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131 results on '"Varsavsky, Thomas"'

1. Flexible visual prompts for in-context learning in computer vision

Author

Foster, Thomas, Croitoru, Ioana, Dorfman, Robert, Edlund, Christoffer, Varsavsky, Thomas, and Almazán, Jon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this work, we address in-context learning (ICL) for the task of image segmentation, introducing a novel approach that adapts a modern Video Object Segmentation (VOS) technique for visual in-context learning. This adaptation is inspired by the VOS method's ability to efficiently and flexibly learn objects from a few examples. Through evaluations across a range of support set sizes and on diverse segmentation datasets, our method consistently surpasses existing techniques. Notably, it excels with data containing classes not encountered during training. Additionally, we propose a technique for support set selection, which involves choosing the most relevant images to include in this set. By employing support set selection, the performance increases for all tested methods without the need for additional training or prompt tuning. The code can be found at https://github.com/v7labs/XMem_ICL/.

Published
2023

2. Augmentation based unsupervised domain adaptation

Author

Orbes-Arteaga, Mauricio, Varsavsky, Thomas, Sorensen, Lauge, Nielsen, Mads, Pai, Akshay, Ourselin, Sebastien, Modat, Marc, and Cardoso, M Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize. Because in realistic clinical scenarios, data acquisition and annotation is expensive, deep learning models trained on small and unrepresentative data tend to outperform when deployed in data that differs from the one used for training (e.g data from different scanners). In this work, we proposed a domain adaptation methodology to alleviate this problem in segmentation models. Our approach takes advantage of the properties of adversarial domain adaptation and consistency training to achieve more robust adaptation. Using two datasets with white matter hyperintensities (WMH) annotations, we demonstrated that the proposed method improves model generalization even in corner cases where individual strategies tend to fail.

Published
2022

3. Acquisition-invariant brain MRI segmentation with informative uncertainties

Author

Borges, Pedro, Shaw, Richard, Varsavsky, Thomas, Klaser, Kerstin, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, and Cardoso, M Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm, that can become robust to the physics of acquisition in the context of segmentation tasks, while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality, but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool., Comment: 25 pages, 8 figures

Published
2021

4. The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties

Author

Borges, Pedro, Shaw, Richard, Varsavsky, Thomas, Klaser, Kerstin, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, and Cardoso, M Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achieve acquisition invariance. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples, providing well calibrated volumetric bounds on these. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation., Comment: 10 pages, 3 figures, published in: Simulation and Synthesis in Medical Imaging 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

Published
2021
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6. Test-time Unsupervised Domain Adaptation

Author

Varsavsky, Thomas, Orbes-Arteaga, Mauricio, Sudre, Carole H., Graham, Mark S., Nachev, Parashkev, and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model's ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subject, Comment: Accepted at MICCAI 2020

Published
2020

7. Hierarchical brain parcellation with uncertainty

Author

Graham, Mark S., Sudre, Carole H., Varsavsky, Thomas, Tudosiu, Petru-Daniel, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are `flat'. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree., Comment: To be published in the MICCAI 2020 workshop: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Published
2020

8. Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

Author

Wood, David A., Lynch, Jeremy, Kafiabadi, Sina, Guilhem, Emily, Busaidi, Aisha Al, Montvila, Antanas, Varsavsky, Thomas, Siddiqui, Juveria, Gadapa, Naveen, Townend, Matthew, Kiik, Martin, Patel, Keena, Barker, Gareth, Ourselin, Sebastian, Cole, James H., and Booth, Thomas C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.

Published
2020

9. Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

Author

Tudosiu, Petru-Daniel, Varsavsky, Thomas, Shaw, Richard, Graham, Mark, Nachev, Parashkev, Ourselin, Sebastien, Sudre, Carole H., and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions. Recently, Vector-Quantised Variational Autoencoders (VQ-VAE) have been proposed as an efficient generative unsupervised learning approach that can encode images to a small percentage of their initial size, while preserving their decoded fidelity. Here, we show a VQ-VAE inspired network can efficiently encode a full-resolution 3D brain volume, compressing the data to $0.825\%$ of the original size while maintaining image fidelity, and significantly outperforming the previous state-of-the-art. We then demonstrate that VQ-VAE decoded images preserve the morphological characteristics of the original data through voxel-based morphology and segmentation experiments. Lastly, we show that such models can be pre-trained and then fine-tuned on different datasets without the introduction of bias.

Published
2020

10. Physics-informed brain MRI segmentation

Author

Borges, Pedro, Sudre, Carole, Varsavsky, Thomas, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different sites and with different parameters exhibit significant differences in contrast and tissue appearance, resulting in downstream issues when quantifying brain anatomy or the presence of pathology. In this work, we propose to combine multiparametric MRI-based static-equation sequence simulations with segmentation convolutional neural networks (CNN), to make these networks robust to variations in acquisition parameters. Results demonstrate that, when given both the image and their associated physics acquisition parameters, CNNs can produce segmentations that exhibit robustness to acquisition variations. We also show that the proposed physics-informed methods can be used to bridge multi-centre and longitudinal imaging studies where imaging acquisition varies across a site or in time., Comment: Accepted at SASHIMI2019

Published
2020
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11. Let's agree to disagree: learning highly debatable multirater labelling

Author

Sudre, Carole H., Anson, Beatriz Gomez, Ingala, Silvia, Lane, Chris D., Jimenez, Daniel, Haider, Lukas, Varsavsky, Thomas, Tanno, Ryutaro, Smith, Lorna, Ourselin, Sébastien, Jäger, Rolf H., and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multirater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency., Comment: Accepted at MICCAI 2019

Published
2019

12. Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

Author

Kläser, Kerstin, Varsavsky, Thomas, Markiewicz, Pawel, Vercauteren, Tom, Atkinson, David, Thielemans, Kris, Hutton, Brian, Cardoso, M Jorge, and Ourselin, Sebastien

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2-norm between the ground truth CT and the pCT. However, given that the ultimate purpose is often to use the pCT as an attenuation map ($\mu$-map) in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily optimal. The main objective should be to predict a pCT that, when used as $\mu$-map, reconstructs a pseudo PET (pPET) which is as close as possible to the gold standard PET. To this end, we propose a novel multi-hypothesis deep learning framework that generates pCTs by minimising a combination of the pixel-wise error between pCT and CT and a proposed metric-loss that itself is represented by a convolutional neural network (CNN) and aims to minimise subsequent PET residuals. The model is trained on a database of 400 paired MR/CT/PET image slices. Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69.68HU) compared to a baseline CNN (66.25HU), but lead to significant improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u., Comment: Aceppted at SASHIMI2019

Published
2019
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13. Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

Author

Orbes-Arteaga, Mauricio, Varsavsky, Thomas, Sudre, Carole H., Eaton-Rosen, Zach, Haddow, Lewis J., Sørensen, Lauge, Nielsen, Mads, Pai, Akshay, Ourselin, Sébastien, Modat, Marc, Nachev, Parashkev, and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines., Comment: Accepted at 1st International Workshop on Domain Adaptation and Representation Transfer held at MICCAI 2019

Published
2019

14. As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging

Author

Eaton-Rosen, Zach, Varsavsky, Thomas, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and suggest ways of deriving predictive intervals from these. We then propose and test a method for calculating intervals as an output of a multi-task network. These predictive intervals are optimised to be as narrow as possible, while also enclosing a desired percentage of the data. We demonstrate the effectiveness of this technique on histopathological cell counting and white matter hyperintensity counting. Finally, we offer insight into other areas where this technique may apply., Comment: Early Accept to MICCAI 2019

Published
2019

15. 3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Author

Sudre, Carole H., Anson, Beatriz Gomez, Ingala, Silvia, Lane, Chris D., Jimenez, Daniel, Haider, Lukas, Varsavsky, Thomas, Smith, Lorna, Jäger, H. Rolf, and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually $<$10 voxels per object for an image of more than $10^6$ voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process.

