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1. Real-time modelling of the SARS-CoV-2 pandemic in England 2020-2023: a challenging data integration

Author: Birrell, Paul J, Blake, Joshua, Kandiah, Joel, Alexopoulos, Angelos, van Leeuwen, Edwin, Pouwels, Koen, Ghosh, Sanmitra, Starr, Colin, Walker, Ann Sarah, House, Thomas A, Gay, Nigel, Finnie, Thomas, Gent, Nick, Charlett, André, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates., Comment: 35 pages, 7 figures, 3 tables
Published: 2024

2. Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs

Author: Ghosh, Sanmitra, Birrell, Paul J., and De Angelis, Daniela
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Computation
Abstract: Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable model, such as a Hidden Markov model (HMM), these methods are designed to only estimate the parameters, rather than the joint distribution of the parameters and the hidden states. Naive application of these methods to a HMM, ignoring the inference of this joint posterior distribution, will thus produce an inaccurate estimate of the posterior predictive distribution, in turn hampering the assessment of goodness-of-fit. To rectify this problem, we propose a novel, sample-efficient likelihood-free method for estimating the high-dimensional hidden states of an implicit HMM. Our approach relies on learning directly the intractable posterior distribution of the hidden states, using an autoregressive-flow, by exploiting the Markov property. Upon evaluating our approach on some implicit HMMs, we found that the quality of the estimates retrieved using our method is comparable to what can be achieved using a much more computationally expensive SMC algorithm.
Published: 2024

3. The NOSTRA model: coherent estimation of infection sources in the case of possible nosocomial transmission

Author: Pascall, David J, Jackson, Chris, Evans, Stephanie, Gouliouris, Theodore, Illingworth, Chris, Piatek, Stefan, Robotham, Julie V, Stirrup, Oliver, Warne, Ben, Breuer, Judith, and De Angelis, Daniela
Subjects: Statistics - Applications, Quantitative Biology - Quantitative Methods
Abstract: Nosocomial infections have important consequences for patients and hospital staff: they worsen patient outcomes and their management stresses already overburdened health systems. Accurate judgements of whether an infection is nosocomial helps staff make appropriate choices to protect other patients within the hospital. Nosocomiality cannot be properly assessed without considering whether the infected patient came into contact with high risk potential infectors within the hospital. We developed a Bayesian model that integrates epidemiological, contact and pathogen genetic data to determine how likely an infection is to be nosocomial and the probability of given infection candidates being the source of the infection.
Published: 2024

4. SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort

Author: Dietz, Elisabeth, Pritchard, Emma, Pouwels, Koen, Ehsaan, Muhammad, Blake, Joshua, Gaughan, Charlotte, Haduli, Eric, Boothe, Hugh, Vihta, Karina-Doris, Peto, Tim, Stoesser, Nicole, Matthews, Philippa, Taylor, Nick, Diamond, Ian, Studley, Ruth, Rourke, Emma, Birrell, Paul, De Angelis, Daniela, Fowler, Tom, Watson, Conall, Eyre, David, House, Thomas, and Walker, Ann Sarah
Published: 2024
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5. The Lifebelt Particle Filter for robust estimation from low-valued count data

Author: Corbella, Alice, McKinley, Trevelyan J., Birrell, Paul J., De Angelis, Daniela, Presanis, Anne M., Roberts, Gareth O., and Spencer, Simon E. F.
Subjects: Statistics - Computation
Abstract: Particle filtering methods can be applied to estimation problems in discrete spaces on bounded domains, to sample from and marginalise over unknown hidden states. As in continuous settings, problems such as particle degradation can arise: proposed particles can be incompatible with the data, lying in low probability regions or outside the boundary constraints, and the discrete system could result in all particles having weights of zero. In this paper we introduce the Lifebelt Particle Filter (LBPF), a novel method for robust likelihood estimation in low-valued count problems. The LBPF combines a standard particle filter with one (or more) lifebelt particles which, by construction, lie within the boundaries of the discrete random variables, and therefore are compatible with the data. A mixture of resampled and non-resampled particles allows for the preservation of the lifebelt particle, which, together with the remaining particle swarm, provides samples from the filtering distribution, and can be used to generate unbiased estimates of the likelihood. The main benefit of the LBPF is that only one or few, wisely chosen, particles are sufficient to prevent particle collapse. Differently from other methods, there is no need to increase the number of particles, and therefore the computational effort, in regions of the parameter space that generate less likely hidden states. The LBPF can be used within a pseudo-marginal scheme to draw inferences on static parameters, $ \boldsymbol{\theta} $, governing the system. We address here the estimation of a parameter governing probabilities of deaths and recoveries of hospitalised patients during an epidemic.
Published: 2022

6. An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling

Author: Ghosh, Sanmitra, Birrell, Paul J., and De Angelis, Daniela
Subjects: Statistics - Computation
Abstract: Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of transmission, but it is nearly impossible to describe, mechanistically, changes in extrinsic environmental factors including public behaviour and seasonal fluctuations. An elegant approach to capturing environmental stochasticity is to model the force of infection as a stochastic process. However, inference in this context requires solving a computationally expensive ``missing data" problem, using data-augmentation techniques. We propose to model the time-varying transmission-potential as an approximate diffusion process using a path-wise series expansion of Brownian motion. This approximation replaces the ``missing data" imputation step with the inference of the expansion coefficients: a simpler and computationally cheaper task. We illustrate the merit of this approach through two examples: modelling influenza using a canonical SIR model, and the modelling of COVID-19 pandemic using a multi-type SEIR model.
Published: 2022
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7. Inferring Epidemics from Multiple Dependent Data via Pseudo-Marginal Methods

Author: Corbella, Alice, Presanis, Anne M, Birrell, Paul J, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from the unobserved epidemic process including transmission and severity dynamics. This paper explores important challenges to the use of state-space models for epidemic inference when multiple dependent datasets are analysed. We propose a new semi-stochastic model that exploits deterministic approximations for large-scale transmission dynamics while retaining stochasticity in the occurrence and reporting of relatively rare severe events. This model is suitable for many real-time situations including large seasonal epidemics and pandemics. Within this context, we develop algorithms to provide exact parameter inference and test them via simulation. Finally, we apply our joint model and the proposed algorithm to several surveillance data on the 2017-18 influenza epidemic in England to reconstruct transmission dynamics and estimate the daily new influenza infections as well as severity indicators such as the case-hospitalisation risk and the hospital-intensive care risk., Comment: 23 pages, 6 figures, 4 tables, 1 algorithm. Submitted to the Annals of Applied Statistics
Published: 2022

