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Dietz E, Pritchard E, Pouwels K, Ehsaan M, Blake J, Gaughan C, Haduli E, Boothe H, Vihta KD, Peto T, Stoesser N, Matthews P, Taylor N, Diamond I, Studley R, Rourke E, Birrell P, De Angelis D, Fowler T, Watson C, Eyre D, House T, Walker AS. SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort. BMC Med 2024; 22:143. [PMID: 38532381 DOI: 10.1186/s12916-024-03351-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. METHODS We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models. RESULTS Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season. CONCLUSIONS Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity.
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Affiliation(s)
- Elisabeth Dietz
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
| | - Koen Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Joshua Blake
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | | | - Eric Haduli
- Berkshire and Surrey Pathology Services, Camberley, UK
| | - Hugh Boothe
- Berkshire and Surrey Pathology Services, Camberley, UK
| | | | - Tim Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Philippa Matthews
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of Infection and Immunity, University College London, London, UK
| | | | | | | | | | - Paul Birrell
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- UK Health Security Agency, London, UK
| | | | - Tom Fowler
- UK Health Security Agency, London, UK
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - David Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
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2
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Cahuantzi R, Lythgoe KA, Hall I, Pellis L, House T. Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proc Natl Acad Sci U S A 2024; 121:e2317284121. [PMID: 38478692 PMCID: PMC10962941 DOI: 10.1073/pnas.2317284121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024] Open
Abstract
Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges provide a motivation to develop complementary methods that can incorporate all of the genetic data available without down-sampling to extract meaningful information rapidly and with minimal curation. In this paper, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies. While not serving as a substitute for current phylogenetic analyses, the proposed methods can be used as a complementary, and fully automatable, approach to identify and confirm new emerging variants.
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Affiliation(s)
- Roberto Cahuantzi
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
- United Kingdom Health Security Agency, University of Oxford, OxfordOX3 7LF, United Kingdom
| | - Katrina A. Lythgoe
- Department of Biology, University of Oxford, OxfordOX1 3SZ, United Kingdom
- Big Data Institute, University of Oxford, OxfordOX3 7LF, United Kingdom
- Pandemic Sciences Institute, University of Oxford, OxfordOX3 7LF, United Kingdom
| | - Ian Hall
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
| | - Thomas House
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
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3
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Ghafari M, Hall M, Golubchik T, Ayoubkhani D, House T, MacIntyre-Cockett G, Fryer HR, Thomson L, Nurtay A, Kemp SA, Ferretti L, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith DL, Bashton M, Nelson A, Crown M, McCann C, Young GR, Santos RAND, Richards Z, Tariq MA, Cahuantzi R, Barrett J, Fraser C, Bonsall D, Walker AS, Lythgoe K. Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature 2024; 626:1094-1101. [PMID: 38383783 PMCID: PMC10901734 DOI: 10.1038/s41586-024-07029-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 01/04/2024] [Indexed: 02/23/2024]
Abstract
Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1-5, give rise to highly divergent lineages6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as 'persistent infections' as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1-0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people11-14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.
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Affiliation(s)
- Mahan Ghafari
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Biology, University of Oxford, Oxford, UK.
- Pandemic Science Institute, University of Oxford, Oxford, UK.
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Daniel Ayoubkhani
- Office for National Statistics, Newport, UK
- Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Helen R Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Steven A Kemp
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Lorne J Lonie
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | - Darren L Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Andrew Nelson
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Clare McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Gregory R Young
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Rui Andre Nunes Dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Mohammad Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | | | | | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Science Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Katrina Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Biology, University of Oxford, Oxford, UK.
- Pandemic Science Institute, University of Oxford, Oxford, UK.
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4
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Fyles M, Vihta KD, Sudre CH, Long H, Das R, Jay C, Wingfield T, Cumming F, Green W, Hadjipantelis P, Kirk J, Steves CJ, Ourselin S, Medley GF, Fearon E, House T. Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets. Sci Rep 2023; 13:21705. [PMID: 38065987 PMCID: PMC10709437 DOI: 10.1038/s41598-023-47488-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.
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Affiliation(s)
- Martyn Fyles
- Department of Mathematics, University of Manchester, Manchester, UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, NW1 2DB, UK
- United Kingdom Health Security Agency (UKHSA), London, UK
| | - Karina-Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Harry Long
- United Kingdom Health Security Agency (UKHSA), London, UK
| | - Rajenki Das
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Caroline Jay
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, NW1 2DB, UK
- Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Tom Wingfield
- Department of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, L7 8XP, UK
- WHO Collaborating Centre on Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Fergus Cumming
- United Kingdom Health Security Agency (UKHSA), London, UK
| | - William Green
- United Kingdom Health Security Agency (UKHSA), London, UK
| | | | - Joni Kirk
- United Kingdom Health Security Agency (UKHSA), London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology King's College London, London, UK
- Department of Ageing and Health Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Graham F Medley
- Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Elizabeth Fearon
- Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
- Institute for Global Health, University College London, London, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK.
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, NW1 2DB, UK.
- IBM Research, Hartree Centre, Daresbury, WA4 4AD, UK.
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5
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Silva MEP, Skeva R, House T, Jay C. Tracking the structure and sentiment of vaccination discussions on Mumsnet. Soc Netw Anal Min 2023; 13:152. [PMID: 38026264 PMCID: PMC10657328 DOI: 10.1007/s13278-023-01155-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/03/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023]
Abstract
Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a UK-based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets-small connected network subgraphs-we show how the topological structure of vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment toward vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis.
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Affiliation(s)
- Miguel E. P. Silva
- Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PY England UK
- LIAAD, INESC-TEC, Porto, Portugal
| | - Rigina Skeva
- Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PY England UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PY England UK
| | - Caroline Jay
- Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PY England UK
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6
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Lythgoe KA, Golubchik T, Hall M, House T, Cahuantzi R, MacIntyre-Cockett G, Fryer H, Thomson L, Nurtay A, Ghafani M, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith D, Bashton M, Nelson A, Crown M, McCann C, Young GR, Andre Nunes dos Santos R, Richards Z, Tariq A, Fraser C, Diamond I, Barrett J, Walker AS, Bonsall D. Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey. Proc Biol Sci 2023; 290:20231284. [PMID: 37848057 PMCID: PMC10581763 DOI: 10.1098/rspb.2023.1284] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/08/2023] [Indexed: 10/19/2023] Open
Abstract
The Office for National Statistics Coronavirus (COVID-19) Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community, and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to their predecessors, although this was also accompanied by a gradual fall in average viral burdens from June 2021 to March 2023. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterized by steadily increasing divergence and diversity within lineages, but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within lineages, and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens.
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Affiliation(s)
- Katrina A. Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Roberto Cahuantzi
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Helen Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Mahan Ghafani
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Lorne J. Lonie
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | | | | | - Darren Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Andrew Nelson
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Clare McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Gregory R. Young
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Rui Andre Nunes dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | | | | | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | | | - Jeff Barrett
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
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7
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Little CL, Druce KL, Dixon WG, Schultz DM, House T, McBeth J. What do people living with chronic pain want from a pain forecast? A research prioritization study. PLoS One 2023; 18:e0292968. [PMID: 37824568 PMCID: PMC10569639 DOI: 10.1371/journal.pone.0292968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023] Open
Abstract
Because people with chronic pain feel uncertain about their future pain, a pain-forecasting model could support individuals to manage their daily pain and improve their quality of life. We conducted two patient and public involvement activities to design the content of a pain-forecasting model by learning participants' priorities in the features provided by a pain forecast and understanding the perceived benefits that such forecasts would provide. The first was a focus group of 12 people living with chronic pain to inform the second activity, a survey of 148 people living with chronic pain. Respondents prioritized forecasting of pain flares (100, or 68%) and fluctuations in pain severity (94, or 64%), particularly the timing of the onset and the severity. Of those surveyed, 75% (or 111) would use a future pain forecast and 80% (or 118) perceived making plans (e.g., shopping, social) as a benefit. For people with chronic pain, the timing of the onset of pain flares, the severity of pain flares and fluctuations in pain severity were prioritized as being key features of a pain forecast, and making plans was prioritized as being a key benefit.
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Affiliation(s)
- Claire L. Little
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - Katie L. Druce
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - William G. Dixon
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - David M. Schultz
- Centre for Atmospheric Science, Department of Earth and Environmental Sciences, University of Manchester, Manchester, United Kingdom
- Centre for Crisis Studies and Mitigation, University of Manchester, Manchester, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - John McBeth
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
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8
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Fryer HR, Golubchik T, Hall M, Fraser C, Hinch R, Ferretti L, Thomson L, Nurtay A, Pellis L, House T, MacIntyre-Cockett G, Trebes A, Buck D, Piazza P, Green A, Lonie LJ, Smith D, Bashton M, Crown M, Nelson A, McCann CM, Tariq MA, Elstob CJ, Dos Santos RN, Richards Z, Xhang X, Hawley J, Lee MR, Carrillo-Barragan P, Chapman I, Harthern-Flint S, Bonsall D, Lythgoe KA. Correction: Viral burden is associated with age, vaccination, and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection-related sampling bias. PLoS Pathog 2023; 19:e1011706. [PMID: 37801435 PMCID: PMC10558069 DOI: 10.1371/journal.ppat.1011706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023] Open
Abstract
[This corrects the article DOI: 10.1371/journal.ppat.1011461.].
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9
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Silva MEP, Fyles M, Pi L, Panovska-Griffiths J, House T, Jay C, Fearon E. The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave. Epidemics 2023; 44:100699. [PMID: 37515954 DOI: 10.1016/j.epidem.2023.100699] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 06/02/2023] [Accepted: 06/12/2023] [Indexed: 07/31/2023] Open
Abstract
Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with 'lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.
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Affiliation(s)
- Miguel E P Silva
- Department of Computer Science, University of Manchester, United Kingdom.
| | - Martyn Fyles
- Department of Mathematics, University of Manchester, United Kingdom; The Alan Turing Institute, London, United Kingdom
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - Jasmina Panovska-Griffiths
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom; The Queen's College, University of Oxford, United Kingdom; Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, United Kingdom
| | - Caroline Jay
- Department of Computer Science, University of Manchester, United Kingdom
| | - Elizabeth Fearon
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, United Kingdom; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom; Institute for Global Health, University College London, United Kingdom.
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10
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House T, Wong HK, Samuel NW, Stephen ID, Brooks KR, Bould H, Attwood AS, Penton-Voak IS. The relationship between body dissatisfaction and attentional bias to thin bodies in Malaysian Chinese and White Australian women: a dot probe study. R Soc Open Sci 2023; 10:230674. [PMID: 37736527 PMCID: PMC10509588 DOI: 10.1098/rsos.230674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 08/15/2023] [Indexed: 09/23/2023]
Abstract
Studies suggest that an attentional bias to thin bodies is common among those with high levels of body dissatisfaction, which is a risk factor for, and symptom of, various eating disorders. However, these studies have predominantly been conducted in Western countries with body stimuli involving images of White people. In a preregistered study, we recruited 150 Malaysian Chinese women and 150 White Australian women for a study using standardized images of East Asian and White Australian bodies. To measure attentional bias to thin bodies, participants completed a dot probe task which presented images of women who self-identified their ethnicity as East Asian or as White Australian. Contrary to previous findings, we found no evidence for an association between body dissatisfaction and attentional bias to thin bodies. This lack of association was not affected by participant ethnicity (Malaysian Chinese versus White Australian) or ethnic congruency between participants and body stimuli (own-ethnicity versus other-ethnicity). However, the internal consistency of the dot probe task was poor. These results suggest that either the relationship between body dissatisfaction and attentional bias to thin bodies is not robust, or the dot probe task may not be a reliable measure of attentional bias to body size.
