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Xia Y, Flores Anato JL, Colijn C, Janjua N, Irvine M, Williamson T, Varughese MB, Li M, Osgood N, Earn DJD, Sander B, Cipriano LE, Murty K, Xiu F, Godin A, Buckeridge D, Hurford A, Mishra S, Maheu-Giroux M. Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2024:10.17269/s41997-024-00910-9. [PMID: 39060710 DOI: 10.17269/s41997-024-00910-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/31/2024] [Indexed: 07/28/2024]
Abstract
SETTING Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies. INTERVENTION Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. OUTCOMES We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. IMPLICATION Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.
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Affiliation(s)
- Yiqing Xia
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Jorge Luis Flores Anato
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Caroline Colijn
- Department of Mathematics, Faculty of Science, Simon Fraser University, Burnaby, BC, Canada
| | - Naveed Janjua
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Mike Irvine
- British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
| | - Marie B Varughese
- Analytics and Performance Reporting Branch, Alberta Health, Edmonton, AB, Canada
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Michael Li
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Nathaniel Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - David J D Earn
- Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada
| | - Beate Sander
- Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Public Health Ontario, Toronto, ON, Canada
- ICES, Toronto, ON, Canada
| | - Lauren E Cipriano
- Ivey Business School, University of Western Ontario, London, ON, Canada
- Departments of Epidemiology & Biostatistics and Medicine, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada
| | - Kumar Murty
- Department of Mathematics, University of Toronto, Toronto, ON, Canada
| | - Fanyu Xiu
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Arnaud Godin
- Department of Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, QC, Canada
| | - David Buckeridge
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Amy Hurford
- Department of Biology and Department of Mathematics and Statistics, Faculty of Science, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada
| | - Sharmistha Mishra
- Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
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Jit M, Cook AR. Informing Public Health Policies with Models for Disease Burden, Impact Evaluation, and Economic Evaluation. Annu Rev Public Health 2024; 45:133-150. [PMID: 37871140 DOI: 10.1146/annurev-publhealth-060222-025149] [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] [Indexed: 10/25/2023]
Abstract
Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.
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Affiliation(s)
- Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom;
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- National University Health System, Singapore
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Md Nadzri MN, Md Zamri ASS, Singh S, Sumarni MG, Lai CH, Tan CV, Aris T, Mohd Ibrahim H, Gill BS, Mohd Ghazali N, Md Iderus NH, Lim MC, Ahmad LCRQ, Kamarudin MK, Ahmad NAR, Tee KK, Zulkifli AA. Description of the COVID-19 epidemiology in Malaysia. Front Public Health 2024; 12:1289622. [PMID: 38544725 PMCID: PMC10968133 DOI: 10.3389/fpubh.2024.1289622] [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: 09/06/2023] [Accepted: 02/26/2024] [Indexed: 05/01/2024] Open
Abstract
Introduction Since the COVID-19 pandemic began, it has spread rapidly across the world and has resulted in recurrent outbreaks. This study aims to describe the COVID-19 epidemiology in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate for each outbreak from the beginning of the pandemic in 2020 till endemicity of COVID-19 in 2022 in Malaysia. Methods Data was sourced from the GitHub repository and the Ministry of Health's official COVID-19 website. The study period was from the beginning of the outbreak in Malaysia, which began during Epidemiological Week (Ep Wk) 4 in 2020, to the last Ep Wk 18 in 2022. Data were aggregated by Ep Wk and analyzed in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate by years (2020 and 2022) and for each outbreak of COVID-19. Results A total of 4,456,736 cases, 35,579 deaths and 58,906,954 COVID-19 tests were reported for the period from 2020 to 2022. The COVID-19 incidence rate, death rate, CFR and test positivity rate were reported at 1.085 and 0.009 per 1,000 populations, 0.80 and 7.57%, respectively, for the period from 2020 to 2022. Higher cases, deaths, testing, incidence/death rate, CFR and test positivity rates were reported in 2021 and during the Delta outbreak. This is evident by the highest number of COVID-19 cases, ICU admissions, ventilatory requirements and deaths observed during the Delta outbreak. Conclusion The Delta outbreak was the most severe compared to other outbreaks in Malaysia's study period. In addition, this study provides evidence that outbreaks of COVID-19, which are caused by highly virulent and transmissible variants, tend to be more severe and devastating if these outbreaks are not controlled early on. Therefore, close monitoring of key epidemiological indicators, as reported in this study, is essential in the control and management of future COVID-19 outbreaks in Malaysia.
