Showing papers by "University of Warwick published in 2021"

Advances and open problems in federated learning

Abstract: The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more. This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems. Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study.

Abstract: Objective To establish whether there is any change in mortality from infection with a new variant of SARS-CoV-2, designated a variant of concern (VOC-202012/1) in December 2020, compared with circulating SARS-CoV-2 variants. Design Matched cohort study. Setting Community based (pillar 2) covid-19 testing centres in the UK using the TaqPath assay (a proxy measure of VOC-202012/1 infection). Participants 54 906 matched pairs of participants who tested positive for SARS-CoV-2 in pillar 2 between 1 October 2020 and 29 January 2021, followed-up until 12 February 2021. Participants were matched on age, sex, ethnicity, index of multiple deprivation, lower tier local authority region, and sample date of positive specimens, and differed only by detectability of the spike protein gene using the TaqPath assay. Main outcome measure Death within 28 days of the first positive SARS-CoV-2 test result. Results The mortality hazard ratio associated with infection with VOC-202012/1 compared with infection with previously circulating variants was 1.64 (95% confidence interval 1.32 to 2.04) in patients who tested positive for covid-19 in the community. In this comparatively low risk group, this represents an increase in deaths from 2.5 to 4.1 per 1000 detected cases. Conclusions The probability that the risk of mortality is increased by infection with VOC-202012/01 is high. If this finding is generalisable to other populations, infection with VOC-202012/1 has the potential to cause substantial additional mortality compared with previously circulating variants. Healthcare capacity planning and national and international control policies are all impacted by this finding, with increased mortality lending weight to the argument that further coordinated and stringent measures are justified to reduce deaths from SARS-CoV-2.

A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies

Abstract: The World Health Organization declared COVID-19 a global pandemic on the 11th of March 2020, but the world is still reeling from its aftermath. Originating from China, cases quickly spread across the globe, prompting the implementation of stringent measures by world governments in efforts to isolate cases and limit the transmission rate of the virus. These measures have however shattered the core sustaining pillars of the modern world economies as global trade and cooperation succumbed to nationalist focus and competition for scarce supplies. Against this backdrop, this paper presents a critical review of the catalogue of negative and positive impacts of the pandemic and proffers perspectives on how it can be leveraged to steer towards a better, more resilient low-carbon economy. The paper diagnosed the danger of relying on pandemic-driven benefits to achieving sustainable development goals and emphasizes a need for a decisive, fundamental structural change to the dynamics of how we live. It argues for a rethink of the present global economic growth model, shaped by a linear economy system and sustained by profiteering and energy-gulping manufacturing processes, in favour of a more sustainable model recalibrated on circular economy (CE) framework. Building on evidence in support of CE as a vehicle for balancing the complex equation of accomplishing profit with minimal environmental harms, the paper outlines concrete sector-specific recommendations on CE-related solutions as a catalyst for the global economic growth and development in a resilient post-COVID-19 world.

Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study.

Abstract: Summary Background The dynamics of vaccination against SARS-CoV-2 are complicated by age-dependent factors, changing levels of infection, and the relaxation of non-pharmaceutical interventions (NPIs) as the perceived risk declines, necessitating the use of mathematical models. Our aims were to use epidemiological data from the UK together with estimates of vaccine efficacy to predict the possible long-term dynamics of SARS-CoV-2 under the planned vaccine rollout. Methods In this study, we used a mathematical model structured by age and UK region, fitted to a range of epidemiological data in the UK, which incorporated the planned rollout of a two-dose vaccination programme (doses 12 weeks apart, protection onset 14 days after vaccination). We assumed default vaccine uptake of 95% in those aged 80 years and older, 85% in those aged 50–79 years, and 75% in those aged 18–49 years, and then varied uptake optimistically and pessimistically. Vaccine efficacy against symptomatic disease was assumed to be 88% on the basis of Pfizer-BioNTech and Oxford-AstraZeneca vaccines being administered in the UK, and protection against infection was varied from 0% to 85%. We considered the combined interaction of the UK vaccination programme with multiple potential future relaxations (or removals) of NPIs, to predict the reproduction number (R) and pattern of daily deaths and hospital admissions due to COVID-19 from January, 2021, to January, 2024. Findings We estimate that vaccination alone is insufficient to contain the outbreak. In the absence of NPIs, even with our most optimistic assumption that the vaccine will prevent 85% of infections, we estimate R to be 1·58 (95% credible intervals [CI] 1·36–1·84) once all eligible adults have been offered both doses of the vaccine. Under the default uptake scenario, removal of all NPIs once the vaccination programme is complete is predicted to lead to 21 400 deaths (95% CI 1400–55 100) due to COVID-19 for a vaccine that prevents 85% of infections, although this number increases to 96 700 deaths (51 800–173 200) if the vaccine only prevents 60% of infections. Although vaccination substantially reduces total deaths, it only provides partial protection for the individual; we estimate that, for the default uptake scenario and 60% protection against infection, 48·3% (95% CI 48·1–48·5) and 16·0% (15·7–16·3) of deaths will be in individuals who have received one or two doses of the vaccine, respectively. Interpretation For all vaccination scenarios we investigated, our predictions highlight the risks associated with early or rapid relaxation of NPIs. Although novel vaccines against SARS-CoV-2 offer a potential exit strategy for the pandemic, success is highly contingent on the precise vaccine properties and population uptake, both of which need to be carefully monitored. Funding National Institute for Health Research, Medical Research Council, and UK Research and Innovation.