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Affiliation(s)
- T. House
- School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia
- School of Psychological Science, University of Bristol, UK
| | - H. K. Wong
- School of Psychology, University of Nottingham Malaysia, Malaysia
| | - N. W. Samuel
- School of Psychology, University of Nottingham Malaysia, Malaysia
| | - I. D. Stephen
- NTU Psychology, Nottingham Trent University, Nottingham, UK
| | - K. R. Brooks
- School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia
- Perception in Action Research Centre (PARC), Macquarie University, Australia
- Lifespan Health and Wellbeing Research Centre, Macquarie University, Australia
| | - H. Bould
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, UK
- Gloucestershire Health and Care NHS Foundation Trust, Gloucester, UK
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, UK
| | - A. S. Attwood
- School of Psychological Science, University of Bristol, UK
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, UK
| | - I. S. Penton-Voak
- School of Psychological Science, University of Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, UK
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11
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Fryer HR, Golubchik T, Hall M, Fraser C, Hinch R, Ferretti L, Thomson L, Nurtay A, Pellis L, House T, MacIntyre-Cockett G, Trebes A, Buck D, Piazza P, Green A, Lonie LJ, Smith D, Bashton M, Crown M, Nelson A, McCann CM, Adnan Tariq M, Elstob CJ, Nunes Dos Santos R, Richards Z, Xhang X, Hawley J, Lee MR, Carrillo-Barragan P, Chapman I, Harthern-Flint S, Bonsall D, Lythgoe KA. Viral burden is associated with age, vaccination, and viral variant in a population-representative study of SARS-CoV-2 that accounts for time-since-infection-related sampling bias. PLoS Pathog 2023; 19:e1011461. [PMID: 37578971 PMCID: PMC10449197 DOI: 10.1371/journal.ppat.1011461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 08/24/2023] [Accepted: 06/05/2023] [Indexed: 08/16/2023] Open
Abstract
In this study, we evaluated the impact of viral variant, in addition to other variables, on within-host viral burden, by analysing cycle threshold (Ct) values derived from nose and throat swabs, collected as part of the UK COVID-19 Infection Survey. Because viral burden distributions determined from community survey data can be biased due to the impact of variant epidemiology on the time-since-infection of samples, we developed a method to explicitly adjust observed Ct value distributions to account for the expected bias. By analysing the adjusted Ct values using partial least squares regression, we found that among unvaccinated individuals with no known prior exposure, viral burden was 44% lower among Alpha variant infections, compared to those with the predecessor strain, B.1.177. Vaccination reduced viral burden by 67%, and among vaccinated individuals, viral burden was 286% higher among Delta variant, compared to Alpha variant, infections. In addition, viral burden increased by 17% for every 10-year age increment of the infected individual. In summary, within-host viral burden increases with age, is reduced by vaccination, and is influenced by the interplay of vaccination status and viral variant.
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Affiliation(s)
- Helen R. Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Robert Hinch
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Luca Ferretti
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | | | - Amy Trebes
- Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - David Buck
- Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Angie Green
- Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Lorne J Lonie
- Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Darren Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Andrew Nelson
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Clare M. McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Mohammed Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Claire J. Elstob
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Rui Nunes Dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Xin Xhang
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Joseph Hawley
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Mark R. Lee
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Priscilla Carrillo-Barragan
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Isobel Chapman
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Sarah Harthern-Flint
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | | | - David Bonsall
- Pandemic Sciences Institute, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Katrina A. Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Old Road Campus, Oxford, United Kingdom
- Department of Biology, University of Oxford, Oxford, United Kingdom
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12
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Brand SPC, Cavallaro M, Cumming F, Turner C, Florence I, Blomquist P, Hilton J, Guzman-Rincon LM, House T, Nokes DJ, Keeling MJ. The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom. Nat Commun 2023; 14:4100. [PMID: 37433797 PMCID: PMC10336136 DOI: 10.1038/s41467-023-38816-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/15/2023] [Indexed: 07/13/2023] Open
Abstract
Beginning in May 2022, Mpox virus spread rapidly in high-income countries through close human-to-human contact primarily amongst communities of gay, bisexual and men who have sex with men (GBMSM). Behavioural change arising from increased knowledge and health warnings may have reduced the rate of transmission and modified Vaccinia-based vaccination is likely to be an effective longer-term intervention. We investigate the UK epidemic presenting 26-week projections using a stochastic discrete-population transmission model which includes GBMSM status, rate of formation of new sexual partnerships, and clique partitioning of the population. The Mpox cases peaked in mid-July; our analysis is that the decline was due to decreased transmission rate per infected individual and infection-induced immunity among GBMSM, especially those with the highest rate of new partners. Vaccination did not cause Mpox incidence to turn over, however, we predict that a rebound in cases due to behaviour reversion was prevented by high-risk group-targeted vaccination.
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Affiliation(s)
- Samuel P C Brand
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK.
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Massimo Cavallaro
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
- Mathematics Institute, University of Warwick, Coventry, UK
| | | | | | | | | | - Joe Hilton
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Laura M Guzman-Rincon
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
| | - D James Nokes
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Matt J Keeling
- The Zeeman Institute for Systems Biology Infectious Disease Epidemiology Research (SBIDER), Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
- Mathematics Institute, University of Warwick, Coventry, UK
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13
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House T, Graham K, Ellis B, Bould H, Attwood AS, Stephen ID, Brooks KR, Penton-Voak IS. Is body dissatisfaction related to an attentional bias towards low weight bodies in non-clinical samples of women? A systematic review and meta-analysis. Body Image 2023; 44:103-119. [PMID: 36563472 DOI: 10.1016/j.bodyim.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
Body dissatisfaction is defined as the negative subjective evaluation of one's body and is considered a risk factor for, and symptom of, eating disorders. Some studies show women with high body dissatisfaction display an attentional bias towards low weight bodies; however, this finding is not consistent, and results are yet to be systematically synthesised. We conducted a qualitative and quantitative synthesis of cross-sectional studies investigating the relationship between body dissatisfaction and attentional bias to low weight bodies in non-clinical samples of women. We searched PubMed, Scopus, Web of Science, PsycINFO, ProQuest, and OpenGrey for studies up until September 2022. We identified 34 eligible studies involving a total of 2857 women. A meta-analysis of 26 studies (75 effects) found some evidence from gaze tracking studies for a positive association between body dissatisfaction and attentional bias to low weight bodies. We found no evidence for an association from studies measuring attention using the dot probe task, electroencephalogram (EEG) recording, or the modified spatial cueing task. The results together provide partial support for the positive association between body dissatisfaction and attentional bias to low weight bodies in women. These findings can be used to inform future attentional bias research.
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Affiliation(s)
- T House
- School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia; School of Psychological Science, University of Bristol, United Kingdom.
| | - K Graham
- Cumbria Northumberland Tyne and Wear NHS Foundation Trust, United Kingdom
| | - B Ellis
- EPSRC CDT in Digital Health and Care, University of Bristol, United Kingdom
| | - H Bould
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom; Gloucestershire Health and Care NHS Foundation Trust, Centre for Academic Mental Health, University of Bristol, United Kingdom; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom
| | - A S Attwood
- School of Psychological Science, University of Bristol, United Kingdom; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, United Kingdom
| | - I D Stephen
- NTU Psychology, Nottingham Trent University, Nottingham, United Kingdom
| | - K R Brooks
- School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia
| | - I S Penton-Voak
- School of Psychological Science, University of Bristol, United Kingdom; National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, United Kingdom
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14
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Das R, Muldoon M, Lunt M, McBeth J, Yimer BB, House T. Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study. PLOS Digit Health 2023; 2:e0000204. [PMID: 36996020 DOI: 10.1371/journal.pdig.0000204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/27/2023] [Indexed: 03/31/2023]
Abstract
It is well-known that mood and pain interact with each other, however individual-level variability in this relationship has been less well quantified than overall associations between low mood and pain. Here, we leverage the possibilities presented by mobile health data, in particular the "Cloudy with a Chance of Pain" study, which collected longitudinal data from the residents of the UK with chronic pain conditions. Participants used an App to record self-reported measures of factors including mood, pain and sleep quality. The richness of these data allows us to perform model-based clustering of the data as a mixture of Markov processes. Through this analysis we discover four endotypes with distinct patterns of co-evolution of mood and pain over time. The differences between endotypes are sufficiently large to play a role in clinical hypothesis generation for personalised treatments of comorbid pain and low mood.
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Affiliation(s)
- Rajenki Das
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Mark Muldoon
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Mark Lunt
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - John McBeth
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - Belay Birlie Yimer
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
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15
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Cunningham PS, Kitchen GB, Jackson C, Papachristos S, Springthorpe T, van Dellen D, Gibbs J, Felton TW, Wilson AJ, Bannard-Smith J, Rutter MK, House T, Dark P, Augustine T, Akman OE, Hazel AL, Blaikley JF. ClinCirc identifies alterations of the circadian peripheral oscillator in critical care patients. J Clin Invest 2023; 133:e162775. [PMID: 36538377 PMCID: PMC9927929 DOI: 10.1172/jci162775] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
BackgroundAssessing circadian rhythmicity from infrequently sampled data is challenging; however, these types of data are often encountered when measuring circadian transcripts in hospitalized patients.MethodsWe present ClinCirc. This method combines 2 existing mathematical methods (Lomb-Scargle periodogram and cosinor) sequentially and is designed to measure circadian oscillations from infrequently sampled clinical data. The accuracy of this method was compared against 9 other methods using simulated and frequently sampled biological data. ClinCirc was then evaluated in 13 intensive care unit (ICU) patients as well as in a separate cohort of 29 kidney-transplant recipients. Finally, the consequences of circadian alterations were investigated in a retrospective cohort of 726 kidney-transplant recipients.ResultsClinCirc had comparable performance to existing methods for analyzing simulated data or clock transcript expression of healthy volunteers. It had improved accuracy compared with the cosinor method in evaluating circadian parameters in PER2:luc cell lines. In ICU patients, it was the only method investigated to suggest that loss of circadian oscillations in the peripheral oscillator was associated with inflammation, a feature widely reported in animal models. Additionally, ClinCirc was able to detect other circadian alterations, including a phase shift following kidney transplantation that was associated with the administration of glucocorticoids. This phase shift could explain why a significant complication of kidney transplantation (delayed graft dysfunction) oscillates according to the time of day kidney transplantation is performed.ConclusionClinCirc analysis of the peripheral oscillator reveals important clinical associations in hospitalized patients.FundingUK Research and Innovation (UKRI), National Institute of Health Research (NIHR), Engineering and Physical Sciences Research Council (EPSRC), National Institute on Academic Anaesthesia (NIAA), Asthma+Lung UK, Kidneys for Life.
