ABSTRACT
Selection bias poses a challenge to statistical inference validity in non-probability surveys. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large non-probability survey, COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against benchmark data from the COVID Vaccine Intelligence Network (CoWIN). Notably, CTIS exhibits a larger estimation error (0.39) compared to CVoter (0.16). Additionally, we investigated the estimation accuracy of the CTIS when using a relative scale and found a significant increase in the effective sample size by altering the estimand from the overall vaccination rate. These results suggest that the big data paradox can manifest in countries beyond the US and it may not apply to every estimand of interest.
Subject(s)
COVID-19ABSTRACT
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.
Subject(s)
COVID-19ABSTRACT
The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2 N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus-associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the models predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework is able to predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.
Subject(s)
COVID-19ABSTRACT
The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data, covering a year post disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct "systemic recovery" profiles with specific progression and resolution of the inflammatory, immune, metabolic and clinical responses, over weeks to several months after infection. In particular, we found a strong intra-patient temporal covariation of innate immune cell numbers, kynurenine- and host lipid-metabolites, which suggested candidate immunometabolic pathways putatively influencing restoration of homeostasis, the risk of death and of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery on the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-systemic-recovery-prediction-app, designed to test our findings prospectively.
Subject(s)
COVID-19 , Severe Acute Respiratory Syndrome , DeathABSTRACT
Multimorbidity is defined as the coexistence of two or more chronic health conditions in an individual. The objective of this study was to examine how diseases in a cluster of physical-mental health multimorbidity with a high all-cause mortality (psychosis, diabetes, and congestive heart failure) develop and coexist over time, and to assess the associated impact of different temporal sequences on mortality. Population-scale, individual-level, anonymised, linked, demographic, administrative and electronic health record data were modelled using multi-state models for 1,675,585 individuals over a 20-year period (2000–2019). Cox regression models were used to estimate baseline hazards for transitions between states, adjusted for gender, age, and area-level deprivation. Our findings suggest that the order of disease acquisition in physical-mental health multimorbidity had an important impact and complex relationship on patient mortality. Individuals developing diabetes, psychosis, and congestive heart failure, in that order, had an increased all-cause mortality rate compared to the development of the same conditions in a different order, resulting in the highest loss in expectation of life of 13 years at age 50 compared to the general population. Congestive heart failure as a single condition and in combination with psychosis had an equally high loss in expectation of life. Identification and therapeutic targets for psychosis and congestive heart failure may be beneficial within 5 years following an initial diagnosis of diabetes. The use of multi-state models offers a flexible framework to assess temporal sequences of diseases and associated patient outcomes, and allows identification of potential risk factors, screening opportunities, and therapeutic targets in multimorbidity.
Subject(s)
Heart Failure , Diabetes Mellitus , Psychotic DisordersABSTRACT
Background: Ethnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK. Method: Using a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate and estimated unbiased prevalence at the Lower Tier Local Authority (LTLA) level, adjusting for confounders and spatio-temporal correlation structure. Findings: Comparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 2.97% to 3.35%. Similarly, prevalence increases by 10% from 0.37% to 0.41%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes more pronounced at the peak of the second wave and then again in May-June 2021. In the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis. Interpretation: IMD and BAME% are both associated with an increased COVID-19 burden in terms of disease spread and monitoring, and the strength of association varies over the course of the pandemic. The consistency of results across the two outcomes suggests that deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits.
Subject(s)
COVID-19 , Severe Acute Respiratory SyndromeABSTRACT
We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England.
Subject(s)
Coronavirus Infections , COVID-19ABSTRACT
Prominent early features of COVID-19 include severe, often clinically silent, hypoxia and a pronounced reduction in B cells, the latter important in defence against SARS-CoV-2. This brought to mind the phenotype of mice with VHL-deficient B cells, in which Hypoxia-Inducible Factors are constitutively active, suggesting hypoxia might drive B cell abnormalities in COVID-19. We demonstrated the breadth of early and persistent defects in B cell subsets in moderate/severe COVID-19, including reduced marginal zone-like, memory and transitional B cells, changes we also observed in B cell VHL-deficient mice. This was corroborated by hypoxia-related transcriptional changes in COVID-19 patients, and by similar B cell abnormalities in mice kept in hypoxic conditions, including reduced marginal zone and germinal center B cells. Thus hypoxia might contribute to B cell pathology in COVID-19, and in other hypoxic states. Through this mechanism it may impact on COVID-19 outcome, and be remediable through early oxygen therapy.