Published
2018

16. PIMMS: Permutation Invariant Multi-Modal Segmentation

Author

Varsavsky, Thomas, Eaton-Rosen, Zach, Sudre, Carole H., Nachev, Parashkev, and Cardoso, M. Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality. If we are to build applications that work in hospitals without significant operational changes in care delivery, algorithms should be designed to cope with the available data in the best possible way. In a clinical environment, imaging protocols are highly flexible, with MRI sequences commonly missing appropriate sequence labeling (e.g. T1, T2, FLAIR). To this end we introduce PIMMS, a Permutation Invariant Multi-Modal Segmentation technique that is able to perform inference over sets of MRI scans without using modality labels. We present results which show that our convolutional neural network can, in some settings, outperform a baseline model which utilizes modality labels, and achieve comparable performance otherwise., Comment: Accepted at the 4th Workshop on Deep Learning in Medical Image Analysis held at MICCAI2018

Published
2018

18. Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Author

Koshy, Cherian, Ash, Amy, Wise, Emma, Moore, Nathan, Mori, Matilde, Cortes, Nick, Lynch, Jessica, Kidd, Stephen, Fairley, Derek J, Curran, Tanya, McKenna, James P, Adams, Helen, Fraser, Christophe, Golubchik, Tanya, Bonsall, David, Hassan-Ibrahim, Mohammed O, Malone, Cassandra S, Cogger, Benjamin J, Wantoch, Michelle, Reynolds, Nicola, Warne, Ben, Maksimovic, Joshua, Spellman, Karla, McCluggage, Kathryn, John, Michaela, Beer, Robert, Afifi, Safiah, Morgan, Sian, Marchbank, Angela, Price, Anna, Kitchen, Christine, Gulliver, Huw, Merrick, Ian, Southgate, Joel, Guest, Martyn, Munn, Robert, Workman, Trudy, Connor, Thomas R, Fuller, William, Bresner, Catherine, Snell, Luke B, Patel, Amita, Charalampous, Themoula, Nebbia, Gaia, Batra, Rahul, Edgeworth, Jonathan, Robson, Samuel C, Beckett, Angela H, Aanensen, David M, Underwood, Anthony P, Yeats, Corin A, Abudahab, Khalil, Taylor, Ben EW, Menegazzo, Mirko, Clark, Gemma, Smith, Wendy, Khakh, Manjinder, Fleming, Vicki M, Lister, Michelle M, Howson-Wells, Hannah C, Berry, Louise, Boswell, Tim, Joseph, Amelia, Willingham, Iona, Jones, Carl, Holmes, Christopher, Bird, Paul, Helmer, Thomas, Fallon, Karlie, Tang, Julian, Raviprakash, Veena, Campbell, Sharon, Sheriff, Nicola, Blakey, Victoria, Williams, Lesley-Anne, Loose, Matthew W, Holmes, Nadine, Moore, Christopher, Carlile, Matthew, Wright, Victoria, Sang, Fei, Debebe, Johnny, Coll, Francesc, Signell, Adrian W, Betancor, Gilberto, Wilson, Harry D, Eldirdiri, Sahar, Kenyon, Anita, Davis, Thomas, Pybus, Oliver G, du Plessis, Louis, Zarebski, Alex E, Raghwani, Jayna, Kraemer, Moritz UG, Francois, Sarah, Attwood, Stephen W, Vasylyeva, Tetyana I, Escalera Zamudio, Marina, Gutierrez, Bernardo, Torok, M. Estee, Hamilton, William L, Goodfellow, Ian G, Hall, Grant, Jahun, Aminu S, Chaudhry, Yasmin, Hosmillo, Myra, Pinckert, Malte L, Georgana, Iliana, Moses, Samuel, Lowe, Hannah, Bedford, Luke, Moore, Jonathan, Stonehouse, Susanne, Fisher, Chloe L, Awan, Ali R, BoYes, John, Breuer, Judith, Harris, Kathryn Ann, Brown, Julianne Rose, Shah, Divya, Atkinson, Laura, Lee, Jack CD, Storey, Nathaniel, Flaviani, Flavia, Alcolea-Medina, Adela, Williams, Rebecca, Vernet, Gabrielle, Chapman, Michael R, Levett, Lisa J, Heaney, Judith, Chatterton, Wendy, Pusok, Monika, Xu-McCrae, Li, Smith, Darren L, Bashton, Matthew, Young, Gregory R, Holmes, Alison, Randell, Paul Anthony, Cox, Alison, Madona, Pinglawathee, Bolt, Frances, Price, James, Mookerjee, Siddharth, Ragonnet-Cronin, Manon, Nascimento, Fabricia F., Jorgensen, David, Siveroni, Igor, Johnson, Rob, Boyd, Olivia, Geidelberg, Lily, Volz, Erik M, Rowan, Aileen, Taylor, Graham P, Smollett, Katherine L, Loman, Nicholas J, Quick, Joshua, McMurray, Claire, Stockton, Joanne, Nicholls, Sam, Rowe, Will, Poplawski, Radoslaw, McNally, Alan, Martinez Nunez, Rocio T, Mason, Jenifer, Robinson, Trevor I, O'Toole, Elaine, Watts, Joanne, Breen, Cassie, Cowell, Angela, Sluga, Graciela, Machin, Nicholas W, Ahmad, Shazaad S Y, George, Ryan P, Halstead, Fenella, Sivaprakasam, Venkat, Hogsden, Wendy, Illingworth, Chris J, Jackson, Chris, Thomson, Emma C, Shepherd, James G, Asamaphan, Patawee, Niebel, Marc O, Li, Kathy K, Shah, Rajiv N, Jesudason, Natasha G, Tong, Lily, Broos, Alice, Mair, Daniel, Nichols, Jenna, Carmichael, Stephen N, Nomikou, Kyriaki, Aranday-Cortes, Elihu, Johnson, Natasha, Starinskij, Igor, da Silva Filipe, Ana, Robertson, David L, Orton, Richard J, Hughes, Joseph, Vattipally, Sreenu, Singer, Joshua B, Nickbakhsh, Seema, Hale, Antony D, Macfarlane-Smith, Louissa R, Harper, Katherine L, Carden, Holli, Taha, Yusri, Payne, Brendan AI, Burton-Fanning, Shirelle, Waugh, Sheila, Collins, Jennifer, Eltringham, Gary, Rushton, Steven, O'Brien, Sarah, Bradley, Amanda, Maclean, Alasdair, Mollett, Guy, Blacow, Rachel, Templeton, Kate E, McHugh, Martin P, Dewar, Rebecca, Wastenge, Elizabeth, Dervisevic, Samir, Stanley, Rachael, Meader, Emma J, Coupland, Lindsay, Smith, Louise, Graham, Clive, Barton, Edward, Padgett, Debra, Scott, Garren, Swindells, Emma, Greenaway, Jane, Nelson, Andrew, McCann, Clare M, Yew, Wen C, Andersson, Monique, Peto, Timothy, Justice, Anita, Eyre, David, Crook, Derrick, Sloan, Tim J, Duckworth, Nichola, Walsh, Sarah, Chauhan, Anoop J, Glaysher, Sharon, Bicknell, Kelly, Wyllie, Sarah, Elliott, Scott, Lloyd, Allyson, Impey, Robert, Levene, Nick, Monaghan, Lynn, Bradley, Declan T, Wyatt, Tim, Allara, Elias, Pearson, Clare, Osman, Husam, Bosworth, Andrew, Robinson, Esther, Muir, Peter, Vipond, Ian B, Hopes, Richard, Pymont, Hannah M, Hutchings, Stephanie, Curran, Martin D, Parmar, Surendra, Lackenby, Angie, Mbisa, Tamyo, Platt, Steven, Miah, Shahjahan, Bibby, David, Manso, Carmen, Hubb, Jonathan, Chand, Meera, Dabrera, Gavin, Ramsay, Mary, Bradshaw, Daniel, Thornton, Alicia, Myers, Richard, Schaefer, Ulf, Groves, Natalie, Gallagher, Eileen, Lee, David, Williams, David, Ellaby, Nicholas, Harrison, Ian, Hartman, Hassan, Manesis, Nikos, Patel, Vineet, Bishop, Chloe, Chalker, Vicki, Ledesma, Juan, Twohig, Katherine A, Holden, Matthew T.G., Shaaban, Sharif, Birchley, Alec, Adams, Alexander, Davies, Alisha, Gaskin, Amy, Plimmer, Amy, Gatica-Wilcox, Bree, McKerr, Caoimhe, Moore, Catherine, Williams, Chris, Heyburn, David, De Lacy, Elen, Hilvers, Ember, Downing, Fatima, Shankar, Giri, Jones, Hannah, Asad, Hibo, Coombes, Jason, Watkins, Joanne, Evans, Johnathan M, Fina, Laia, Gifford, Laura, Gilbert, Lauren, Graham, Lee, Perry, Malorie, Morgan, Mari, Bull, Matthew, Cronin, Michelle, Pacchiarini, Nicole, Craine, Noel, Jones, Rachel, Howe, Robin, Corden, Sally, Rey, Sara, Kumziene-SummerhaYes, Sara, Taylor, Sarah, Cottrell, Simon, Jones, Sophie, Edwards, Sue, O'Grady, Justin, Page, Andrew J, Mather, Alison E, Baker, David J, Rudder, Steven, Aydin, Alp, Kay, Gemma L, Trotter, Alexander J, Alikhan, Nabil-Fareed, de Oliveira Martins, Leonardo, Le-Viet, Thanh, Meadows, Lizzie, Casey, Anna, Ratcliffe, Liz, Simpson, David A, Molnar, Zoltan, Thompson, Thomas, Acheson, Erwan, Masoli, Jane AH, Knight, Bridget A, Ellard, Sian, Auckland, Cressida, Jones, Christopher R, Mahungu, Tabitha W, Irish-Tavares, Dianne, Haque, Tanzina, Hart, Jennifer, Witele, Eric, Fenton, Melisa Louise, Dadrah, Ashok, Symmonds, Amanda, Saluja, Tranprit, Bourgeois, Yann, Scarlett, Garry P, Loveson, Katie F, Goudarzi, Salman, Fearn, Christopher, Cook, Kate, Dent, Hannah, Paul, Hannah, Partridge, David G, Raza, Mohammad, Evans, Cariad, Johnson, Kate, Liggett, Steven, Baker, Paul, Bonner, Stephen, Essex, Sarah, Lyons, Ronan A, Saeed, Kordo, Mahanama, Adhyana I.K, Samaraweera, Buddhini, Silveira, Siona, Pelosi, Emanuela, Wilson-Davies, Eleri, Williams, Rachel J, Kristiansen, Mark, Roy, Sunando, Williams, Charlotte A, Cotic, Marius, Bayzid, Nadua, Westhorpe, Adam P, Hartley, John A, Jannoo, Riaz, Lowe, Helen L, Karamani, Angeliki, Ensell, Leah, Prieto, Jacqui A, Jeremiah, Sarah, Grammatopoulos, Dimitris, Pandey, Sarojini, Berry, Lisa, Jones, Katie, Richter, Alex, Beggs, Andrew, Best, Angus, Percival, Benita, Mirza, Jeremy, Megram, Oliver, Mayhew, Megan, Crawford, Liam, Ashcroft, Fiona, Moles-Garcia, Emma, Cumley, Nicola, Smith, Colin P, Bucca, Giselda, Hesketh, Andrew R, Blane, Beth, Girgis, Sophia T, Leek, Danielle, Sridhar, Sushmita, Forrest, Sally, Cormie, Claire, Gill, Harmeet K, Dias, Joana, Higginson, Ellen E, Maes, Mailis, Young, Jamie, Kermack, Leanne M, Gupta, Ravi Kumar, Ludden, Catherine, Peacock, Sharon J, Palmer, Sophie, Churcher, Carol M, Hadjirin, Nazreen F, Carabelli, Alessandro M, Brooks, Ellena, Smith, Kim S, Galai, Katerina, McManus, Georgina M, Ruis, Chris, Davidson, Rose K, Rambaut, Andrew, Williams, Thomas, Balcazar, Carlos E, Gallagher, Michael D, O'Toole, Áine, Rooke, Stefan, Hill, Verity, Williamson, Kathleen A, Stanton, Thomas D, Michell, Stephen L, Bewshea, Claire M, Temperton, Ben, Michelsen, Michelle L, Warwick-Dugdale, Joanna, Manley, Robin, Farbos, Audrey, Harrison, James W, Sambles, Christine M, Studholme, David J, Jeffries, Aaron R, Darby, Alistair C, Hiscox, Julian A, Paterson, Steve, Iturriza-Gomara, Miren, Jackson, Kathryn A, Lucaci, Anita O, Vamos, Edith E, Hughes, Margaret, Rainbow, Lucille, Eccles, Richard, Nelson, Charlotte, Whitehead, Mark, Turtle, Lance, Haldenby, Sam T, Gregory, Richard, Gemmell, Matthew, Wierzbicki, Claudia, Webster, Hermione J, de Silva, Thushan I, Smith, Nikki, Angyal, Adrienn, Lindsey, Benjamin B, Groves, Danielle C, Green, Luke R, Wang, Dennis, Freeman, Timothy M, Parker, Matthew D, Keeley, Alexander J, Parsons, Paul J, Tucker, Rachel M, Brown, Rebecca, Wyles, Matthew, Whiteley, Max, Zhang, Peijun, Gallis, Marta, Louka, Stavroula F, Constantinidou, Chrystala, Unnikrishnan, Meera, Ott, Sascha, Cheng, Jeffrey K.J., Bridgewater, Hannah E., Frost, Lucy R., Taylor-Joyce, Grace, Stark, Richard, Baxter, Laura, Alam, Mohammad T., Brown, Paul E, Aggarwal, Dinesh, Cerda, Alberto C, Merrill, Tammy V, Wilson, Rebekah E, McClure, Patrick C, Chappell, Joseph G, Tsoleridis, Theocharis, Ball, Jonathan, Buck, David, Todd, John A, Green, Angie, Trebes, Amy, MacIntyre-Cockett, George, de Cesare, Mariateresa, Alderton, Alex, Amato, Roberto, Ariani, Cristina V, Beale, Mathew A, Beaver, Charlotte, Bellis, Katherine L, Betteridge, Emma, Bonfield, James, Danesh, John, Dorman, Matthew J, Drury, Eleanor, Farr, Ben W, Foulser, Luke, Goncalves, Sonia, Goodwin, Scott, Gourtovaia, Marina, Harrison, Ewan M, Jackson, David K, Jamrozy, Dorota, Johnston, Ian, Kane, Leanne, Kay, Sally, Keatley, Jon-Paul, Kwiatkowski, Dominic, Langford, Cordelia F, Lawniczak, Mara, Letchford, Laura, Livett, Rich, Lo, Stephanie, Martincorena, Inigo, McGuigan, Samantha, Nelson, Rachel, Palmer, Steve, Park, Naomi R, Patel, Minal, Prestwood, Liam, Puethe, Christoph, Quail, Michael A, Rajatileka, Shavanthi, Scott, Carol, Shirley, Lesley, Sillitoe, John, Spencer Chapman, Michael H, Thurston, Scott AJ, Tonkin-Hill, Gerry, Weldon, Danni, Rajan, Diana, Bronner, Iraad F, Aigrain, Louise, Redshaw, Nicholas M, Lensing, Stefanie V, Davies, Robert, Whitwham, Andrew, Liddle, Jennifier, Lewis, Kevin, Tovar-Corona, Jaime M, Leonard, Steven, Durham, Jillian, Bassett, Andrew R, McCarthy, Shane, Moll, Robin J, James, Keith, Oliver, Karen, Makunin, Alex, Barrett, Jeff, Gunson, Rory N, Graham, Mark S, Sudre, Carole H, May, Anna, Antonelli, Michela, Murray, Benjamin, Varsavsky, Thomas, Kläser, Kerstin, Canas, Liane S, Molteni, Erika, Modat, Marc, Drew, David A, Nguyen, Long H, Polidori, Lorenzo, Selvachandran, Somesh, Hu, Christina, Capdevila, Joan, Hammers, Alexander, Chan, Andrew T, Wolf, Jonathan, Spector, Tim D, Steves, Claire J, and Ourselin, Sebastien