8. A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19

Author: Jackson, Christopher, Tom, Brian, Kirwan, Peter, Mandal, Sema, Seaman, Shaun, Kunzmann, Kevin, Presanis, Anne, and De Angelis, Daniela
Subjects: Statistics - Methodology
Abstract: We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to ICU, the probability of death in hospital for patients before and after ICU admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular ICU admission, and these are best represented by using "cure-rate" models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the "flexsurv" R package, which allows arbitrarily-flexible distributions to be used to represent the cause-specific hazards or times to events.
Published: 2022
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9. Protection of vaccine boosters and prior infection against mild/asymptomatic and moderate COVID-19 infection in the UK SIREN healthcare worker cohort: October 2023 to March 2024

Author: Kirwan, Peter D., Foulkes, Sarah, Munro, Katie, Sparkes, Dominic, Singh, Jasleen, Henry, Amanda, Dunne, Angela, Timeyin, Jean, Russell, Sophie, Khawam, Jameel, Blick, Debbie, Otter, Ashley D., Hettiarachchi, Nipunadi, Cairns, Michelle D., Jackson, Christopher H., Seaman, Shaun, Brown, Colin S., Atti, Ana, Islam, Jasmin, Charlett, Andre, De Angelis, Daniela, Presanis, Anne M., Hall, Victoria J., and Hopkins, Susan
Published: 2024
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10. A genetic history of continuity and mobility in the Iron Age central Mediterranean

Author: Moots, Hannah M., Antonio, Margaret, Sawyer, Susanna, Spence, Jeffrey P., Oberreiter, Victoria, Weiß, Clemens L., Lucci, Michaela, Cherifi, Yahia Mehdi Seddik, La Pastina, Francesco, Genchi, Francesco, Praxmeier, Elisa, Zagorc, Brina, Cheronet, Olivia, Özdoğan, Kadir T., Demetz, Lea, Amrani, Selma, Candilio, Francesca, De Angelis, Daniela, Gasperetti, Gabriella, Fernandes, Daniel, Gao, Ziyue, Fantar, Mounir, Coppa, Alfredo, Pritchard, Jonathan K., and Pinhasi, Ron
Published: 2023
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11. Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study

Author: Kirwan, Peter, Charlett, Andre, Birrell, Paul, Elgohari, Suzanne, Hope, Russell, Mandal, Sema, De Angelis, Daniela, and Presanis, Anne
Subjects: Statistics - Applications
Abstract: Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of admission and vaccination status among those hospitalised with COVID-19 in England between March 2020 to September 2021, controlling for demographic factors and hospital load. Among 259,727 hospitalised COVID-19 cases, 51,948 (20.0%) experienced mortality in hospital. Hospitalised fatality risk ranged from 40.3% (95% confidence interval 39.4-41.3%) in March 2020 to 8.1% (7.2-9.0%) in June 2021. Older individuals and those with multiple co-morbidities were more likely to die or else experienced longer stays prior to discharge. Compared to unvaccinated people, the hazard of hospitalised mortality was 0.71 (0.67-0.77) with a first vaccine dose, and 0.56 (0.52-0.61) with a second vaccine dose. Compared to hospital load at 0-20% of the busiest week, the hazard of hospitalised mortality during periods of peak load (90-100%), was 1.23 (1.12-1.34). The prognosis for people hospitalised with COVID-19 in England has varied substantially throughout the pandemic and according to case-mix, vaccination, and hospital load. Our estimates provide an indication for demands on hospital resources, and the relationship between hospital burden and outcomes.
Published: 2021
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12. Evaluating the impact of local tracing partnerships on the performance of contact tracing for COVID-19 in England

Author: Samartsidis, Pantelis, Seaman, Shaun R., Harrison, Abbie, Alexopoulos, Angelos, Hughes, Gareth J., Rawlinson, Christopher, Anderson, Charlotte, Charlett, Andre, Oliver, Isabel, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: Assessing the impact of an intervention using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. In this paper, we present a novel method to estimate intervention effects in such a setting by generalising existing approaches based on the factor analysis model and developing a Bayesian algorithm for inference. Our method is one of the few that can simultaneously: deal with outcomes of mixed type (continuous, binomial, count); increase efficiency in the estimates of the causal effects by jointly modelling multiple outcomes affected by the intervention; easily provide uncertainty quantification for all causal estimands of interest. We use the proposed approach to evaluate the impact that local tracing partnerships (LTP) had on the effectiveness of England's Test and Trace (TT) programme for COVID-19. Our analyses suggest that, overall, LTPs had a small positive impact on TT. However, there is considerable heterogeneity in the estimates of the causal effects over units and time.
Published: 2021

