1
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Watson LM, Plank MJ, Armstrong BA, Chapman JR, Hewitt J, Morris H, Orsi A, Bunce M, Donnelly CA, Steyn N. Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand. COMMUNICATIONS MEDICINE 2024; 4:143. [PMID: 39009723 PMCID: PMC11250817 DOI: 10.1038/s43856-024-00570-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 07/04/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care. METHODS We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods. RESULTS We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. CONCLUSIONS Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.
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Affiliation(s)
- Leighton M Watson
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
| | - Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | | | - Joanne R Chapman
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Joanne Hewitt
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Helen Morris
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Alvaro Orsi
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Michael Bunce
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| | - Nicholas Steyn
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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2
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Kiani P, Hendriksen PA, Kim AJ, Garssen J, Verster JC. Comparative Analysis of the Clinical Presentation of Individuals Who Test Positive or Negative for SARS-CoV-2: Results from a Test Street Study. Viruses 2024; 16:1031. [PMID: 39066194 PMCID: PMC11281701 DOI: 10.3390/v16071031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/17/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
The common cold, the flu, and the 2019 coronavirus disease (COVID-19) have many symptoms in common. As such, without testing for severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2), it is difficult to conclude whether or not one is infected with SARS-CoV-2. The aim of the current study was to compare the presence and severity of COVID-19-related symptoms among those who tested positive or negative for the beta variant of SARS-CoV-2 (B.1.351) and identify the clinical presentation with the greatest likelihood of testing positive for SARS-CoV-2. n = 925 individuals that were tested for SARS-CoV-2 at Dutch mass testing sites (i.e., test streets) were invited to complete a short online survey. The presence and severity of 17 COVID-19-related symptoms were assessed. In addition, mood, health correlates, and quality of life were assessed for the week before the test. Of the sample, n = 88 tested positive and n = 837 tested negative for SARS-CoV-2. Individuals who tested positive for SARS-CoV-2 reported experiencing a significantly greater number, as well as greater overall symptom severity, compared to individuals who tested negative for SARS-CoV-2. A binary logistic regression analysis revealed that increased severity levels of congestion, coughing, shivering, or loss of smell were associated with an increase in the odds of testing positive for SARS-CoV-2, whereas an increase in the severity levels of runny nose, sore throat, or fatigue were associated with an increase in the odds of testing negative for SARS-CoV-2. No significant differences in mood or health correlates were found between those who tested positive or negative for SARS-CoV-2, except for a significantly higher stress score among those who tested negative for SARS-CoV-2. In conclusion, individuals that tested positive for SARS-CoV-2 experienced a significantly greater number and more severe COVID-19-related symptoms compared to those who tested negative for SARS-CoV-2. Experiencing shivering and loss of smell may be the best indicators for increased likelihood of testing positive for SARS-CoV-2.
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Affiliation(s)
- Pantea Kiani
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584CG Utrecht, The Netherlands; (P.K.); (P.A.H.); (J.G.)
| | - Pauline A. Hendriksen
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584CG Utrecht, The Netherlands; (P.K.); (P.A.H.); (J.G.)
| | - Andy J. Kim
- Department of Psychology and Neuroscience, Dalhousie University, 1355 Oxford Str., Halifax, NS B3H 4R2, Canada;
| | - Johan Garssen
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584CG Utrecht, The Netherlands; (P.K.); (P.A.H.); (J.G.)
- Danone, Global Research & Innovation Center, 3584CT Utrecht, The Netherlands
| | - Joris C. Verster
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584CG Utrecht, The Netherlands; (P.K.); (P.A.H.); (J.G.)
- Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne, VIC 3122, Australia
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3
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Pouwels KB, Eyre DW, House T, Aspey B, Matthews PC, Stoesser N, Newton JN, Diamond I, Studley R, Taylor NGH, Bell JI, Farrar J, Kolenchery J, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS. Improving the representativeness of UK's national COVID-19 Infection Survey through spatio-temporal regression and post-stratification. Nat Commun 2024; 15:5340. [PMID: 38914564 PMCID: PMC11196632 DOI: 10.1038/s41467-024-49201-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK's national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.
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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, Oxford, UK.
| | - 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, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech, Daresbury, UK
| | - Ben Aspey
- Office for National Statistics, Newport, UK
| | - Philippa C Matthews
- The Francis Crick Institute, London, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- 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
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | - Jaison Kolenchery
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- 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
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim E A Peto
- 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
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - A Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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4
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McCabe R, Danelian G, Panovska-Griffiths J, Donnelly CA. Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey. Infect Dis Model 2024; 9:299-313. [PMID: 38371874 PMCID: PMC10867655 DOI: 10.1016/j.idm.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Key epidemiological parameters, including the effective reproduction number, R ( t ) , and the instantaneous growth rate, r ( t ) , generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the "emergency" to "endemic" phase of the pandemic. The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the "ONS-based" R ( t ) and r ( t ) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters. Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates. Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.
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Affiliation(s)
- Ruth McCabe
- Department of Statistics, University of Oxford, UK
- National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, UK
- United Kingdom Health Security Agency, UK
| | | | - Jasmina Panovska-Griffiths
- United Kingdom Health Security Agency, UK
- The Queen's College, University of Oxford, UK
- The Pandemic Sciences Institute, University of Oxford, UK
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, UK
- National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, UK
- The Pandemic Sciences Institute, University of Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, UK
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5
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Shearer FM, McCaw JM, Ryan GE, Hao T, Tierney NJ, Lydeamore MJ, Wu L, Ward K, Ellis S, Wood J, McVernon J, Golding N. Estimating the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission in Australia. Epidemics 2024; 47:100764. [PMID: 38552550 DOI: 10.1016/j.epidem.2024.100764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 12/15/2023] [Accepted: 03/14/2024] [Indexed: 06/17/2024] Open
Abstract
BACKGROUND Australian states and territories used test-trace-isolate-quarantine (TTIQ) systems extensively in their response to the COVID-19 pandemic in 2020-2021. We report on an analysis of Australian case data to estimate the impact of test-trace-isolate-quarantine systems on SARS-CoV-2 transmission. METHODS Our analysis uses a novel mathematical modelling framework and detailed surveillance data on COVID-19 cases including dates of infection and dates of isolation. First, we directly translate an empirical distribution of times from infection to isolation into reductions in potential for onward transmission during periods of relatively low caseloads (tens to hundreds of reported cases per day). We then apply a simulation approach, validated against case data, to assess the impact of case-initiated contact tracing on transmission during a period of relatively higher caseloads and system stress (up to thousands of cases per day). RESULTS We estimate that under relatively low caseloads in the state of New South Wales (tens of cases per day), TTIQ contributed to a 54% reduction in transmission. Under higher caseloads in the state of Victoria (hundreds of cases per day), TTIQ contributed to a 42% reduction in transmission. Our results also suggest that case-initiated contact tracing can support timely quarantine in times of system stress (thousands of cases per day). CONCLUSION Contact tracing systems for COVID-19 in Australia were highly effective and adaptable in supporting the national suppression strategy from 2020-21, prior to the emergence of the Omicron variant in November 2021. TTIQ systems were critical to the maintenance of the strong suppression strategy and were more effective when caseloads were (relatively) low.
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Affiliation(s)
- Freya M Shearer
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia.
| | - James M McCaw
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Gerard E Ryan
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia
| | - Tianxiao Hao
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia
| | | | - Michael J Lydeamore
- Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
| | - Logan Wu
- Walter and Eliza Hall Institute, Melbourne, Australia
| | - Kate Ward
- Public Health Response Branch, NSW Ministry of Health, Australia
| | - Sally Ellis
- Public Health Response Branch, NSW Ministry of Health, Australia
| | - James Wood
- School of Population Health, The University of New South Wales, Sydney, Australia
| | - Jodie McVernon
- Department of Infectious Diseases at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia; Victorian Infectious Diseases Reference Laboratory Epidemiology Unit at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia
| | - Nick Golding
- Infectious Disease Dynamics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Telethon Kids Institute, Perth, Australia; Curtin University, Perth, Australia.
