute, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wey Wen Lim
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Dongxuan Chen
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Mingwei Li
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Justin K. Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, 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
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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Abeysuriya RG, Sacks-Davis R, Heath K, Delport D, Russell FM, Danchin M, Hellard M, McVernon J, Scott N. Keeping kids in school: modelling school-based testing and quarantine strategies during the COVID-19 pandemic in Australia. Front Public Health 2023; 11:1150810. [PMID: 37333560 PMCID: PMC10272722 DOI: 10.3389/fpubh.2023.1150810] [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: 01/25/2023] [Accepted: 05/05/2023] [Indexed: 06/20/2023] Open
Abstract
Background In 2021, the Australian Government Department of Health commissioned a consortium of modelling groups to generate evidence assisting the transition from a goal of no community COVID-19 transmission to 'living with COVID-19', with adverse health and social consequences limited by vaccination and other measures. Due to the extended school closures over 2020-21, maximizing face-to-face teaching was a major objective during this transition. The consortium was tasked with informing school surveillance and contact management strategies to minimize infections and support this goal. Methods Outcomes considered were infections and days of face-to-face teaching lost in the 45 days following an outbreak within an otherwise COVID-naïve school setting. A stochastic agent-based model of COVID-19 transmission was used to evaluate a 'test-to-stay' strategy using daily rapid antigen tests (RATs) for close contacts of a case for 7 days compared with home quarantine; and an asymptomatic surveillance strategy involving twice-weekly screening of all students and/or teachers using RATs. Findings Test-to-stay had similar effectiveness for reducing school infections as extended home quarantine, without the associated days of face-to-face teaching lost. Asymptomatic screening was beneficial in reducing both infections and days of face-to-face teaching lost and was most beneficial when community prevalence was high. Interpretation Use of RATs in school settings for surveillance and contact management can help to maximize face-to-face teaching and minimize outbreaks. This evidence supported the implementation of surveillance testing in schools in several Australian jurisdictions from January 2022.
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Affiliation(s)
- Romesh G. Abeysuriya
- Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Rachel Sacks-Davis
- Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Katherine Heath
- Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia
| | - Dominic Delport
- Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia
| | - Fiona M. Russell
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Margie Danchin
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
- The Royal Children’s Hospital, Melbourne, VIC, Australia
| | - Margaret Hellard
- Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, VIC, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Jodie McVernon
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Victorian Infectious Diseases Epidemiology Unit, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Nick Scott
- Disease Elimination Program, Burnet Institute, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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Scott N, Abeysuriya RG, Delport D, Sacks-Davis R, Nolan J, West D, Sutton B, Wallace EM, Hellard M. COVID-19 epidemic modelling for policy decision support in Victoria, Australia 2020-2021. BMC Public Health 2023; 23:988. [PMID: 37237343 DOI: 10.1186/s12889-023-15936-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Policy responses to COVID-19 in Victoria, Australia over 2020-2021 have been supported by evidence generated through mathematical modelling. This study describes the design, key findings, and process for policy translation of a series of modelling studies conducted for the Victorian Department of Health COVID-19 response team during this period. METHODS An agent-based model, Covasim, was used to simulate the impact of policy interventions on COVID-19 outbreaks and epidemic waves. The model was continually adapted to enable scenario analysis of settings or policies being considered at the time (e.g. elimination of community transmission versus disease control). Model scenarios were co-designed with government, to fill evidence gaps prior to key decisions. RESULTS Understanding outbreak risk following incursions was critical to eliminating community COVID-19 transmission. Analyses showed risk depended on whether the first detected case was the index case, a primary contact of the index case, or a 'mystery case'. There were benefits of early lockdown on first case detection and gradual easing of restrictions to minimise resurgence risk from undetected cases. As vaccination coverage increased and the focus shifted to controlling rather than eliminating community transmission, understanding health system demand was critical. Analyses showed that vaccines alone could not protect health systems and need to be complemented with other public health measures. CONCLUSIONS Model evidence offered the greatest value when decisions needed to be made pre-emptively, or for questions that could not be answered with empiric data and data analysis alone. Co-designing scenarios with policy-makers ensured relevance and increased policy translation.
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Affiliation(s)
- Nick Scott
- Disease Elimination Program, Burnet Institute, 85 Commercial Rd, Melbourne, Victoria, Australia.