13. Hospitalisation risk for COVID-19 patients infected with SARS-CoV-2 variant B.1.1.7: cohort analysis

Author: Nyberg, Tommy, Twohig, Katherine A., Harris, Ross J., Seaman, Shaun R., Flannagan, Joe, Allen, Hester, Charlett, Andre, De Angelis, Daniela, Dabrera, Gavin, and Presanis, Anne M.
Subjects: Statistics - Applications, Quantitative Biology - Populations and Evolution
Abstract: Objective: To evaluate the relationship between coronavirus disease 2019 (COVID-19) diagnosis with SARS-CoV-2 variant B.1.1.7 (also known as Variant of Concern 202012/01) and the risk of hospitalisation compared to diagnosis with wildtype SARS-CoV-2 variants. Design: Retrospective cohort, analysed using stratified Cox regression. Setting: Community-based SARS-CoV-2 testing in England, individually linked with hospitalisation data. Participants: 839,278 laboratory-confirmed COVID-19 patients, of whom 36,233 had been hospitalised within 14 days, tested between 23rd November 2020 and 31st January 2021 and analysed at a laboratory with an available TaqPath assay that enables assessment of S-gene target failure (SGTF). SGTF is a proxy test for the B.1.1.7 variant. Patient data were stratified by age, sex, ethnicity, deprivation, region of residence, and date of positive test. Main outcome measures: Hospitalisation between 1 and 14 days after the first positive SARS-CoV-2 test. Results: 27,710 of 592,409 SGTF patients (4.7%) and 8,523 of 246,869 non-SGTF patients (3.5%) had been hospitalised within 1-14 days. The stratum-adjusted hazard ratio (HR) of hospitalisation was 1.52 (95% confidence interval [CI] 1.47 to 1.57) for COVID-19 patients infected with SGTF variants, compared to those infected with non-SGTF variants. The effect was modified by age (P<0.001), with HRs of 0.93-1.21 for SGTF compared to non-SGTF patients below age 20 years, 1.29 in those aged 20-29, and 1.45-1.65 in age groups 30 years or older. Conclusions: The results suggest that the risk of hospitalisation is higher for individuals infected with the B.1.1.7 variant compared to wildtype SARS-CoV-2, likely reflecting a more severe disease. The higher severity may be specific to adults above the age of 30., Comment: 29 pages, 9 figures
Published: 2021
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14. Quantifying efficiency gains of innovative designs of two-arm vaccine trials for COVID-19 using an epidemic simulation model

Author: Johnson, Rob, Jackson, Chris, Presanis, Anne, Villar, Sofia S., and De Angelis, Daniela
Subjects: Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract: Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements may be evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomised two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritising participants at high risk of infection, can result in substantial improvement in terms of power, if sufficiently many contacts of observed cases are at high risk. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomisation, discussing their advantages and disadvantages in this two-arm context and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.
Published: 2021

15. Trends in risks of severe events and lengths of stay for COVID-19 hospitalisations in England over the pre-vaccination era: results from the Public Health England SARI-Watch surveillance scheme

Author: Kirwan, Peter D., Elgohari, Suzanne, Jackson, Christopher H., Tom, Brian D. M., Mandal, Sema, De Angelis, Daniela, and Presanis, Anne M.
Subjects: Statistics - Applications
Abstract: Background: Trends in hospitalised case-fatality risk (HFR), risk of intensive care unit (ICU) admission and lengths of stay for patients hospitalised for COVID-19 in England over the pre-vaccination era are unknown. Methods: Data on hospital and ICU admissions with COVID-19 at 31 NHS trusts in England were collected by Public Health England's Severe Acute Respiratory Infections surveillance system and linked to death information. We applied parametric multi-state mixture models, accounting for censored outcomes and regressing risks and times between events on month of admission, geography, and baseline characteristics. Findings: 20,785 adults were admitted with COVID-19 in 2020. Between March and June/July/August estimated HFR reduced from 31.9% (95% confidence interval 30.3-33.5%) to 10.9% (9.4-12.7%), then rose steadily from 21.6% (18.4-25.5%) in September to 25.7% (23.0-29.2%) in December, with steeper increases among older patients, those with multi-morbidity and outside London/South of England. ICU admission risk reduced from 13.9% (12.8-15.2%) in March to 6.2% (5.3-7.1%) in May, rising to a high of 14.2% (11.1-17.2%) in September. Median length of stay in non-critical care increased during 2020, from 6.6 to 12.3 days for those dying, and from 6.1 to 9.3 days for those discharged. Interpretation: Initial improvements in patient outcomes, corresponding to developments in clinical practice, were not sustained throughout 2020, with HFR in December approaching the levels seen at the start of the pandemic, whilst median hospital stays have lengthened. The role of increased transmission, new variants, case-mix and hospital pressures in increasing COVID-19 severity requires urgent further investigation., Comment: 45 pages, 12 figures
Published: 2021

16. Genomic reconstruction of the SARS-CoV-2 epidemic in England

Author: Robson, Samuel C, Connor, Thomas R, Loman, Nicholas J, Golubchik, Tanya, Martinez Nunez, Rocio T, Bonsall, David, Rambaut, Andrew, Snell, Luke B, Ludden, Catherine, Corden, Sally, Nastouli, Eleni, Nebbia, Gaia, Lythgoe, Katrina, Torok, M Estee, Goodfellow, Ian G, Prieto, Jacqui A, Saeed, Kordo, Houlihan, Catherine, Frampton, Dan, Hamilton, William L, Witney, Adam A, Bucca, Giselda, Pope, Cassie F, Moore, Catherine, Thomson, Emma C, Harrison, Ewan M, Smith, Colin P, Rogan, Fiona, Beckwith, Shaun M, Murray, Abigail, Singleton, Dawn, Eastick, Kirstine, Sheridan, Liz A, Randell, Paul, Jackson, Leigh M, Gonçalves, Sónia, Fairley, Derek J, Loose, Matthew W, Watkins, Joanne, Moses, Samuel, Nicholls, Sam, Bull, Matthew, Smith, Darren L, Aanensen, David M, Aggarwal, Dinesh, Shepherd, James G, Curran, Martin D, Parmar, Surendra, Parker, Matthew D, Williams, Catryn, Glaysher, Sharon, Underwood, Anthony P, Bashton, Matthew, Pacchiarini, Nicole, Loveson, Katie F, Byott, Matthew, Carabelli, Alessandro M, Templeton, Kate E, de Silva, Thushan I, Wang, Dennis, Langford, Cordelia F, Gunson, Rory N, Cottrell, Simon, O’Grady, Justin, Kwiatkowski, Dominic, Lillie, Patrick J, Cortes, Nicholas, Moore, Nathan, Thomas, Claire, Burns, Phillipa J, Mahungu, Tabitha W, Liggett, Steven, Beckett, Angela H, Holden, Matthew TG, Levett, Lisa J, Osman, Husam, Hassan-Ibrahim, Mohammed O, Simpson, David A, Chand, Meera, Gupta, Ravi K, Darby, Alistair C, Paterson, Steve, Pybus, Oliver G, Volz, Erik, de Angelis, Daniela, Robertson, David L, Page, Andrew J, Bassett, Andrew R, Wong, Nick, Taha, Yusri, Erkiert, Michelle J, Spencer Chapman, Michael H, Dewar, Rebecca, McHugh, Martin P, Mookerjee, Siddharth, Aplin, Stephen, Harvey, Matthew, Sass, Thea, Umpleby, Helen, and Wheeler, Helen
Subjects: Genetics, Prevention, Emerging Infectious Diseases, Infectious Diseases, Lung, Good Health and Well Being, Amino Acid Substitution, COVID-19, England, Epidemiological Monitoring, Genome, Viral, Genomics, Humans, Molecular Epidemiology, Mutation, Quarantine, SARS-CoV-2, Spatio-Temporal Analysis, Spike Glycoprotein, Coronavirus, Wellcome Sanger Institute COVID-19 Surveillance Team, COVID-19 Genomics UK (COG-UK) Consortium*, General Science & Technology
Abstract: The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021.
Published: 2021