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Affiliation(s)
- Mohamad Nadzmi Md Nadzri
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Ahmed Syahmi Syafiq Md Zamri
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Sarbhan Singh
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Mohd Ghazali Sumarni
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Chee Herng Lai
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Cia Vei Tan
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Tahir Aris
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | | | - Balvinder Singh Gill
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Nur’Ain Mohd Ghazali
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Nuur Hafizah Md Iderus
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Mei Cheng Lim
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Lonny Chen Rong Qi Ahmad
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Mohd Kamarulariffin Kamarudin
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Nur Ar Rabiah Ahmad
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Kok Keng Tee
- Department of Medical Microbiology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Asrul Anuar Zulkifli
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Setia Alam, Malaysia
- International Medical School, Management and Science University, Shah Alam, Selangor, Malaysia
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Sherratt K, Carnegie AC, Kucharski A, Cori A, Pearson CAB, Jarvis CI, Overton C, Weston D, Hill EM, Knock E, Fearon E, Nightingale E, Hellewell J, Edmunds WJ, Villabona Arenas J, Prem K, Pi L, Baguelin M, Kendall M, Ferguson N, Davies N, Eggo RM, van Elsland S, Russell T, Funk S, Liu Y, Abbott S. Improving modelling for epidemic responses: reflections from members of the UK infectious disease modelling community on their experiences during the COVID-19 pandemic. Wellcome Open Res 2024; 9:12. [PMID: 38784437 PMCID: PMC11112301 DOI: 10.12688/wellcomeopenres.19601.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 05/25/2024] Open
Abstract
Background The COVID-19 pandemic both relied and placed significant burdens on the experts involved from research and public health sectors. The sustained high pressure of a pandemic on responders, such as healthcare workers, can lead to lasting psychological impacts including acute stress disorder, post-traumatic stress disorder, burnout, and moral injury, which can impact individual wellbeing and productivity. Methods As members of the infectious disease modelling community, we convened a reflective workshop to understand the professional and personal impacts of response work on our community and to propose recommendations for future epidemic responses. The attendees represented a range of career stages, institutions, and disciplines. This piece was collectively produced by those present at the session based on our collective experiences. Results Key issues we identified at the workshop were lack of institutional support, insecure contracts, unequal credit and recognition, and mental health impacts. Our recommendations include rewarding impactful work, fostering academia-public health collaboration, decreasing dependence on key individuals by developing teams, increasing transparency in decision-making, and implementing sustainable work practices. Conclusions Despite limitations in representation, this workshop provided valuable insights into the UK COVID-19 modelling experience and guidance for future public health crises. Recognising and addressing the issues highlighted is crucial, in our view, for ensuring the effectiveness of epidemic response work in the future.
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Affiliation(s)
- Katharine Sherratt
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Anna C Carnegie
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Carl A B Pearson
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | - Christopher I Jarvis
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher Overton
- All Hazards Intelligence, Data Analytics and Surveillance, UK Health Security Agency, London, UK
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
- Department of Mathematics, The University of Manchester, Manchester, UK
| | - Dale Weston
- Emergency Response Department Science & Technology Behavioural Science, UK Health Security Agency, London, UK
| | - Edward M Hill
- Warwick Mathematics Institute and The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
- Joint UNIversities Pandemic and Epidemiological Research, JUNIPER, https://maths.org/juniper/, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Elizabeth Fearon
- Institute for Global Health, University College London, London, UK
| | - Emily Nightingale
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Joel Hellewell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Julián Villabona Arenas
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Kiesha Prem
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Marc Baguelin
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Michelle Kendall
- Warwick Mathematics Institute and The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Neil Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Nicholas Davies
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Sabine van Elsland
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Timothy Russell
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Yang Liu
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene & Tropical Medicine, London, UK
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Jit M, Ainslie K, Althaus C, Caetano C, Colizza V, Paolotti D, Beutels P, Willem L, Edmunds J, Nunes B, Namorado S, Faes C, Low N, Wallinga J, Hens N. Reflections On Epidemiological Modeling To Inform Policy During The COVID-19 Pandemic In Western Europe, 2020-23. Health Aff (Millwood) 2023; 42:1630-1636. [PMID: 38048502 DOI: 10.1377/hlthaff.2023.00688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
We reflect on epidemiological modeling conducted throughout the COVID-19 pandemic in Western Europe, specifically in Belgium, France, Italy, the Netherlands, Portugal, Switzerland, and the United Kingdom. Western Europe was initially one of the worst-hit regions during the COVID-19 pandemic. Western European countries deployed a range of policy responses to the pandemic, which were often informed by mathematical, computational, and statistical models. Models differed in terms of temporal scope, pandemic stage, interventions modeled, and analytical form. This diversity was modulated by differences in data availability and quality, government interventions, societal responses, and technical capacity. Many of these models were decisive to policy making at key junctures, such as during the introduction of vaccination and the emergence of the Alpha, Delta, and Omicron variants. However, models also faced intense criticism from the press, other scientists, and politicians around their accuracy and appropriateness for decision making. Hence, evaluating the success of models in terms of accuracy and influence is an essential task. Modeling needs to be supported by infrastructure for systems to collect and share data, model development, and collaboration between groups, as well as two-way engagement between modelers and both policy makers and the public.