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6
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Fong KJ, Summers C, Cook TM. NHS hospital capacity during covid-19: overstretched staff, space, systems, and stuff. BMJ 2024; 385:e075613. [PMID: 38569726 DOI: 10.1136/bmj-2023-075613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Affiliation(s)
- Kevin J Fong
- University College London Hospitals NHS Trust, London, UK
- Department of Science, Technology, Engineering and Public Policy, University College London, UK
| | - Charlotte Summers
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Tim M Cook
- Royal United Hospitals Foundation Trust, Bath, UK
- School of Medicine, University of Bristol, Bristol, UK
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7
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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] [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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8
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Wei J, Stoesser N, Matthews PC, Khera T, Gethings O, Diamond I, Studley R, Taylor N, Peto TEA, Walker AS, Pouwels KB, Eyre DW. Risk of SARS-CoV-2 reinfection during multiple Omicron variant waves in the UK general population. Nat Commun 2024; 15:1008. [PMID: 38307854 PMCID: PMC10837445 DOI: 10.1038/s41467-024-44973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/10/2024] [Indexed: 02/04/2024] Open
Abstract
SARS-CoV-2 reinfections increased substantially after Omicron variants emerged. Large-scale community-based comparisons across multiple Omicron waves of reinfection characteristics, risk factors, and protection afforded by previous infection and vaccination, are limited. Here we studied ~45,000 reinfections from the UK's national COVID-19 Infection Survey and quantified the risk of reinfection in multiple waves, including those driven by BA.1, BA.2, BA.4/5, and BQ.1/CH.1.1/XBB.1.5 variants. Reinfections were associated with lower viral load and lower percentages of self-reporting symptoms compared with first infections. Across multiple Omicron waves, estimated protection against reinfection was significantly higher in those previously infected with more recent than earlier variants, even at the same time from previous infection. Estimated protection against Omicron reinfections decreased over time from the most recent infection if this was the previous or penultimate variant (generally within the preceding year). Those 14-180 days after receiving their most recent vaccination had a lower risk of reinfection than those >180 days from their most recent vaccination. Reinfection risk was independently higher in those aged 30-45 years, and with either low or high viral load in their most recent previous infection. Overall, the risk of Omicron reinfection is high, but with lower severity than first infections; both viral evolution and waning immunity are independently associated with reinfection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Nicole Stoesser
- 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
- 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
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | | | | | | | | | | | - Tim E A Peto
- 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
- 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
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, 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, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - 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, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, 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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9
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Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, Vandemaele K, Viboud C, Riley S, McCaw JM, Shearer FM. Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerg Infect Dis 2024; 30:e230768. [PMID: 38190760 PMCID: PMC10826770 DOI: 10.3201/eid3002.230768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.
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10
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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] [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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11
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Plank MJ, Watson L, Maclaren OJ. Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. PLoS Comput Biol 2024; 20:e1011752. [PMID: 38190380 PMCID: PMC10798620 DOI: 10.1371/journal.pcbi.1011752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/19/2024] [Accepted: 12/12/2023] [Indexed: 01/10/2024] Open
Abstract
Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.
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Affiliation(s)
- Michael J. Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Leighton Watson
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Oliver J. Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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12
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Heath B, Evans S, Robertson DS, Robotham JV, Villar SS, Presanis AM. Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals. BMC Infect Dis 2023; 23:900. [PMID: 38129789 PMCID: PMC10740241 DOI: 10.1186/s12879-023-08881-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing). METHODS Two different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a 'typical' English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to 'normal life' without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the 'ward' testing scheme only healthcare workers who work on a single ward are eligible and in the 'full' testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers. RESULTS Including a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2[Formula: see text] in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests. CONCLUSIONS Whether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown.
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Affiliation(s)
- Bethany Heath
- MRC Biostatistics Unit, Univeristy of Cambridge, Robinson Way, Cambridge, CB2 0SR, Cambridgeshire, United Kingdom.
| | - Stephanie Evans
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
- Statistics, Modelling and Economics Division, UK Health Security Agency, London, United Kingdom
| | - David S Robertson
- MRC Biostatistics Unit, Univeristy of Cambridge, Robinson Way, Cambridge, CB2 0SR, Cambridgeshire, United Kingdom
| | - Julie V Robotham
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom
- Statistics, Modelling and Economics Division, UK Health Security Agency, London, United Kingdom
- NIHR Health Protection Research Unit in Modelling and Health Economics at Imperial College London in partnership with the UK Health Security Agency and London School of Hygiene and Tropical Medicine, London, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with the UK Health Security Agency, Oxford, United Kingdom
| | - Sofía S Villar
- MRC Biostatistics Unit, Univeristy of Cambridge, Robinson Way, Cambridge, CB2 0SR, Cambridgeshire, United Kingdom
| | - Anne M Presanis
- MRC Biostatistics Unit, Univeristy of Cambridge, Robinson Way, Cambridge, CB2 0SR, Cambridgeshire, United Kingdom
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol in partnership with the UK Health Security Agency and MRC Biostatistics Unit, University of Cambridge, Bristol, United Kingdom
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13
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Rhodes S, Demou E, Wilkinson J, Cherrie M, Edge R, Gittins M, Katikireddi SV, Kromydas T, Mueller W, Pearce N, van Tongeren M. Potential contribution of vaccination uptake to occupational differences in risk of SARS-CoV-2: analysis of the ONS COVID-19 Infection Survey. Occup Environ Med 2023; 81:oemed-2023-108931. [PMID: 38124150 PMCID: PMC10850636 DOI: 10.1136/oemed-2023-108931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 09/30/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES To assess variation in vaccination uptake across occupational groups as a potential explanation for variation in risk of SARS-CoV-2 infection. DESIGN We analysed data from the UK Office of National Statistics COVID-19 Infection Survey linked to vaccination data from the National Immunisation Management System in England from 1 December 2020 to 11 May 2022. We analysed vaccination uptake and SARS-CoV-2 infection risk by occupational group and assessed whether adjustment for vaccination reduced the variation in risk between occupational groups. RESULTS Estimated rates of triple vaccination were high across all occupational groups (80% or above), but were lowest for food processing (80%), personal care (82%), hospitality (83%), manual occupations (84%) and retail (85%). High rates were observed for individuals working in health (95% for office based, 92% for those in patient-facing roles) and education (91%) and office-based workers not included in other categories (90%). The impact of adjusting for vaccination when estimating relative risks of infection was generally modest (ratio of hazard ratios across all occupational groups reduced from 1.37 to 1.32), but was consistent with the hypothesis that low vaccination rates contribute to elevated risk in some groups. CONCLUSIONS Variation in vaccination coverage might account for a modest proportion of occupational differences in infection risk. Vaccination rates were uniformly very high in this cohort, which may suggest that the participants are not representative of the general population. Accordingly, these results should be considered tentative pending the accumulation of additional evidence.
