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2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308689

ABSTRACT

Digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. Real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308614

ABSTRACT

Background: Emerging evidence shows the substantial real-world impact of authorised vaccines against COVID-19 and provides insight into the potential role of vaccines in curbing the pandemic. However, there remains uncertainty about the efficacy of vaccines against different variants of the virus. Here we assessed efficacy of ChAdOx1 nCoV-19 (AZD1222) against lineages of SARS-CoV-2 circulating in Brazil from June 2020 until early 2021. Methods: Participants aged 18 and above were enrolled into a randomised phase 3 trial of ChAdOx1 nCoV-19 vaccine against symptomatic SARS-CoV-2 infection. Participants received two doses of ChAdOx1 nCoV-19 or control (1st dose: Men ACWY vaccine, 2nd dose: normal saline). Nasopharyngeal and oropharyngeal swabbing was performed if participants developed symptoms of COVID-19 (cough, shortness of breath, fever >37.8°C, ageusia, anosmia). Swabs were tested by nucleic acid amplification (NAAT) for SARS-CoV-2, sequenced, and viral load determined. For those samples where a genotype could not be ascertained from sequencing, allele specific PCR was performed. The efficacy analysis included symptomatic COVID-19 in seronegative participants with a NAAT positive swab more than 14 days after a second dose of vaccine. Participants were unblinded after the vaccine was authorised for use, and the control participants offered vaccination. Infections occurring after unblinding were excluded from analysis. Vaccine efficacy was calculated as 100% x (1 – relative risk (RR)), where RR was estimated from a robust Poisson model. The trial is registered at ISRCTN89951424. Findings: 9433 participants were eligible for inclusion in the pre-specified primary efficacy population, having reached more than 14 days after a second dose of ChAdOx1 nCoV-19, of whom 307 were NAAT+, in this post-hoc analysis. From June 2020 to February 2021, the two most frequently identified lineages were P.2 (N=153) and B.1.1.28 (N=49). P.1 emerged during the study (N=18) but became dominant only after study unblinding. Viral loads were highest amongst those with P.1 infection. Vaccine efficacy (VE) for B.1.1.33 (88.2%, 95%CI 5, 99), B.1.1.28 (73%, 95% CI, 46, 86), P.2 (69% 95% CI, 55, 78) and P.1 (64%, 95% CI, -2, 87) was estimated. In participants who had received two doses of vaccine, one COVID-19 hospitalisation occurred in the ChAdOx1 nCoV-19 group and 18 in the control group, with VE against hospitalisation 95% (95% CI 61, 99). There were 2 COVID-19 deaths in the control group and none in the vaccine group. Interpretation ChAdOx1 nCoV-19 provides high efficacy against hospitalisation, severe disease and death from COVID-19 in Brazil and there is strong evidence of protection being maintained against P.2, despite the presence of the spike protein mutation E484K. Real world effectiveness studies are ongoing in Brazil to further establish protection against P.1 and other emerging variants.

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-295666

ABSTRACT

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. Data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland.

7.
Nat Commun ; 12(1): 5861, 2021 10 06.
Article in English | MEDLINE | ID: covidwho-1454761

ABSTRACT

Several COVID-19 vaccines have shown good efficacy in clinical trials, but there remains uncertainty about the efficacy of vaccines against different variants. Here, we investigate the efficacy of ChAdOx1 nCoV-19 (AZD1222) against symptomatic COVID-19 in a post-hoc exploratory analysis of a Phase 3 randomised trial in Brazil (trial registration ISRCTN89951424). Nose and throat swabs were tested by PCR in symptomatic participants. Sequencing and genotyping of swabs were performed to determine the lineages of SARS-CoV-2 circulating during the study. Protection against any symptomatic COVID-19 caused by the Zeta (P.2) variant was assessed in 153 cases with vaccine efficacy (VE) of 69% (95% CI 55, 78). 49 cases of B.1.1.28 occurred and VE was 73% (46, 86). The Gamma (P.1) variant arose later in the trial and fewer cases (N = 18) were available for analysis. VE was 64% (-2, 87). ChAdOx1 nCoV-19 provided 95% protection (95% CI 61%, 99%) against hospitalisation due to COVID-19. In summary, we report that ChAdOx1 nCoV-19 protects against emerging variants in Brazil despite the presence of the spike protein mutation E484K.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/immunology , COVID-19/virology , Phylogeny , SARS-CoV-2/immunology , Adolescent , Adult , Aged , Brazil , Cohort Studies , Dose-Response Relationship, Immunologic , Female , Hospitalization , Humans , Male , Middle Aged , Treatment Outcome , Vaccination , Viral Load/immunology , Young Adult
8.
PLoS Comput Biol ; 17(7): e1009146, 2021 07.
Article in English | MEDLINE | ID: covidwho-1305573