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
| | - Romesh G Abeysuriya
- Disease Elimination Program, Burnet Institute, 85 Commercial Rd, Melbourne, Victoria, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dominic Delport
- Disease Elimination Program, Burnet Institute, 85 Commercial Rd, Melbourne, Victoria, Australia
| | - Rachel Sacks-Davis
- Disease Elimination Program, Burnet Institute, 85 Commercial Rd, Melbourne, Victoria, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jonathan Nolan
- Victorian Government Department of Health, Victoria, Australia
| | - Daniel West
- Victorian Government Department of Health, Victoria, Australia
| | - Brett Sutton
- Victorian Government Department of Health, Victoria, Australia
| | - Euan M Wallace
- Victorian Government Department of Health, Victoria, Australia
| | - Margaret Hellard
- Disease Elimination Program, Burnet Institute, 85 Commercial Rd, Melbourne, Victoria, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, Victoria, Australia
- Department of Infectious Diseases, Doherty Institute, The University of Melbourne, Parkville, Victoria, Australia
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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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8
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Čiva LM, Beganović A, Busuladžić M, Jusufbegović M, Awad-Dedić T, Vegar-Zubović S. Dose Descriptors and Assessment of Risk of Exposure-Induced Death in Patients Undergoing COVID-19 Related Chest Computed Tomography. Diagnostics (Basel) 2022; 12:diagnostics12082012. [PMID: 36010362 PMCID: PMC9407529 DOI: 10.3390/diagnostics12082012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/14/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
For more than two years, coronavirus disease 19 (COVID-19) has represented a threat to global health and lifestyles. Computed tomography (CT) imaging provides useful information in patients with COVID-19 pneumonia. However, this diagnostic modality is based on exposure to ionizing radiation, which is associated with an increased risk of radiation-induced cancer. In this study, we evaluated the common dose descriptors, CTDIvol and DLP, for 1180 adult patients. This data was used to estimate the effective dose, and risk of exposure-induced death (REID). Awareness of the extensive use of CT as a diagnostic tool in the management of COVID-19 during the pandemic is vital for the evaluation of radiation exposure parameters, dose reduction methods development and radiation protection.
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Affiliation(s)
- Lejla M. Čiva
- Sarajevo Medical School, University Sarajevo School of Science and Technology, 71210 Ilidža, Bosnia and Herzegovina
| | - Adnan Beganović
- Radiation Protection and Medical Physics Department, Sarajevo University Clinical Center, 71000 Sarajevo, Bosnia and Herzegovina
- Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
- Correspondence:
| | - Mustafa Busuladžić
- Faculty of Medicine, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Merim Jusufbegović
- Radiology Clinic, Sarajevo University Clinical Center, 71000 Sarajevo, Bosnia and Herzegovina
| | - Ta’a Awad-Dedić
- Healthcare Center of Sarajevo Canton, 71000 Sarajevo, Bosnia and Herzegovina
| | - Sandra Vegar-Zubović
- Radiology Clinic, Sarajevo University Clinical Center, 71000 Sarajevo, Bosnia and Herzegovina
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9
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Sanz-Leon P, Stevenson NJ, Stuart RM, Abeysuriya RG, Pang JC, Lambert SB, Kerr CC, Roberts JA. Risk of sustained SARS-CoV-2 transmission in Queensland, Australia. Sci Rep 2022; 12:6309. [PMID: 35428853 PMCID: PMC9012253 DOI: 10.1038/s41598-022-10349-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 03/29/2022] [Indexed: 12/23/2022] Open
Abstract
We used an agent-based model Covasim to assess the risk of sustained community transmission of SARSCoV-2/COVID-19 in Queensland (Australia) in the presence of high-transmission variants of the virus. The model was calibrated using the demographics, policies, and interventions implemented in the state. Then, using the calibrated model, we simulated possible epidemic trajectories that could eventuate due to leakage of infected cases with high-transmission variants, during a period without recorded cases of locally acquired infections, known in Australian settings as “zero community transmission”. We also examined how the threat of new variants reduces given a range of vaccination levels. Specifically, the model calibration covered the first-wave period from early March 2020 to May 2020. Predicted epidemic trajectories were simulated from early February 2021 to late March 2021. Our simulations showed that one infected agent with the ancestral (A.2.2) variant has a 14% chance of crossing a threshold of sustained community transmission (SCT) (i.e., > 5 infections per day, more than 3 days in a row), assuming no change in the prevailing preventative and counteracting policies. However, one agent carrying the alpha (B.1.1.7) variant has a 43% chance of crossing the same threshold; a threefold increase with respect to the ancestral strain; while, one agent carrying the delta (B.1.617.2) variant has a 60% chance of the same threshold, a fourfold increase with respect to the ancestral strain. The delta variant is 50% more likely to trigger SCT than the alpha variant. Doubling the average number of daily tests from ∼ 6,000 to 12,000 results in a decrease of this SCT probability from 43 to 33% for the alpha variant. However, if the delta variant is circulating we would need an average of 100,000 daily tests to achieve a similar decrease in SCT risk. Further, achieving a full-vaccination coverage of 70% of the adult population, with a vaccine with 70% effectiveness against infection, would decrease the probability of SCT from a single seed of alpha from 43 to 20%, on par with the ancestral strain in a naive population. In contrast, for the same vaccine coverage and same effectiveness, the probability of SCT from a single seed of delta would decrease from 62 to 48%, a risk slightly above the alpha variant in a naive population. Our results demonstrate that the introduction of even a small number of people infected with high-transmission variants dramatically increases the probability of sustained community transmission in Queensland. Until very high vaccine coverage is achieved, a swift implementation of policies and interventions, together with high quarantine adherence rates, will be required to minimise the probability of sustained community transmission.
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Affiliation(s)
- Paula Sanz-Leon
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Robyn M Stuart
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - James C Pang
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Stephen B Lambert
- National Centre for Immunisation Research and Surveillance for Vaccine Preventable Diseases, Westmead, Sydney, NSW, Australia
| | - Cliff C Kerr
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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