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Affiliation(s)
- Sarah Rhodes
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | - Evangelia Demou
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Jack Wilkinson
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | - Mark Cherrie
- Institute of Occupational Medicine, Edinburgh, UK
| | | | - Matthew Gittins
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | | | - Theocharis Kromydas
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | | | - Neil Pearce
- Faculty of Epidemiology and Population Health, London School of Hygeine and Tropical Medicine, London, UK
| | - Martie van Tongeren
- Centre for Occupational and Environmental Health, University of Manchester, Manchester, UK
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14
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Omiyale W, Holliday J, Doherty N, Callen H, Wood N, Horn E, Burnett F, Young A, Lewington S, Fry D, Bešević J, Conroy M, Sheard S, Feng Q, Welsh S, Effingham M, Young A, Collins R, Lacey B, Allen N. Social determinants of ethnic disparities in SARS-CoV-2 infection: UK Biobank SARS-CoV-2 Serology Study. J Epidemiol Community Health 2023; 78:3-10. [PMID: 37699665 PMCID: PMC10715462 DOI: 10.1136/jech-2023-220353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/25/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND The social determinants of ethnic disparities in risk of SARS-CoV-2 infection during the first wave of the pandemic in the UK remain unclear. METHODS In May 2020, a total of 20 195 adults were recruited from the general population into the UK Biobank SARS-CoV-2 Serology Study. Between mid-May and mid-November 2020, participants provided monthly blood samples. At the end of the study, participants completed a questionnaire on social factors during different periods of the pandemic. Logistic regression yielded ORs for the association between ethnicity and SARS-CoV-2 immunoglobulin G antibodies (indicating prior infection) using blood samples collected in July 2020, immediately after the first wave. RESULTS After exclusions, 14 571 participants (mean age 56; 58% women) returned a blood sample in July, of whom 997 (7%) had SARS-CoV-2 antibodies. Seropositivity was strongly related to ethnicity: compared with those of White ethnicity, ORs (adjusted for age and sex) for Black, South Asian, Chinese, Mixed and Other ethnic groups were 2.66 (95% CI 1.94-3.60), 1.66 (1.15-2.34), 0.99 (0.42-1.99), 1.42 (1.03-1.91) and 1.79 (1.27-2.47), respectively. Additional adjustment for social factors reduced the overall likelihood ratio statistics for ethnicity by two-thirds (67%; mostly from occupational factors and UK region of residence); more precise measurement of social factors may have further reduced the association. CONCLUSIONS This study identifies social factors that are likely to account for much of the ethnic disparities in SARS-CoV-2 infection during the first wave in the UK, and highlights the particular relevance of occupation and residential region in the pathway between ethnicity and SARS-CoV-2 infection.
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Affiliation(s)
- Wemimo Omiyale
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jo Holliday
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | | | - Howard Callen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Natasha Wood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Edward Horn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Frances Burnett
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Allen Young
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Sarah Lewington
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Jelena Bešević
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Megan Conroy
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Qi Feng
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | - Alan Young
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Ben Lacey
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Naomi Allen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
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15
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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] [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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16
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Nguyen HT, Denkinger CM, Brenner S, Koeppel L, Brugnara L, Burk R, Knop M, Bärnighausen T, Deckert A, De Allegri M. Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2023; 24:1545-1559. [PMID: 36656403 PMCID: PMC9850332 DOI: 10.1007/s10198-022-01561-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION The COVID-19 pandemic has entered its third year and continues to affect most countries worldwide. Active surveillance, i.e. testing individuals irrespective of symptoms, presents a promising strategy to accurately measure the prevalence of SARS-CoV-2. We aimed to identify the most cost-effective active surveillance strategy for COVID-19 among the four strategies tested in a randomised control trial between 18th November 2020 and 23rd December 2020 in Germany. The four strategies included: (A1) direct testing of individuals; (A2) direct testing of households; (B1) testing conditioned on upstream COVID-19 symptom pre-screening of individuals; and (B2) testing conditioned on upstream COVID-19 symptom pre-screening of households. METHODS We adopted a health system perspective and followed an activity-based approach to costing. Resource consumption data were collected prospectively from a digital individual database, daily time records, key informant interviews and direct observations. Our cost-effectiveness analysis compared each strategy with the status quo and calculated the average cost-effective ratios (ACERs) for one primary outcome (sample tested) and three secondary outcomes (responder recruited, case detected and asymptomatic case detected). RESULTS Our results showed that A2, with cost per sample tested at 52,89 EURO, had the lowest ACER for the primary outcome, closely followed by A1 (63,33 EURO). This estimate was much higher for both B1 (243,84 EURO) and B2 (181,06 EURO). CONCLUSION A2 (direct testing at household level) proved to be the most cost-effective of the four evaluated strategies and should be considered as an option to strengthen the routine surveillance system in Germany and similar settings.
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Affiliation(s)
- Hoa Thi Nguyen
- Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.
| | - Claudia M Denkinger
- Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 324, 69120, Heidelberg, Germany
- German Center for Infection Research (DZIF), Im Neuenheimer Feld 344, Heidelberg, Germany
| | - Stephan Brenner
- Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Lisa Koeppel
- Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 324, 69120, Heidelberg, Germany
| | - Lucia Brugnara
- Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
- evaplan GmbH at the University Hospital Heidelberg, Ringstr.19B, 69115, Heidelberg, Germany
| | - Robin Burk
- Center for Molecular Biology (ZMBH), Heidelberg University, Im Neuenheimer Feld 282, 69120, Heidelberg, Germany
| | - Michael Knop
- Center for Molecular Biology (ZMBH), Heidelberg University, Im Neuenheimer Feld 282, 69120, Heidelberg, Germany
- German Cancer Research Center (DKFZ), ZMBH Alliance, 69120, Heidelberg, Germany
| | - Till Bärnighausen
- Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Andreas Deckert
- Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University, Im Neuenheimer Feld 324, 69120, Heidelberg, Germany
| | - Manuela De Allegri
- Heidelberg Institute of Global Health, University Hospital and Medical Faculty, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
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17
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Brainard J, Lake IR, Morbey RA, Jones NR, Elliot AJ, Hunter PR. Comparison of surveillance systems for monitoring COVID-19 in England: a retrospective observational study. Lancet Public Health 2023; 8:e850-e858. [PMID: 37832574 DOI: 10.1016/s2468-2667(23)00219-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/01/2023] [Accepted: 09/15/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND During the COVID-19 pandemic, cases were tracked using multiple surveillance systems. Some systems were completely novel, and others incorporated multiple data streams to estimate case incidence and prevalence. How well these different surveillance systems worked as epidemic indicators is unclear, which has implications for future disease surveillance and outbreak management. The aim of this study was to compare case counts, prevalence and incidence, timeliness, and comprehensiveness of different COVID-19 surveillance systems in England. METHODS For this retrospective observational study of COVID-19 surveillance systems in England, data from 12 surveillance systems were extracted from publicly available sources (Jan 1, 2020-Nov 30, 2021). The main outcomes were correlations between different indicators of COVID-19 incidence or prevalence. These data were integrated as daily time-series and comparisons undertaken using Spearman correlation between candidate alternatives and the most timely (updated daily, clinical case register) and the least biased (from comprehensive household sampling) COVID-19 epidemic indicators, with comparisons focused on the period of Sept 1, 2020-Nov 30, 2021. FINDINGS Spearman statistic correlations during the full focus period between the least biased indicator (from household surveys) and other epidemic indicator time-series were 0·94 (95% CI 0·92 to 0·95; clinical cases, the most timely indicator), 0·92 (0·90 to 0·94; estimates of incidence generated after incorporating information about self-reported case status on the ZoeApp, which is a digital app), 0·67 (95% CI 0·60 to 0·73, emergency department attendances), 0·64 (95% CI 0·60 to 0·68, NHS 111 website visits), 0·63 (95% CI 0·56 to 0·69, wastewater viral genome concentrations), 0·60 (95% CI 0·52 to 0·66, admissions to hospital with positive COVID-19 status), 0·45 (95% CI 0·36 to 0·52, NHS 111 calls), 0·08 (95% CI -0·03 to 0·18, Google search rank for "covid"), -0·04 (95% CI -0·12 to 0·05, in-hours consultations with general practitioners), and -0·37 (95% CI -0·46 to -0·28, Google search rank for "coronavirus"). Time lags (-14 to +14 days) did not markedly improve these rho statistics. Clinical cases (the most timely indicator) captured a more consistent proportion of cases than the self-report digital app did. INTERPRETATION A suite of monitoring systems is useful. The household survey system was the most comprehensive and least biased epidemic monitor, but not very timely. Data from laboratory testing, the self-reporting digital app, and attendances to emergency departments were comparatively useful, fairly accurate, and timely epidemic trackers. FUNDING National Institute for Health and Care Research Health Protection Research Unit in Emergency Preparedness and Response, a partnership between the UK Health Security Agency, King's College London, and the University of East Anglia.