ABSTRACT

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.


Subject(s)
COVID-19/prevention & control , Contact Tracing , Systems Analysis , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , COVID-19 Testing , COVID-19 Vaccines/administration & dosage , Disease Outbreaks , Humans , Physical Distancing , Quarantine , SARS-CoV-2/isolation & purification
9.
Nature ; 594(7863): 408-412, 2021 06.
Article in English | MEDLINE | ID: covidwho-1225509

ABSTRACT

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention1-6, but its epidemiological impact has remained uncertain7. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/instrumentation , Contact Tracing/methods , Mobile Applications/statistics & numerical data , Basic Reproduction Number , COVID-19/mortality , COVID-19/transmission , England/epidemiology , Humans , Mortality , National Health Programs , Quarantine , Wales/epidemiology
10.
NPJ Digit Med ; 4(1): 49, 2021 Mar 12.
Article in English | MEDLINE | ID: covidwho-1132107

ABSTRACT

Contact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple and Google's Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties to explore how digital exposure notifications combined with other non-pharmaceutical interventions influence COVID-19 disease spread under various adoption, compliance, and mobility scenarios. In a model with 15% participation, we found that exposure notification could reduce infections and deaths by approximately 8% and 6% and could effectively complement traditional contact tracing. We believe this can provide health authorities in Washington state and beyond with guidance on how exposure notification can complement traditional interventions to suppress the spread of COVID-19.

11.
Science ; 372(6539)2021 04 16.
Article in English | MEDLINE | ID: covidwho-1125076

ABSTRACT

Extensive global sampling and sequencing of the pandemic virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have enabled researchers to monitor its spread and to identify concerning new variants. Two important determinants of variant spread are how frequently they arise within individuals and how likely they are to be transmitted. To characterize within-host diversity and transmission, we deep-sequenced 1313 clinical samples from the United Kingdom. SARS-CoV-2 infections are characterized by low levels of within-host diversity when viral loads are high and by a narrow bottleneck at transmission. Most variants are either lost or occasionally fixed at the point of transmission, with minimal persistence of shared diversity, patterns that are readily observable on the phylogenetic tree. Our results suggest that transmission-enhancing and/or immune-escape SARS-CoV-2 variants are likely to arise infrequently but could spread rapidly if successfully transmitted.


Subject(s)
COVID-19/transmission , COVID-19/virology , Genetic Variation , SARS-CoV-2/genetics , COVID-19/immunology , Coinfection/virology , Coronavirus Infections/virology , Coronavirus OC43, Human , Family Characteristics , Genome, Viral , Humans , Immune Evasion , Mutation , Phylogeny , RNA, Viral/genetics , RNA-Seq , SARS-CoV-2/pathogenicity , SARS-CoV-2/physiology , Selection, Genetic , Spike Glycoprotein, Coronavirus/genetics , United Kingdom , Viral Load
13.
Lancet Digit Health ; 2(12): e658-e666, 2020 12.
Article in English | MEDLINE | ID: covidwho-857316