Published
2021
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19. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study

Author

Varsavsky, Thomas, Graham, Mark S, Canas, Liane S, Ganesh, Sajaysurya, Capdevila Pujol, Joan, Sudre, Carole H, Murray, Benjamin, Modat, Marc, Jorge Cardoso, M, Astley, Christina M, Drew, David A, Nguyen, Long H, Fall, Tove, Gomez, Maria F, Franks, Paul W, Chan, Andrew T, Davies, Richard, Wolf, Jonathan, Steves, Claire J, Spector, Tim D, and Ourselin, Sebastien

Published
2021
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20. Attributes and predictors of long COVID

Author

Sudre, Carole H., Murray, Benjamin, Varsavsky, Thomas, Graham, Mark S., Penfold, Rose S., Bowyer, Ruth C., Pujol, Joan Capdevila, Klaser, Kerstin, Antonelli, Michela, Canas, Liane S., Molteni, Erika, Modat, Marc, Cardoso, M. Jorge, May, Anna, Ganesh, Sajaysurya, Davies, Richard, and Nguyen, Long H.

Subjects
Biological sciences, Health
Abstract

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called 'long COVID', are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app.sup.1. A total of 558 (13.3%) participants reported symptoms lasting [greater than or equal to]28 days, 189 (4.5%) for [greater than or equal to]8 weeks and 95 (2.3%) for [greater than or equal to]12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76-4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services. Analysis of data from the COVID Symptom Study app reveals fatigue, headache, dyspnea and anosmia as key attributes of long COVID, with those experiencing five or more symptoms during the first week of being at increased risk of prolonged disease., Author(s): Carole H. Sudre [sup.1] [sup.2] [sup.3] , Benjamin Murray [sup.3] , Thomas Varsavsky [sup.3] , Mark S. Graham [sup.3] , Rose S. Penfold [sup.4] , Ruth C. Bowyer [sup.5] [...]