A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Abstract: Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This article applies advanced machine-learning techniques to achieve effective future capacities and RUL prediction for lithium-ion (Li-ion) batteries with reliable uncertainty management. To be specific, after using the empirical mode decomposition (EMD) method, the original battery capacity data is decomposed into some intrinsic mode functions (IMFs) and a residual. Then, the long short-term memory (LSTM) submodel is applied to estimate the residual while the Gaussian process regression (GPR) submodel is utilized to fit the IMFs with the uncertainty level. Consequently, both the long-term dependence of capacity and uncertainty quantification caused by the capacity regenerations can be captured directly and simultaneously. Experimental aging data from different batteries are deployed to evaluate the performance of proposed LSTM+GPR model in comparison with the solo GPR, solo LSTM, GPR+EMD, and LSTM+EMD models. Illustrative results demonstrate the combined LSTM+GPR model outperforms other counterparts and is capable of achieving accurate results for both 1-step and multistep ahead capacity predictions. Even predicting the RUL at the early battery cycle stage, the proposed data-driven approach still presents good adaptability and reliable uncertainty quantification for battery health diagnosis.

Managing the long term effects of covid-19: summary of NICE, SIGN, and RCGP rapid guideline.

Abstract: ### What you need to know For a proportion of people covid-19 leads to long term effects that can have a significant impact on quality of life. According to the Office for National Statistics, around one in five people testing positive for covid-19 exhibit symptoms for a period of five weeks or more.1 This presents challenges for determining best-practice standards of care. As yet, no commonly agreed clinical definition of long term covid-19 exists, nor a clear definition of treatment pathway. To assist clinicians, the National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network (SIGN), and the Royal College of General Practitioners (RCGP) have developed the “COVID-19 rapid guideline: managing the long term effects of COVID-19.”2 It covers care for people with signs and symptoms that continue for more than four weeks, and which developed during or after an infection consistent with covid-19, and which are not explained by alternative diagnoses. The guideline provides clinical definitions of the effects of covid-19 at different times and provides advice on diagnosis and management based on the best available evidence and the knowledge and experience of the expert panel. It will be subject to a “living” approach, which means that targeted areas of the guideline will be reviewed weekly and updated in response to emerging evidence and evolving expert experience. This article summarises the guideline recommendations as published on 18 …

Gaussian Process Regression for Materials and Molecules.

Abstract: We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

Virus-like particles: preparation, immunogenicity and their roles as nanovaccines and drug nanocarriers.

Abstract: Virus-like particles (VLPs) are virus-derived structures made up of one or more different molecules with the ability to self-assemble, mimicking the form and size of a virus particle but lacking the genetic material so they are not capable of infecting the host cell. Expression and self-assembly of the viral structural proteins can take place in various living or cell-free expression systems after which the viral structures can be assembled and reconstructed. VLPs are gaining in popularity in the field of preventive medicine and to date, a wide range of VLP-based candidate vaccines have been developed for immunization against various infectious agents, the latest of which is the vaccine against SARS-CoV-2, the efficacy of which is being evaluated. VLPs are highly immunogenic and are able to elicit both the antibody- and cell-mediated immune responses by pathways different from those elicited by conventional inactivated viral vaccines. However, there are still many challenges to this surface display system that need to be addressed in the future. VLPs that are classified as subunit vaccines are subdivided into enveloped and non- enveloped subtypes both of which are discussed in this review article. VLPs have also recently received attention for their successful applications in targeted drug delivery and for use in gene therapy. The development of more effective and targeted forms of VLP by modification of the surface of the particles in such a way that they can be introduced into specific cells or tissues or increase their half-life in the host is likely to expand their use in the future. Recent advances in the production and fabrication of VLPs including the exploration of different types of expression systems for their development, as well as their applications as vaccines in the prevention of infectious diseases and cancers resulting from their interaction with, and mechanism of activation of, the humoral and cellular immune systems are discussed in this review.