ABSTRACT

Background: In May 2020, the UK National Health Service (NHS) Test and Trace programme was launched in England in response to the COVID-19 pandemic. The programme was first rolled out on the Isle of Wight and included version 1 of the NHS contact tracing app. The aim of the study was to make a preliminary assessment of the epidemiological impact of the Test and Trace programme using publicly available data. Methods: We used COVID-19 daily case data from Public Health England to infer incidence of new infections and estimate the reproduction number (R) for each of the 150 Upper-Tier Local Authorities (UTLAs) in England and nationally, before and after the launch of the Test and Trace programme on the Isle of Wight. We used Bayesian and maximum-likelihood methods to estimate R and compared the Isle of Wight with other UTLAs using a synthetic control method. Findings: We observed significant decreases in incidence and R on the Isle of Wight immediately after the launch of the Test and Trace programme. The Isle of Wight had a marked reduction in R, from 1·3 before the Test and Trace programme to 0·5 after by one of our measures, and went from having the third highest R before the Test and Trace programme, to the twelfth lowest afterwards compared with other UTLAs. Interpretation: Our results show that the epidemic on the Isle of Wight was controlled quickly and effectively after the launch of Test and Trace. Our findings highlight the need for further research to determine the causes of the reduction in the spread of the disease, as these could be translated into local and national non-pharmaceutical intervention strategies in the period before a treatment or vaccination for COVID-19 becomes available. Funding: Li Ka Shing Foundation and UK Economic and Social Research Council.


Subject(s)
COVID-19 Testing/methods , COVID-19/epidemiology , Contact Tracing/methods , Islands/epidemiology , Adolescent , Adult , Age Factors , Aged , COVID-19/diagnosis , COVID-19/prevention & control , COVID-19 Testing/statistics & numerical data , Child , Child, Preschool , Contact Tracing/statistics & numerical data , England/epidemiology , Humans , Infant , Infant, Newborn , Likelihood Functions , Middle Aged , State Medicine , Thiocarbamates , United Kingdom/epidemiology , Young Adult
14.
J Med Ethics ; 46(7): 427-431, 2020 07.
Article in English | MEDLINE | ID: covidwho-214131

ABSTRACT

In this paper we discuss ethical implications of the use of mobile phone apps in the control of the COVID-19 pandemic. Contact tracing is a well-established feature of public health practice during infectious disease outbreaks and epidemics. However, the high proportion of pre-symptomatic transmission in COVID-19 means that standard contact tracing methods are too slow to stop the progression of infection through the population. To address this problem, many countries around the world have deployed or are developing mobile phone apps capable of supporting instantaneous contact tracing. Informed by the on-going mapping of 'proximity events' these apps are intended both to inform public health policy and to provide alerts to individuals who have been in contact with a person with the infection. The proposed use of mobile phone data for 'intelligent physical distancing' in such contexts raises a number of important ethical questions. In our paper, we outline some ethical considerations that need to be addressed in any deployment of this kind of approach as part of a multidimensional public health response. We also, briefly, explore the implications for its use in future infectious disease outbreaks.


Subject(s)
Cell Phone , Contact Tracing/ethics , Contact Tracing/methods , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Betacoronavirus , Bioethical Issues , COVID-19 , Communicable Disease Control/methods , Freedom , Humans , Mobile Applications , Pandemics , Privacy , SARS-CoV-2 , Trust
15.
Science ; 368(6491)2020 05 08.
Article in English | MEDLINE | ID: covidwho-20745

ABSTRACT

The newly emergent human virus SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact tracing needed to stop the epidemic. Although SARS-CoV-2 is spreading too fast to be contained by manual contact tracing, it could be controlled if this process were faster, more efficient, and happened at scale. A contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without resorting to mass quarantines ("lockdowns") that are harmful to society. We discuss the ethical requirements for an intervention of this kind.


Subject(s)
Betacoronavirus , Cell Phone , Contact Tracing/methods , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Mobile Applications , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Algorithms , Asymptomatic Diseases , Basic Reproduction Number , COVID-19 , China/epidemiology , Contact Tracing/ethics , Coronavirus Infections/epidemiology , Epidemics/prevention & control , Humans , Infection Control , Mobile Applications/ethics , Models, Theoretical , Pneumonia, Viral/epidemiology , Probability , Quarantine , SARS-CoV-2 , Time Factors
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