17. Effect of second booster vaccinations and prior infection against SARS-CoV-2 in the UK SIREN healthcare worker cohort

Author: Lewis, Tracy, Bain, Steve, Thomas, Rebeccah, Geen, John, Pothecary, Carla, Cutler, Sean, Northfield, John, Price, Cathy, Tomlinson, Johanne, Knight, Sarah, Macnaughton, Emily, Watson, Ekaterina, Lazarus, Rajeka, Sinclair, Aaran, Galliford, Joanne, Masunda, Bridgett, Mahungu, Tabitha, Rodger, Alison, Hanison, Esther, Warren, Simon, Jain, Swati, Mirfenderesky, Mariyam, Mahabir, Natasha, Pritchard-Jones, Rowan, Wycherley, Diane, Gabriel, Claire, Matovu, Elijah, Bakker, Philippa, Guha, Simantee, Gormley, S., Pethick, James, Butt, Georgina, Pepper, Stacey, Bedford, Luke, Ridley, Paul, Democratis, Jane, Meda, Manjula, Chawla, Anu, Westwell, Fran, Kalakonda, Nagesh, Khanduri, Sheena, Doel, Allison, Pai, Sumita, Hacon, Christian, Nwaka, Davis, Moro, Veronica Mendez, Moody, A., Auckland, Cressida, Prince, Stephanie, de Silva, Thushan, Shulver, Helen, Shah, A., Jones, C., Subhro-Osuji, Banerjee, Houston, Angela, Planche, Tim, Booth, Martin, Duff, Christopher, Aeron-Thomas, Jonnie, Chaudhuri, Ray, Hilton, David, Jory, Hannah, Al-Khafaji, Zehra'a, Kemsley, Philippa, Longfellow, Ruth, Boss, David, Brake, Simon, Coke, Louise, Elumogo, Ngozi, Latham, Scott, Subudhi, Chinari, Hoad, Ina, Thomas, Claire, Chitalia, Nihil, Edmunds, Tracy, Ashby, Helen, Elliott, John, Wilkinson, Beverley, Rand, Abby, Thompson, Catherine, Agwuh, K., Grice, Anna, Moran, Kelly, Waykar, Vijayendra, Lester, Yvonne, Sach, Lauren, Court, Kathryn, White, Nikki, Favager, Clair, Holliday, Kyra, Harwood, Jayne, Payne, Brendan, Burns, Karen, Fothergill, Lynda, Arenas-Pinto, Alejandro, Severn, Abigail, Brown, Kerryanne, Gray, Katherine, Dare, Jane, Zheng, Qi, Hollinshead, Kathryn, Shorten, Robert, Roebuck, Alun, Holmes, Christopher, Wiselka, Martin, Faris, Barzo, Marsh, Liane, McAdam, Clare, Ditchfield, Lisa, Qazzafi, Zaman, Boyd, G., Wong, N., Brand, Sarah, Squires, Jack, Ashcroft, John, Rosario, Ismaelette Del, Howard, Joanne, Ward, Emma, Harrison, Gemma, Morgan, Joely, Corless, Claire, Penn, Ruth, Wong, Nick, Bagary, Manny, Starkova, Nadezda, Beekes, Mandy, Carnahan, Mandy, Khan, Shivani, Mackay, Shekoo, Lewis, Keneisha, Pickard, Graham, Dawson, Joy, Finlayson, Lauren, Cameron, Euan, Todd, Anne, Fagegaltier, Sebastien, Mavin, Sally, Cochrane, Alexandra, Gibson, Andrew, Donaldson, Sam, Templeton, Kate, Malcolm, Martin, Smith, Beth, Dhasmana, Devesh, Fowler, Susan, Ho, Antonia, Murphy, Michael, Beith, Claire, Patel, Manish, Boyd, Elizabeth, Irvine, Val, Grant, Alison, Temple-Purcell, Rebecca, Loughrey, Clodagh, Hanna, Elinor, Johnston, Frances, Boulos, Angel, Thompson, Fiona, Protaschik, Yuri, Regan, Susan, Donaghy, Tracy, O'Kane, Maurice, Akinbami, Omolola, Barbero, Paola, Brooks, Tim, Chand, Meera, Insalata, Ferdinando, Joshi, Palak, O'Connell, Anne-Marie, Ramsay, Mary, Saei, Ayoub, Zambon, Maria, Linley, Ezra, Tonge, Simon, Adaji, Enemona, Adebiyi, Omoyeni, Andrews, Nick, Conneely, Joanna, Conneely, Paul, Dunne, Angela, Dyer, Simone, Emmett, Hannah, Hettiarachchi, Nipunadi, Kapirial, Nishanthan, Khawam, Jameel, Monk, Edward, Russell, Sophie, Taylor-Kerr, Andrew, Timeyin, Jean, D'Arcangelo, Silvia, Rowe, Cathy, Semper, Amanda, Gallagher, Eileen, Kyffin, Robert, Cromey, Lisa, Areghan, Desmond, Bishop, Jennifer, Dembinsky, Melanie, Dobbie, Laura, Evans, Josie, Goldberg, David, Haahr, Lynne, Jorgenson, Annelysse, Matuluko, Ayodeji, Naismith, Laura, Nuryunarsih, Desy, Olaoye, Alexander, Plank, Caitlin, Price, Lesley, Sergenson, Nicole, Stewart, Sally, Telfer, Andrew, Weir, Jennifer, De Lacy, Ellen, Ellis, Yvette, Froude, Susannah, Stevens, Guy, Tyson, Linda, Dunachie, Susanna, Klenerman, Paul, Duncan, Chris, Payne, Rebecca, Turtle, Lance, Richter, Alex, De Silva, Thushan, Barnes, Eleanor, Wootton, Daniel, Galgut, Oliver, Heeney, Jonathan, Baxendale, Helen, Castillo-Olivares, Javier, Beale, Rupert, Carr, Edward, Barclay, Wendy, Moshe, Maya, Palmarini, Massimo, Willett, Brian, Baillie, John Kenneth, Evans, Jennie, Aquino, Erika, Kirwan, Peter D., Hall, Victoria J., Foulkes, Sarah, Otter, Ashley D., Munro, Katie, Sparkes, Dominic, Howells, Anna, Platt, Naomi, Broad, Jonathan, Crossman, David, Norman, Chris, Corrigan, Diane, Jackson, Christopher H., Cole, Michelle, Brown, Colin S., Atti, Ana, Islam, Jasmin, Presanis, Anne M., Charlett, Andre, De Angelis, Daniela, and Hopkins, Susan
Published: 2024
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18. HIV transmission in men who have sex with men in England: on track for elimination by 2030?