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Affiliation(s)
- Peter S. Cunningham
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Gareth B. Kitchen
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Callum Jackson
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Stavros Papachristos
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Thomas Springthorpe
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - David van Dellen
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Julie Gibbs
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Timothy W. Felton
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Wythenshawe Hospital, MFT, Manchester, United Kingdom
| | - Anthony J. Wilson
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Jonathan Bannard-Smith
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Martin K. Rutter
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Paul Dark
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Northern Care Alliance NHS Foundation Trust (Salford Care Organisation), Salford, United Kingdom
| | - Titus Augustine
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Royal Infirmary, Manchester University NHS Foundation Trust (MFT), Manchester, United Kingdom
| | - Ozgur E. Akman
- School of Mathematics, University of Exeter, Exeter, United Kingdom
| | - Andrew L. Hazel
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - John F. Blaikley
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Wythenshawe Hospital, MFT, Manchester, United Kingdom
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16
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Wing K, Grint DJ, Mathur R, Gibbs HP, Hickman G, Nightingale E, Schultze A, Forbes H, Nafilyan V, Bhaskaran K, Williamson E, House T, Pellis L, Herrett E, Gautam N, Curtis HJ, Rentsch CT, Wong AYS, MacKenna B, Mehrkar A, Bacon S, Douglas IJ, Evans SJW, Tomlinson L, Goldacre B, Eggo RM. Association between household composition and severe COVID-19 outcomes in older people by ethnicity: an observational cohort study using the OpenSAFELY platform. Int J Epidemiol 2022; 51:1745-1760. [PMID: 35962974 PMCID: PMC9384728 DOI: 10.1093/ije/dyac158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/22/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Ethnic differences in the risk of severe COVID-19 may be linked to household composition. We quantified the association between household composition and risk of severe COVID-19 by ethnicity for older individuals. METHODS With the approval of NHS England, we analysed ethnic differences in the association between household composition and severe COVID-19 in people aged 67 or over in England. We defined households by number of age-based generations living together, and used multivariable Cox regression stratified by location and wave of the pandemic and accounted for age, sex, comorbidities, smoking, obesity, housing density and deprivation. We included 2 692 223 people over 67 years in Wave 1 (1 February 2020-31 August 2020) and 2 731 427 in Wave 2 (1 September 2020-31 January 2021). RESULTS Multigenerational living was associated with increased risk of severe COVID-19 for White and South Asian older people in both waves [e.g. Wave 2, 67+ living with three other generations vs 67+-year-olds only: White hazard ratio (HR) 1.61 95% CI 1.38-1.87, South Asian HR 1.76 95% CI 1.48-2.10], with a trend for increased risks of severe COVID-19 with increasing generations in Wave 2. There was also an increased risk of severe COVID-19 in Wave 1 associated with living alone for White (HR 1.35 95% CI 1.30-1.41), South Asian (HR 1.47 95% CI 1.18-1.84) and Other (HR 1.72 95% CI 0.99-2.97) ethnicities, an effect that persisted for White older people in Wave 2. CONCLUSIONS Both multigenerational living and living alone were associated with severe COVID-19 in older adults. Older South Asian people are over-represented within multigenerational households in England, especially in the most deprived settings, whereas a substantial proportion of White older people live alone. The number of generations in a household, number of occupants, ethnicity and deprivation status are important considerations in the continued roll-out of COVID-19 vaccination and targeting of interventions for future pandemics.
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Affiliation(s)
- Kevin Wing
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Daniel J Grint
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Rohini Mathur
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Hamish P Gibbs
- Department of Geography, University College London, London, UK
| | - George Hickman
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
| | - Emily Nightingale
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anna Schultze
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Harriet Forbes
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Vahé Nafilyan
- Health Modelling Hub, Office of National Statistics, Newport, UK
| | - Krishnan Bhaskaran
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Elizabeth Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Emily Herrett
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Nileesa Gautam
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Aetion Inc, Boston, USA
| | - Helen J Curtis
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
| | - Christopher T Rentsch
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Angel Y S Wong
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Brian MacKenna
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
| | - Amir Mehrkar
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
| | - Seb Bacon
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
| | - Ian J Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Stephen J W Evans
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Laurie Tomlinson
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Ben Goldacre
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
| | - Rosalind M Eggo
- The DataLab, University of Oxford Nuffield Department of Primary Care Health Sciences, Oxford, UK
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17
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Hilton J, Riley H, Pellis L, Aziza R, Brand SPC, K. Kombe I, Ojal J, Parisi A, Keeling MJ, Nokes DJ, Manson-Sawko R, House T. A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic. PLoS Comput Biol 2022; 18:e1010390. [PMID: 36067212 PMCID: PMC9481179 DOI: 10.1371/journal.pcbi.1010390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 09/16/2022] [Accepted: 07/14/2022] [Indexed: 11/18/2022] Open
Abstract
The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.
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Affiliation(s)
- Joe Hilton
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Heather Riley
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, United Kingdom
| | - Rabia Aziza
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
| | - Samuel P. C. Brand
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Ivy K. Kombe
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - John Ojal
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Andrea Parisi
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
| | - Matt J. Keeling
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - D. James Nokes
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, United Kingdom
- IBM Research Europe, Hartree Centre, Daresbury, United Kingdom
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18
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Marshall GC, Skeva R, Jay C, Silva MEP, Fyles M, House T, Davis EL, Pi L, Medley GF, Quilty BJ, Dyson L, Yardley L, Fearon E. Public perceptions and interactions with UK COVID-19 Test, Trace and Isolate policies, and implications for pandemic infectious disease modelling. F1000Res 2022. [DOI: 10.12688/f1000research.124627.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Background The efforts to contain SARS-CoV-2 and reduce the impact of the COVID-19 pandemic have been supported by Test, Trace and Isolate (TTI) systems in many settings, including the United Kingdom. Mathematical models of transmission and TTI interventions, used to inform design and policy choices, make assumptions about the public’s behaviour in the context of a rapidly unfolding and changeable emergency. This study investigates public perceptions and interactions with UK TTI policy in July 2021, assesses them against how TTI processes are conceptualised and represented in models, and then interprets the findings with modellers who have been contributing evidence to TTI policy. Methods 20 members of the public recruited via social media were interviewed for one hour about their perceptions and interactions with the UK TTI system. Thematic analysis identified key themes, which were then presented back to a workshop of pandemic infectious disease modellers who assessed these findings against assumptions made in TTI intervention modelling. Workshop members co-drafted this report. Results Themes included education about SARS-CoV-2, perceived risks, trust, mental health and practical concerns. Findings covered testing practices, including the uses of and trust in different types of testing, and the challenges of testing and isolating faced by different demographic groups. This information was judged as consequential to the modelling process, from guiding the selection of research questions, influencing choice of model structure, informing parameter ranges and validating or challenging assumptions, to highlighting where model assumptions are reasonable or where their poor reflection of practice might lead to uninformative results. Conclusions We conclude that deeper engagement with members of the public should be integrated at regular stages of public health intervention modelling.
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19
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De Angelis D, Birrell P, Funk S, House T. Editorial. Stat Methods Med Res 2022; 31:1639-1640. [PMID: 36112917 DOI: 10.1177/09622802221118613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Daniela De Angelis
- MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge CB2 0SR
- Data Analytics and Surveillance, UK Health Security Agency
| | - Paul Birrell
- MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge CB2 0SR
- Data Analytics and Surveillance, UK Health Security Agency
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, M13 9PL
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20
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House T, Riley H, Pellis L, Pouwels KB, Bacon S, Eidukas A, Jahanshahi K, Eggo RM, Sarah Walker A. Inferring risks of coronavirus transmission from community household data. Stat Methods Med Res 2022; 31:1738-1756. [PMID: 36112916 PMCID: PMC9465559 DOI: 10.1177/09622802211055853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics COVID-19 Infection Survey data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) susceptible-Infectious transmission probabilities of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range of 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants.
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Affiliation(s)
- Thomas House
- Department of Mathematics, 5292University of Manchester, Manchester UK
- IBM Research, Hartree Centre, Daresbury UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London UK
| | - Heather Riley
- Department of Mathematics, 5292University of Manchester, Manchester UK
| | - Lorenzo Pellis
- Department of Mathematics, 5292University of Manchester, Manchester UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, London UK
| | - Koen B Pouwels
- 105596Nuffield Department of Medicine, University of Oxford, Oxford UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, , Oxford UK
| | - Sebastian Bacon
- The DataLab, 12205Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford UK
| | | | | | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene and Tropical Medicine, London UK
| | - A Sarah Walker
- 105596Nuffield Department of Medicine, University of Oxford, Oxford UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford UK
- MRC Clinical Trials Unit at UCL, UCL, London UK
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21
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Vihta KD, Pouwels KB, Peto TEA, Pritchard E, Eyre DW, House T, Gethings O, Studley R, Rourke E, Cook D, Diamond I, Crook D, Matthews PC, Stoesser N, Walker AS. Symptoms and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity in the General Population in the United Kingdom. Clin Infect Dis 2022; 75:e329-e337. [PMID: 34748629 PMCID: PMC8767848 DOI: 10.1093/cid/ciab945] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND "Classic" symptoms (cough, fever, loss of taste/smell) prompt severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing in the United Kingdom. Studies have assessed the ability of different symptoms to identify infection, but few have compared symptoms over time (reflecting variants) and by vaccination status. METHODS Using the COVID-19 Infection Survey, sampling households across the United Kingdom, we compared symptoms in PCR-positives vs PCR-negatives, evaluating sensitivity of combinations of 12 symptoms (percentage symptomatic PCR-positives reporting specific symptoms) and tests per case (TPC) (PCR-positives or PCR-negatives reporting specific symptoms/ PCR-positives reporting specific symptoms). RESULTS Between April 2020 and August 2021, 27 869 SARS-CoV-2 PCR-positive episodes occurred in 27 692 participants (median 42 years), of whom 13 427 (48%) self-reported symptoms ("symptomatic PCR-positives"). The comparator comprised 3 806 692 test-negative visits (457 215 participants); 130 612 (3%) self-reported symptoms ("symptomatic PCR-negatives"). Symptom reporting in PCR-positives varied by age, sex, and ethnicity, and over time, reflecting changes in prevalence of viral variants, incidental changes (eg, seasonal pathogens (with sore throat increasing in PCR-positives and PCR-negatives from April 2021), schools reopening) and vaccination rollout. After May 2021 when Delta emerged, headache and fever substantially increased in PCR-positives, but not PCR-negatives. Sensitivity of symptom-based detection increased from 74% using "classic" symptoms, to 81% adding fatigue/weakness, and 90% including all 8 additional symptoms. However, this increased TPC from 4.6 to 5.3 to 8.7. CONCLUSIONS Expanded symptom combinations may provide modest benefits for sensitivity of PCR-based case detection, but this will vary between settings and over time, and increases tests/case. Large-scale changes to targeted PCR-testing approaches require careful evaluation given substantial resource and infrastructure implications.