Published
2021
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21. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study

Author

Albert, Christine M., Andreotti, Gabriella, Bala, Bijal, Balasubramanian, Bijal A., Beane-Freeman, Laura E., Brownstein, John S., Bruinsma, Fiona J., Coresh, Joe, Costa, Rui, Cowan, Annie N., Deka, Anusila, Deming-Halverson, Sandra L., Elena Martinez, Maria, Ernst, Michael E., Figueiredo, Jane C., Fortuna, Pedro, Franks, Paul W., Freeman, Laura Beane, Gardner, Christopher D., Ghobrial, Irene M., Haiman, Christopher A., Hall, Janet E., Kang, Jae H., Kirpach, Brenda, Koenen, Karestan C., Kubzansky, Laura D., Lacey, Jr, James V., Le Marchand, Loic, Lin, Xihong, Lutsey, Pam, Marinac, Catherine R., Martinez, Maria Elena, Milne, Roger L., Murray, Anne M., Nash, Denis, Palmer, Julie R., Patel, Alpa V., Pierce, Eric, Robertson, McKaylee M., Rosenberg, Lynn, Sandler, Dale P., Schurman, Shepherd H., Sewalk, Kara, Sharma, Shreela V., Sidey-Gibbons, Christopher J., Slevin, Liz, Smoller, Jordan W.., Steves, Claire J., Tiirikainen, Maarit I., Weiss, Scott T., Wilkens, Lynne R., Zhang, Feng, Nguyen, Long H, Drew, David A, Graham, Mark S, Joshi, Amit D, Guo, Chuan-Guo, Ma, Wenjie, Mehta, Raaj S, Warner, Erica T, Sikavi, Daniel R, Lo, Chun-Han, Kwon, Sohee, Song, Mingyang, Mucci, Lorelei A, Stampfer, Meir J, Willett, Walter C, Eliassen, A Heather, Hart, Jaime E, Chavarro, Jorge E, Rich-Edwards, Janet W, Davies, Richard, Capdevila, Joan, Lee, Karla A, Lochlainn, Mary Ni, Varsavsky, Thomas, Sudre, Carole H, Cardoso, M Jorge, Wolf, Jonathan, Spector, Tim D, Ourselin, Sebastien, Steves, Claire J, and Chan, Andrew T

Published
2020
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22. Real-time tracking of self-reported symptoms to predict potential COVID-19

Author

Menni, Cristina, Valdes, Ana M., Freidin, Maxim B., Sudre, Carole H., Nguyen, Long H., Drew, David A., Ganesh, Sajaysurya, Varsavsky, Thomas, Cardoso, M. Jorge, El-Sayed Moustafa, Julia S., Visconti, Alessia, Hysi, Pirro, Bowyer, Ruth C. E., Mangino, Massimo, Falchi, Mario, Wolf, Jonathan, Ourselin, Sebastien, Chan, Andrew T., Steves, Claire J., and Spector, Tim D.

Published
2020
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23. Bayesian Volumetric Autoregressive Generative Models for Better Semisupervised Learning

Author

Pombo, Guilherme, Gray, Robert, Varsavsky, Thomas, Ashburner, John, Nachev, Parashkev, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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24. As Easy as 1, 2...4? Uncertainty in Counting Tasks for Medical Imaging

Author

Eaton-Rosen, Zach, Varsavsky, Thomas, Ourselin, Sebastien, Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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27. Test-Time Unsupervised Domain Adaptation

Author

Varsavsky, Thomas, primary, Orbes-Arteaga, Mauricio, additional, Sudre, Carole H., additional, Graham, Mark S., additional, Nachev, Parashkev, additional, and Cardoso, M. Jorge, additional

Published
2020
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29. Author Correction: Attributes and predictors of long COVID

Author

Sudre, Carole H., Murray, Benjamin, Varsavsky, Thomas, Graham, Mark S., Penfold, Rose S., Bowyer, Ruth C., Pujol, Joan Capdevila, Klaser, Kerstin, Antonelli, Michela, Canas, Liane S., Molteni, Erika, Modat, Marc, Jorge Cardoso, M., May, Anna, Ganesh, Sajaysurya, Davies, Richard, Nguyen, Long H., Drew, David A., Astley, Christina M., Joshi, Amit D., Merino, Jordi, Tsereteli, Neli, Fall, Tove, Gomez, Maria F., Duncan, Emma L., Menni, Cristina, Williams, Frances M. K., Franks, Paul W., Chan, Andrew T., Wolf, Jonathan, Ourselin, Sebastien, Spector, Tim, and Steves, Claire J.

Published
2021
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30. Let’s Agree to Disagree: Learning Highly Debatable Multirater Labelling

Author

Sudre, Carole H., primary, Anson, Beatriz Gomez, additional, Ingala, Silvia, additional, Lane, Chris D., additional, Jimenez, Daniel, additional, Haider, Lukas, additional, Varsavsky, Thomas, additional, Tanno, Ryutaro, additional, Smith, Lorna, additional, Ourselin, Sébastien, additional, Jäger, Rolf H., additional, and Cardoso, M. Jorge, additional

Published
2019
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31. Physics-Informed Brain MRI Segmentation

Author

Borges, Pedro, primary, Sudre, Carole, additional, Varsavsky, Thomas, additional, Thomas, David, additional, Drobnjak, Ivana, additional, Ourselin, Sebastien, additional, and Cardoso, M. Jorge, additional

Published
2019
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33. Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning

Author

Orbes-Arteaga, Mauricio, primary, Varsavsky, Thomas, additional, Sudre, Carole H., additional, Eaton-Rosen, Zach, additional, Haddow, Lewis J., additional, Sørensen, Lauge, additional, Nielsen, Mads, additional, Pai, Akshay, additional, Ourselin, Sébastien, additional, Modat, Marc, additional, Nachev, Parashkev, additional, and Cardoso, M. Jorge, additional

Published
2019
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35. Knowledge barriers in a national symptomatic-COVID-19 testing programme

Author

Graham, Mark S., primary, May, Anna, additional, Varsavsky, Thomas, additional, Sudre, Carole H., additional, Murray, Benjamin, additional, Kläser, Kerstin, additional, Antonelli, Michela, additional, Canas, Liane S., additional, Molteni, Erika, additional, Modat, Marc, additional, Cardoso, M. Jorge, additional, Drew, David A., additional, Nguyen, Long H., additional, Rader, Benjamin, additional, Hu, Christina, additional, Capdevila, Joan, additional, Hammers, Alexander, additional, Chan, Andrew T., additional, Wolf, Jonathan, additional, Brownstein, John S., additional, Spector, Tim D., additional, Ourselin, Sebastien, additional, Steves, Claire J., additional, and Astley, Christina M., additional

Published
2022
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36. Aspirin and NSAID use and the risk of COVID-19

Author

Drew, David A., primary, Guo, Chuan-Guo, additional, Lee, Karla A., additional, Nguyen, Long H., additional, Joshi, Amit D., additional, Lo, Chun-Han, additional, Ma, Wenjie, additional, Mehta, Raaj S., additional, Kwon, Sohee, additional, Astley, Christina M., additional, Song, Mingyang, additional, Davies, Richard, additional, Capdevila, Joan, additional, Lochlainn, Mary Ni, additional, Sudre, Carole H., additional, Graham, Mark S., additional, Varsavsky, Thomas, additional, Gomez, Maria F., additional, Kennedy, Beatrice, additional, Fitipaldi, Hugo, additional, Wolf, Jonathan, additional, Spector, Tim D., additional, Ourselin, Sebastien, additional, Steves, Claire J., additional, and Chan, Andrew T., additional

Published
2021
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37. Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Author