Author: Brizzi, Francesco, Birrell, Paul J, Kirwan, Peter, Ogaz, Dana, Brown, Alison E, Delpech, Valerie C, Gill, O Noel, and De Angelis, Daniela
Subjects: Quantitative Biology - Quantitative Methods
Abstract: Background: After a decade of a treatment as prevention (TasP) strategy based on progressive HIV testing scale-up and earlier treatment, a reduction in the estimated number of new infections in men-who-have-sex-with-men (MSM) in England had yet to be identified by 2010. To achieve internationally agreed targets for HIV control and elimination, test-and-treat prevention efforts have been dramatically intensified over the period 2010-2015, and, from 2016, further strengthened by pre-exposure prophylaxis (PrEP). Methods: Application of a novel age-stratified back-calculation approach to data on new HIV diagnoses and CD4 count-at-diagnosis, enabled age-specific estimation of HIV incidence, undiagnosed infections and mean time-to-diagnosis across both the 2010-2015 and 2016-2018 periods. Estimated incidence trends were then extrapolated, to quantify the likelihood of achieving HIV elimination by 2030. Findings: A fall in HIV incidence in MSM is estimated to have started in 2012/3, eighteen months before the observed fall in new diagnoses. A steep decrease from 2,770 annual infections (95% credible interval 2.490-3,040) in 2013 to 1,740 (1,500-2,010) in 2015 is estimated, followed by steady decline from 2016, reaching 854 (441-1,540) infections in 2018. A decline is consistently estimated in all age groups, with a fall particularly marked in the 24-35 age group, and slowest in the 45+ group. Comparable declines are estimated in the number of undiagnosed infections. Interpretation: The peak and subsequent sharp decline in HIV incidence occurred prior to the phase-in of PrEP. Definining elimination as a public health threat to be < 50 new infections (1.1 infections per 10,000 at risk), 40% of incidence projections hit this threshold by 2030. In practice, targeted policies will be required, particularly among the 45+y where STIs are increasing most rapidly., Comment: 20 pages, 5 figures
Published: 2020

19. Implications for HIV elimination by 2030 of recent trends in undiagnosed infection in England: an evidence synthesis

Author: Presanis, Anne M, Kirwan, Peter, Miltz, Ada, Croxford, Sara, Harris, Ross, Heinsbroek, Ellen, Jackson, Chris, Mohammed, Hamish, Brown, Alison, Delpech, Valerie, Gill, O Noel, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: A target to eliminate Human Immuno-deficiency Virus (HIV) transmission in England by 2030 was set in early 2019. Estimates of recent trends in HIV prevalence, particularly the number of people living with undiagnosed HIV, by exposure group, ethnicity, gender, age group and region, are essential to monitor progress towards elimination. A Bayesian synthesis of evidence from multiple surveillance, demographic and survey datasets relevant to HIV in England is employed to estimate trends in: the number of people living with HIV (PLWH); the proportion of these people unaware of their HIV infection; and the corresponding prevalence of undiagnosed HIV. All estimates are stratified by exposure group, ethnicity, gender, age group (15-34, 35-44, 45-59, 60-74), region (London, outside London) and year (2012-2017). The total number of PLWH aged 15-74 in England increased from 82,400 (95% credible interval, CrI, 78,700 to 89,100) in 2012 to 89,500 (95% CrI 87,400 to 93,300) in 2017. The proportion diagnosed steadily increased from 84% (95% CrI 77 to 88%) to 92% (95% CrI 89 to 94%) over the same time period, corresponding to a halving in the number of undiagnosed infections from 13,500 (95% CrI 9,800 to 20,200) to 6,900 (95% CrI 4,900 to 10,700). This decrease is equivalent to a halving in prevalence of undiagnosed infection and is reflected in all sub-groups of gay, bisexual and other men who have sex with men and most sub-groups of black African heterosexuals. However, decreases were not detected for some sub-groups of other ethnicity heterosexuals, particularly outside London. In 2016, the Joint United Nations Programme on HIV/ AIDS target of diagnosing 90% of people living with HIV was reached in England. To achieve HIV elimination by 2030, current testing efforts should be enhanced to address the numbers of heterosexuals living with undiagnosed HIV, especially outside London.
Published: 2019

