Publications
140 results found
Bhatt S, 2021, Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe, Nature Communications, Vol: 12, Pages: 1-12, ISSN: 2041-1723
As European governments face resurging waves of COVID-19, non-pharmaceutical interventions (NPIs) continue to be the primary tool for infection control. However, updated estimates of their relative effectiveness have been absent for Europe’s second wave, largely due to a lack of collated data that considers the increased subnational variation and diversity of NPIs. We collect the largest dataset of NPI implementation dates in Europe, spanning 114 subnational areas in 7 countries, with a systematic categorisation of interventions tailored to the second wave. Using a hierarchical Bayesian transmission model, we estimate the effectiveness of 17 NPIs from local case and death data. We manually validate the data, address limitations in modelling from previous studies, and extensively test the robustness of our estimates. The combined effect of all NPIs was smaller relative to estimates from the first half of 2020, indicating the strong influence of safety measures and individual protective behaviours--such as distancing--that persisted after the first wave. Closing specific businesses was highly effective. Gathering restrictions were highly effective but only for the strictest limits. We find smaller effects for closing educational institutions compared to the first wave, suggesting that safer operation of schools was possible with a set of stringent safety measures including testing and tracing, preventing mixing, and smaller classes. These results underscore that effectiveness estimates from the early stage of an epidemic are measured relative to pre-pandemic behaviour. Updated estimates are required to inform policy in an ongoing pandemic.
Vollmer MAC, Radhakrishnan S, Kont MD, et al., 2021, The impact of the COVID-19 pandemic on patterns of attendance at emergency departments in two large London hospitals: an observational study, BMC Health Services Research, Vol: 21, Pages: 1-9, ISSN: 1472-6963
Background Hospitals in England have undergone considerable change to address the surgein demand imposed by the COVID-19 pandemic. The impact of this on emergencydepartment (ED) attendances is unknown, especially for non-COVID-19 related emergencies.Methods This analysis is an observational study of ED attendances at the Imperial CollegeHealthcare NHS Trust (ICHNT). We calibrated auto-regressive integrated moving averagetime-series models of ED attendances using historic (2015-2019) data. Forecasted trendswere compared to present year ICHNT data for the period between March 12, 2020 (whenEngland implemented the first COVID-19 public health measure) and May 31, 2020. Wecompared ICHTN trends with publicly available regional and national data. Lastly, wecompared hospital admissions made via the ED and in-hospital mortality at ICHNT duringthe present year to the historic 5-year average.Results ED attendances at ICHNT decreased by 35% during the period after the firstlockdown was imposed on March 12, 2020 and before May 31, 2020, reflecting broadertrends seen for ED attendances across all England regions, which fell by approximately 50%for the same time frame. For ICHNT, the decrease in attendances was mainly amongst thoseaged <65 years and those arriving by their own means (e.g. personal or public transport) andnot correlated with any of the spatial dependencies analysed such as increasing distance frompostcode of residence to the hospital. Emergency admissions of patients without COVID-19after March 12, 2020 fell by 48%; we did not observe a significant change to the crudemortality risk in patients without COVID-19 (RR 1.13, 95%CI 0.94-1.37, p=0.19).Conclusions Our study findings reflect broader trends seen across England and give anindication how emergency healthcare seeking has drastically changed. At ICHNT, we findthat a larger proportion arrived by ambulance and that hospitalisation outcomes of patientswithout COVID-19 did not differ from previous years. The ext
Wilde H, Mellan T, Hawryluk I, et al., 2021, The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study., BMC Medicine, Vol: 19, Pages: 1-12, ISSN: 1741-7015
BACKGROUND: The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. METHODS: A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). RESULTS: One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)]. CONCLUSION: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU
Ratmann O, Bhatt S, Flaxman S, 2021, Implications of a highly transmissible variant of SARS-CoV-2 for children, Archives of Disease in Childhood, Vol: 106, Pages: 1-1, ISSN: 0003-9888
Mishra S, Scott JA, Laydon DJ, et al., 2021, Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling, SCIENTIFIC REPORTS, Vol: 11, Pages: 1-9, ISSN: 2045-2322
The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others’ policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country’s first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.