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Affiliation(s)
- Karina Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - David W Eyre
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Sci-Tech Daresbury, United Kingdom
| | - Owen Gethings
- Office for National Statistics, Newport, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Duncan Cook
- Office for National Statistics, Newport, United Kingdom
| | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
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22
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Vihta KD, Pouwels KB, Peto TEA, Pritchard E, House T, Studley R, Rourke E, Cook D, Diamond I, Crook D, Clifton DA, Matthews PC, Stoesser N, Eyre DW, Walker AS. Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom. Clin Infect Dis 2022; 76:ciac613. [PMID: 35917440 PMCID: PMC9384604 DOI: 10.1093/cid/ciac613] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/14/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The SARS-CoV-2 Delta variant has been replaced by the highly transmissible Omicron BA.1 variant, and subsequently by Omicron BA.2. It is important to understand how these changes in dominant variants affect reported symptoms, while also accounting for symptoms arising from other co-circulating respiratory viruses. METHODS In a nationally representative UK community study, the COVID-19 Infection Survey, we investigated symptoms in PCR-positive infection episodes vs. PCR-negative study visits over calendar time, by age and vaccination status, comparing periods when the Delta, Omicron BA.1 and BA.2 variants were dominant. RESULTS Between October-2020 and April-2022, 120,995 SARS-CoV-2 PCR-positive episodes occurred in 115,886 participants, with 70,683 (58%) reporting symptoms. The comparator comprised 4,766,366 PCR-negative study visits (483,894 participants); 203,422 (4%) reporting symptoms. Symptom reporting in PCR-positives varied over time, with a marked reduction in loss of taste/smell as Omicron BA.1 dominated, maintained with BA.2 (44%/45% 17 October 2021, 16%/13% 2 January 2022, 15%/12% 27 March 2022). Cough, fever, shortness of breath, myalgia, fatigue/weakness and headache also decreased after Omicron BA.1 dominated, but sore throat increased, the latter to a greater degree than concurrent increases in PCR-negatives. Fatigue/weakness increased again after BA.2 dominated, although to a similar degree to concurrent increases in PCR-negatives. Symptoms were consistently more common in adults aged 18-65 years than in children or older adults. CONCLUSIONS Increases in sore throat (also common in the general community), and a marked reduction in loss of taste/smell, make Omicron harder to detect with symptom-based testing algorithms, with implications for institutional and national testing policies.
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Affiliation(s)
- Karina-Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Sci-Tech Daresbury, Daresbury, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Duncan Cook
- Office for National Statistics, Newport, United Kingdom
| | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - David A Clifton
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Francis Crick Institute, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Infection, University College London Hospitals, London, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - David W Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
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23
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Waites W, Pearson CAB, Gaskell KM, House T, Pellis L, Johnson M, Gould V, Hunt A, Stone NRH, Kasstan B, Chantler T, Lal S, Roberts CH, Goldblatt D, Marks M, Eggo RM. Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK. Sci Rep 2022; 12:8550. [PMID: 35595824 PMCID: PMC9121858 DOI: 10.1038/s41598-022-12517-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 05/12/2022] [Indexed: 11/22/2022] Open
Abstract
Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.
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Affiliation(s)
- William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland, UK.
| | - Carl A B Pearson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Katherine M Gaskell
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester, UK
| | - Lorenzo Pellis
- School of Mathematics, University of Manchester, Manchester, UK
| | - Marina Johnson
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Victoria Gould
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Adam Hunt
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Neil R H Stone
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Hospital for Tropical Diseases, University College London Hospital NHS Foundation Trust, London, UK
| | - Ben Kasstan
- Centre for Health, Law and Society, University of Bristol Law School, Bristol, UK
- Department of Sociology and Anthropology, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tracey Chantler
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Sham Lal
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Chrissy H Roberts
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - David Goldblatt
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Hospital for Tropical Diseases, University College London Hospital NHS Foundation Trust, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
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24
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House T, Stephen ID, Penton-Voak IS, Brooks KR. The effect of attention on body size adaptation and body dissatisfaction. R Soc Open Sci 2022; 9:211718. [PMID: 35223063 PMCID: PMC8864361 DOI: 10.1098/rsos.211718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/31/2022] [Indexed: 05/03/2023]
Abstract
Attentional bias to low-fat bodies is thought to be associated with body dissatisfaction-a symptom and risk factor of eating disorders. However, the causal nature of this relationship is unclear. In three preregistered experiments, we trained 370 women to attend towards either high- or low-fat body stimuli using an attention training dot probe task. For each experiment, we analysed the effect of the attention training on (i) attention to subsequently presented high- versus low-fat body stimuli, (ii) visual adaptation to body size, and (iii) body dissatisfaction. The attention training had no effect on attention towards high- or low-fat bodies in an online setting (Experiment 1), but did increase attention to high-fat bodies in a laboratory setting (Experiment 2). Neither perceptions of a 'normal' body size nor levels of body dissatisfaction changed as a result of the attention training in either setting. The results in the online setting did not change when we reduced the stimulus onset-asynchrony of the dot probe task from 500 to 100 ms (Experiment 3). Our results provide no evidence that the dot probe training task used here has robust effects on attention to body size, body image disturbance or body dissatisfaction.
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Affiliation(s)
- T. House
- School of Psychological Science, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Department of Psychology, University of Bristol, Bristol, UK
| | - I. D. Stephen
- Department of Psychology, Nottingham Trent University, Nottingham, UK
| | - I. S. Penton-Voak
- Department of Psychology, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, UK
| | - K. R. Brooks
- School of Psychological Science, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
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25
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Pritchard E, Jones J, Vihta KD, Stoesser N, Matthews PPC, Eyre DW, House T, Bell JI, Newton PJN, Farrar J, Crook PD, Hopkins S, Cook D, Rourke E, Studley R, Diamond PI, Peto PT, Pouwels KB, Walker PAS. Monitoring populations at increased risk for SARS-CoV-2 infection in the community using population-level demographic and behavioural surveillance. Lancet Reg Health Eur 2022; 13:100282. [PMID: 34927119 PMCID: PMC8665900 DOI: 10.1016/j.lanepe.2021.100282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND The COVID-19 pandemic is rapidly evolving, with emerging variants and fluctuating control policies. Real-time population screening and identification of groups in whom positivity is highest could help monitor spread and inform public health messaging and strategy. METHODS To develop a real-time screening process, we included results from nose and throat swabs and questionnaires taken 19 July 2020-17 July 2021 in the UK's national COVID-19 Infection Survey. Fortnightly, associations between SARS-CoV-2 positivity and 60 demographic and behavioural characteristics were estimated using logistic regression models adjusted for potential confounders, considering multiple testing, collinearity, and reverse causality. FINDINGS Of 4,091,537 RT-PCR results from 482,677 individuals, 29,903 (0·73%) were positive. As positivity rose September-November 2020, rates were independently higher in younger ages, and those living in Northern England, major urban conurbations, more deprived areas, and larger households. Rates were also higher in those returning from abroad, and working in healthcare or outside of home. When positivity peaked December 2020-January 2021 (Alpha), high positivity shifted to southern geographical regions. With national vaccine roll-out from December 2020, positivity reduced in vaccinated individuals. Associations attenuated as rates decreased between February-May 2021. Rising positivity rates in June-July 2021 (Delta) were independently higher in younger, male, and unvaccinated groups. Few factors were consistently associated with positivity. 25/45 (56%) confirmed associations would have been detected later using 28-day rather than 14-day periods. INTERPRETATION Population-level demographic and behavioural surveillance can be a valuable tool in identifying the varying characteristics driving current SARS-CoV-2 positivity, allowing monitoring to inform public health policy. FUNDING Department of Health and Social Care (UK), Welsh Government, Department of Health (on behalf of the Northern Ireland Government), Scottish Government, National Institute for Health Research.
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Affiliation(s)
- Emma Pritchard
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Karina-Doris Vihta
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Prof Philippa C. Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - David W. Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | | | - Prof Derrick Crook
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Susan Hopkins
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | | | | | | | | | - Prof Tim Peto
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Koen B. Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Prof A. Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - COVID-19 Infection Survey Team
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Office for National Statistics, Newport, UK
- Department of Engineering, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
- Health Improvement Directorate, Public Health England, London, UK
- Wellcome Trust, London, UK
- Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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26
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Yimer BB, Schultz DM, Beukenhorst AL, Lunt M, Pisaniello HL, House T, Sergeant JC, McBeth J, Dixon WG. Heterogeneity in the association between weather and pain severity among patients with chronic pain: a Bayesian multilevel regression analysis. Pain Rep 2022; 7:e963. [PMID: 35047712 PMCID: PMC8759613 DOI: 10.1097/pr9.0000000000000963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 12/31/2022] Open
Abstract
INTRODUCTION Previous studies on the association between weather and pain severity among patients with chronic pain have produced mixed results. In part, this inconsistency may be due to differences in individual pain responses to the weather. METHODS To test the hypothesis that there might be subgroups of participants with different pain responses to different weather conditions, we examined data from a longitudinal smartphone-based study, Cloudy with a Chance of Pain, conducted between January 2016 and April 2017. The study recruited more than 13,000 participants and recorded daily pain severity on a 5-point scale (range: no pain to very severe pain) along with hourly local weather data for up to 15 months. We used a Bayesian multilevel model to examine the weather-pain association. RESULTS We found 1 in 10 patients with chronic pain were sensitive to the temperature, 1 in 25 to relative humidity, 1 in 50 to pressure, and 3 in 100 to wind speed, after adjusting for age, sex, belief in the weather-pain association, mood, and activity level. The direction of the weather-pain association differed between people. Although participants seem to be differentially sensitive to weather conditions, there is no definite indication that participants' underlying pain conditions play a role in weather sensitivity. CONCLUSION This study demonstrated that weather sensitivity among patients with chronic pain is more apparent in some subgroups of participants. In addition, among those sensitive to the weather, the direction of the weather-pain association can differ.