Correlative operando microscopy of oxygen evolution electrocatalysts

Abstract: Transition metal (oxy)hydroxides are promising electrocatalysts for the oxygen evolution reaction1–3. The properties of these materials evolve dynamically and heterogeneously4 with applied voltage through ion insertion redox reactions, converting materials that are inactive under open circuit conditions into active electrocatalysts during operation5. The catalytic state is thus inherently far from equilibrium, which complicates its direct observation. Here, using a suite of correlative operando scanning probe and X-ray microscopy techniques, we establish a link between the oxygen evolution activity and the local operational chemical, physical and electronic nanoscale structure of single-crystalline β-Co(OH)2 platelet particles. At pre-catalytic voltages, the particles swell to form an α-CoO2H1.5·0.5H2O-like structure—produced through hydroxide intercalation—in which the oxidation state of cobalt is +2.5. Upon increasing the voltage to drive oxygen evolution, interlayer water and protons de-intercalate to form contracted β-CoOOH particles that contain Co3+ species. Although these transformations manifest heterogeneously through the bulk of the particles, the electrochemical current is primarily restricted to their edge facets. The observed Tafel behaviour is correlated with the local concentration of Co3+ at these reactive edge sites, demonstrating the link between bulk ion-insertion and surface catalytic activity. Mapping the operational chemical, physical and electronic structure of an oxygen evolution electrocatalyst at the nanoscale links the properties of the material with the observed oxygen evolution activity.

Coronavirus Perceptions And Economic Anxiety

Abstract: We provide one of the first systematic assessments of the development and determinants of economic anxiety at the onset of the coronavirus pandemic Using a global dataset on internet searches and two representative surveys from the US, we document a substantial increase in economic anxiety during and after the arrival of the coronavirus We also document a large dispersion in beliefs about the pandemic risk factors of the coronavirus, and demonstrate that these beliefs causally affect individuals' economic anxieties Finally, we show that individuals' mental models of infectious disease spread understate non-linear growth and shape the extent of economic anxiety

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

Abstract: Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84–5.29) for men of European ancestry to 3.74 (95% CI, 3.36–4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14–2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71–0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Averages of b-hadron, c-hadron, and $$\tau $$-lepton properties as of 2018: Heavy Flavor Averaging Group (HFLAV)

Abstract: This paper reports world averages of measurements of b -hadron, c -hadron, and -lepton properties obtained by the Heavy Flavour Averaging Group using results available through September 2018. In rare cases, significant results obtained several months later are also used. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, violation parameters, parameters of semileptonic decays, and Cabibbo–Kobayashi–Maskawa matrix elements.

2020 taxonomic update for phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

Abstract: In March 2020, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. At the genus rank, 20 new genera were added, two were deleted, one was moved, and three were renamed. At the species rank, 160 species were added, four were deleted, ten were moved and renamed, and 30 species were renamed. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.

Physics-Inspired Structural Representations for Molecules and Materials.

Abstract: The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.

Improving Survival of Critical Care Patients With Coronavirus Disease 2019 in England: A National Cohort Study, March to June 2020.