Graham, Mark S, primary, Sudre, Carole H, additional, May, Anna, additional, Antonelli, Michela, additional, Murray, Benjamin, additional, Varsavsky, Thomas, additional, Kläser, Kerstin, additional, Canas, Liane S, additional, Molteni, Erika, additional, Modat, Marc, additional, Drew, David A, additional, Nguyen, Long H, additional, Polidori, Lorenzo, additional, Selvachandran, Somesh, additional, Hu, Christina, additional, Capdevila, Joan, additional, Hammers, Alexander, additional, Chan, Andrew T, additional, Wolf, Jonathan, additional, Spector, Tim D, additional, Steves, Claire J, additional, Ourselin, Sebastien, additional, Koshy, Cherian, additional, Ash, Amy, additional, Wise, Emma, additional, Moore, Nathan, additional, Mori, Matilde, additional, Cortes, Nick, additional, Lynch, Jessica, additional, Kidd, Stephen, additional, Fairley, Derek J, additional, Curran, Tanya, additional, McKenna, James P, additional, Adams, Helen, additional, Fraser, Christophe, additional, Golubchik, Tanya, additional, Bonsall, David, additional, Hassan-Ibrahim, Mohammed O, additional, Malone, Cassandra S, additional, Cogger, Benjamin J, additional, Wantoch, Michelle, additional, Reynolds, Nicola, additional, Warne, Ben, additional, Maksimovic, Joshua, additional, Spellman, Karla, additional, McCluggage, Kathryn, additional, John, Michaela, additional, Beer, Robert, additional, Afifi, Safiah, additional, Morgan, Sian, additional, Marchbank, Angela, additional, Price, Anna, additional, Kitchen, Christine, additional, Gulliver, Huw, additional, Merrick, Ian, additional, Southgate, Joel, additional, Guest, Martyn, additional, Munn, Robert, additional, Workman, Trudy, additional, Connor, Thomas R, additional, Fuller, William, additional, Bresner, Catherine, additional, Snell, Luke B, additional, Patel, Amita, additional, Charalampous, Themoula, additional, Nebbia, Gaia, additional, Batra, Rahul, additional, Edgeworth, Jonathan, additional, Robson, Samuel C, additional, Beckett, Angela H, additional, Aanensen, David M, additional, Underwood, Anthony P, additional, Yeats, Corin A, additional, Abudahab, Khalil, additional, Taylor, Ben EW, additional, Menegazzo, Mirko, additional, Clark, Gemma, additional, Smith, Wendy, additional, Khakh, Manjinder, additional, Fleming, Vicki M, additional, Lister, Michelle M, additional, Howson-Wells, Hannah C, additional, Berry, Louise, additional, Boswell, Tim, additional, Joseph, Amelia, additional, Willingham, Iona, additional, Jones, Carl, additional, Holmes, Christopher, additional, Bird, Paul, additional, Helmer, Thomas, additional, Fallon, Karlie, additional, Tang, Julian, additional, Raviprakash, Veena, additional, Campbell, Sharon, additional, Sheriff, Nicola, additional, Blakey, Victoria, additional, Williams, Lesley-Anne, additional, Loose, Matthew W, additional, Holmes, Nadine, additional, Moore, Christopher, additional, Carlile, Matthew, additional, Wright, Victoria, additional, Sang, Fei, additional, Debebe, Johnny, additional, Coll, Francesc, additional, Signell, Adrian W, additional, Betancor, Gilberto, additional, Wilson, Harry D, additional, Eldirdiri, Sahar, additional, Kenyon, Anita, additional, Davis, Thomas, additional, Pybus, Oliver G, additional, du Plessis, Louis, additional, Zarebski, Alex E, additional, Raghwani, Jayna, additional, Kraemer, Moritz UG, additional, Francois, Sarah, additional, Attwood, Stephen W, additional, Vasylyeva, Tetyana I, additional, Escalera Zamudio, Marina, additional, Gutierrez, Bernardo, additional, Torok, M. Estee, additional, Hamilton, William L, additional, Goodfellow, Ian G, additional, Hall, Grant, additional, Jahun, Aminu S, additional, Chaudhry, Yasmin, additional, Hosmillo, Myra, additional, Pinckert, Malte L, additional, Georgana, Iliana, additional, Moses, Samuel, additional, Lowe, Hannah, additional, Bedford, Luke, additional, Moore, Jonathan, additional, Stonehouse, Susanne, additional, Fisher, Chloe L, additional, Awan, Ali R, additional, BoYes, John, additional, Breuer, Judith, additional, Harris, Kathryn Ann, additional, Brown, Julianne Rose, additional, Shah, Divya, additional, Atkinson, Laura, additional, Lee, Jack CD, additional, Storey, Nathaniel, additional, Flaviani, Flavia, additional, Alcolea-Medina, Adela, additional, Williams, Rebecca, additional, Vernet, Gabrielle, additional, Chapman, Michael R, additional, Levett, Lisa J, additional, Heaney, Judith, additional, Chatterton, Wendy, additional, Pusok, Monika, additional, Xu-McCrae, Li, additional, Smith, Darren L, additional, Bashton, Matthew, additional, Young, Gregory R, additional, Holmes, Alison, additional, Randell, Paul Anthony, additional, Cox, Alison, additional, Madona, Pinglawathee, additional, Bolt, Frances, additional, Price, James, additional, Mookerjee, Siddharth, additional, Ragonnet-Cronin, Manon, additional, Nascimento, Fabricia F., additional, Jorgensen, David, additional, Siveroni, Igor, additional, Johnson, Rob, additional, Boyd, Olivia, additional, Geidelberg, Lily, additional, Volz, Erik M, additional, Rowan, Aileen, additional, Taylor, Graham P, additional, Smollett, Katherine L, additional, Loman, Nicholas J, additional, Quick, Joshua, additional, McMurray, Claire, additional, Stockton, Joanne, additional, Nicholls, Sam, additional, Rowe, Will, additional, Poplawski, Radoslaw, additional, McNally, Alan, additional, Martinez Nunez, Rocio T, additional, Mason, Jenifer, additional, Robinson, Trevor I, additional, O'Toole, Elaine, additional, Watts, Joanne, additional, Breen, Cassie, additional, Cowell, Angela, additional, Sluga, Graciela, additional, Machin, Nicholas W, additional, Ahmad, Shazaad S Y, additional, George, Ryan P, additional, Halstead, Fenella, additional, Sivaprakasam, Venkat, additional, Hogsden, Wendy, additional, Illingworth, Chris J, additional, Jackson, Chris, additional, Thomson, Emma C, additional, Shepherd, James G, additional, Asamaphan, Patawee, additional, Niebel, Marc O, additional, Li, Kathy K, additional, Shah, Rajiv N, additional, Jesudason, Natasha G, additional, Tong, Lily, additional, Broos, Alice, additional, Mair, Daniel, additional, Nichols, Jenna, additional, Carmichael, Stephen N, additional, Nomikou, Kyriaki, additional, Aranday-Cortes, Elihu, additional, Johnson, Natasha, additional, Starinskij, Igor, additional, da Silva Filipe, Ana, additional, Robertson, David L, additional, Orton, Richard J, additional, Hughes, Joseph, additional, Vattipally, Sreenu, additional, Singer, Joshua B, additional, Nickbakhsh, Seema, additional, Hale, Antony D, additional, Macfarlane-Smith, Louissa R, additional, Harper, Katherine L, additional, Carden, Holli, additional, Taha, Yusri, additional, Payne, Brendan AI, additional, Burton-Fanning, Shirelle, additional, Waugh, Sheila, additional, Collins, Jennifer, additional, Eltringham, Gary, additional, Rushton, Steven, additional, O'Brien, Sarah, additional, Bradley, Amanda, additional, Maclean, Alasdair, additional, Mollett, Guy, additional, Blacow, Rachel, additional, Templeton, Kate E, additional, McHugh, Martin P, additional, Dewar, Rebecca, additional, Wastenge, Elizabeth, additional, Dervisevic, Samir, additional, Stanley, Rachael, additional, Meader, Emma J, additional, Coupland, Lindsay, additional, Smith, Louise, additional, Graham, Clive, additional, Barton, Edward, additional, Padgett, Debra, additional, Scott, Garren, additional, Swindells, Emma, additional, Greenaway, Jane, additional, Nelson, Andrew, additional, McCann, Clare M, additional, Yew, Wen C, additional, Andersson, Monique, additional, Peto, Timothy, additional, Justice, Anita, additional, Eyre, David, additional, Crook, Derrick, additional, Sloan, Tim J, additional, Duckworth, Nichola, additional, Walsh, Sarah, additional, Chauhan, Anoop J, additional, Glaysher, Sharon, additional, Bicknell, Kelly, additional, Wyllie, Sarah, additional, Elliott, Scott, additional, Lloyd, Allyson, additional, Impey, Robert, additional, Levene, Nick, additional, Monaghan, Lynn, additional, Bradley, Declan T, additional, Wyatt, Tim, additional, Allara, Elias, additional, Pearson, Clare, additional, Osman, Husam, additional, Bosworth, Andrew, additional, Robinson, Esther, additional, Muir, Peter, additional, Vipond, Ian B, additional, Hopes, Richard, additional, Pymont, Hannah M, additional, Hutchings, Stephanie, additional, Curran, Martin D, additional, Parmar, Surendra, additional, Lackenby, Angie, additional, Mbisa, Tamyo, additional, Platt, Steven, additional, Miah, Shahjahan, additional, Bibby, David, additional, Manso, Carmen, additional, Hubb, Jonathan, additional, Chand, Meera, additional, Dabrera, Gavin, additional, Ramsay, Mary, additional, Bradshaw, Daniel, additional, Thornton, Alicia, additional, Myers, Richard, additional, Schaefer, Ulf, additional, Groves, Natalie, additional, Gallagher, Eileen, additional, Lee, David, additional, Williams, David, additional, Ellaby, Nicholas, additional, Harrison, Ian, additional, Hartman, Hassan, additional, Manesis, Nikos, additional, Patel, Vineet, additional, Bishop, Chloe, additional, Chalker, Vicki, additional, Ledesma, Juan, additional, Twohig, Katherine