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Affiliation(s)
- Belay B. Yimer
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Greater Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - David M. Schultz
- Department of Earth and Environmental Sciences, Centre for Atmospheric Science, University of Manchester, Manchester, United Kingdom
- Centre for Crisis Studies and Mitigation, University of Manchester, Manchester, United Kingdom
| | - Anna L. Beukenhorst
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Mark Lunt
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Greater Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Huai L. Pisaniello
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- Discipline of Medicine, The University of Adelaide, Adelaide, Australia
| | - Thomas House
- School of Mathematics, The University of Manchester, Manchester, United Kingdom
| | - Jamie C. Sergeant
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- Centre for Biostatistics, University of Manchester, Manchester, United Kingdom
| | - John McBeth
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Greater Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - William G. Dixon
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- NIHR Greater Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
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27
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Walker AS, Vihta KD, Gethings O, Pritchard E, Jones J, House T, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Studley R, Rourke E, Hay J, Hopkins S, Crook D, Peto T, Matthews PC, Eyre DW, Stoesser N, Pouwels KB. Tracking the Emergence of SARS-CoV-2 Alpha Variant in the United Kingdom. N Engl J Med 2021; 385:2582-2585. [PMID: 34879193 PMCID: PMC8693687 DOI: 10.1056/nejmc2103227] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | | | - Owen Gethings
- Office for National Statistics, Newport, United Kingdom
| | | | - Joel Jones
- Office for National Statistics, Newport, United Kingdom
| | - Thomas House
- University of Manchester, Manchester, United Kingdom
| | - Iain Bell
- Office for National Statistics, Newport, United Kingdom
| | - John I Bell
- University of Oxford, Oxford, United Kingdom
| | - John N Newton
- Office for Health Improvement and Disparities, London, United Kingdom
| | | | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Jodie Hay
- University of Glasgow, Glasgow, United Kingdom
| | | | | | - Tim Peto
- University of Oxford, Oxford, United Kingdom
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28
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Pouwels KB, Pritchard E, Matthews PC, Stoesser N, Eyre DW, Vihta KD, House T, Hay J, Bell JI, Newton JN, Farrar J, Crook D, Cook D, Rourke E, Studley R, Peto TEA, Diamond I, Walker AS. Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK. Nat Med 2021; 27:2127-2135. [PMID: 34650248 PMCID: PMC8674129 DOI: 10.1038/s41591-021-01548-7] [Citation(s) in RCA: 319] [Impact Index Per Article: 106.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/21/2021] [Indexed: 12/13/2022]
Abstract
The effectiveness of the BNT162b2 and ChAdOx1 vaccines against new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections requires continuous re-evaluation, given the increasingly dominant B.1.617.2 (Delta) variant. In this study, we investigated the effectiveness of these vaccines in a large, community-based survey of randomly selected households across the United Kingdom. We found that the effectiveness of BNT162b2 and ChAdOx1 against infections (new polymerase chain reaction (PCR)-positive cases) with symptoms or high viral burden is reduced with the B.1.617.2 variant (absolute difference of 10-13% for BNT162b2 and 16% for ChAdOx1) compared to the B.1.1.7 (Alpha) variant. The effectiveness of two doses remains at least as great as protection afforded by prior natural infection. The dynamics of immunity after second doses differed significantly between BNT162b2 and ChAdOx1, with greater initial effectiveness against new PCR-positive cases but faster declines in protection against high viral burden and symptomatic infection with BNT162b2. There was no evidence that effectiveness varied by dosing interval, but protection was higher in vaccinated individuals after a prior infection and in younger adults. With B.1.617.2, infections occurring after two vaccinations had similar peak viral burden as those in unvaccinated individuals. SARS-CoV-2 vaccination still reduces new infections, but effectiveness and attenuation of peak viral burden are reduced with B.1.617.2.
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Affiliation(s)
- Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Emma Pritchard
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - David W Eyre
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Karina-Doris Vihta
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
| | - Jodie Hay
- Glasgow Lighthouse Laboratory, Glasgow, UK
- University of Glasgow, Glasgow, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | - Derrick Crook
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | - Tim E A Peto
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- MRC Clinical Trials Unit at UCL, University College London, London, UK
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29
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Dyson L, Hill EM, Moore S, Curran-Sebastian J, Tildesley MJ, Lythgoe KA, House T, Pellis L, Keeling MJ. Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics. Nat Commun 2021; 12:5730. [PMID: 34593807 PMCID: PMC8484271 DOI: 10.1038/s41467-021-25915-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/08/2021] [Indexed: 11/09/2022] Open
Abstract
Viral reproduction of SARS-CoV-2 provides opportunities for the acquisition of advantageous mutations, altering viral transmissibility, disease severity, and/or allowing escape from natural or vaccine-derived immunity. We use three mathematical models: a parsimonious deterministic model with homogeneous mixing; an age-structured model; and a stochastic importation model to investigate the effect of potential variants of concern (VOCs). Calibrating to the situation in England in May 2021, we find epidemiological trajectories for putative VOCs are wide-ranging and dependent on their transmissibility, immune escape capability, and the introduction timing of a postulated VOC-targeted vaccine. We demonstrate that a VOC with a substantial transmission advantage over resident variants, or with immune escape properties, can generate a wave of infections and hospitalisations comparable to the winter 2020-2021 wave. Moreover, a variant that is less transmissible, but shows partial immune-escape could provoke a wave of infection that would not be revealed until control measures are further relaxed.
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Affiliation(s)
- Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom.
- Joint Universities Pandemic and Epidemiological Research, .
| | - Edward M Hill
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Joint Universities Pandemic and Epidemiological Research
| | - Sam Moore
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Joint Universities Pandemic and Epidemiological Research
| | - Jacob Curran-Sebastian
- Joint Universities Pandemic and Epidemiological Research
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Michael J Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Joint Universities Pandemic and Epidemiological Research
| | - Katrina A Lythgoe
- Big Data Institute, Old Road Campus, University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Joint Universities Pandemic and Epidemiological Research
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Daresbury, United Kingdom
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, United Kingdom
| | - Lorenzo Pellis
- Joint Universities Pandemic and Epidemiological Research
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- The Alan Turing Institute for Data Science and Artificial Intelligence, London, United Kingdom
| | - Matt J Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Joint Universities Pandemic and Epidemiological Research
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30
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Vekaria B, Overton C, Wiśniowski A, Ahmad S, Aparicio-Castro A, Curran-Sebastian J, Eddleston J, Hanley NA, House T, Kim J, Olsen W, Pampaka M, Pellis L, Ruiz DP, Schofield J, Shryane N, Elliot MJ. Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning. BMC Infect Dis 2021; 21:700. [PMID: 34294037 PMCID: PMC8295642 DOI: 10.1186/s12879-021-06371-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 06/29/2021] [Indexed: 11/24/2022] Open
Abstract
Background Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. Conclusions Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist. Supplementary Information The online version contains supplementary material available at (10.1186/s12879-021-06371-6).
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Affiliation(s)
- Bindu Vekaria
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. .,Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PL, UK. .,Clinical Data Science Unit, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WU, UK.
| | - Christopher Overton
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. .,Department of Mathematics, University of Liverpool, Peach Street, Liverpool, L69 7ZL, UK.
| | - Arkadiusz Wiśniowski
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
| | - Shazaad Ahmad
- Department of Virology, Manchester Medical Microbiology Partnership, Manchester Foundation Trust, Manchester Academic Health Sciences Centre, Oxford Road, Manchester, M13 9WU, UK
| | - Andrea Aparicio-Castro
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Jacob Curran-Sebastian
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.,Clinical Data Science Unit, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WU, UK
| | - Jane Eddleston
- Clinical Data Science Unit, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WU, UK
| | - Neil A Hanley
- Division of Diabetes, Endocrinology & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.,Clinical Data Science Unit, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WU, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.,IBM Research, Hartree Centre, Daresbury, UK.,Clinical Data Science Unit, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WU, UK
| | - Jihye Kim
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Wendy Olsen
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Maria Pampaka
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Diego Perez Ruiz
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - John Schofield
- Clinical Data Science Unit, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WU, UK
| | - Nick Shryane
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Mark J Elliot
- Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
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31
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Brooks-Pollock E, Read JM, House T, Medley GF, Keeling MJ, Danon L. The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200273. [PMID: 34053263 PMCID: PMC8165584 DOI: 10.1098/rstb.2020.0273] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/17/2021] [Indexed: 02/06/2023] Open
Abstract
Many countries have banned groups and gatherings as part of their response to the pandemic caused by the coronavirus, SARS-CoV-2. Although there are outbreak reports involving mass gatherings, the contribution to overall transmission is unknown. We used data from a survey of social contact behaviour that specifically asked about contact with groups to estimate the population attributable fraction (PAF) due to groups as the relative change in the basic reproduction number when groups are prevented. Groups of 50+ individuals accounted for 0.5% of reported contact events, and we estimate that the PAF due to groups of 50+ people is 5.4% (95% confidence interval 1.4%, 11.5%). The PAF due to groups of 20+ people is 18.9% (12.7%, 25.7%) and the PAF due to groups of 10+ is 25.2% (19.4%, 31.4%). Under normal circumstances with pre-COVID-19 contact patterns, large groups of individuals have a relatively small epidemiological impact; small- and medium-sized groups between 10 and 50 people have a larger impact on an epidemic. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Ellen Brooks-Pollock
- Bristol Veterinary School, University of Bristol, Bristol BS40 5DU, UK
- Population Health Sciences, Bristol Medical School, Bristol, BS8 2BN, UK
| | - Jonathan M. Read
- Lancaster Medical School, Lancaster University, Lancaster LA1 4YW, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
| | - Matt J. Keeling
- Mathematics Institute and Department of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Leon Danon
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK
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32
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Fyles M, Fearon E, Overton C, Wingfield T, Medley GF, Hall I, Pellis L, House T. Using a household-structured branching process to analyse contact tracing in the SARS-CoV-2 pandemic. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200267. [PMID: 34053253 PMCID: PMC8165594 DOI: 10.1098/rstb.2020.0267] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2021] [Indexed: 01/19/2023] Open
Abstract
We explore strategies of contact tracing, case isolation and quarantine of exposed contacts to control the SARS-CoV-2 epidemic using a branching process model with household structure. This structure reflects higher transmission risks among household members than among non-household members. We explore strategic implementation choices that make use of household structure, and investigate strategies including two-step tracing, backwards tracing, smartphone tracing and tracing upon symptom report rather than test results. The primary model outcome is the effect of contact tracing, in combination with different levels of physical distancing, on the growth rate of the epidemic. Furthermore, we investigate epidemic extinction times to indicate the time period over which interventions must be sustained. We consider effects of non-uptake of isolation/quarantine, non-adherence, and declining recall of contacts over time. Our results find that, compared to self-isolation of cases without contact tracing, a contact tracing strategy designed to take advantage of household structure allows for some relaxation of physical distancing measures but cannot completely control the epidemic absent of other measures. Even assuming no imported cases and sustainment of moderate physical distancing, testing and tracing efforts, the time to bring the epidemic to extinction could be in the order of months to years. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Martyn Fyles
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Elizabeth Fearon
- Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | | | - Tom Wingfield
- Department of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool L7 8XP, UK
- WHO Collaborating Centre on Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Graham F. Medley
- Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Ian Hall
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Public Health England, UK
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/, Daresbury WA4 4AD, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/, Daresbury WA4 4AD, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/, Daresbury WA4 4AD, UK
- IBM Research, Hartree Centre, Daresbury WA4 4AD, UK
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33
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Walker AS, Pritchard E, House T, Robotham JV, Birrell PJ, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Studley R, Hay J, Vihta KD, Peto TEA, Stoesser N, Matthews PC, Eyre DW, Pouwels KB. Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time. eLife 2021; 10:e64683. [PMID: 34250907 PMCID: PMC8282332 DOI: 10.7554/elife.64683] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 07/06/2021] [Indexed: 12/22/2022] Open
Abstract
Background Information on SARS-CoV-2 in representative community surveillance is limited, particularly cycle threshold (Ct) values (a proxy for viral load). Methods We included all positive nose and throat swabs 26 April 2020 to 13 March 2021 from the UK's national COVID-19 Infection Survey, tested by RT-PCR for the N, S, and ORF1ab genes. We investigated predictors of median Ct value using quantile regression. Results Of 3,312,159 nose and throat swabs, 27,902 (0.83%) were RT-PCR-positive, 10,317 (37%), 11,012 (40%), and 6550 (23%) for 3, 2, or 1 of the N, S, and ORF1ab genes, respectively, with median Ct = 29.2 (~215 copies/ml; IQR Ct = 21.9-32.8, 14-56,400 copies/ml). Independent predictors of lower Cts (i.e. higher viral load) included self-reported symptoms and more genes detected, with at most small effects of sex, ethnicity, and age. Single-gene positives almost invariably had Ct > 30, but Cts varied widely in triple-gene positives, including without symptoms. Population-level Cts changed over time, with declining Ct preceding increasing SARS-CoV-2 positivity. Of 6189 participants with IgG S-antibody tests post-first RT-PCR-positive, 4808 (78%) were ever antibody-positive; Cts were significantly higher in those remaining antibody negative. Conclusions Marked variation in community SARS-CoV-2 Ct values suggests that they could be a useful epidemiological early-warning indicator. Funding Department of Health and Social Care, National Institutes of Health Research, Huo Family Foundation, Medical Research Council UK; Wellcome Trust.