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Affiliation(s)
- Julii Brainard
- Norwich Medical School, University of East Anglia, Norwich, UK.
| | - Iain R Lake
- School of Environmental Sciences, University of East Anglia, Norwich, UK
| | - Roger A Morbey
- Real-time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, UK
| | - Natalia R Jones
- School of Environmental Sciences, University of East Anglia, Norwich, UK
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham, UK
| | - Paul R Hunter
- Norwich Medical School, University of East Anglia, Norwich, UK
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18
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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] [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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19
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Thomson LJM, Spiro N, Williamon A, Chatterjee HJ. The Impact of Culture-, Health- and Nature-Based Engagement on Mitigating the Adverse Effects of Public Health Restrictions on Wellbeing, Social Connectedness and Loneliness during COVID-19: Quantitative Evidence from a Smaller- and Larger-Scale UK Survey. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6943. [PMID: 37887681 PMCID: PMC10606618 DOI: 10.3390/ijerph20206943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/02/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023]
Abstract
Numerous UK surveys conducted during COVID-19 examined the pandemic's detrimental effects on health, and the consequences of lockdown and other public health restrictions on mental health. Some surveys considered specific populations and social inequities exacerbated during COVID-19. Fewer surveys examined the ways in which the adverse effects of public health restrictions, such as lockdown, shielding and social distancing, might be alleviated. Drawing upon self-determination theory, the purpose of the current study was to assess whether culture-, health- and nature-based engagement would mitigate the effects of these restrictions on psychological wellbeing, social connectedness and loneliness. Quantitative data from a smaller-scale survey (n = 312) and a subset of questions embedded in a larger-scale survey (n = 3647) were analyzed using univariate and multivariate methods. Frequency of engagement, whether participation was online or offline and with or without other people, and the extent to which type of participation was associated with psychological wellbeing, social connectedness and loneliness were examined. Sports and fitness, gardening and reading occurred frequently in both surveys. For the smaller-scale survey, increases in connectedness and frequency of participation and decreases in loneliness were significantly associated with improved wellbeing, whereas the type of participation and age range were not significant predictors. Outcomes from the smaller-scale survey approximated the larger-scale survey for measures of loneliness, type and frequency of participation and proportion of respondents in each age range. As the frequency of participation was a significant predictor of wellbeing, but the type of participation was not significant, the findings implied that any type of participation in a sufficient quantity would be likely to boost wellbeing.
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Affiliation(s)
- Linda J. M. Thomson
- UCL Arts & Sciences, University College London, London WC1 6BT, UK
- UCL Biosciences, University College London, London WC1 6BT, UK
| | - Neta Spiro
- Centre for Performance Science, Royal College of Music, London SW7 2BS, UK
- Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; (N.S.); (A.W.)
| | - Aaron Williamon
- Centre for Performance Science, Royal College of Music, London SW7 2BS, UK
- Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; (N.S.); (A.W.)
| | - Helen J. Chatterjee
- UCL Arts & Sciences, University College London, London WC1 6BT, UK
- UCL Biosciences, University College London, London WC1 6BT, UK
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20
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Volz E. Fitness, growth and transmissibility of SARS-CoV-2 genetic variants. Nat Rev Genet 2023; 24:724-734. [PMID: 37328556 DOI: 10.1038/s41576-023-00610-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2023] [Indexed: 06/18/2023]
Abstract
The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.
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Affiliation(s)
- Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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21
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Carter AR, Clayton GL, Borges MC, Howe LD, Hughes RA, Smith GD, Lawlor DA, Tilling K, Griffith GJ. Time-sensitive testing pressures and COVID-19 outcomes: are socioeconomic inequalities over the first year of the pandemic explained by selection bias? BMC Public Health 2023; 23:1863. [PMID: 37752486 PMCID: PMC10521522 DOI: 10.1186/s12889-023-16767-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND There are many ways in which selection bias might impact COVID-19 research. Here we focus on selection for receiving a polymerase-chain-reaction (PCR) SARS-CoV-2 test and how known changes to selection pressures over time may bias research into COVID-19 infection. METHODS Using UK Biobank (N = 420,231; 55% female; mean age = 66.8 [SD = 8·11]) we estimate the association between socio-economic position (SEP) and (i) being tested for SARS-CoV-2 infection versus not being tested (ii) testing positive for SARS-CoV-2 infection versus testing negative and (iii) testing negative for SARS-CoV-2 infection versus not being tested. We construct four distinct time-periods between March 2020 and March 2021, representing distinct periods of testing pressures and lockdown restrictions and specify both time-stratified and combined models for each outcome. We explore potential selection bias by examining associations with positive and negative control exposures. RESULTS The association between more disadvantaged SEP and receiving a SARS-CoV-2 test attenuated over time. Compared to individuals with a degree, individuals whose highest educational qualification was a GCSE or equivalent had an OR of 1·27 (95% CI: 1·18 to 1·37) in March-May 2020 and 1·13 (95% CI: 1.·10 to 1·16) in January-March 2021. The magnitude of the association between educational attainment and testing positive for SARS-CoV-2 infection increased over the same period. For the equivalent comparison, the OR for testing positive increased from 1·25 (95% CI: 1·04 to 1·47), to 1·69 (95% CI: 1·55 to 1·83). We found little evidence of an association between control exposures, and any considered outcome. CONCLUSIONS The association between SEP and SARS-CoV-2 testing changed over time, highlighting the potential of time-specific selection pressures to bias analyses of COVID-19. Positive and negative control analyses suggest that changes in the association between SEP and SARS-CoV-2 infection over time likely reflect true increases in socioeconomic inequalities.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - M Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rachael A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gareth J Griffith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
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22
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Qasmieh SA, Robertson MM, Nash D. "Boosting" Surveillance for a More Impactful Public Health Response During Protracted and Evolving Infectious Disease Threats: Insights From the COVID-19 Pandemic. Health Secur 2023; 21:S47-S55. [PMID: 37643313 PMCID: PMC10818055 DOI: 10.1089/hs.2023.0046] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Affiliation(s)
- Saba A. Qasmieh
- Saba A. Qasmieh, MPH, is a Research Scientist, Institute for Implementation Science in Population Health, and a PhD Student, Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, University of New York, New York, NY
| | - McKaylee M. Robertson
- McKaylee M. Robertson, PhD, MPH, is an Investigator, Institute for Implementation Science in Population Health, University of New York, New York, NY
| | - Denis Nash
- Denis Nash, PhD, MPH, is Executive Director, Institute for Implementation Science in Population Health, and Distinguished Professor of Epidemiology, Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, University of New York, New York, NY
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23
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Pak TR, Rhee C, Wang R, Klompas M. Discontinuation of Universal Admission Testing for SARS-CoV-2 and Hospital-Onset COVID-19 Infections in England and Scotland. JAMA Intern Med 2023; 183:877-880. [PMID: 37273229 PMCID: PMC10242507 DOI: 10.1001/jamainternmed.2023.1261] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 02/28/2023] [Indexed: 06/06/2023]
Abstract
This quality improvement study examines the association between the discontinuation of universal admission testing for SARS-CoV-2 infections and hospital-onset SARS-CoV-2 infections in England and Scotland.
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Affiliation(s)
- Theodore R. Pak
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
| | - Chanu Rhee
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
| | - Rui Wang
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
| | - Michael Klompas
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
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24
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Pakkanen MS, Miscouridou X, Penn MJ, Whittaker C, Berah T, Mishra S, Mellan TA, Bhatt S. Unifying incidence and prevalence under a time-varying general branching process. J Math Biol 2023; 87:35. [PMID: 37526739 PMCID: PMC10393927 DOI: 10.1007/s00285-023-01958-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 12/23/2022] [Accepted: 04/29/2023] [Indexed: 08/02/2023]
Abstract
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump-Mode-Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman-Harris process and one that arises from an inhomogeneous Poisson process model of transmission. We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox.