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Affiliation(s)
- Mark Jit
- Mark Jit , London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kylie Ainslie
- Kylie Ainslie, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | | | - Constantino Caetano
- Constantino Caetano, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
| | | | | | | | | | - John Edmunds
- John Edmunds, London School of Hygiene and Tropical Medicine
| | - Baltazar Nunes
- Baltazar Nunes, National Institute of Health Doutor Ricardo Jorge
| | - Sónia Namorado
- Sónia Namorado, National Institute of Health Doutor Ricardo Jorge
| | | | | | - Jacco Wallinga
- Jacco Wallinga, National Institute for Public Health and the Environment (RIVM)
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McCabe R, Donnelly CA. Public awareness of and opinions on the use of mathematical transmission modelling to inform public health policy in the United Kingdom. J R Soc Interface 2023; 20:20230456. [PMID: 38113928 PMCID: PMC10730285 DOI: 10.1098/rsif.2023.0456] [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: 08/04/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Mathematical modelling is used to inform public health policy, particularly so during the COVID-19 pandemic. As the public are key stakeholders, understanding the public perceptions of these tools is vital. To complement our previous study on the science-policy interface, novel survey data were collected via an online panel ('representative' sample) and social media ('non-probability' sample). Many questions were asked twice, in reference to the period 'prior to' (retrospectively) and 'during' the COVID-19 pandemic. Respondents reported being increasingly aware of modelling in informing policy during the pandemic, with higher levels of awareness among social media respondents. Modelling informing policy was perceived as more reliable during the pandemic than in reference to the pre-pandemic period in both samples. Trust in government public health advice remained high within both samples but was lower during the pandemic in comparison with the (retrospective) pre-pandemic period. The decay in trust was greater among social media respondents. Many respondents explicitly made the distinction that their trust was reserved for 'scientists' and not 'politicians'. Almost all respondents believed governments have responsibility for communicating modelling to the public. These results provide a reminder of the skewed conclusions that could be drawn from non-representative samples.
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Affiliation(s)
- Ruth McCabe
- Department of Statistics, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, UK
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, UK
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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7
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Hogan AB, Wu SL, Toor J, Olivera Mesa D, Doohan P, Watson OJ, Winskill P, Charles G, Barnsley G, Riley EM, Khoury DS, Ferguson NM, Ghani AC. Long-term vaccination strategies to mitigate the impact of SARS-CoV-2 transmission: A modelling study. PLoS Med 2023; 20:e1004195. [PMID: 38016000 PMCID: PMC10715640 DOI: 10.1371/journal.pmed.1004195] [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: 01/17/2023] [Revised: 12/12/2023] [Accepted: 10/25/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Vaccines have reduced severe disease and death from Coronavirus Disease 2019 (COVID-19). However, with evidence of waning efficacy coupled with continued evolution of the virus, health programmes need to evaluate the requirement for regular booster doses, considering their impact and cost-effectiveness in the face of ongoing transmission and substantial infection-induced immunity. METHODS AND FINDINGS We developed a combined immunological-transmission model parameterised with data on transmissibility, severity, and vaccine effectiveness. We simulated Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission and vaccine rollout in characteristic global settings with different population age-structures, contact patterns, health system capacities, prior transmission, and vaccine uptake. We quantified the impact of future vaccine booster dose strategies with both ancestral and variant-adapted vaccine products, while considering the potential future emergence of new variants with modified transmission, immune escape, and severity properties. We found that regular boosting of the oldest age group (75+) is an efficient strategy, although large numbers of hospitalisations and deaths could be averted by extending vaccination to younger age groups. In countries with low vaccine coverage and high infection-derived immunity, boosting older at-risk groups was more effective than continuing primary vaccination into younger ages in our model. Our study is limited by uncertainty in key parameters, including the long-term durability of vaccine and infection-induced immunity as well as uncertainty in the future evolution of the virus. CONCLUSIONS Our modelling suggests that regular boosting of the high-risk population remains an important tool to reduce morbidity and mortality from current and future SARS-CoV-2 variants. Our results suggest that focusing vaccination in the highest-risk cohorts will be the most efficient (and hence cost-effective) strategy to reduce morbidity and mortality.