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Affiliation(s)
- A Sarah Walker
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of OxfordOxfordUnited Kingdom
- MRC Clinical Trials Unit at UCL, UCLLondonUnited Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
| | - Thomas House
- Department of Mathematics, University of ManchesterManchesterUnited Kingdom
- IBM Research, Hartree CentreSci-Tech DaresburyUnited Kingdom
| | - Julie V Robotham
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- National Infection Service, Public Health EnglandLondonUnited Kingdom
| | - Paul J Birrell
- National Infection Service, Public Health EnglandLondonUnited Kingdom
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public HealthCambridgeUnited Kingdom
| | - Iain Bell
- Office for National StatisticsNewportUnited Kingdom
| | - John I Bell
- Office of the Regius Professor of Medicine, University of OxfordOxfordUnited Kingdom
| | - John N Newton
- Health Improvement Directorate, Public Health EnglandLondonUnited Kingdom
| | | | - Ian Diamond
- Office for National StatisticsNewportUnited Kingdom
| | - Ruth Studley
- Office for National StatisticsNewportUnited Kingdom
| | - Jodie Hay
- University of GlasgowGlasgowUnited Kingdom
- Lighthouse Laboratory in Glasgow, Queen Elizabeth University HospitalGlasgowUnited Kingdom
| | - Karina-Doris Vihta
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
| | - Timothy EA Peto
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of OxfordOxfordUnited Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe HospitalOxfordUnited Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of OxfordOxfordUnited Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe HospitalOxfordUnited Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe HospitalOxfordUnited Kingdom
| | - David W Eyre
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- Lighthouse Laboratory in Glasgow, Queen Elizabeth University HospitalGlasgowUnited Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of OxfordOxfordUnited Kingdom
| | - Koen B Pouwels
- Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of OxfordOxfordUnited Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of OxfordOxfordUnited Kingdom
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34
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Fearon E, Buchan IE, Das R, Davis EL, Fyles M, Hall I, Hollingsworth TD, House T, Jay C, Medley GF, Pellis L, Quilty BJ, Silva MEP, Stage HB, Wingfield T. SARS-CoV-2 antigen testing: weighing the false positives against the costs of failing to control transmission. Lancet Respir Med 2021; 9:685-687. [PMID: 34139150 PMCID: PMC8203180 DOI: 10.1016/s2213-2600(21)00234-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 05/11/2021] [Indexed: 01/19/2023]
Affiliation(s)
- Elizabeth Fearon
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK,Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
| | - Iain E Buchan
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Rajenki Das
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Emma L Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
| | - Martyn Fyles
- Department of Mathematics, University of Manchester, Manchester, UK,The Alan Turing Institute, London, UK
| | - Ian Hall
- Department of Mathematics, University of Manchester, Manchester, UK,Emergency Response Department, Public Health England, Salisbury, UK
| | | | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK,IBM Research, Warrington, UK
| | - Caroline Jay
- School of Computer Science, University of Manchester, Manchester, UK
| | - Graham F Medley
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK,Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Billy J Quilty
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
| | - Miguel E P Silva
- School of Computer Science, University of Manchester, Manchester, UK
| | - Helena B Stage
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Tom Wingfield
- Department of Clinical Sciences, and Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK,Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK,WHO Collaborating Centre in Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, Solna, Sweden
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35
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Pritchard E, Matthews PC, Stoesser N, Eyre DW, Gethings O, Vihta KD, Jones J, House T, VanSteenHouse H, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Rourke E, Studley R, Crook D, Peto TEA, Walker AS, Pouwels KB. Impact of vaccination on new SARS-CoV-2 infections in the United Kingdom. Nat Med 2021; 27:1370-1378. [PMID: 34108716 PMCID: PMC8363500 DOI: 10.1038/s41591-021-01410-w] [Citation(s) in RCA: 183] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022]
Abstract
The effectiveness of COVID-19 vaccination in preventing new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general community is still unclear. Here, we used the Office for National Statistics COVID-19 Infection Survey—a large community-based survey of individuals living in randomly selected private households across the United Kingdom—to assess the effectiveness of the BNT162b2 (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca; ChAdOx1) vaccines against any new SARS-CoV-2 PCR-positive tests, split according to self-reported symptoms, cycle threshold value (<30 versus ≥30; as a surrogate for viral load) and gene positivity pattern (compatible with B.1.1.7 or not). Using 1,945,071 real-time PCR results from nose and throat swabs taken from 383,812 participants between 1 December 2020 and 8 May 2021, we found that vaccination with the ChAdOx1 or BNT162b2 vaccines already reduced SARS-CoV-2 infections ≥21 d after the first dose (61% (95% confidence interval (CI) = 54–68%) versus 66% (95% CI = 60–71%), respectively), with greater reductions observed after a second dose (79% (95% CI = 65–88%) versus 80% (95% CI = 73–85%), respectively). The largest reductions were observed for symptomatic infections and/or infections with a higher viral burden. Overall, COVID-19 vaccination reduced the number of new SARS-CoV-2 infections, with the largest benefit received after two vaccinations and against symptomatic and high viral burden infections, and with no evidence of a difference between the BNT162b2 and ChAdOx1 vaccines. Results from the Office of National Statistics COVID-19 Infection Survey in the United Kingdom demonstrate that the ChAdOx1 nCoV-19 and BNT162b2 vaccines reduce the incidence of new SARS-CoV-2 infections by up to 65% with a single dose and up to 80% after two doses, with no significant differences in efficacy observed between the two vaccines.
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Affiliation(s)
- Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - David W Eyre
- National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Karina-Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK.,IBM Research, Hartree Centre, Daresbury, UK
| | | | - Iain Bell
- Office for National Statistics, Newport, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | | | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford, University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,MRC Clinical Trials Unit at UCL, University College London, London, UK
| | - Koen B Pouwels
- National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK. .,Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK. .,Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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36
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Hall I, Lewkowicz H, Webb L, House T, Pellis L, Sedgwick J, Gent N. Outbreaks in care homes may lead to substantial disease burden if not mitigated. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200269. [PMID: 34053256 PMCID: PMC8165603 DOI: 10.1098/rstb.2020.0269] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The number of COVID-19 outbreaks reported in UK care homes rose rapidly in early March of 2020. Owing to the increased co-morbidities and therefore worse COVID-19 outcomes for care home residents, it is important that we understand this increase and its future implications. We demonstrate the use of an SIS model where each nursing home is an infective unit capable of either being susceptible to an outbreak (S) or in an active outbreak (I). We use a generalized additive model to approximate the trend in growth rate of outbreaks in care homes and find the fit to be improved in a model where the growth rate is proportional to the number of current care home outbreaks compared with a model with a constant growth rate. Using parameters found from the outbreak-dependent growth rate, we predict a 73% prevalence of outbreaks in UK care homes without intervention as a reasonable worst-case planning assumption. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
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Affiliation(s)
- Ian Hall
- Department of Mathematics, University of Manchester, Manchester, UK.,The Alan Turing Institute, London, UK
| | - Hugo Lewkowicz
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Luke Webb
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK.,The Alan Turing Institute, London, UK.,IBM Research, Hartree Centre, SciTech Daresbury, Warrington, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, UK.,The Alan Turing Institute, London, UK
| | | | - Nick Gent
- Public Health England, Emergency Response, London, UK
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37
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Eyre RW, House T, Xavier Gómez-Olivé F, Griffiths FE. Bayesian belief network modelling of household food security in rural South Africa. BMC Public Health 2021; 21:935. [PMID: 34001089 PMCID: PMC8130258 DOI: 10.1186/s12889-021-10938-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 04/28/2021] [Indexed: 11/14/2022] Open
Abstract
Background Achieving food security remains a key challenge for public policy throughout the world. As such, understanding the determinants of food insecurity and the causal relationships between them is an important scientific question. We aim to construct a Bayesian belief network model of food security in rural South Africa to act as a tool for decision support in the design of interventions. Methods Here, we use data from the Agincourt Health and Socio-demographic Surveillance System (HDSS) study area, which is close to the Mozambique border in a low-income region of South Africa, together with Bayesian belief network (BBN) methodology to address this question. Results We find that a combination of expert elicitation and learning from data produces the most credible set of causal relationships, as well as the greatest predictive performance with 10-fold cross validation resulting in a Briers score 0.0846, information reward of 0.5590, and Bayesian information reward of 0.0057. We report the resulting model as a directed acyclic graph (DAG) that can be used to model the expected effects of complex interventions to improve food security. Applications to sensitivity analyses and interventional simulations show ways the model can be applied as tool for decision support for human experts in deciding on interventions. Conclusions The resulting models can form the basis of the iterative generation of a robust causal model of household food security in the Agincourt HDSS study area and in other similar populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-10938-y.
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Affiliation(s)
- Robert W Eyre
- Spectra Analytics, 70 Gracechurch Street, London, EC3V 0HR, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - F Xavier Gómez-Olivé
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Frances E Griffiths
- Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK. .,University of the Witwatersrand, Johannesburg, South Africa.
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Pouwels KB, House T, Pritchard E, Robotham JV, Birrell PJ, Gelman A, Vihta KD, Bowers N, Boreham I, Thomas H, Lewis J, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Benton P, Walker AS. Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey. Lancet Public Health 2021; 6:e30-e38. [PMID: 33308423 PMCID: PMC7786000 DOI: 10.1016/s2468-2667(20)30282-6] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. METHODS Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. FINDINGS Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29-0·54) to 0·06% (0·04-0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17-24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17-24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45-68%, dependent on calendar time. INTERPRETATION Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards. FUNDING Department of Health and Social Care.