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Affiliation(s)
- Mikko S Pakkanen
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
- Department of Mathematics, Imperial College London, London, UK.
| | | | - Matthew J Penn
- Department of Statistics, University of Oxford, Oxford, UK
| | - Charles Whittaker
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Tresnia Berah
- Department of Mathematics, Imperial College London, London, UK
| | - Swapnil Mishra
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Thomas A Mellan
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
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25
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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] [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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26
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Cannings-John R, Schoenbuchner S, Jones H, Lugg-Widger FV, Akbari A, Brookes-Howell L, Hood K, John A, Thomas DR, Prout H, Robling M. Impact of the COVID-19 pandemic on domiciliary care workers in Wales, UK: a data linkage cohort study using the SAIL Databank. BMJ Open 2023; 13:e070637. [PMID: 37263685 DOI: 10.1136/bmjopen-2022-070637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2023] Open
Abstract
OBJECTIVES To quantify population health risks for domiciliary care workers (DCWs) in Wales, UK, working during the COVID-19 pandemic. DESIGN A population-level retrospective study linking occupational registration data to anonymised electronic health records maintained by the Secure Anonymised Information Linkage Databank in a privacy-protecting trusted research environment. SETTING Registered DCW population in Wales. PARTICIPANTS Records for all linked DCWs from 1 March 2020 to 30 November 2021. PRIMARY AND SECONDARY OUTCOME MEASURES Our primary outcome was confirmed COVID-19 infection; secondary outcomes included contacts for suspected COVID-19, mental health including self-harm, fit notes, respiratory infections not necessarily recorded as COVID-19, deaths involving COVID-19 and all-cause mortality. RESULTS Confirmed and suspected COVID-19 infection rates increased over the study period to 24% by 30 November 2021. Confirmed COVID-19 varied by sex (males: 19% vs females: 24%) and age (>55 years: 19% vs <35 years: 26%) and were higher for care workers employed by local authority social services departments compared with the private sector (27% and 23%, respectively). 34% of DCWs required support for a mental health condition, with mental health-related prescribing increasing in frequency when compared with the prepandemic period. Events for self-harm increased from 0.2% to 0.4% over the study period as did the issuing of fit notes. There was no evidence to suggest a miscoding of COVID-19 infection with non-COVID-19 respiratory conditions. COVID-19-related and all-cause mortality were no greater than for the general population aged 15-64 years in Wales (0.1% and 0.034%, respectively). A comparable DCW workforce in Scotland and England would result in a comparable rate of COVID-19 infection, while the younger workforce in Northern Ireland may result in a greater infection rate. CONCLUSIONS While initial concerns about excess mortality are alleviated, the substantial pre-existing and increased mental health burden for DCWs will require investment to provide long-term support to the sector's workforce.
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Affiliation(s)
| | | | - Hywel Jones
- Division of Population Medicine, Cardiff University, Cardiff, UK
| | | | - Ashley Akbari
- Faculty of Medicine, Health & Life Science, Swansea University Medical School, Swansea, UK
| | | | - Kerenza Hood
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Ann John
- Health Data Research UK | Administrative Data Research Wales, Swansea University, Swansea, UK
- DECIPHer-Centre for Development, Evaluation, Complexity and Implementation in Public Health Improvement, Cardiff University, Cardiff, UK
| | - Daniel Rh Thomas
- Communicable Disease Surveillance Centre, Public Health Wales, Cardiff, UK
- Cardiff Metropolitan University, Cardiff, UK
| | - Hayley Prout
- Centre for Trials Research, Cardiff University, Cardiff, UK
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27
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Buckell J, Jones J, Matthews PC, Diamond SI, Rourke E, Studley R, Cook D, Walker AS, Pouwels KB. COVID-19 vaccination, risk-compensatory behaviours, and contacts in the UK. Sci Rep 2023; 13:8441. [PMID: 37231004 PMCID: PMC10209557 DOI: 10.1038/s41598-023-34244-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
The physiological effects of vaccination against SARS-CoV-2 (COVID-19) are well documented, yet the behavioural effects not well known. Risk compensation suggests that gains in personal safety, as a result of vaccination, are offset by increases in risky behaviour, such as socialising, commuting and working outside the home. This is potentially important because transmission of SARS-CoV-2 is driven by contacts, which could be amplified by vaccine-related risk compensation. Here, we show that behaviours were overall unrelated to personal vaccination, but-adjusting for variation in mitigation policies-were responsive to the level of vaccination in the wider population: individuals in the UK were risk compensating when rates of vaccination were rising. This effect was observed across four nations of the UK, each of which varied policies autonomously.
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Affiliation(s)
- John Buckell
- Health Economics Research Centre, Richard Doll Building, Old Road Campus, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
- Health Behaviours, Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Philippa C Matthews
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
- Division of Infection and Immunity, University College London, Gower St, London, WC1E 6BT, UK
- Department of Infection, University College London Hospitals, 235 Euston Rd, London, NW1 2BU, UK
| | | | | | | | | | - Ann Sarah Walker
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- 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
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Richard Doll Building, Old Road Campus, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, 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
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28
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Rozenbaum MH, Begier E, Kurosky SK, Whelan J, Bem D, Pouwels KB, Postma M, Bont L. Incidence of Respiratory Syncytial Virus Infection in Older Adults: Limitations of Current Data. Infect Dis Ther 2023:10.1007/s40121-023-00802-4. [PMID: 37310617 DOI: 10.1007/s40121-023-00802-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/30/2023] [Indexed: 06/14/2023] Open
Abstract
INTRODUCTION Respiratory syncytial virus (RSV) is an important cause of severe respiratory illness in older adults and adults with respiratory or cardiovascular comorbidities. Published estimates of its incidence and prevalence in adult groups vary widely. This article reviews the potential limitations affecting RSV epidemiology studies and suggests points to consider when evaluating or designing them. METHODS Studies reporting the incidence or prevalence of RSV infection in adults in high-income Western countries from 2000 onwards were identified via a rapid literature review. Author-reported limitations were recorded, together with presence of other potential limitations. Data were synthesized narratively, with a focus on factors affecting incidence estimates for symptomatic infection in older adults. RESULTS A total of 71 studies met the inclusion criteria, most in populations with medically attended acute respiratory illness (ARI). Only a minority used case definitions and sampling periods tailored specifically to RSV; many used influenza-based or other criteria that are likely to result in RSV cases being missed. The great majority relied solely on polymerase chain reaction (PCR) testing of upper respiratory tract samples, which is likely to miss RSV cases compared with dual site sampling and/or addition of serology. Other common limitations were studying a single season, which has potential for bias due to seasonal variability; failure to stratify results by age, which underestimates the burden of severe disease in older adults; limited generalizability beyond a limited study setting; and absence of measures of uncertainty in the reporting of results. CONCLUSIONS A significant proportion of studies are likely to underestimate the incidence of RSV infection in older adults, although the effect size is unclear and there is also potential for overestimation. Well-designed studies, together with increased testing for RSV in patients with ARI in clinical practice, are required to accurately capture both the burden of RSV and the potential public health impact of vaccines.
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Affiliation(s)
| | | | | | | | | | | | | | - Louis Bont
- University Medical Center Utrecht, Utrecht, The Netherlands
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29
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Wei J, Matthews PC, Stoesser N, Newton JN, Diamond I, Studley R, Taylor N, Bell JI, Farrar J, Kolenchery J, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Pouwels KB, Eyre DW. Protection against SARS-CoV-2 Omicron BA.4/5 variant following booster vaccination or breakthrough infection in the UK. Nat Commun 2023; 14:2799. [PMID: 37193713 PMCID: PMC10187514 DOI: 10.1038/s41467-023-38275-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/21/2023] [Indexed: 05/18/2023] Open
Abstract
Following primary SARS-CoV-2 vaccination, whether boosters or breakthrough infections provide greater protection against SARS-CoV-2 infection is incompletely understood. Here we investigated SARS-CoV-2 antibody correlates of protection against new Omicron BA.4/5 (re-)infections and anti-spike IgG antibody trajectories after a third/booster vaccination or breakthrough infection following second vaccination in 154,149 adults ≥18 y from the United Kingdom general population. Higher antibody levels were associated with increased protection against Omicron BA.4/5 infection and breakthrough infections were associated with higher levels of protection at any given antibody level than boosters. Breakthrough infections generated similar antibody levels to boosters, and the subsequent antibody declines were slightly slower than after boosters. Together our findings show breakthrough infection provides longer-lasting protection against further infections than booster vaccinations. Our findings, considered alongside the risks of severe infection and long-term consequences of infection, have important implications for vaccine policy.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- 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
- 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
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | - Jaison Kolenchery
- 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
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- 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
- 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
| | - Tim E A Peto
- 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
- 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
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, 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, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - 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, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, 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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30
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Whitfield CA, van Tongeren M, Han Y, Wei H, Daniels S, Regan M, Denning DW, Verma A, Pellis L, Hall I. Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector. PLoS One 2023; 18:e0284805. [PMID: 37146037 PMCID: PMC10162531 DOI: 10.1371/journal.pone.0284805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/06/2023] [Indexed: 05/07/2023] Open
Abstract
OBJECTIVE We aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector. METHODS We devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures. RESULTS The baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times. CONCLUSION This work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.