Abstract: Objectives: To measure temporal trends in survival over time in people with severe coronavirus disease 2019 requiring critical care (high dependency unit or ICU) management, and to assess whether temporal variation in mortality was explained by changes in patient demographics and comorbidity burden over time. Design: Retrospective observational cohort; based on data reported to the COVID-19 Hospitalisation in England Surveillance System. The primary outcome was in-hospital 30-day all-cause mortality. Unadjusted survival was estimated by calendar week of admission, and Cox proportional hazards models were used to estimate adjusted survival, controlling for age, sex, ethnicity, major comorbidities, and geographical region. Setting: One hundred eight English critical care units. Patients: All adult (18 yr +) coronavirus disease 2019 specific critical care admissions between March 1, 2020, and June 27, 2020. Interventions: Not applicable. Measurements and Main Results: Twenty-one thousand eighty-two critical care patients (high dependency unit n = 15,367; ICU n = 5,715) were included. Unadjusted survival at 30 days was lowest for people admitted in late March in both high dependency unit (71.6% survival) and ICU (58.0% survival). By the end of June, survival had improved to 92.7% in high dependency unit and 80.4% in ICU. Improvements in survival remained after adjustment for patient characteristics (age, sex, ethnicity, and major comorbidities) and geographical region. Conclusions: There has been a substantial improvement in survival amongst people admitted to critical care with coronavirus disease 2019 in England, with markedly higher survival rates in people admitted in May and June compared with those admitted in March and April. Our analysis suggests this improvement is not due to temporal changes in the age, sex, ethnicity, or major comorbidity burden of admitted patients.

The L-functions and modular forms database

Abstract: The LMFDB is a database of mathematical objects and the connections between them. A web interface allows visitors to browse and query the database in a flexible and powerful way. At the core of the LMFDB is the Langlands program (see Figure 1), a set of conjectures relating objects from arithmetic algebraic geometry (such as number fields, elliptic curves, and abelian varieties) with modular and automorphic forms via Galois representations and Lfunctions (the most basic of which is the Riemann zeta function). The LMFDB catalogs these objects and the ways in which they link to one another, as well as other closely related objects like permutation groups and p-adic fields. Currently, the LMFDB includes tens of millions of objects totalling over 4.8 terabytes of data. Hosted on Google Cloud Platform, in 2020, it was used by more than 30 000 users in 146

Green finance, fintech and environmental protection: Evidence from China

Abstract: This paper is one of the first to offer a comprehensive analysis of the impact of green finance related policies in China, utilizing text analysis and panel data from 290 cities between 2011 and 2018. Employing the Semi-parametric Difference-in-Differences (SDID) we show that overall China's green finance related policies have led to a significant reduction in industrial gas emissions in the review period. Additionally, we found that Fintech development contributes to the depletion of sulphur dioxide emissions and has a positive impact on environmental protection investment initiatives. China is poised to be a global leader in green finance policy implementation and regulators need to accelerate the formulation of green finance products and enhance the capacity of financial institutions to offer green credit. While minimizing the systemic risk fintech poses, policy makers should encourage fintechs to actively participate in environmental protection initiatives that promote green consumption.

Physics-inspired structural representations for molecules and materials.

Abstract: The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks, and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.

SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead

Abstract: Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this article, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost.

Green synthesis of metal nanoparticles using microorganisms and their application in the agrifood sector.

Abstract: The agricultural sector is currently facing many global challenges, such as climate change, and environmental problems such as the release of pesticides and fertilizers, which will be exacerbated in the face of population growth and food shortages. Therefore, the need to change traditional farming methods and replace them with new technologies is essential, and the application of nanotechnology, especially green technology offers considerable promise in alleviating these problems. Nanotechnology has led to changes and advances in many technologies and has the potential to transform various fields of the agricultural sector, including biosensors, pesticides, fertilizers, food packaging and other areas of the agricultural industry. Due to their unique properties, nanomaterials are considered as suitable carriers for stabilizing fertilizers and pesticides, as well as facilitating controlled nutrient transfer and increasing crop protection. The production of nanoparticles by physical and chemical methods requires the use of hazardous materials, advanced equipment, and has a negative impact on the environment. Thus, over the last decade, research activities in the context of nanotechnology have shifted towards environmentally friendly and economically viable ‘green’ synthesis to support the increasing use of nanoparticles in various industries. Green synthesis, as part of bio-inspired protocols, provides reliable and sustainable methods for the biosynthesis of nanoparticles by a wide range of microorganisms rather than current synthetic processes. Therefore, this field is developing rapidly and new methods in this field are constantly being invented to improve the properties of nanoparticles. In this review, we consider the latest advances and innovations in the production of metal nanoparticles using green synthesis by different groups of microorganisms and the application of these nanoparticles in various agricultural sectors to achieve food security, improve crop production and reduce the use of pesticides. In addition, the mechanism of synthesis of metal nanoparticles by different microorganisms and their advantages and disadvantages compared to other common methods are presented.

The epidemiological impact of the NHS COVID-19 App.

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. Statistical analysis of COVID-19 transmission among users of a smartphone-based digital contact-tracing app suggests that such apps can be an effective measure for reducing disease spread.