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Affiliation(s)
- Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK.
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK; IBM Research, Hartree Centre, Sci-Tech, Daresbury, UK
| | - Emma Pritchard
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Paul J Birrell
- National Infection Service, Public Health England, London, UK; Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge, UK
| | - Andrew Gelman
- Department of Statistics, Columbia University, New York, NY, USA
| | - Karina-Doris Vihta
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | | | | | | | - Iain Bell
- Office for National Statistics, Newport, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | - Ann Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; MRC Clinical Trials Unit at University College London, London, UK
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39
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Thompson RN, Hollingsworth TD, Isham V, Arribas-Bel D, Ashby B, Britton T, Challenor P, Chappell LHK, Clapham H, Cunniffe NJ, Dawid AP, Donnelly CA, Eggo RM, Funk S, Gilbert N, Glendinning P, Gog JR, Hart WS, Heesterbeek H, House T, Keeling M, Kiss IZ, Kretzschmar ME, Lloyd AL, McBryde ES, McCaw JM, McKinley TJ, Miller JC, Morris M, O'Neill PD, Parag KV, Pearson CAB, Pellis L, Pulliam JRC, Ross JV, Tomba GS, Silverman BW, Struchiner CJ, Tildesley MJ, Trapman P, Webb CR, Mollison D, Restif O. Key questions for modelling COVID-19 exit strategies. Proc Biol Sci 2020; 287:20201405. [PMID: 32781946 PMCID: PMC7575516 DOI: 10.1098/rspb.2020.1405] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/21/2020] [Indexed: 12/15/2022] Open
Abstract
Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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Affiliation(s)
- Robin N. Thompson
- Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
- Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | | | - Valerie Isham
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
| | - Daniel Arribas-Bel
- School of Environmental Sciences, University of Liverpool, Brownlow Street, Liverpool L3 5DA, UK
- The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | - Ben Ashby
- Department of Mathematical Sciences, University of Bath, North Road, Bath BA2 7AY, UK
| | - Tom Britton
- Department of Mathematics, Stockholm University, Kräftriket, 106 91 Stockholm, Sweden
| | - Peter Challenor
- College of Engineering, Mathematical and Physical Sciences, University of Exeter, Exeter EX4 4QE, UK
| | - Lauren H. K. Chappell
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK
| | - Hannah Clapham
- Saw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive, Singapore117549, Singapore
| | - Nik J. Cunniffe
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - A. Philip Dawid
- Statistical Laboratory, University of Cambridge, Wilberforce Road, Cambridge CB3 0WB, UK
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial CollegeLondon, Norfolk Place, London W2 1PG, UK
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Sebastian Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Nigel Gilbert
- Department of Sociology, University of Surrey, Stag Hill, Guildford GU2 7XH, UK
| | - Paul Glendinning
- Department of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Julia R. Gog
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - William S. Hart
- Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Yalelaan, 3584 CL Utrecht, The Netherlands
| | - Thomas House
- IBM Research, The Hartree Centre, Daresbury, Warrington WA4 4AD, UK
- Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Matt Keeling
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - István Z. Kiss
- School of Mathematical and Physical Sciences, University of Sussex, Falmer, Brighton BN1 9QH, UK
| | - Mirjam E. Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Alun L. Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA
| | - Emma S. McBryde
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland 4811, Australia
| | - James M. McCaw
- School of Mathematics and Statistics, University of Melbourne, Carlton, Victoria 3010, Australia
| | - Trevelyan J. McKinley
- College of Medicine and Health, University of Exeter, Barrack Road, Exeter EX2 5DW, UK
| | - Joel C. Miller
- Department of Mathematics and Statistics, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Martina Morris
- Department of Sociology, University of Washington, Savery Hall, Seattle, WA 98195, USA
| | - Philip D. O'Neill
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial CollegeLondon, Norfolk Place, London W2 1PG, UK
| | - Carl A. B. Pearson
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Jonkershoek Road, Stellenbosch 7600, South Africa
| | - Lorenzo Pellis
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Juliet R. C. Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Jonkershoek Road, Stellenbosch 7600, South Africa
| | - Joshua V. Ross
- School of Mathematical Sciences, University of Adelaide, South Australia 5005, Australia
| | | | - Bernard W. Silverman
- Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, UK
- Rights Lab, University of Nottingham, Highfield House, Nottingham NG7 2RD, UK
| | - Claudio J. Struchiner
- Escola de Matemática Aplicada, Fundação Getúlio Vargas, Praia de Botafogo, 190 Rio de Janeiro, Brazil
| | - Michael J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Pieter Trapman
- Department of Mathematics, Stockholm University, Kräftriket, 106 91 Stockholm, Sweden
| | - Cerian R. Webb
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, UK
| | - Olivier Restif
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
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40
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Overton CE, Stage HB, Ahmad S, Curran-Sebastian J, Dark P, Das R, Fearon E, Felton T, Fyles M, Gent N, Hall I, House T, Lewkowicz H, Pang X, Pellis L, Sawko R, Ustianowski A, Vekaria B, Webb L. Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example. Infect Dis Model 2020; 5:409-441. [PMID: 32691015 PMCID: PMC7334973 DOI: 10.1016/j.idm.2020.06.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 06/30/2020] [Accepted: 06/30/2020] [Indexed: 01/12/2023] Open
Abstract
During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
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Affiliation(s)
- Christopher E Overton
- Department of Mathematics, University of Manchester, UK
- Department of Mathematical Sciences, University of Liverpool, UK
| | | | - Shazaad Ahmad
- Department of Virology, Manchester Medical Microbiology Partnership, Manchester Foundation Trust, UK
- Manchester Academic Health Sciences Centre, UK
| | | | - Paul Dark
- Division of Infection, Immunity and Respiratory Medicine, NIHR Biomedical Research Centre, University of Manchester, UK
- Critical Care Unit, Salford Royal Hospital, Northern Care Alliance NHS Group, UK
| | - Rajenki Das
- Department of Mathematics, University of Manchester, UK
| | - Elizabeth Fearon
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, UK
| | - Timothy Felton
- Division of Infection, Immunity and Respiratory Medicine, NIHR Biomedical Research Centre, University of Manchester, UK
- Intensive Care Unit, Wythenshawe Hospital, Manchester University NHS Foundation Trust, UK
| | - Martyn Fyles
- Department of Mathematics, University of Manchester, UK
- The Alan Turing Institute, UK
| | - Nick Gent
- Emergency Response Department, Public Health England, UK
| | - Ian Hall
- Department of Mathematics, University of Manchester, UK
- Emergency Response Department, Public Health England, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, UK
- IBM Research, Hartree Centre, SciTech Daresbury, UK
| | - Hugo Lewkowicz
- Department of Health Sciences, University of Manchester, UK
- Department of Mathematics, University of Manchester, UK
| | - Xiaoxi Pang
- Department of Mathematics, University of Manchester, UK
| | | | - Robert Sawko
- IBM Research, Hartree Centre, SciTech Daresbury, UK
| | - Andrew Ustianowski
- Regional Infectious Diseases Unit, North Manchester General Hospital, UK
- School of Medical Sciences, University of Manchester, UK
| | - Bindu Vekaria
- Department of Mathematics, University of Manchester, UK
| | - Luke Webb
- Department of Mathematics, University of Manchester, UK
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41
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Alahmadi A, Belet S, Black A, Cromer D, Flegg JA, House T, Jayasundara P, Keith JM, McCaw JM, Moss R, Ross JV, Shearer FM, Tun STT, Walker J, White L, Whyte JM, Yan AWC, Zarebski AE. Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges. Epidemics 2020; 32:100393. [PMID: 32674025 DOI: 10.1016/j.epidem.2020.100393] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 04/25/2020] [Indexed: 12/16/2022] Open
Abstract
Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.
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Affiliation(s)
- Amani Alahmadi
- School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia
| | - Sarah Belet
- School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Andrew Black
- School of Mathematical Sciences, University of Adelaide, Adelaide, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Deborah Cromer
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, Australia and School of Mathematics and Statistics, UNSW Sydney, Sydney, Australia
| | - Jennifer A Flegg
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK; IBM Research, Hartree Centre, Sci-Tech Daresbury, Warrington, UK.
| | | | - Jonathan M Keith
- School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - James M McCaw
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia.
| | - Robert Moss
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Joshua V Ross
- School of Mathematical Sciences, University of Adelaide, Adelaide, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).
| | - Freya M Shearer
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Sai Thein Than Tun
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, UK
| | - James Walker
- School of Mathematical Sciences, University of Adelaide, Adelaide, Australia
| | - Lisa White
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, UK
| | - Jason M Whyte
- Centre of Excellence for Biosecurity Risk Analysis (CEBRA), School of BioSciences, University of Melbourne, Melbourne, Australia; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
| | - Ada W C Yan
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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Affiliation(s)
- Jo Middleton
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Falmer BN1 9PH, UK; Evolution, Behaviour and Environment, School of Life Sciences, University of Sussex, Falmer, UK.
| | - Stephen L Walker
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK; Hospital for Tropical Diseases and Department of Dermatology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester, UK
| | - Michael G Head
- Faculty of Medicine and Global Health Research Institute, University of Southampton, Southampton, UK
| | - Jackie A Cassel
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Falmer BN1 9PH, UK; Surrey and Sussex Health Protection Team, Public Health England, Horsham, UK
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43
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Hill EM, House T, Dhingra MS, Kalpravidh W, Morzaria S, Osmani MG, Brum E, Yamage M, Kalam MA, Prosser DJ, Takekawa JY, Xiao X, Gilbert M, Tildesley MJ. The impact of surveillance and control on highly pathogenic avian influenza outbreaks in poultry in Dhaka division, Bangladesh. PLoS Comput Biol 2018; 14:e1006439. [PMID: 30212472 PMCID: PMC6155559 DOI: 10.1371/journal.pcbi.1006439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 09/25/2018] [Accepted: 08/16/2018] [Indexed: 11/19/2022] Open
Abstract
In Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh. Ongoing circulation of avian influenza H5N1 viruses in poultry pose a global public health risk and cause extensive damage to the livestock industry. One of several countries in South Asia gravely affected is Bangladesh, where the poultry industry is an economically and socially important sector. Identifying the optimal control response in anticipation of further outbreaks is therefore a key challenge for policy-makers. This study tested a series of culling, vaccination and active surveillance intervention actions, assessing specific control objectives related to the burden or duration of H5N1 outbreaks among commercial poultry farms in the Dhaka division. This assessment was achieved through performing computational simulations of a previously developed H5N1 influenza transmission mathematical model. The findings of this assessment indicate that the impact of reactive culling and vaccination control policies are dependent upon transmission characteristics, control objectives and availability of resources to enact the control action, whereas proactive surveillance schemes significantly outperform reactive surveillance procedures irrespective of these conditions.