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Affiliation(s)
- Carl A. Whitfield
- Department of Mathematics, University of Manchester, Manchester, England
- Division of Infection, Immunity & Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester, England
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
| | - Martie van Tongeren
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Yang Han
- Department of Mathematics, University of Manchester, Manchester, England
| | - Hua Wei
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Sarah Daniels
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Martyn Regan
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
- National COVID-19 Response Centre, UK Health Security Agency, London, England
| | - David W. Denning
- Division of Infection, Immunity & Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester, England
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
| | - Arpana Verma
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, England
| | - Ian Hall
- Department of Mathematics, University of Manchester, Manchester, England
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Public Health Advice, Guidance and Expertise, UK Health Security Agency, London, England
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31
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Cook TM, Lawton T. Surgery soon after COVID-19: transparent big data have value but careful interpretation is still required. Anaesthesia 2023; 78:671-676. [PMID: 37094781 DOI: 10.1111/anae.16031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 04/26/2023]
Affiliation(s)
- T M Cook
- Royal United Hospitals Bath NHS Foundation Trust, Bath, UK
- School of Medicine, University of Bristol, Bristol, UK
| | - T Lawton
- Improvement Academy, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
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32
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Lyu X, Luo Z, Shao L, Awbi H, Lo Piano S. Safe CO 2 threshold limits for indoor long-range airborne transmission control of COVID-19. BUILDING AND ENVIRONMENT 2023; 234:109967. [PMID: 36597420 PMCID: PMC9801696 DOI: 10.1016/j.buildenv.2022.109967] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/16/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
CO2-based infection risk monitoring is highly recommended during the current COVID-19 pandemic. However, the CO2 monitoring thresholds proposed in the literature are mainly for spaces with fixed occupants. Determining CO2 threshold is challenging in spaces with changing occupancy due to the co-existence of quanta and CO2 remaining from previous occupants. Here, we propose a new calculation framework for deriving safe excess CO2 thresholds (above outdoor level), C t, for various spaces with fixed/changing occupancy and analyze the uncertainty involved. We categorized common indoor spaces into three scenarios based on their occupancy conditions, e.g., fixed or varying infection ratios (infectors/occupants). We proved that the rebreathed fraction-based model can be applied directly for deriving C t in the case of a fixed infection ratio (Scenario 1 and Scenario 2). In the case of varying infection ratios (Scenario 3), C t derivation must follow the general calculation framework due to the existence of initial quanta/excess CO2. Otherwise, C t can be significantly biased (e.g., 260 ppm) when the infection ratio varies greatly. C t can vary significantly based on specific space factors such as occupant number, physical activity, and community prevalence, e.g., 7 ppm for gym and 890 ppm for lecture hall, indicating C t must be determined on a case-by-case basis. An uncertainty of up to 6 orders of magnitude for C t was found for all cases due to uncertainty in emissions of quanta and CO2, thus emphasizing the role of accurate emissions data in determining C t.
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Affiliation(s)
- Xiaowei Lyu
- School of the Built Environment, University of Reading, UK
| | - Zhiwen Luo
- Welsh School of Architecture, Cardiff University, UK
| | - Li Shao
- School of the Built Environment, University of Reading, UK
| | - Hazim Awbi
- School of the Built Environment, University of Reading, UK
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33
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Cohen C, Pulliam J. COVID-19 infection, reinfection, and the transition to endemicity. Lancet 2023; 401:798-800. [PMID: 36930672 PMCID: PMC9934854 DOI: 10.1016/s0140-6736(22)02634-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 12/20/2022] [Indexed: 02/18/2023]
Affiliation(s)
- Cheryl Cohen
- Centre for Respiratory Disease and Meningitis, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg 2193, South Africa; School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Juliet Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, South Africa
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34
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Lustig A, Vattiato G, Maclaren O, Watson LM, Datta S, Plank MJ. Modelling the impact of the Omicron BA.5 subvariant in New Zealand. J R Soc Interface 2023; 20:20220698. [PMID: 36722072 PMCID: PMC9890098 DOI: 10.1098/rsif.2022.0698] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/06/2023] [Indexed: 02/02/2023] Open
Abstract
New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.
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Affiliation(s)
| | - Giorgia Vattiato
- Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of Physics, The University of Auckland, Auckland, New Zealand
| | - Oliver Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Leighton M. Watson
- School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
| | - Samik Datta
- National Institute of Water and Atmospheric Research, Wellington, New Zealand
| | - Michael J. Plank
- Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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35
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Modelling the impact of repeat asymptomatic testing policies for staff on SARS-CoV-2 transmission potential. J Theor Biol 2023; 557:111335. [PMID: 36334850 PMCID: PMC9626407 DOI: 10.1016/j.jtbi.2022.111335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
Repeat asymptomatic testing in order to identify and quarantine infectious individuals has become a widely-used intervention to control SARS-CoV-2 transmission. In some workplaces, and in particular health and social care settings with vulnerable patients, regular asymptomatic testing has been deployed to staff to reduce the likelihood of workplace outbreaks. We have developed a model based on data available in the literature to predict the potential impact of repeat asymptomatic testing on SARS-CoV-2 transmission. The results highlight features that are important to consider when modelling testing interventions, including population heterogeneity of infectiousness and correlation with test-positive probability, as well as adherence behaviours in response to policy. Furthermore, the model based on the reduction in transmission potential presented here can be used to parameterise existing epidemiological models without them having to explicitly simulate the testing process. Overall, we find that even with different model paramterisations, in theory, regular asymptomatic testing is likely to be a highly effective measure to reduce transmission in workplaces, subject to adherence. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Hoover CM, Skaff NK, Blumberg S, Fukunaga R. Aligning staff schedules, testing, and isolation reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001302. [PMID: 36962883 PMCID: PMC10022395 DOI: 10.1371/journal.pgph.0001302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/30/2022] [Indexed: 01/07/2023]
Abstract
COVID-19 outbreaks in congregate settings remain a serious threat to the health of disproportionately affected populations such as people experiencing incarceration or homelessness, the elderly, and essential workers. An individual-based model accounting for individual infectiousness over time, staff work schedules, and testing and isolation schedules was developed to simulate community transmission of SARS-CoV-2 to staff in a congregate facility and subsequent transmission within the facility that could cause an outbreak. Systematic testing strategies in which staff are tested on the first day of their workweek were found to prevent up to 16% more infections than testing strategies unrelated to staff schedules. Testing staff at the beginning of their workweek, implementing timely isolation following testing, limiting test turnaround time, and increasing test frequency in high transmission scenarios can supplement additional mitigation measures to aid outbreak prevention in congregate settings.
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Affiliation(s)
- Christopher M. Hoover
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
| | - Nicholas K. Skaff
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California, United States of America
- Division of Hospital Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Rena Fukunaga
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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37
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D'Aoust PM, Tian X, Towhid ST, Xiao A, Mercier E, Hegazy N, Jia JJ, Wan S, Kabir MP, Fang W, Fuzzen M, Hasing M, Yang MI, Sun J, Plaza-Diaz J, Zhang Z, Cowan A, Eid W, Stephenson S, Servos MR, Wade MJ, MacKenzie AE, Peng H, Edwards EA, Pang XL, Alm EJ, Graber TE, Delatolla R. Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 853:158547. [PMID: 36067855 PMCID: PMC9444156 DOI: 10.1016/j.scitotenv.2022.158547] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/10/2022] [Accepted: 09/01/2022] [Indexed: 05/14/2023]
Abstract
Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.