Hillis S, Unwin H, Chen Y, et al., 2021, Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study, The Lancet, Vol: 398, Pages: 391-402, ISSN: 0140-6736
Background: The COVID-19 pandemic response has focused on prevention, detection, and response. Beyond morbidity and mortality, pandemics carry secondary impacts, such as children orphaned or bereft of their caregivers. Such children often face adverse consequences, including poverty, abuse, and institutionalization. We provide estimates for the magnitude of this problem resulting from COVID-19 and describe the need for resource allocation.Methods: We use mortality and fertility data to model minimum estimates and rates of COVID-19-associated orphanhood (death of 1 or both parents) and deaths of custodial and co-residing grandparents for 21 countries. We use these estimates to model global extrapolations for the number of children experiencing COVID-19-associated deaths of parents and grandparents ages 60-84.Results: Globally, from March 1, 2020-March 31, 2021, we estimate 974,000 children experienced death of primary caregivers, including parents or custodial grandparents; >1.3 million experienced death of primary caregivers and co-residing grandparents (or kin). Countries with rates of primary caregiver deaths >1/1000 children included Peru, South Africa, Mexico, Colombia, Brazil, I.R. Iran, U.S.A., and Russia (range, 1.0-8.5/1000). Numbers of children orphaned exceeded numbers of deaths among those aged 15 – 44; 2 – 5 times more children had deceased fathers than deceased mothers. Conclusions: Orphanhood and caregiver deaths are a hidden pandemic resulting from COVID-19-associated deaths. Accelerating equitable vaccine delivery is key to prevention. Psychosocial and economic support can help families nurture children bereft of caregivers and help ensure institutionalization is avoided. These data demonstrate the need for an additional pillar of our response: prevent, detect, respond, and care for children.
Mishra S, Mindermann S, Sharma M, et al., 2021, Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England, EClinicalMedicine, Vol: 39, Pages: 1-8, ISSN: 2589-5370
BackgroundSince its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital.MethodsWe examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021.FindingsAcross data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May.InterpretationThe outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control effortsFundingNational Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy
Meyerowitz-Katz G, Bhatt S, Ratmann O, et al., 2021, Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19, BMJ Global Health, Vol: 6, Pages: 1-6, ISSN: 2059-7908
Krawczyk K, Chelkowski T, Laydon DJ, et al., 2021, Correction: Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource., J Med Internet Res, Vol: 23
[This corrects the article DOI: 10.2196/28253.].
Salvatore M, Bhattacharyya R, Purkayastha S, et al., 2021, Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions
<jats:title>Abstract</jats:title><jats:p>India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness.</jats:p><jats:sec><jats:title>One Sentence Summary</jats:title><jats:p>Altered epidemiological characteristics of B.1.617.2 and delayed public health interventions contributed to the resurgence of SARS-CoV-2 in India from February to May 2021.</jats:p></jats:sec>
Krawczyk K, Chelkowski T, Laydon DJ, et al., 2021, Correction: Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource (Preprint)
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Wolock T, Flaxman S, Risher K, et al., 2021, Evaluating distributional regression strategies for modelling self-reported sexual age-mixing, eLife, Vol: 10, Pages: 1-38, ISSN: 2050-084X
The age dynamics of sexual partnership formation determine patterns of sexually transmitted disease transmission and have long been a focus of researchers studying human immunodeficiency virus. Data on self-reported sexual partner age distributions are available from a variety of sources. We sought to explore statistical models that accurately predict the distribution of sexual partner ages over age and sex. We identified which probability distributions and outcome specifications best captured variation in partner age and quantified the benefits of modelling these data using distributional regression. We found that distributional regression with a sinh-arcsinh distribution replicated observed partner age distributions most accurately across three geographically diverse data sets. This framework can be extended with well-known hierarchical modelling tools and can help improve estimates of sexual age-mixing dynamics.
Smith TP, Flaxman S, Gallinat AS, et al., 2021, Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions, Proceedings of the National Academy of Sciences of USA, Vol: 118, Pages: 1-8, ISSN: 0027-8424
As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention (“lockdown”) and reductions in individuals’ mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.