Subject(s)
COVID-19ABSTRACT
Targeted surveillance testing schemes for SARS-CoV-2 focus on certain subsets of the population, such as individuals experiencing one or more of a prescribed list of symptoms. These schemes have routinely been used to monitor the spread of SARS-CoV-2 in countries across the world. The number of positive tests in a given region can provide local insights into important epidemiological parameters, such as prevalence and effective reproduction number. Moreover, targeted testing data has been used inform the deployment of localised non-pharmaceutical interventions. However, surveillance schemes typically suffer from ascertainment bias; the individuals who are tested are not necessarily representative of the wider population of interest. Here, we show that data from randomised testing schemes, such as the REACT study in the UK, can be used to debias fine-scale targeted testing data in order to provide accurate localised estimates of the number of infectious individuals. We develop a novel, integrative causal framework that explicitly models the process underlying the selection of individuals for targeted testing. The output from our model can readily be incorporated into longitudinal analyses to provide local estimates of the reproduction number. We apply our model to characterise the size of the infectious population in England between June 2020 and January 2021. Our local estimates of the effective reproduction number are predictive of future changes in positive case numbers. We also capture local increases in both prevalence and effective reproductive number in the South East from November 2020 to December 2020, reflecting the spread of the Kent variant. Our results illustrate the complementary roles of randomised and targeted testing schemes. Preparations for future epidemics should ensure the rapid deployment of both types of schemes to accurately monitor the spread of emerging and ongoing infectious diseases.
Subject(s)
COVID-19ABSTRACT
During Covid-19 outbreaks, school closures are employed as part of governments' non-pharmaceutical interventions around the world to reduce the number of contacts and keep the reproduction number below 1. Yet, prolonged school closures have profound negative impact on the future opportunities of pupils, particularly from disadvantaged backgrounds, as well as additional economic and social impacts by preventing their parents from returning to work. Data on Covid-19 in children are sparse and policy frameworks are evolving quickly. We compare a set of potential policies to accompany the reopening of schools by means of an agent-based simulation tool. The policies and scenarios we model reflect the public discussion and government guidelines in early March 2021 in England before the planned nationwide reopening of schools on the 8th of March. A point of particular interest is the potential contribution of a more wide-spread use of screening tests based on lateral flow devices. We compare policies both with respect to their potential to contain new outbreaks of Covid-19 in schools and the proportion of schooldays lost due to isolation of pupils. We find that regular asymptomatic screening of the whole school as an addition to a policy built around isolation of symptomatic pupils and their closest contacts is beneficial across a wide range of scenarios, including when screening tests with relatively low test sensitivity are used. Multiple screening tests per week bring only small additional benefits in some scenarios. These findings remain valid when test compliance is not enforced although the effectiveness of outbreak control is reduced.
Subject(s)
COVID-19ABSTRACT
In a study of 207 SARS-CoV2-infected individuals with a range of severities followed over 12 weeks from symptom onset, we demonstrate that an early robust immune response, without systemic inflammation, is characteristic of asymptomatic or mild disease. Those presenting to hospital had delayed adaptive responses and systemic inflammation already evident at around symptom onset. Such early evidence of inflammation suggests immunopathology may be inevitable in some individuals, or that preventative intervention might be needed before symptom onset. Viral load does not correlate with the development of this pathological response, but does with its subsequent severity. Immune recovery is complex, with profound persistent cellular abnormalities correlating with a change in the nature of the inflammatory response, where signatures characteristic of increased oxidative phosphorylation and reactive-oxygen species-associated inflammation replace those driven by TNF and IL-6. These late immunometabolic inflammatory changes and unresolved immune cell defects, if persistent, may contribute to "long COVID".
Subject(s)
Severe Acute Respiratory Syndrome , Chronobiology Disorders , COVID-19 , InflammationABSTRACT
In a study of 207 SARS-CoV2-infected individuals with a range of severities followed over 12 weeks from symptom onset, we demonstrate that an early robust immune response, without systemic inflammation, is characteristic of asymptomatic or mild disease. Those presenting to hospital had delayed adaptive responses and systemic inflammation already evident at around symptom onset. Such early evidence of inflammation suggests immunopathology may be inevitable in some individuals, or that preventative intervention might be needed before symptom onset. Viral load does not correlate with the development of this pathological response, but does with its subsequent severity. Immune recovery is complex, with profound persistent cellular abnormalities correlating with a change in the nature of the inflammatory response, where signatures characteristic of increased oxidative phosphorylation and reactive-oxygen species-associated inflammation replace those driven by TNF and IL-6. These late immunometabolic inflammatory changes and unresolved immune cell defects, if persistent, may contribute to “long COVID”.Funding: We are grateful for the generous support of CVC Capital Partners, the Evelyn Trust (20/75), UKRI COVID Immunology Consortium, Addenbrooke’s Charitable Trust (12/20A) and the NIHR Cambridge Biomedical Research Centre for their financial support. K.G.C.S. is the recipient of a Wellcome Investigator Award (200871/Z/16/Z); M.P.W. is the recipient of Wellcome Senior Clinical Research Fellowship (108070/Z/15/Z); C.H. was funded by a Wellcome COVID-19 Rapid Response DCF and the Fondation Botnar; N.M. was funded by the MRC (CSF MR/P008801/1) and NHSBT (WPA15-02); I.G.G. is a Wellcome Senior Fellow and was supported by funding from the Wellcome (Ref: 207498/Z/17/Z).Conflict of Interest: The authors declare they have no competing interests.