20. Investigation of hospital discharge cases and SARS-CoV-2 introduction into Lothian care homes

Author: Robson, Samuel C., Connor, Thomas R., Loman, Nicholas J., Golubchik, Tanya, Martinez Nunez, Rocio T., Bonsall, David, Rambaut, Andrew, Snell, Luke B., Livett, Rich, Ludden, Catherine, Corden, Sally, Nastouli, Eleni, Nebbia, Gaia, Johnston, Ian, Prieto, Jacqui A., Saeed, Kordo, Jackson, David K., Houlihan, Catherine, Frampton, Dan, Hamilton, William L., Witney, Adam A., Bucca, Giselda, Pope, Cassie F., Moore, Catherine, Thomson, Emma C., Cutino-Moguel, Teresa, Harrison, Ewan M., Smith, Colin P., Rogan, Fiona, Beckwith, Shaun M., Murray, Abigail, Singleton, Dawn, Eastick, Kirstine, Sheridan, Liz A., Randell, Paul, Jackson, Leigh M., Ariani, Cristina V., Gonçalves, Sónia, Fairley, Derek J., Loose, Matthew W., Watkins, Joanne, Moses, Samuel, Nicholls, Sam, Bull, Matthew, Amato, Roberto, Smith, Darren L., Aanensen, David M., Barrett, Jeffrey C., Kele, Beatrix, Aggarwal, Dinesh, Shepherd, James G., Curran, Martin D., Parmar, Surendra, Parker, Matthew D., Williams, Catryn, Glaysher, Sharon, Underwood, Anthony P., Bashton, Matthew, Pacchiarini, Nicole, Loveson, Katie F., Byott, Matthew, Carabelli, Alessandro M., Templeton, Kate E., Peacock, Sharon J., de Silva, Thushan I., Wang, Dennis, Langford, Cordelia F., Sillitoe, John, Gunson, Rory N., Cottrell, Simon, O’Grady, Justin, Kwiatkowski, Dominic, Lillie, Patrick J., Cortes, Nicholas, Moore, Nathan, Thomas, Claire, Burns, Phillipa J., Mahungu, Tabitha W., Liggett, Steven, Beckett, Angela H., Holden, Matthew TG., Levett, Lisa J., Osman, Husam, Hassan-Ibrahim, Mohammed O., Simpson, David A., Chand, Meera, Gupta, Ravi K., Darby, Alistair C., Paterson, Steve, Pybus, Oliver G., Volz, Erik M., de Angelis, Daniela, Robertson, David L., Page, Andrew J., Martincorena, Inigo, Aigrain, Louise, Bassett, Andrew R., Wong, Nick, Taha, Yusri, Erkiert, Michelle J., Spencer Chapman, Michael H., Dewar, Rebecca, McHugh, Martin P., Mookerjee, Siddharth, Aplin, Stephen, Harvey, Matthew, Sass, Thea, Umpleby, Helen, Wheeler, Helen, McKenna, James P., Warne, Ben, Taylor, Joshua F., Chaudhry, Yasmin, Izuagbe, Rhys, Jahun, Aminu S., Young, Gregory R., McMurray, Claire, McCann, Clare M., Nelson, Andrew, Elliott, Scott, Lowe, Hannah, Price, Anna, Crown, Matthew R., Rey, Sara, Roy, Sunando, Temperton, Ben, Shaaban, Sharif, Hesketh, Andrew R., Laing, Kenneth G., Monahan, Irene M., Heaney, Judith, Pelosi, Emanuela, Silviera, Siona, Wilson-Davies, Eleri, Fryer, Helen, Adams, Helen, du Plessis, Louis, Johnson, Rob, Harvey, William T., Hughes, Joseph, Orton, Richard J., Spurgin, Lewis G., Bourgeois, Yann, Ruis, Chris, O'Toole, Áine, Gourtovaia, Marina, Sanderson, Theo, Fraser, Christophe, Edgeworth, Jonathan, Breuer, Judith, Michell, Stephen L., Todd, John A., John, Michaela, Buck, David, Gajee, Kavitha, Kay, Gemma L., Heyburn, David, Charalampous, Themoula, Alcolea-Medina, Adela, Kitchman, Katie, McNal, Alan, Pritch, David T., Dervisevic, Samir, Muir, Peter, Robinson, Esther, Vipond, Barry B., Ramadan, Newara A., Jeanes, Christopher, Weldon, Danni, Catalan, Jana, Jones, Neil, da Silva Filipe, Ana, Williams, Chris, Fuchs, Marc, Miskelly, Julia, Jeffries, Aaron R., Oliver, Karen, Park, Naomi R., Ash, Amy, Koshy, Cherian, Barrow, Magdalena, Buchan, Sarah L., Mantzouratou, Anna, Clark, Gemma, Holmes, Christopher W., Campbell, Sharon, Davis, Thomas, Tan, Ngee Keong, Brown, Julianne R., Harris, Kathryn A., Kidd, Stephen P., Grant, Paul R., Xu-McCrae, Li, Cox, Alison, Madona, Pinglawathee, Pond, Marcus, Randell, Paul A., Withell, Karen T., Williams, Cheryl, Graham, Clive, Denton-Smith, Rebecca, Swindells, Emma, Turnbull, Robyn, Sloan, Tim J., Bosworth, Andrew, Hutchings, Stephanie, Pymont, Hannah M., Casey, Anna, Ratcliffe, Liz, Jones, Christopher R., Knight, Bridget A., Haque, Tanzina, Hart, Jennifer, Irish-Tavares, Dianne, Witele, Eric, Mower, Craig, Watson DipHE, Louisa K., Collins, Jennifer, Eltringham, Gary, Crudgington, Dorian, Macklin, Ben, Iturriza-Gomara, Miren, Lucaci, Anita O., McClure, Patrick C., Carlile, Matthew, Holmes, Nadine, Moore, Christopher, Storey, Nathaniel, Rooke, Stefan, Yebra, Gonzalo, Craine, Noel, Perry, Malorie, Alikhan, Nabil-Fareed, Bridgett, Stephen, Cook, Kate F., Fearn, Christopher, Goudarzi, Salman, Lyons, Ronan A., Williams, Thomas, Haldenby, Sam T., Durham, Jillian, Leonard, Steven, Davies, Robert M., Batra, Rahul, Blane, Beth, Spyer, Moira J., Smith, Perminder, Yavus, Mehmet, Williams, Rachel J., Mahanama, Adhyana IK., Samaraweera, Buddhini, Girgis, Sophia T., Hansford, Samantha E., Green, Angie, Beaver, Charlotte, Bellis, Katherine L., Dorman, Matthew J., Kay, Sally, Prestwood, Liam, Rajatileka, Shavanthi, Quick, Joshua, Poplawski, Radoslaw, Reynolds, Nicola, Mack, Andrew, Morriss, Arthur, Whalley, Thomas, Patel, Bindi, Georgana, Iliana, Hosmillo, Myra, Pinckert, Malte L., Stockton, Joanne, Henderson, John H., Hollis, Amy, Stanley, William, Yew, Wen C., Myers, Richard, Thornton, Alicia, Adams, Alexander, Annett, Tara, Asad, Hibo, Birchley, Alec, Coombes, Jason, Evans, Johnathan M., Fina, Laia, Gatica-Wilcox, Bree, Gilbert, Lauren, Graham, Lee, Hey, Jessica, Hilvers, Ember, Jones, Sophie, Jones, Hannah, Kumziene-Summerhayes, Sara, McKerr, Caoimhe, Powell, Jessica, Pugh, Georgia, Taylor, Sarah, Trotter, Alexander J., Williams, Charlotte A., Kermack, Leanne M., Foulkes, Benjamin H., Gallis, Marta, Hornsby, Hailey R., Louka, Stavroula F., Pohare, Manoj, Wolverson, Paige, Zhang, Peijun, MacIntyre-Cockett, George, Trebes, Amy, Moll, Robin J., Ferguson, Lynne, Goldstein, Emily J., Maclean, Alasdair, Tomb, Rachael, Starinskij, Igor, Thomson, Laura, Southgate, Joel, Kraemer, Moritz UG., Raghwani, Jayna, Zarebski, Alex