Bradley VC, Kuriwaki S, Isakov M, et al., 2021, Unrepresentative Big Surveys Significantly Overestimate US Vaccine Uptake
<jats:title>Abstract</jats:title><jats:p>Surveys are a crucial tool for understanding public opinion and behavior, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the impact of survey bias – an instance of the Big Data Paradox <jats:sup>1</jats:sup>. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults: Delphi-Facebook <jats:sup>2,3</jats:sup> (about 250,000 responses per week) and Census Household Pulse <jats:sup>4</jats:sup> (about 75,000 per week). By May 2021, Delphi-Facebook overestimated uptake by 17 percentage points and Census Household Pulse by 14, compared to a benchmark from the Centers for Disease Control and Prevention (CDC). Moreover, their large data sizes led to minuscule margins of error on the incorrect estimates. In contrast, an Axios-Ipsos online panel <jats:sup>5</jats:sup> with about 1,000 responses following survey research best practices <jats:sup>6</jats:sup> provided reliable estimates and uncertainty. We decompose observed error using a recent analytic framework <jats:sup>1</jats:sup> to explain the inaccuracy in the three surveys. We then analyze the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters far more than data quantity, and compensating the former with the latter is a mathematically provable losing proposition.</jats:p>
Krawczyk K, Chelkowski T, Laydon DJ, et al., 2021, Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 23, ISSN: 1438-8871
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- Citations: 38
Faria NR, Mellan TA, Whittaker C, et al., 2021, Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, Vol: 372, Pages: 815-821, ISSN: 0036-8075
Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.
Volz E, Mishra S, Chand M, et al., 2021, Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England, Nature, Vol: 593, Pages: 266-269, ISSN: 0028-0836
The SARS-CoV-2 lineage B.1.1.7, designated a Variant of Concern 202012/01 (VOC) by Public Health England1, originated in the UK in late Summer to early Autumn 20202. Whole genome SARS-CoV-2 sequence data collected from community-based diagnostic testing shows an unprecedentedly rapid expansion of the B.1.1.7 lineage during Autumn 2020, suggesting a selective advantage. We find that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S-gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that the VOC has higher transmissibility than non-VOC lineages, even if the VOC has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with a larger share of under 20 year olds among reported VOC than non-VOC cases. Time-varying reproduction numbers for the VOC and cocirculating lineages were estimated using SGTF and genomic data. The best supported models did not indicate a substantial difference in VOC transmissibility among different age groups. There is a consensus among all analyses that the VOC has a substantial transmission advantage with a 50% to 100% higher reproduction number.
Suel E, Bhatt S, Brauer M, et al., 2021, Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas, Remote Sensing of Environment: an interdisciplinary journal, Vol: 257, ISSN: 0034-4257
Data collected at large scale and low cost (e.g. satellite and street level imagery) have the potential to substantially improve resolution, spatial coverage, and temporal frequency of measurement of urban inequalities. Multiple types of data from different sources are often available for a given geographic area. Yet, most studies utilize a single type of input data when making measurements due to methodological difficulties in their joint use. We propose two deep learning-based methods for jointly utilizing satellite and street level imagery for measuring urban inequalities. We use London as a case study for three selected outputs, each measured in decile classes: income, overcrowding, and environmental deprivation. We compare the performances of our proposed multimodal models to corresponding unimodal ones using mean absolute error (MAE). First, satellite tiles are appended to street level imagery to enhance predictions at locations where street images are available leading to improvements in accuracy by 20, 10, and 9% in units of decile classes for income, overcrowding, and living environment. The second approach, novel to the best of our knowledge, uses a U-Net architecture to make predictions for all grid cells in a city at high spatial resolution (e.g. for 3 m × 3 m pixels in London in our experiments). It can utilize city wide availability of satellite images as well as more sparse information from street-level images where they are available leading to improvements in accuracy by 6, 10, and 11%. We also show examples of prediction maps from both approaches to visually highlight performance differences.