A, additional, Holden, Matthew T.G., additional, Shaaban, Sharif, additional, Birchley, Alec, additional, Adams, Alexander, additional, Davies, Alisha, additional, Gaskin, Amy, additional, Plimmer, Amy, additional, Gatica-Wilcox, Bree, additional, McKerr, Caoimhe, additional, Moore, Catherine, additional, Williams, Chris, additional, Heyburn, David, additional, De Lacy, Elen, additional, Hilvers, Ember, additional, Downing, Fatima, additional, Shankar, Giri, additional, Jones, Hannah, additional, Asad, Hibo, additional, Coombes, Jason, additional, Watkins, Joanne, additional, Evans, Johnathan M, additional, Fina, Laia, additional, Gifford, Laura, additional, Gilbert, Lauren, additional, Graham, Lee, additional, Perry, Malorie, additional, Morgan, Mari, additional, Bull, Matthew, additional, Cronin, Michelle, additional, Pacchiarini, Nicole, additional, Craine, Noel, additional, Jones, Rachel, additional, Howe, Robin, additional, Corden, Sally, additional, Rey, Sara, additional, Kumziene-SummerhaYes, Sara, additional, Taylor, Sarah, additional, Cottrell, Simon, additional, Jones, Sophie, additional, Edwards, Sue, additional, O'Grady, Justin, additional, Page, Andrew J, additional, Mather, Alison E, additional, Baker, David J, additional, Rudder, Steven, additional, Aydin, Alp, additional, Kay, Gemma L, additional, Trotter, Alexander J, additional, Alikhan, Nabil-Fareed, additional, de Oliveira Martins, Leonardo, additional, Le-Viet, Thanh, additional, Meadows, Lizzie, additional, Casey, Anna, additional, Ratcliffe, Liz, additional, Simpson, David A, additional, Molnar, Zoltan, additional, Thompson, Thomas, additional, Acheson, Erwan, additional, Masoli, Jane AH, additional, Knight, Bridget A, additional, Ellard, Sian, additional, Auckland, Cressida, additional, Jones, Christopher R, additional, Mahungu, Tabitha W, additional, Irish-Tavares, Dianne, additional, Haque, Tanzina, additional, Hart, Jennifer, additional, Witele, Eric, additional, Fenton, Melisa Louise, additional, Dadrah, Ashok, additional, Symmonds, Amanda, additional, Saluja, Tranprit, additional, Bourgeois, Yann, additional, Scarlett, Garry P, additional, Loveson, Katie F, additional, Goudarzi, Salman, additional, Fearn, Christopher, additional, Cook, Kate, additional, Dent, Hannah, additional, Paul, Hannah, additional, Partridge, David G, additional, Raza, Mohammad, additional, Evans, Cariad, additional, Johnson, Kate, additional, Liggett, Steven, additional, Baker, Paul, additional, Bonner, Stephen, additional, Essex, Sarah, additional, Lyons, Ronan A, additional, Saeed, Kordo, additional, Mahanama, Adhyana I.K, additional, Samaraweera, Buddhini, additional, Silveira, Siona, additional, Pelosi, Emanuela, additional, Wilson-Davies, Eleri, additional, Williams, Rachel J, additional, Kristiansen, Mark, additional, Roy, Sunando, additional, Williams, Charlotte A, additional, Cotic, Marius, additional, Bayzid, Nadua, additional, Westhorpe, Adam P, additional, Hartley, John A, additional, Jannoo, Riaz, additional, Lowe, Helen L, additional, Karamani, Angeliki, additional, Ensell, Leah, additional, Prieto, Jacqui A, additional, Jeremiah, Sarah, additional, Grammatopoulos, Dimitris, additional, Pandey, Sarojini, additional, Berry, Lisa, additional, Jones, Katie, additional, Richter, Alex, additional, Beggs, Andrew, additional, Best, Angus, additional, Percival, Benita, additional, Mirza, Jeremy, additional, Megram, Oliver, additional, Mayhew, Megan, additional, Crawford, Liam, additional, Ashcroft, Fiona, additional, Moles-Garcia, Emma, additional, Cumley, Nicola, additional, Smith, Colin P, additional, Bucca, Giselda, additional, Hesketh, Andrew R, additional, Blane, Beth, additional, Girgis, Sophia T, additional, Leek, Danielle, additional, Sridhar, Sushmita, additional, Forrest, Sally, additional, Cormie, Claire, additional, Gill, Harmeet K, additional, Dias, Joana, additional, Higginson, Ellen E, additional, Maes, Mailis, additional, Young, Jamie, additional, Kermack, Leanne M, additional, Gupta, Ravi Kumar, additional, Ludden, Catherine, additional, Peacock, Sharon J, additional, Palmer, Sophie, additional, Churcher, Carol M, additional, Hadjirin, Nazreen F, additional, Carabelli, Alessandro M, additional, Brooks, Ellena, additional, Smith, Kim S, additional, Galai, Katerina, additional, McManus, Georgina M, additional, Ruis, Chris, additional, Davidson, Rose K, additional, Rambaut, Andrew, additional, Williams, Thomas, additional, Balcazar, Carlos E, additional, Gallagher, Michael D, additional, O'Toole, Áine, additional, Rooke, Stefan, additional, Hill, Verity, additional, Williamson, Kathleen A, additional, Stanton, Thomas D, additional, Michell, Stephen L, additional, Bewshea, Claire M, additional, Temperton, Ben, additional, Michelsen, Michelle L, additional, Warwick-Dugdale, Joanna, additional, Manley, Robin, additional, Farbos, Audrey, additional, Harrison, James W, additional, Sambles, Christine M, additional, Studholme, David J, additional, Jeffries, Aaron R, additional, Darby, Alistair C, additional, Hiscox, Julian A, additional, Paterson, Steve, additional, Iturriza-Gomara, Miren, additional, Jackson, Kathryn A, additional, Lucaci, Anita O, additional, Vamos, Edith E, additional, Hughes, Margaret, additional, Rainbow, Lucille, additional, Eccles, Richard, additional, Nelson, Charlotte, additional, Whitehead, Mark, additional, Turtle, Lance, additional, Haldenby, Sam T, additional, Gregory, Richard, additional, Gemmell, Matthew, additional, Wierzbicki, Claudia, additional, Webster, Hermione J, additional, de Silva, Thushan I, additional, Smith, Nikki, additional, Angyal, Adrienn, additional, Lindsey, Benjamin B, additional, Groves, Danielle C, additional, Green, Luke R, additional, Wang, Dennis, additional, Freeman, Timothy M, additional, Parker, Matthew D, additional, Keeley, Alexander J, additional, Parsons, Paul J, additional, Tucker, Rachel M, additional, Brown, Rebecca, additional, Wyles, Matthew, additional, Whiteley, Max, additional, Zhang, Peijun, additional, Gallis, Marta, additional, Louka, Stavroula F, additional, Constantinidou, Chrystala, additional, Unnikrishnan, Meera, additional, Ott, Sascha, additional, Cheng, Jeffrey K.J., additional, Bridgewater, Hannah E., additional, Frost, Lucy R., additional, Taylor-Joyce, Grace, additional, Stark, Richard, additional, Baxter, Laura, additional, Alam, Mohammad T., additional, Brown, Paul E, additional, Aggarwal, Dinesh, additional, Cerda, Alberto C, additional, Merrill, Tammy V, additional, Wilson, Rebekah E, additional, McClure, Patrick C, additional, Chappell, Joseph G, additional, Tsoleridis, Theocharis, additional, Ball, Jonathan, additional, Buck, David, additional, Todd, John A, additional, Green, Angie, additional, Trebes, Amy, additional, MacIntyre-Cockett, George, additional, de Cesare, Mariateresa, additional, Alderton, Alex, additional, Amato, Roberto, additional, Ariani, Cristina V, additional, Beale, Mathew A, additional, Beaver, Charlotte, additional, Bellis, Katherine L, additional, Betteridge, Emma, additional, Bonfield, James, additional, Danesh, John, additional, Dorman, Matthew J, additional, Drury, Eleanor, additional, Farr, Ben W, additional, Foulser, Luke, additional, Goncalves, Sonia, additional, Goodwin, Scott, additional, Gourtovaia, Marina, additional, Harrison, Ewan M, additional, Jackson, David K, additional, Jamrozy, Dorota, additional, Johnston, Ian, additional, Kane, Leanne, additional, Kay, Sally, additional, Keatley, Jon-Paul, additional, Kwiatkowski, Dominic, additional, Langford, Cordelia F, additional, Lawniczak, Mara, additional, Letchford, Laura, additional, Livett, Rich, additional, Lo, Stephanie, additional, Martincorena, Inigo, additional, McGuigan, Samantha, additional, Nelson, Rachel, additional, Palmer, Steve, additional, Park, Naomi R, additional, Patel, Minal, additional, Prestwood, Liam, additional, Puethe, Christoph, additional, Quail, Michael A, additional, Rajatileka, Shavanthi, additional, Scott, Carol, additional, Shirley, Lesley, additional, Sillitoe, John, additional, Spencer Chapman, Michael H, additional, Thurston, Scott AJ, additional, Tonkin-Hill, Gerry, additional, Weldon, Danni, additional, Rajan, Diana, additional, Bronner, Iraad F, additional, Aigrain, Louise, additional, Redshaw, Nicholas M, additional, Lensing, Stefanie V, additional, Davies, Robert, additional, Whitwham, Andrew, additional, Liddle, Jennifier, additional, Lewis, Kevin, additional, Tovar-Corona, Jaime M, additional, Leonard, Steven, additional, Durham, Jillian, additional, Bassett, Andrew R, additional, McCarthy, Shane, additional, Moll, Robin J, additional, James, Keith, additional, Oliver, Karen, additional, Makunin, Alex, additional, Barrett, Jeff, additional, and Gunson, Rory N, additional