E., Boyd, Olivia, Geidelberg, Lily, Illingworth, Chris J., Jackson, Chris, Pascall, David, Vattipally, Sreenu, Freeman, Timothy M., Hsu, Sharon N., Lindsey, Benjamin B., James, Keith, Lewis, Kevin, Tonkin-Hill, Gerry, Tovar-Corona, Jaime M., Cox, MacGregor, Abudahab, Khalil, Menegazzo, Mirko, Taylor, Ben EW., Yeats, Corin A., Mukaddas, Afrida, Wright, Derek W., de Oliveira Martins, Leonardo, Colquhoun, Rachel, Hill, Verity, Jackson, Ben, McCrone, J.T., Medd, Nathan, Scher, Emily, Keatley, Jon-Paul, Curran, Tanya, Morgan, Sian, Maxwell, Patrick, Smith, Ken, Eldirdiri, Sahar, Kenyon, Anita, Holmes, Alison H., Price, James R., Wyatt, Tim, Mather, Alison E., Skvortsov, Timofey, Hartley, John A., Guest, Martyn, Kitchen, Christine, Merrick, Ian, Munn, Robert, Bertolusso, Beatrice, Lynch, Jessica, Vernet, Gabrielle, Kirk, Stuart, Wastnedge, Elizabeth, Stanley, Rachael, Idle, Giles, Bradley, Declan T., Killough, Nicholas F., Poyner, Jennifer, Mori, Matilde, Jones, Owen, Wright, Victoria, Brooks, Ellena, Churcher, Carol M., Delgado Callico, Laia, Fragakis, Mireille, Galai, Katerina, Jermy, Andrew, Judges, Sarah, Markov, Anna, McManus, Georgina M., Smith, Kim S., Thomas-McEwen, Peter MD., Westwick, Elaine, Attwood, Stephen W., Bolt, Frances, Davies, Alisha, De Lacy, Elen, Downing, Fatima, Edwards, Sue, Meadows, Lizzie, Jeremiah, Sarah, Smith, Nikki, Foulser, Luke, Patel, Amita, Berry, Louise, Boswell, Tim, Fleming, Vicki M., Howson-Wells, Hannah C., Joseph, Amelia, Khakh, Manjinder, Lister, Michelle M., Bird, Paul W., Fallon, Karlie, Helmer, Thomas, McMurray, Claire L., Odedra, Mina, Shaw, Jessica, Tang, Julian W., Willford, Nicholas J., Blakey, Victoria, Raviprakash, Veena, Sheriff, Nicola, Williams, Lesley-Anne, Feltwell, Theresa, Bedford, Luke, Cargill, James S., Hughes, Warwick, Moore, Jonathan, Stonehouse, Susanne, Atkinson, Laura, Lee, Jack CD., Shah, Divya, Ohemeng-Kumi, Natasha, Ramble, John, Sehmi, Jasveen, Williams, Rebecca, Chatterton, Wendy, Pusok, Monika, Everson, William, Castigador, Anibolina, Macnaughton, Emily, El Bouzidi, Kate, Lampejo, Temi, Sudhanva, Malur, Breen, Cassie, Sluga, Graciela, Ahmad, Shazaad SY., George, Ryan P., Machin, Nicholas W., Binns, Debbie, James, Victoria, Blacow, Rachel, Coupland, Lindsay, Smith, Louise, Barton, Edward, Padgett, Debra, Scott, Garren, Cross, Aidan, Mirfenderesky, Mariyam, Greenaway, Jane, Cole, Kevin, Clarke, Phillip, Duckworth, Nichola, Walsh, Sarah, Bicknell, Kelly, Impey, Robert, Wyllie, Sarah, Hopes, Richard, Bishop, Chloe, Chalker, Vicki, Harrison, Ian, Gifford, Laura, Molnar, Zoltan, Auckland, Cressida, Evans, Cariad, Johnson, Kate, Partridge, David G., Raza, Mohammad, Baker, Paul, Bonner, Stephen, Essex, Sarah, Murray, Leanne J., Lawton, Andrew I., Burton-Fanning, Shirelle, Payne, Brendan AI., Waugh, Sheila, Gomes, Andrea N., Kimuli, Maimuna, Murray, Darren R., Ashfield, Paula, Dobie, Donald, Ashford, Fiona, Best, Angus, Crawford, Liam, Cumley, Nicola, Mayhew, Megan, Megram, Oliver, Mirza, Jeremy, Moles-Garcia, Emma, Percival, Benita, Driscoll, Megan, Ensell, Leah, Lowe, Helen L., Maftei, Laurentiu, Mondani, Matteo, Chaloner, Nicola J., Cogger, Benjamin J., Easton, Lisa J., Huckson, Hannah, Lewis, Jonathan, Lowdon, Sarah, Malone, Cassandra S., Munemo, Florence, Mutingwende, Manasa, Nicodemi, Roberto, Podplomyk FD, Olga, Somassa, Thomas, Beggs, Andrew, Richter, Alex, Cormie, Claire, Dias, Joana, Forrest, Sally, Higginson, Ellen E., Maes, Mailis, Young, Jamie, Davidson, Rose K., Jackson, Kathryn A., Keeley, Alexander J., Ball, Jonathan, Byaruhanga, Timothy, Chappell, Joseph G., Dey, Jayasree, Hill, Jack D., Park, Emily J., Fanaie, Arezou, Hilson, Rachel A., Yaze, Geraldine, Lo, Stephanie, Afifi, Safiah, Beer, Robert, Maksimovic, Joshua, McCluggage, Kathryn, Spellman, Karla, Bresner, Catherine, Fuller, William, Marchbank, Angela, Workma, Trudy, Shelest, Ekaterina, Debebe, Johnny, Sang, Fei, Francois, Sarah, Gutierrez, Bernardo, Vasylyeva, Tetyana I., Flaviani, Flavia, Ragonnet-Cronin, Manon, Smollett, Katherine L., Broos, Alice, Mair, Daniel, Nichols, Jenna, Nomikou, Kyriaki, Tong, Lily, Tsatsani, Ioulia, O'Brien, Sarah, Rushton, Steven, Sanderson, Roy, Perkins, Jon, Cotton, Seb, Gallagher, Abbie, Allara, Elias, Pearson, Clare, Bibby, David, Dabrer, Gavin, Ellaby, Nicholas, Gallagher, Eileen, Hubb, Jonathan, Lackenby, Angie, Lee, David, Manesis, Nikos, Mbisa, Tamyo, Platt, Steven, Twohig, Katherine A., Morgan, Mari, Aydin, Alp, Baker, David J., Foster-Nyarko, Ebenezer, Prosolek, Sophie J., Rudder, Steven, Baxter, Chris, Carvalho, Sílvia F., Lavin, Deborah, Mariappan, Arun, Radulescu, Clara, Singh, Aditi, Tang, Miao, Morcrette, Helen, Bayzid, Nadua, Cotic, Marius, Balcazar, Carlos E., Gallagher, Michael D., Maloney, Daniel, Stanton, Thomas D., Williamson, Kathleen A., Manley, Robin, Michelsen, Michelle L., Sambles, Christine M., Studholme, David J., Warwick-Dugdale, Joanna, Eccles, Richard, Gemmell, Matthew, Gregory, Richard, Hughes, Margaret, Nelson, Charlotte, Rainbow, Lucille, Vamos, Edith E., Webster, Hermione J., Whitehead, Mark, Wierzbicki, Claudia, Angyal, Adrienn, Green, Luke R., Whiteley, Max, Betteridge, Emma, Bronner, Iraad F., Farr, Ben W., Goodwin, Scott, Lensing, Stefanie V., McCarthy, Shane A., Quail, Michael A., Rajan, Diana, Redshaw, Nicholas M., Scott, Carol, Shirley, Lesley, Thurston, Scott AJ., Rowe, Will, Gaskin, Amy, Le-Viet, Thanh, Bonfield, James, Liddle, Jennifier, Whitwham, Andrew, Cotton, S., McHugh, M.P., Dewar, R., Haas, J.G., and Templeton, K.
Published: 2023
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21. Modelling hepatitis C infection acquired from blood transfusions in the UK between 1970 and 1991 for the Infected Blood Inquiry