Laydon D, Mishra S, Hinsley W, et al., 2021, Modelling the impact of the Tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns, BMJ Open, Vol: 11, ISSN: 2044-6055
Objective To measure the effects of the tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern.Design This is a modelling study combining estimates of real-time reproduction number Rt (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities to account for broader national trends in addition to subnational effects from tiers.Setting The UK at lower tier local authority (LTLA) level. 310 LTLAs were included in the analysis.Primary and secondary outcome measures Reduction in real-time reproduction number Rt.Results Nationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, Rt averaged 1.3 (0.9–1.6) across LTLAs, but declined to an average of 1.1 (0.86–1.42) 2 weeks later. Decline in transmission was not solely attributable to tiers. Tier 1 had negligible effects. Tiers 2 and 3, respectively, reduced transmission by 6% (5%–7%) and 23% (21%–25%). 288 LTLAs (93%) would have begun to suppress their epidemics if every LTLA had gone into tier 3 by the second national lockdown, whereas only 90 (29%) did so in reality.Conclusions The relatively small effect sizes found in this analysis demonstrate that interventions at least as stringent as tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed.
Watson O, Alhaffar M, Mehchy Z, et al., 2021, Leveraging community mortality indicators to infer COVID-19 mortality and transmission dynamics in Damascus, Syria, Nature Communications, Vol: 12, Pages: 1-10, ISSN: 2041-1723
The COVID-19 pandemic has resulted in substantial mortality worldwide. However, to date, countries in the Middle East and Africa have reported considerably lower mortality rates than in Europe and the Americas. Motivated by reports of an overwhelmed health system, we estimate the likely under-ascertainment of COVID-19 mortality in Damascus, Syria. Using all-cause mortality data, we fit a mathematical model of COVID-19 transmission to reported mortality, estimating that 1.25% of COVID-19 deaths (sensitivity range 1.00% – 3.00%) have been reported as of 2 September 2020. By 2 September, we estimate that 4,380 (95% CI: 3,250 – 5,550) COVID-19 deaths in Damascus may have been missed, with 39.0% (95% CI: 32.5% – 45.0%) of the population in Damascus estimated to have been infected. Accounting for under-ascertainment corroborates reports of exceeded hospital bed capacity and is validated by community-uploaded obituary notifications, which confirm extensive unreported mortality in Damascus.
Unwin HJT, Routledge I, Flaxman S, et al., 2021, Using Hawkes Processes to model imported and local malaria cases in near-elimination settings, PLoS Computational Biology, Vol: 17, Pages: 1-18, ISSN: 1553-734X
Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.
Laydon DJ, Mishra S, Hinsley WR, et al., 2021, Impact of the Tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Measure the effects of the Tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern.</jats:p></jats:sec><jats:sec><jats:title>Design</jats:title><jats:p>Modelling study combining estimates of the real-time reproduction number <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities, to account for broader national trends in addition to subnational effects from Tiers.</jats:p></jats:sec><jats:sec><jats:title>Setting</jats:title><jats:p>The UK at Lower Tier Local Authority (LTLA) level.</jats:p></jats:sec><jats:sec><jats:title>Primary and secondary outcome measures</jats:title><jats:p>Reduction in real-time reproduction number <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Nationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> averaged 1.3 (0.9 – 1.6) across LTLAs, but declined to an average of 1.1 (0.86 – 1.42) two weeks later. Decline in transmission was not solely attributable to Tiers. Tier 1 had negligible effects. Tiers 2 and 3 respectively reduced transmission by 6% (5%-7%) and 2
Bourne RRA, Steinmetz JD, Flaxman S, et al., 2021, Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study, LANCET GLOBAL HEALTH, Vol: 9, Pages: E130-E143, ISSN: 2214-109X
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- Citations: 257
Steinmetz JD, Bourne RRA, Briant PS, et al., 2021, Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study, LANCET GLOBAL HEALTH, Vol: 9, Pages: E144-E160, ISSN: 2214-109X
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- Citations: 778
Vollmer MAC, Glampson B, Mellan TA, et al., 2021, A unified machine learning approach to time series forecasting applied to demand at emergency departments, BMC Emergency Medicine, Vol: 21, Pages: 1-14, ISSN: 1471-227X
There were 25.6 million attendances at Emergency Departments (EDs) in Englandin 2019 corresponding to an increase of 12 million attendances over the pastten years. The steadily rising demand at EDs creates a constant challenge toprovide adequate quality of care while maintaining standards and productivity.Managing hospital demand effectively requires an adequate knowledge of thefuture rate of admission. Using 8 years of electronic admissions data from twomajor acute care hospitals in London, we develop a novel ensemble methodologythat combines the outcomes of the best performing time series and machinelearning approaches in order to make highly accurate forecasts of demand, 1, 3and 7 days in the future. Both hospitals face an average daily demand of 208and 106 attendances respectively and experience considerable volatility aroundthis mean. However, our approach is able to predict attendances at theseemergency departments one day in advance up to a mean absolute error of +/- 14and +/- 10 patients corresponding to a mean absolute percentage error of 6.8%and 8.6% respectively. Our analysis compares machine learning algorithms tomore traditional linear models. We find that linear models often outperformmachine learning methods and that the quality of our predictions for any of theforecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Inaddition to comparing and combining state-of-the-art forecasting methods topredict hospital demand, we consider two different hyperparameter tuningmethods, enabling a faster deployment of our models without compromisingperformance. We believe our framework can readily be used to forecast a widerange of policy relevant indicators.