Published
2021
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38. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application

Author

Varsavsky, Thomas, Graham, Mark S., Canas, Liane S., Ganesh, Sajaysurya, Puyol, Joan Capdevila, Sudre, Carole H., Murray, Benjamin, Modat, Marc, Cardoso, M. Jorge, Astley, Christina M., Drew, David A, Nguyen, Long H., Fall, Tove, Gomez, Maria F, Franks, Paul W., Chan, Andrew T., Davies, Richard, Wolf, Jonathan, Steves, Claire J., Spector, Tim D., and Ourselin, Sebastien

Subjects
Article
Abstract

BACKGROUND: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. METHODS: We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence and effective reproduction number. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots. FINDINGS: More than 2.6 million app users in England provided 115 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence showed similar sensitivity to changes as two national community surveys: the ONS and REACT studies. On a geographically granular level, our estimates were able to highlight regions before they were subject to local government lockdowns. Between 12 May and 29 September we were able to flag between 35-80% of regions appearing in the Government’s hotspot list. INTERPRETATION: Self-reported data from mobile applications can provide a cost-effective and agile resource to inform a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance. FUNDING: Zoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimer’s Society. RESEARCH IN CONTEXT: EVIDENCE BEFORE THIS STUDY: To identify instances of the use of digital tools to perform COVID-19 surveillance, we searched PubMed for peer-reviewed articles between 1 January and 14 October 2020, using the keywords COVID-19 AND ((mobile application) OR (web tool) OR (digital survey)). Of the 382 results, we found eight that utilised user-reported data to ascertain a user’s COVID-19 status. Of these, none sought to provide disease surveillance on a national level, or to compare these predictions to other tools to ascertain their accuracy. Furthermore, none of these papers sought to use their data to highlight geographical areas of concern. ADDED VALUE OF THIS STUDY: To our knowledge, we provide the first demonstration of mobile technology to provide national-level disease surveillance. Using over 115 million reports from more than 2.6 million users across England, we estimate incidence, prevalence, and the effective reproduction number. We compare these estimates to those from national community surveys to understand the effectiveness of these digital tools. Furthermore, we demonstrate the large number of users can be used to provide disease surveillance with high geographical granularity, potentially providing a valuable source of information for policymakers seeking to understand the spread of the disease. IMPLICATIONS OF ALL THE AVAILABLE EVIDENCE: Our findings suggest that mobile technology can be used to provide real-time data on the national and local state of the pandemic, enabling policymakers to make informed decisions in a fast-moving pandemic.

Published
2020

39. Geo-social gradients in predicted COVID-19 prevalence and severity in Great Britain: results from 2,266,235 users of the COVID-19 Symptoms Tracker app

Author

Bowyer, Ruth, Varsavsky, Thomas, Sudre, Carole H, Murray, Benjamin, Freidin, Maxim, Yarand, Darioush, Ganesh, Sajaysurya, Capdevila, Joan, Thompson, Ellen J, Bakker, Elco, Cardoso, M Jorge, Davies, Richard, Wolf, Jonathan, Spector, Tim D, Ourselin, Sebastien, Steves, Claire J, and Menni, Cristina

Subjects
education.field_of_study, Coronavirus disease 2019 (COVID-19), business.industry, 05 social sciences, Population, Declaration, Distribution (economics), 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Geography, Environmental health, 0502 economics and business, Pandemic, Health care, Resource allocation, 030212 general & internal medicine, business, education, 050203 business & management, Public health policy
Abstract

Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 2,266,235 unique GB users of the COVID Symptom Tracker app, we find that COVID-19 prevalence and severity became rapidly distributed across the UK within a month of the WHO declaration of the pandemic, with significant evidence of “urban hot-spots”. We found a geo-social gradient associated with disease severity and prevalence suggesting resources should focus on urban areas and areas of higher deprivation. Our results demonstrate use of self-reported data to inform public health policy and resource allocation.

Published
2020
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40. Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

Author

Sudre, Carole H., primary, Lee, Karla A., additional, Lochlainn, Mary Ni, additional, Varsavsky, Thomas, additional, Murray, Benjamin, additional, Graham, Mark S., additional, Menni, Cristina, additional, Modat, Marc, additional, Bowyer, Ruth C. E., additional, Nguyen, Long H., additional, Drew, David A., additional, Joshi, Amit D., additional, Ma, Wenjie, additional, Guo, Chuan-Guo, additional, Lo, Chun-Han, additional, Ganesh, Sajaysurya, additional, Buwe, Abubakar, additional, Pujol, Joan Capdevila, additional, du Cadet, Julien Lavigne, additional, Visconti, Alessia, additional, Freidin, Maxim B., additional, El-Sayed Moustafa, Julia S., additional, Falchi, Mario, additional, Davies, Richard, additional, Gomez, Maria F., additional, Fall, Tove, additional, Cardoso, M. Jorge, additional, Wolf, Jonathan, additional, Franks, Paul W., additional, Chan, Andrew T., additional, Spector, Tim D., additional, Steves, Claire J., additional, and Ourselin, Sébastien, additional

Published
2021
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41. Knowledge barriers in the symptomatic-COVID-19 testing programme in the UK: an observational study

Author

Graham, Mark S., primary, May, Anna, additional, Varsavsky, Thomas, additional, Sudre, Carole H., additional, Murray, Benjamin, additional, Kläser, Kerstin, additional, Antonelli, Michela, additional, Canas, Liane S., additional, Molteni, Erika, additional, Modat, Marc, additional, Cardoso, M. Jorge, additional, Drew, David A., additional, Nguyen, Long H., additional, Rader, Benjamin, additional, Hu, Christina, additional, Capdevila, Joan, additional, Hammers, Alexander, additional, Chan, Andrew T., additional, Wolf, Jonathan, additional, Brownstein, John S., additional, Spector, Tim D., additional, Ourselin, Sebastien, additional, Steves, Claire J., additional, and Astley, Christina M., additional

Published
2021
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42. Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

Author

Bowyer, Ruth C E, primary, Varsavsky, Thomas, additional, Thompson, Ellen J, additional, Sudre, Carole H, additional, Murray, Benjamin A K, additional, Freidin, Maxim B, additional, Yarand, Darioush, additional, Ganesh, Sajaysurya, additional, Capdevila, Joan, additional, Bakker, Elco, additional, Cardoso, M Jorge, additional, Davies, Richard, additional, Wolf, Jonathan, additional, Spector, Tim D, additional, Ourselin, Sebastien, additional, Steves, Claire J, additional, and Menni, Cristina, additional