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Affiliation(s)
- Edward M. Hill
- Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- * E-mail:
| | - Thomas House
- School of Mathematics, The University of Manchester, Manchester, United Kingdom
| | - Madhur S. Dhingra
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
- Food and Agricultural Organization of the United Nations, Rome, Italy
| | - Wantanee Kalpravidh
- Food and Agricultural Organization of the United Nations Regional Office for Asia and the Pacific, Bangkok, Thailand
| | - Subhash Morzaria
- Food and Agricultural Organization of the United Nations, Rome, Italy
| | | | - Eric Brum
- Emergency Centre for Transboundary Animal Diseases (ECTAD), Food and Agriculture Organization of the United Nations, Dhaka, Bangladesh
| | - Mat Yamage
- Emergency Centre for Transboundary Animal Diseases (ECTAD), Food and Agriculture Organization of the United Nations, Dhaka, Bangladesh
| | - Md. A. Kalam
- Institute of Epidemiology, Disease Control & Research (IEDCR), Dhaka, Bangladesh
| | - Diann J. Prosser
- USGS Patuxent Wildlife Research Center, Laurel, Maryland, United States of America
| | - John Y. Takekawa
- U.S. Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station, Vallejo, California, United States of America
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - Michael J. Tildesley
- Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
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Eyre RW, House T, Gómez-Olivé FX, Griffiths FE. Modelling fertility in rural South Africa with combined nonlinear parametric and semi-parametric methods. Emerg Themes Epidemiol 2018; 15:5. [PMID: 29507596 PMCID: PMC5833110 DOI: 10.1186/s12982-018-0073-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 02/23/2018] [Indexed: 11/10/2022] Open
Abstract
Background Central to the study of populations, and therefore to the analysis of the development of countries undergoing major transitions, is the calculation of fertility patterns and their dependence on different variables such as age, education, and socio-economic status. Most epidemiological research on these matters rely on the often unjustified assumption of (generalised) linearity, or alternatively makes a parametric assumption (e.g. for age-patterns). Methods We consider nonlinearity of fertility in the covariates by combining an established nonlinear parametric model for fertility over age with nonlinear modelling of fertility over other covariates. For the latter, we use the semi-parametric method of Gaussian process regression which is a popular methodology in many fields including machine learning, computer science, and systems biology. We applied the method to data from the Agincourt Health and Socio-Demographic Surveillance System, annual census rounds performed on a poor rural region of South Africa since 1992, to analyse fertility patterns over age and socio-economic status. Results We capture a previously established age-pattern of fertility, whilst being able to more robustly model the relationship between fertility and socio-economic status without unjustified a priori assumptions of linearity. Peak fertility over age is shown to be increasing over time, as well as for adolescents but not for those later in life for whom fertility is generally decreasing over time. Conclusions Combining Gaussian process regression with nonlinear parametric modelling of fertility over age allowed for the incorporation of further covariates into the analysis without needing to assume a linear relationship. This enabled us to provide further insights into the fertility patterns of the Agincourt study area, in particular the interaction between age and socio-economic status.
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Affiliation(s)
- Robert W Eyre
- 1Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL UK
| | - Thomas House
- 2School of Mathematics, University of Manchester, Manchester, M13 9PL UK
| | - F Xavier Gómez-Olivé
- 3Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Frances E Griffiths
- 4Warwick Medical School, University of Warwick, Coventry, CV4 7AL UK.,5Centre for Health Policy, University of the Witwatersrand, Johannesburg, South Africa
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Kinyanjui T, Middleton J, Güttel S, Cassell J, Ross J, House T. Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units. PLoS Comput Biol 2018; 14:e1006046. [PMID: 29579037 PMCID: PMC5898763 DOI: 10.1371/journal.pcbi.1006046] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 04/13/2018] [Accepted: 02/20/2018] [Indexed: 12/03/2022] Open
Abstract
In the context of an ageing population, understanding the transmission of infectious diseases such as scabies through well-connected sub-units of the population, such as residential care homes, is particularly important for the design of efficient interventions to mitigate against the effects of those diseases. Here, we present a modelling methodology based on the efficient solution of a large-scale system of linear differential equations that allows statistical calibration of individual-based random models to real data on scabies in residential care homes. In particular, we review and benchmark different numerical methods for the integration of the differential equation system, and then select the most appropriate of these methods to perform inference using Markov chain Monte Carlo. We test the goodness-of-fit of this model using posterior predictive intervals and propagate forward the resulting parameter uncertainty in a Bayesian framework to consider the economic cost of delayed interventions against scabies, quantifying the benefits of prompt action in the event of detection. We also revisit the previous methodology used to assess the safety of treatments in small population sub-units-in this context ivermectin-and demonstrate that even a very slight relaxation of the implicit assumption of homogeneous death rates significantly increases the plausibility of the hypothesis that ivermectin does not cause excess mortality based upon the data of Barkwell and Shields.
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Affiliation(s)
| | - Jo Middleton
- Department of Primary Care and Public Health Medicine, Brighton and Sussex Medical School, Falmer, Brighton, United Kingdom
| | - Stefan Güttel
- School of Mathematics, University of Manchester, United Kingdom
| | - Jackie Cassell
- Department of Primary Care and Public Health Medicine, Brighton and Sussex Medical School, Falmer, Brighton, United Kingdom
| | - Joshua Ross
- School of Mathematical Sciences, The University of Adelaide, Adelaide, Australia
| | - Thomas House
- School of Mathematics, University of Manchester, United Kingdom
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Buckingham-Jeffery E, Isham V, House T. Gaussian process approximations for fast inference from infectious disease data. Math Biosci 2018; 301:111-120. [PMID: 29471011 DOI: 10.1016/j.mbs.2018.02.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 02/13/2018] [Accepted: 02/17/2018] [Indexed: 10/18/2022]
Abstract
We present a flexible framework for deriving and quantifying the accuracy of Gaussian process approximations to non-linear stochastic individual-based models of epidemics. We develop this for the SIR and SEIR models, and we show how it can be used to perform quick maximum likelihood inference for the underlying parameters given population estimates of the number of infecteds or cases at given time points. We also show how the unobserved processes can be inferred at the same time as the underlying parameters.
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Affiliation(s)
- Elizabeth Buckingham-Jeffery
- Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL, UK; School of Mathematics, University of Manchester, Manchester M13 9PL, UK.
| | - Valerie Isham
- Department of Statistical Science, University College London, London, WC1E 6BT, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester M13 9PL, UK
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House T, Ford A, Lan S, Bilson S, Buckingham-Jeffery E, Girolami M. Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry. J R Soc Interface 2017; 13:rsif.2016.0279. [PMID: 27558850 PMCID: PMC5014059 DOI: 10.1098/rsif.2016.0279] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 07/25/2016] [Indexed: 12/14/2022] Open
Abstract
Infectious diseases exert a large and in many contexts growing burden on human health, but violate most of the assumptions of classical epidemiological statistics and hence require a mathematically sophisticated approach. Viral shedding data are collected during human studies—either where volunteers are infected with a disease or where existing cases are recruited—in which the levels of live virus produced over time are measured. These have traditionally been difficult to analyse due to strong, complex correlations between parameters. Here, we show how a Bayesian approach to the inverse problem together with modern Markov chain Monte Carlo algorithms based on information geometry can overcome these difficulties and yield insights into the disease dynamics of two of the most prevalent human pathogens—influenza and norovirus—as well as Ebola virus disease.
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Affiliation(s)
- Thomas House
- School of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK Warwick Infectious Disease Epidemiology Research Centre (WIDER), Warwick Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Ashley Ford
- School of Mathematics, University of Bristol, Bristol BS8 1TW, UK
| | - Shiwei Lan
- Department of Statistics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Samuel Bilson
- Warwick Infectious Disease Epidemiology Research Centre (WIDER), Warwick Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Elizabeth Buckingham-Jeffery
- Warwick Infectious Disease Epidemiology Research Centre (WIDER), Warwick Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK Complexity Science Doctoral Training Centre, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Mark Girolami
- Department of Statistics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
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Eyre RW, House T, Hill EM, Griffiths FE. Spreading of components of mood in adolescent social networks. R Soc Open Sci 2017; 4:170336. [PMID: 28989740 PMCID: PMC5627080 DOI: 10.1098/rsos.170336] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 08/21/2017] [Indexed: 06/07/2023]
Abstract
Recent research has provided evidence that mood can spread over social networks via social contagion, but that, in seeming contradiction to this, depression does not. Here, we investigate whether there is evidence for the individual components of mood (such as appetite, tiredness and sleep) spreading through US adolescent friendship networks while adjusting for confounding by modelling the transition probabilities of changing mood state over time. We find that having more friends with worse mood is associated with a higher probability of an adolescent worsening in mood and a lower probability of improving, and vice versa for friends with better mood, for the overwhelming majority of mood components. We also show, however, that this effect is not strong enough in the negative direction to lead to a significant increase in depression incidence, helping to resolve the seeming contradictory nature of existing research. Our conclusions, therefore, link in to current policy discussions on the importance of subthreshold levels of depressive symptoms and could help inform interventions against depression in high schools.
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Affiliation(s)
- Robert W. Eyre
- Centre for Complexity Science, Zeeman Building, University of Warwick, Coventry CV4 7AL, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Edward M. Hill
- Centre for Complexity Science, Zeeman Building, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - Frances E. Griffiths
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Centre for Health Policy, University of the Witwatersrand, Johannesburg, South Africa
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Abstract
Social influence can lead to behavioural 'fads' that are briefly popular and quickly die out. Various models have been proposed for these phenomena, but empirical evidence of their accuracy as real-world predictive tools has so far been absent. Here we find that a 'complex contagion' model accurately describes the spread of behaviours driven by online sharing. We found that standard, 'simple', contagion often fails to capture both the rapid spread and the long tails of popularity seen in real fads, where our complex contagion model succeeds. Complex contagion also has predictive power: it successfully predicted the peak time and duration of the ALS Icebucket Challenge. The fast spread and longer duration of fads driven by complex contagion has important implications for activities such as publicity campaigns and charity drives.
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Affiliation(s)
- Daniel A. Sprague
- Spectra Analytics, 40-42 Scrutton St, London EC2A 4PP, United Kingdom
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester, M13 9PL, United Kingdom
- * E-mail:
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Buckingham-Jeffery E, Morbey R, House T, Elliot AJ, Harcourt S, Smith GE. Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats. BMC Public Health 2017; 17:477. [PMID: 28525991 PMCID: PMC5438549 DOI: 10.1186/s12889-017-4372-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 05/07/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. METHODS The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. RESULTS The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing curves. CONCLUSIONS The results from this study make it possible to identify trends and unusual activity in syndromic surveillance data from GP services in real-time independently of the effects caused by day of the week and public holidays, thereby improving the public health action resulting from the analysis of these data.
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Affiliation(s)
- Elizabeth Buckingham-Jeffery
- Centre for Complexity Science and Warwick Infectious Disease Epidemiology Research Centre, University of Warwick, Coventry, UK. .,School of Mathematics, University of Manchester, Manchester, UK.
| | - Roger Morbey
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester, UK
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK
| | - Sally Harcourt
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK
| | - Gillian E Smith
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, UK
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