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Eales O, Wang H, Haw D, Ainslie KEC, Walters CE, Atchison C, Cooke G, Barclay W, Ward H, Darzi A, Ashby D, Donnelly CA, Elliott P, Riley S. Trends in SARS-CoV-2 infection prevalence during England's roadmap out of lockdown, January to July 2021. PLoS Comput Biol 2022; 18:e1010724. [PMID: 36417468 PMCID: PMC9728904 DOI: 10.1371/journal.pcbi.1010724] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/07/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Following rapidly rising COVID-19 case numbers, England entered a national lockdown on 6 January 2021, with staged relaxations of restrictions from 8 March 2021 onwards. AIM We characterise how the lockdown and subsequent easing of restrictions affected trends in SARS-CoV-2 infection prevalence. METHODS On average, risk of infection is proportional to infection prevalence. The REal-time Assessment of Community Transmission-1 (REACT-1) study is a repeat cross-sectional study of over 98,000 people every round (rounds approximately monthly) that estimates infection prevalence in England. We used Bayesian P-splines to estimate prevalence and the time-varying reproduction number (Rt) nationally, regionally and by age group from round 8 (beginning 6 January 2021) to round 13 (ending 12 July 2021) of REACT-1. As a comparator, a separate segmented-exponential model was used to quantify the impact on Rt of each relaxation of restrictions. RESULTS Following an initial plateau of 1.54% until mid-January, infection prevalence decreased until 13 May when it reached a minimum of 0.09%, before increasing until the end of the study to 0.76%. Following the first easing of restrictions, which included schools reopening, the reproduction number Rt increased by 82% (55%, 108%), but then decreased by 61% (82%, 53%) at the second easing of restrictions, which was timed to match the Easter school holidays. Following further relaxations of restrictions, the observed Rt increased steadily, though the increase due to these restrictions being relaxed was offset by the effects of vaccination and also affected by the rapid rise of Delta. There was a high degree of synchrony in the temporal patterns of prevalence between regions and age groups. CONCLUSION High-resolution prevalence data fitted to P-splines allowed us to show that the lockdown was effective at reducing risk of infection with school holidays/closures playing a significant part.
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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
| | - 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
| | - David Haw
- 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
| | - Christina Atchison
- School of Public Health, Imperial College London, London, United Kingdom
| | - Graham Cooke
- Department of Infectious Disease, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
| | - 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, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
| | - Ara Darzi
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
- Institute of Global Health Innovation at 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
| | - Paul Elliott
- School of Public Health, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- National Institute for Health Research Imperial Biomedical Research Centre, London
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Health Data Research (HDR) UK London at Imperial College, London, United Kingdom
- UK Dementia Research Institute at Imperial College, 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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White LF, Moser CB, Thompson RN, Pagano M. Statistical Estimation of the Reproductive Number From Case Notification Data. Am J Epidemiol 2021; 190:611-620. [PMID: 33034345 DOI: 10.1093/aje/kwaa211] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 09/24/2020] [Accepted: 10/02/2020] [Indexed: 12/20/2022] Open
Abstract
The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.
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Chong KC, Hu P, Lau S, Jia KM, Liang W, Wang MH, Zee BCY, Sun R, Zheng H. Monitoring the age-specificity of measles transmissions during 2009-2016 in Southern China. PLoS One 2018; 13:e0205339. [PMID: 30296273 PMCID: PMC6175510 DOI: 10.1371/journal.pone.0205339] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 09/24/2018] [Indexed: 12/19/2022] Open
Abstract
Background Despite several immunization efforts, China saw a resurgence of measles in 2012. Monitoring of transmissions of individuals from different age groups could offer information that would be valuable for planning adequate disease control strategies. We compared the age-specific effective reproductive numbers (R) of measles during 2009–2016 in Guangdong, China. Methods We estimated the age-specific R values for 7 age groups: 0–8 months, 9–18 months, 19 months to 6 years, 7–15 years, 16–25 years, 26–45 years, and ≥46 years adapting the contact matrix of China. The daily numbers of laboratory and clinically confirmed cases reported to the Center for Disease Control and Prevention of Guangdong were used. Results The peak R values of the entire population were above unity from 2012 to 2016, indicating the persistence of measles in the population. In general, children aged 0–6 years and adults aged 26–45 years had larger values of R when comparing with other age groups after 2012. While the peaks of R values for children aged 0–6 years dropped steadily after 2013, the peaks of R values for adults aged 26–45 years kept at a high range every year. Conclusions Although the provincial supplementary immunization activities (SIAs) conducted in 2009 and 2010 were able to reduce the transmissions from 2009 to 2011, larger values of R for children aged 0–6 years were observed after 2012, indicating that the benefits of the SIAs were short-lived. In addition, the transmissions from adults aged between 26 and 45 years increased over time. Disease control strategies should target children and adult groups that carry high potential for measles transmission.
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Affiliation(s)
- Ka Chun Chong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China
| | - Pei Hu
- Center for Disease Control and Prevention of Guangdong Province, Guangzhou, China
| | - Steven Lau
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Katherine Min Jia
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Wenjia Liang
- Center for Disease Control and Prevention of Guangdong Province, Guangzhou, China
| | - Maggie Haitian Wang
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China
| | - Benny Chung Ying Zee
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China
| | - Riyang Sun
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- * E-mail: (HZ); (RS)
| | - Huizhen Zheng
- Center for Disease Control and Prevention of Guangdong Province, Guangzhou, China
- * E-mail: (HZ); (RS)
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