Published
2020
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43. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study

Author

Varsavsky, Thomas, primary, Graham, Mark S., additional, Canas, Liane S., additional, Ganesh, Sajaysurya, additional, Pujol, Joan Capdevila, additional, Sudre, Carole H., additional, Murray, Benjamin, additional, Modat, Marc, additional, Cardoso, M. Jorge, additional, Astley, Christina M., additional, Drew, David A, additional, Nguyen, Long H., additional, Fall, Tove, additional, Gomez, Maria F, additional, Franks, Paul W., additional, Chan, Andrew T., additional, Davies, Richard, additional, Wolf, Jonathan, additional, Steves, Claire J., additional, Spector, Tim D., additional, and Ourselin, Sebastien, additional

Published
2020
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44. Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App

Author

Sudre, Carole H., primary, Murray, Benjamin, additional, Varsavsky, Thomas, additional, Graham, Mark S., additional, Penfold, Rose S., additional, Bowyer, Ruth C., additional, Pujol, Joan Capdevila, additional, Klaser, Kerstin, additional, Antonelli, Michela, additional, Canas, Liane S., additional, Molteni, Erika, additional, Modat, Marc, additional, Cardoso, M. Jorge, additional, May, Anna, additional, Ganesh, Sajaysurya, additional, Davies, Richard, additional, Nguyen, Long H, additional, Drew, David A., additional, Astley, Christina M., additional, Joshi, Amit D., additional, Merino, Jordi, additional, Tsereteli, Neli, additional, Fall, Tove, additional, Gomez, Maria F., additional, Duncan, Emma L., additional, Menni, Cristina, additional, Williams, Frances M.K., additional, Franks, Paul W., additional, Chan, Andrew T., additional, Wolf, Jonathan, additional, Ourselin, Sebastien, additional, Spector, Tim, additional, and Steves, Claire J., additional

Published
2020
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46. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study

Author

Nguyen, Long H, primary, Drew, David A, additional, Graham, Mark S, additional, Joshi, Amit D, additional, Guo, Chuan-Guo, additional, Ma, Wenjie, additional, Mehta, Raaj S, additional, Warner, Erica T, additional, Sikavi, Daniel R, additional, Lo, Chun-Han, additional, Kwon, Sohee, additional, Song, Mingyang, additional, Mucci, Lorelei A, additional, Stampfer, Meir J, additional, Willett, Walter C, additional, Eliassen, A Heather, additional, Hart, Jaime E, additional, Chavarro, Jorge E, additional, Rich-Edwards, Janet W, additional, Davies, Richard, additional, Capdevila, Joan, additional, Lee, Karla A, additional, Lochlainn, Mary Ni, additional, Varsavsky, Thomas, additional, Sudre, Carole H, additional, Cardoso, M Jorge, additional, Wolf, Jonathan, additional, Spector, Tim D, additional, Ourselin, Sebastien, additional, Steves, Claire J, additional, Chan, Andrew T, additional, Albert, Christine M., additional, Andreotti, Gabriella, additional, Bala, Bijal, additional, Balasubramanian, Bijal A., additional, Beane-Freeman, Laura E., additional, Brownstein, John S., additional, Bruinsma, Fiona J., additional, Coresh, Joe, additional, Costa, Rui, additional, Cowan, Annie N., additional, Deka, Anusila, additional, Deming-Halverson, Sandra L., additional, Elena Martinez, Maria, additional, Ernst, Michael E., additional, Figueiredo, Jane C., additional, Fortuna, Pedro, additional, Franks, Paul W., additional, Freeman, Laura Beane, additional, Gardner, Christopher D., additional, Ghobrial, Irene M., additional, Haiman, Christopher A., additional, Hall, Janet E., additional, Kang, Jae H., additional, Kirpach, Brenda, additional, Koenen, Karestan C., additional, Kubzansky, Laura D., additional, Lacey, Jr, James V., additional, Le Marchand, Loic, additional, Lin, Xihong, additional, Lutsey, Pam, additional, Marinac, Catherine R., additional, Martinez, Maria Elena, additional, Milne, Roger L., additional, Murray, Anne M., additional, Nash, Denis, additional, Palmer, Julie R., additional, Patel, Alpa V., additional, Pierce, Eric, additional, Robertson, McKaylee M., additional, Rosenberg, Lynn, additional, Sandler, Dale P., additional, Schurman, Shepherd H., additional, Sewalk, Kara, additional, Sharma, Shreela V., additional, Sidey-Gibbons, Christopher J., additional, Slevin, Liz, additional, Smoller, Jordan W.., additional, Steves, Claire J., additional, Tiirikainen, Maarit I., additional, Weiss, Scott T., additional, Wilkens, Lynne R., additional, and Zhang, Feng, additional

Published
2020
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47. Racial and ethnic determinants of Covid-19 risk

Author

Lo, Chun-Han, primary, Nguyen, Long H., additional, Drew, David A., additional, Graham, Mark S., additional, Warner, Erica T., additional, Joshi, Amit D., additional, Astley, Christina M., additional, Guo, Chuan-Guo, additional, Ma, Wenjie, additional, Mehta, Raaj S., additional, Kwon, Sohee, additional, Song, Mingyang, additional, Davies, Richard, additional, Capdevila, Joan, additional, Lee, Karla A., additional, Lochlainn, Mary Ni, additional, Varsavsky, Thomas, additional, Sudre, Carole H., additional, Wolf, Jonathan, additional, Cozier, Yvette C., additional, Rosenberg, Lynn, additional, Wilkens, Lynne R., additional, Haiman, Christopher A., additional, Le Marchand, Loïc, additional, Palmer, Julie R., additional, Spector, Tim D., additional, Ourselin, Sebastien, additional, Steves, Claire J., additional, and Chan, Andrew T., additional

Published
2020
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48. Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app

Author

Sudre, Carole H., primary, Lee, Karla A., additional, Lochlainn, Mary Ni, additional, Varsavsky, Thomas, additional, Murray, Benjamin, additional, Graham, Mark S., additional, Menni, Cristina, additional, Modat, Marc, additional, Bowyer, Ruth C E, additional, Nguyen, Long H., additional, Drew, David A., additional, Joshi, Amit D., additional, Ma, Wenjie, additional, Guo, Chuan-Guo, additional, Lo, Chun-Han, additional, Ganesh, Sajaysurya, additional, Buwe, Abubakar, additional, Pujol, Joan Capdevila, additional, du Cadet, Julien Lavigne, additional, Visconti, Alessia, additional, Freidin, Maxim B, additional, El-Sayed Moustafa, Julia S., additional, Falchi, Mario, additional, Davies, Richard, additional, Gomez, Maria F., additional, Fall, Tove, additional, Cardoso, M. Jorge, additional, Wolf, Jonathan, additional, Franks, Paul W., additional, Chan, Andrew T., additional, Spector, Tim D., additional, Steves, Claire J, additional, and Ourselin, Sébastien, additional

Published
2020
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49. Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals

Author

Lochlainn, Mary Ni, primary, Lee, Karla A., additional, Sudre, Carole H., additional, Varsavsky, Thomas, additional, Cardoso, M. Jorge, additional, Menni, Cristina, additional, Bowyer, Ruth C. E., additional, Nguyen, Long H., additional, Drew, David A., additional, Ganesh, Sajaysurya, additional, du Cadet, Julien Lavigne, additional, Visconti, Alessia, additional, Freidin, Maxim B., additional, Modat, Marc, additional, Graham, Mark S, additional, Pujol, Joan Capdevila, additional, Murray, Benjamin, additional, Moustafa, Julia S El-Sayed, additional, Zhang, Xinyuan, additional, Davies, Richard, additional, Falchi, Mario, additional, Wolf, Jonathan, additional, Spector, Tim D., additional, Chan, Andrew T., additional, Ourselin, Sebastien, additional, and Steves, Claire J., additional

Published
2020
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50. Geo-social gradients in predicted COVID-19 prevalence and severity in Great Britain: results from 2,266,235 users of the COVID-19 Symptoms Tracker app

Author

Bowyer, Ruth C E, primary, Varsavsky, Thomas, additional, Sudre, Carole H., additional, Murray, Benjamin A K, additional, Freidin, Maxim B, additional, Yarand, Darioush, additional, Ganesh, Sajaysurya, additional, Capdevila, Joan, additional, Thompson, Ellen J, additional, Bakker, Elco, additional, Cardoso, M. Jorge, additional, Davies, Richard, additional, Wolf, Jonathan, additional, Spector, Tim D, additional, Ourselin, Sebastien, additional, Steves, Claire J, additional, and Menni, Cristina, additional

Published
2020
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