Wilde H, Mellan TA, Hawryluk I, et al., 2021, A national retrospective cohort study of mechanical ventilator availability and its association with mortality risk in intensive care patients with COVID-19
<jats:p>Objectives: To determine if there is an association between survival rates in intensive care units (ICU) and occupancy of the unit on the day of admission.Design: National retrospective observational cohort study during the COVID-19 pandemic.Setting: 90 English hospital trusts (i.e. groups of hospitals functioning as single operational units).Participants: 6,686 adults admitted to an ICU in England between 2nd April and 1st December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria.Interventions: N/AMain Outcomes and Measures: A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible) bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).Results: 121,151 patient-days were observed, with a mortality rate of 20.8 per 1,000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (>85% occupancy versus the baseline of 45 to 85%) [OR 1.18 (95% posterior credible interval (PCI): 1.00 to 1.38)]. In contrast, mortality was decreased for admissions during periods of low occupancy (<45% relative to the baseline) [OR 0.79 (95% PCI: 0.69 to 0.90)].Conclusion and Relevance: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Public health interventions (such as expeditious vaccination programmes and non-pharmaceutical interventions) to control both inciden
Holbrook AJ, Loeffler CE, Flaxman SR, et al., 2021, Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data, STATISTICS AND COMPUTING, Vol: 31, ISSN: 0960-3174
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- Citations: 6
Fu H, Wang H, Xi X, et al., 2021, A database for the epidemic trends and control measures during the first wave of COVID-19 in mainland China, International Journal of Infectious Diseases, Vol: 102, Pages: 463-471, ISSN: 1201-9712
Objectives: This data collation effort aims to provide a comprehensive database to describe the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19)across main provinces in China. Methods: From mid-January to March 2020, we extracted publicly available data on the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted a descriptive analysis of the epidemics in the six most-affected provinces. Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends were different across provinces. Compared to Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as local transmission of COVID-19 declined, switching the focus of measures to testing and quarantine of inbound travellers could help to sustain the control of the epidemic. Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database with these indicators and information on control measures provides useful source for exploring further research and policy planning for response to the COVID-19 epidemic.
Krawczyk K, Chelkowski T, Mishra S, et al., 2020, Quantifying the online news media coverage of the COVID-19 pandemic
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Non-pharmaceutical interventions such as lockdowns, mask wearing and social distancing have been the primary measures to effectively combat the COVID-19 pandemic. Such measures are highly effective when there is strong population wide adherence which needs to be facilitated by information on the current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place. Here we address the issue of communication on the pandemic by offering data and analysis of online news media coverage of COVID-19.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available at<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://sciride.org">http://sciride.org</jats:ext-link>). Using topic detection we identified COVID-19-related content to quantify the proportion of total coverage pandemic received in 2020. Sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 articles to reveal the leading themes in pandemic reporting and their respective emotional polarizations.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>We find that COVID-19 coverage accounted for approximately 25% of all front-page online news articles between January and October 2020. Sentiment analysis of English-speaking sources reveals that the overall COVID-19 coverage cannot be simply classified as negative due to the disease subject matter, suggesting a wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 n
Flaxman S, Mishra S, Scott J, et al., 2020, The effect of interventions on COVID-19 Reply, NATURE, Vol: 588, Pages: E29-E32, ISSN: 0028-0836
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