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Affiliation(s)
- Patrick M D'Aoust
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Xin Tian
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | | | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Elisabeth Mercier
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Nada Hegazy
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Jian-Jun Jia
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Shen Wan
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Md Pervez Kabir
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Wanting Fang
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Meghan Fuzzen
- Department of Biology, University of Waterloo, Waterloo, Canada
| | - Maria Hasing
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Minqing Ivy Yang
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
| | - Jianxian Sun
- Department of Chemistry, University of Toronto, Toronto, Canada
| | - Julio Plaza-Diaz
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Zhihao Zhang
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Aaron Cowan
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada
| | - Walaa Eid
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Sean Stephenson
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Mark R Servos
- Department of Biology, University of Waterloo, Waterloo, Canada
| | - Matthew J Wade
- Data, Analytics and Surveillance Group, UK Health Security Agency, London, United Kingdom
| | - Alex E MacKenzie
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Hui Peng
- Department of Chemistry, University of Toronto, Toronto, Canada
| | - Elizabeth A Edwards
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
| | - Xiao-Li Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Tyson E Graber
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Robert Delatolla
- Department of Civil Engineering, University of Ottawa, Ottawa, Canada.
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Gokani SA, Ta NH, Espehana A, Garden EM, Klyvyte G, Luke L, Myuran T, Uththerakunaseelan V, Boak DC, Philpott CM. The growing burden of long COVID in the United Kingdom: Insights from the UK Coronavirus Infection Survey. Int Forum Allergy Rhinol 2022:10.1002/alr.23103. [PMID: 36479948 PMCID: PMC9877687 DOI: 10.1002/alr.23103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/19/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Shyam Ajay Gokani
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK
| | - Ngan Hong Ta
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK,Institute of Head and Neck Studies and EducationUniversity of BirminghamBirminghamUK
| | - Andreas Espehana
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK
| | - Elizabeth Mairenn Garden
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK
| | - Gabija Klyvyte
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK
| | - Louis Luke
- ENT DepartmentJames Paget University HospitalGreat YarmouthNorfolkUK
| | - Tharsika Myuran
- ENT DepartmentAddenbrooke's HospitalCambridgeCambridgeshireUK
| | - Vinushy Uththerakunaseelan
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK
| | | | - Carl Martin Philpott
- Rhinology & ENT Research Group, Norwich Medical SchoolUniversity of East Anglia, Norwich Research ParkNorwichUK,ENT DepartmentJames Paget University HospitalGreat YarmouthNorfolkUK
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39
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Groenheit R, Beser J, Kühlmann Berenzon S, Galanis I, van Straten E, Duracz J, Rapp M, Hansson D, Mansjö M, Söderholm S, Muradrasoli S, Risberg A, Ölund R, Wiklund A, Metzkes K, Lundberg M, Bacchus P, Tegmark Wisell K, Bråve A. Point prevalence of SARS-CoV-2 infection in Sweden at six time points during 2020. BMC Infect Dis 2022; 22:861. [PMCID: PMC9672540 DOI: 10.1186/s12879-022-07858-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
Abstract
Background
In order to estimate the prevalence and understand the spread of SARS-CoV-2 in Sweden, the Public Health Agency of Sweden, with support from the Swedish Armed Forces, conducted a series of point prevalence surveys between March and December 2020.
Methods
Sampling material and instructions on how to perform self-sampling of the upper respiratory tract were delivered to the homes of the participants. Samples were analysed by real-time PCR, and the participants completed questionnaires regarding symptoms.
Findings
The first survey in the Stockholm region in March 2020 included 707 participants and showed a SARS-CoV-2 prevalence of 2.5%. The following five surveys, performed on a national level, with between 2461 and 2983 participants, showed SARS-CoV-2 prevalences of 0.9% (April), 0.3% (May), 0.0% (August), 0.0% (September), and 0.7% (December). All positive cases who responded to questionnaires reported experiencing symptoms that occurred from 2 weeks before the date of sampling up to and including the date of sampling.
Interpretation
None of the individuals shown to be PCR-positive were asymptomatic at the time of sampling or in the 14 days prior to sampling. This is in contrast to many other surveys in which a substantial proportion of positive cases have been reported to be asymptomatic. Our surveys demonstrate a decreasing ratio between notified cases and the observed prevalence throughout the year, in line with increasing testing capacity and the consecutive inclusion of all symptomatic individuals in the case definition for testing.
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40
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Teh YW, Elesedy B, He B, Hutchinson M, Zaidi S, Bhoopchand A, Paquet U, Tomasev N, Read J, Diggle PJ. Efficient Bayesian inference of instantaneous reproduction numbers at fine spatial scales, with an application to mapping and nowcasting the Covid-19 epidemic in British local authorities. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:S65-S85. [PMID: 38607892 PMCID: PMC9877716 DOI: 10.1111/rssa.12971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Affiliation(s)
- Yee Whye Teh
- Department of StatisticsUniversity of OxfordOxfordUK
| | - Bryn Elesedy
- Department of StatisticsUniversity of OxfordOxfordUK
| | - Bobby He
- Department of StatisticsUniversity of OxfordOxfordUK
| | | | | | - Avishkar Bhoopchand
- Department of StatisticsUniversity of Oxford, seconded from DeepMindOxfordUK
| | - Ulrich Paquet
- Department of StatisticsUniversity of Oxford, seconded from DeepMindOxfordUK
| | - Nenad Tomasev
- Department of StatisticsUniversity of Oxford, seconded from DeepMindOxfordUK
| | - Jonathan Read
- CHICAS, Lancaster Medical SchoolLancaster UniversityLancasterUK
| | - Peter J. Diggle
- CHICAS, Lancaster Medical SchoolLancaster UniversityLancasterUK
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41
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Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA. Nat Commun 2022; 13:5547. [PMID: 36138039 PMCID: PMC9499975 DOI: 10.1038/s41467-022-33317-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 09/12/2022] [Indexed: 01/08/2023] Open
Abstract
Public health indicators typically used for COVID-19 surveillance can be biased or lag changing community transmission patterns. In this study, we investigate whether sentinel surveillance of recently symptomatic individuals receiving outpatient diagnostic testing for SARS-CoV-2 could accurately assess the instantaneous reproductive number R(t) and provide early warning of changes in transmission. We use data from community-based diagnostic testing sites in the United States city of Chicago. Patients tested at community-based diagnostic testing sites between September 2020 and June 2021, and reporting symptom onset within four days preceding their test, formed the sentinel population. R(t) calculated from sentinel cases agreed well with R(t) from other indicators. Retrospectively, trends in sentinel cases did not precede trends in COVID-19 hospital admissions by any identifiable lead time. In deployment, sentinel surveillance held an operational recency advantage of nine days over hospital admissions. The promising performance of opportunistic sentinel surveillance suggests that deliberately designed outpatient sentinel surveillance would provide robust early warning of increasing transmission.
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42
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Eales O, Ainslie KEC, Walters CE, Wang H, Atchison C, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P, Riley S. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics 2022; 40:100604. [PMID: 35780515 PMCID: PMC9220254 DOI: 10.1016/j.epidem.2022.100604] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/31/2022] [Accepted: 06/17/2022] [Indexed: 02/09/2023] Open
Abstract
The time-varying reproduction number (Rt) can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of Rt from case data. However, these are not easily adapted to point prevalence data nor can they infer Rt across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020-December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of Rt over the period of two subsequent rounds (6-8 weeks) and single rounds (2-3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in Rt over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in Rt over the summer of 2020 as restrictions were eased, and a reduction in Rt during England's second national lockdown followed by an increase as the Alpha variant surged. The model is robust against both under-fitting and over-fitting and is able to interpolate between periods of available data; it is a particularly versatile model when growth rate can change over small timescales, as in the current SARS-CoV-2 pandemic. This work highlights the importance of pairing robust methods with representative samples to track pandemics.