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Affiliation(s)
- Alexandra B. Hogan
- School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Sean L. Wu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States of America
| | - Jaspreet Toor
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Daniela Olivera Mesa
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Patrick Doohan
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Oliver J. Watson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Giovanni Charles
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Gregory Barnsley
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Eleanor M. Riley
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - David S. Khoury
- Kirby Institute, University of New South Wales, Sydney, Australia
| | - Neil M. Ferguson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
| | - Azra C. Ghani
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
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Brooks-Pollock E, Northstone K, Pellis L, Scarabel F, Thomas A, Nixon E, Matthews DA, Bowyer V, Garcia MP, Steves CJ, Timpson NJ, Danon L. Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England. BMC Med 2023; 21:25. [PMID: 36658548 PMCID: PMC9851586 DOI: 10.1186/s12916-022-02714-5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 12/15/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Predicting the likely size of future SARS-CoV-2 waves is necessary for public health planning. In England, voluntary "plan B" mitigation measures were introduced in December 2021 including increased home working and face coverings in shops but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown. METHODS We developed a rapid online survey of risk mitigation behaviours ahead of the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/COVID Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we predicted the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave. RESULTS Over 95% of survey respondents (NALSPAC = 2686 and NTwins = 6155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to "plan B". We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 12,000 and 46,000 cumulative deaths, depending on assumptions about severity and vaccine effectiveness. The actual number of deaths was 15,208 (26 November 2021-1 March 2022). We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%. CONCLUSIONS Predicting future infection burden is affected by uncertainty in disease severity and vaccine effectiveness estimates. In addition to biological uncertainty, we show that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant but that voluntary measures alone would be unlikely to completely control transmission.
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Affiliation(s)
- Ellen Brooks-Pollock
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Kate Northstone
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - Amy Thomas
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emily Nixon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Alan Turing Institute, London, UK
| | - David A Matthews
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Vicky Bowyer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Maria Paz Garcia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicholas J Timpson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
| | - Leon Danon
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
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9
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Adams S, Lancaster K, Rhodes T. Undoing elimination: Modelling Australia's way out of the COVID-19 pandemic. Glob Public Health 2023; 18:2195899. [PMID: 37054450 DOI: 10.1080/17441692.2023.2195899] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
In the middle of 2020, with its borders tightly closed to the rest of the world, Australia almost achieved the local elimination of COVID-19 and subsequently maintained 'COVID-zero' in most parts of the country for the following year. Australia has since faced the relatively unique challenge of deliberately 'undoing' these achievements by progressively easing restrictions and reopening. Exploring the role of mathematical modelling in navigating a course through the pandemic through qualitative interviews with modellers and others working closely with modelling, we argue that each of these two significant phases of Australia's COVID-19 experience can be understood as distinct forms of 'model society'. This refers at once to the society enacted through the governance of risk, and to the visions of societal outcomes - whether to be sought or to be avoided - that are offered up by models. Each of the two model societies came about through a reflexive engagement with risk facilitated by models, and the iterative relationship between the representations of society enacted within models and the possibilities that these representations generate in the material world beyond them.
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Affiliation(s)
- Sophie Adams
- Centre for Social Research in Health, University of New South Wales, Kensington, Sydney, Australia
| | - Kari Lancaster
- Centre for Social Research in Health, University of New South Wales, Kensington, Sydney, Australia
| | - Tim Rhodes
- London School of Hygiene and Tropical Medicine, London, UK
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10
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Ioannidis JP, Powis SH. COVID-19 models and expectations - Learning from the pandemic. Adv Biol Regul 2022; 86:100922. [PMID: 36241518 PMCID: PMC9546779 DOI: 10.1016/j.jbior.2022.100922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 11/12/2022]
Affiliation(s)
- John P.A. Ioannidis
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Science and of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, USA,Corresponding author. 1265 Welch Rd, Medical School Office Building, Room X306, Stanford, CA, 94305, USA
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