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Affiliation(s)
- Oliver Eales
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Kylie E C Ainslie
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
| | - Caroline E Walters
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Haowei Wang
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
| | - Christina Atchison
- School of Public Health, Imperial College London, London, United Kingdom.
| | - Deborah Ashby
- School of Public Health, Imperial College London, London, United Kingdom.
| | - Christl A Donnelly
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom; Department of Statistics, University of Oxford, Oxford, United Kingdom.
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom.
| | - Wendy Barclay
- Department of Infectious Disease, Imperial College London, London, United Kingdom.
| | - Helen Ward
- School of Public Health, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom.
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom; Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, Imperial College London, London, United Kingdom; National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom; MRC Centre for Environment and Health, Imperial College London, London, United Kingdom; Health Data Research (HDR), Imperial College London, London, United Kingdom; UK Dementia Research Institute, Imperial College London, London, United Kingdom.
| | - Steven Riley
- School of Public Health, Imperial College London, London, United Kingdom; MRC Centre for Global infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.
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43
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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: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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44
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Sheridan C, Klompmaker J, Cummins S, James P, Fecht D, Roscoe C. Associations of air pollution with COVID-19 positivity, hospitalisations, and mortality: Observational evidence from UK Biobank. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 308:119686. [PMID: 35779662 PMCID: PMC9243647 DOI: 10.1016/j.envpol.2022.119686] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 05/26/2023]
Abstract
Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March-December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM10 were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM2.5 and NO2 was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.
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Affiliation(s)
- Charlotte Sheridan
- London School of Hygiene & Tropical Medicine, Keppel St., London, WC1E 7HT, United Kingdom.
| | - Jochem Klompmaker
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, United States.
| | - Steven Cummins
- Population Health Innovation Lab, Department of Public Health, Environments and Society, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, Keppel St., London, United Kingdom.
| | - Peter James
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, United States; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, United States.
| | - Daniela Fecht
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Medicine, St Mary's Campus, Imperial College London, London, W2 1PG, United Kingdom.
| | - Charlotte Roscoe
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, United States; MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Medicine, St Mary's Campus, Imperial College London, London, W2 1PG, United Kingdom; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, United States.
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45
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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] [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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46
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Naylor NR, Evans S, Pouwels KB, Troughton R, Lamagni T, Muller-Pebody B, Knight GM, Atun R, Robotham JV. Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England. Front Public Health 2022; 10:803943. [PMID: 36033764 PMCID: PMC9413182 DOI: 10.3389/fpubh.2022.803943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 07/04/2022] [Indexed: 01/21/2023] Open
Abstract
Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the "primary effects" of AMR. Previous estimates of the burden of AMR have largely ignored the potential "secondary effects," such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.
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Affiliation(s)
- Nichola R. Naylor
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United Kingdom,Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Antimicrobial Resistance (AMR) Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom,Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom,*Correspondence: Nichola R. Naylor
| | - Stephanie Evans
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
| | - Koen B. Pouwels
- Nuffield Department of Population Health, Health Economics Research Centre, 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
| | - Rachael Troughton
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United Kingdom
| | - Theresa Lamagni
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
| | - Berit Muller-Pebody
- Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
| | - Gwenan M. Knight
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Antimicrobial Resistance (AMR) Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rifat Atun
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States,Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Julie V. Robotham
- The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United Kingdom,Healthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United Kingdom
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47
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Naylor NR, Evans S, Pouwels KB, Troughton R, Lamagni T, Muller-Pebody B, Knight GM, Atun R, Robotham JV. Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England. Front Public Health 2022. [DOI: 10.5210.3389/fpubh.2022.803943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the “primary effects” of AMR. Previous estimates of the burden of AMR have largely ignored the potential “secondary effects,” such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.
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48
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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: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [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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49
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Morvan M, Jacomo AL, Souque C, Wade MJ, Hoffmann T, Pouwels K, Lilley C, Singer AC, Porter J, Evens NP, Walker DI, Bunce JT, Engeli A, Grimsley J, O'Reilly KM, Danon L. An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence. Nat Commun 2022; 13:4313. [PMID: 35879277 PMCID: PMC9312315 DOI: 10.1038/s41467-022-31753-y] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 06/28/2022] [Indexed: 12/23/2022] Open
Abstract
Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4-5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.
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Affiliation(s)
- Mario Morvan
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- Department of Physics and Astronomy, University College London, London, WC1E 6BT, UK
| | - Anna Lo Jacomo
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- Department of Engineering Mathematics, Ada Lovelace Building, University Walk, Bristol, BS8 1TW, UK
| | - Celia Souque
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- Department of Zoology, University of Oxford, Oxford, OX1 3SZ, UK
| | - Matthew J Wade
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
| | - Till Hoffmann
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK
| | - Koen Pouwels
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Chris Lilley
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
| | - Andrew C Singer
- UK Centre for Ecology & Hydrology, Wallingford, OX10 8BB, UK
| | - Jonathan Porter
- Environment Agency, National Monitoring, Starcross, Exeter, EX6 8FD, UK
| | - Nicholas P Evens
- Environment Agency, National Monitoring, Starcross, Exeter, EX6 8FD, UK
| | - David I Walker
- Centre for Environment, Fisheries and Aquaculture Science, Weymouth, DT4 8UB, UK
| | - Joshua T Bunce
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
- Department for Environment, Food and Rural Affairs, London, SW1P 4DF, UK
| | - Andrew Engeli
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
| | - Jasmine Grimsley
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
| | - Kathleen M O'Reilly
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK.
- Centre for Mathematical Modelling of Infectious Diseases & Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
| | - Leon Danon
- Data, Analytics, and Surveillance Group, UK Health Security Agency (Formerly part of the Joint Biosecurity Centre, Department of Health and Social Care), London, SW1P 3JR, UK
- Department of Engineering Mathematics, Ada Lovelace Building, University Walk, Bristol, BS8 1TW, UK
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50
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Rhodes S, Wilkinson J, Pearce N, Mueller W, Cherrie M, Stocking K, Gittins M, Katikireddi SV, Tongeren MV. Occupational differences in SARS-CoV-2 infection: analysis of the UK ONS COVID-19 infection survey. J Epidemiol Community Health 2022; 76:jech-2022-219101. [PMID: 35817467 PMCID: PMC9484374 DOI: 10.1136/jech-2022-219101] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/28/2022] [Indexed: 01/03/2023]
Abstract
BACKGROUND Concern remains about how occupational SARS-CoV-2 risk has evolved during the COVID-19 pandemic. We aimed to ascertain occupations with the greatest risk of SARS-CoV-2 infection and explore how relative differences varied over the pandemic. METHODS Analysis of cohort data from the UK Office of National Statistics COVID-19 Infection Survey from April 2020 to November 2021. This survey is designed to be representative of the UK population and uses regular PCR testing. Cox and multilevel logistic regression were used to compare SARS-CoV-2 infection between occupational/sector groups, overall and by four time periods with interactions, adjusted for age, sex, ethnicity, deprivation, region, household size, urban/rural neighbourhood and current health conditions. RESULTS Based on 3 910 311 observations (visits) from 312 304 working age adults, elevated risks of infection can be seen overall for social care (HR 1.14; 95% CI 1.04 to 1.24), education (HR 1.31; 95% CI 1.23 to 1.39), bus and coach drivers (1.43; 95% CI 1.03 to 1.97) and police and protective services (HR 1.45; 95% CI 1.29 to 1.62) when compared with non-essential workers. By time period, relative differences were more pronounced early in the pandemic. For healthcare elevated odds in the early waves switched to a reduction in the later stages. Education saw raises after the initial lockdown and this has persisted. Adjustment for covariates made very little difference to effect estimates. CONCLUSIONS Elevated risks among healthcare workers have diminished over time but education workers have had persistently higher risks. Long-term mitigation measures in certain workplaces may be warranted.
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Affiliation(s)
- Sarah Rhodes
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | - Jack Wilkinson
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | - Neil Pearce
- Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Mark Cherrie
- Institute of Occupational Medicine, Edinburgh, UK
| | - Katie Stocking
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | - Matthew Gittins
- Centre for Biostatistics, University of Manchester, Manchester, UK
| | | | - Martie Van Tongeren
- Centre for Occupation and Environmental Health, The University of Manchester, Manchester, UK
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