Author: Hayes, Sarah, primary, McCabe, Ruth, additional, De Angelis, Daniela, additional, Donnelly, Christl A., additional, Evans, Stephen J. W., additional, Medley, Graham F., additional, Spiegelhalter, David J., additional, and Bird, Sheila M., additional
Published: 2024
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22. Analysing Multiple Epidemic Data Sources

Author: De Angelis, Daniela and Presanis, Anne M.
Subjects: Statistics - Methodology, Statistics - Applications
Abstract: Evidence-based knowledge of infectious disease burden, including prevalence, incidence, severity and transmission, in different population strata and locations, and possibly in real time, is crucial to the planning and evaluation of public health policies. Direct observation of a disease process is rarely possible. However, latent characteristics of an epidemic and its evolution can often be inferred from the synthesis of indirect information from various routine data sources, as well as expert opinion. The simultaneous synthesis of multiple data sources, often conveniently carried out in a Bayesian framework, poses a number of statistical and computational challenges: the heterogeneity in type, relevance and granularity of the data, together with selection and informative observation biases, lead to complex probabilistic models that are difficult to build and fit, and challenging to criticize. Using motivating case studies of influenza, this chapter illustrates the cycle of model development and criticism in the context of Bayesian evidence synthesis, highlighting the challenges of complex model building, computationally efficient inference, and conflicting evidence., Comment: This manuscript is a preprint of a Chapter to appear in the "Handbook of Infectious Disease Data Analysis", Held, L., Hens, N., O'Neill, P.D. and Wallinga, J. (Eds.). Chapman & Hall/CRC, 2018. Please use the book for possible citations
Published: 2018

23. Assessing the causal effect of binary interventions from observational panel data with few treated units

Author: Samartsidis, Pantelis, Seaman, Shaun R., Presanis, Anne M., Hickman, Matthew, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.
Published: 2018
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24. Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study

Author: Kirwan, Peter D., Charlett, Andre, Birrell, Paul, Elgohari, Suzanne, Hope, Russell, Mandal, Sema, De Angelis, Daniela, and Presanis, Anne M.
Published: 2022
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25. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

Author: Nyberg, Tommy, Ferguson, Neil M, Nash, Sophie G, Webster, Harriet H, Flaxman, Seth, Andrews, Nick, Hinsley, Wes, Lopez Bernal, Jamie, Kall, Meaghan, Bhatt, Samir, Blomquist, Paula, Zaidi, Asad, Volz, Erik, Abdul Aziz, Nurin, Harman, Katie, Funk, Sebastian, Abbott, Sam, Hope, Russell, Charlett, Andre, Chand, Meera, Ghani, Azra C, Seaman, Shaun R, Dabrera, Gavin, De Angelis, Daniela, Presanis, Anne M, Thelwall, Simon, Bernal, Jamie Lopez, and Aziz, Nurin Abdul
Published: 2022
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26. Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Author: Swallow, Ben, Birrell, Paul, Blake, Joshua, Burgman, Mark, Challenor, Peter, Coffeng, Luc E., Dawid, Philip, De Angelis, Daniela, Goldstein, Michael, Hemming, Victoria, Marion, Glenn, McKinley, Trevelyan J., Overton, Christopher E., Panovska-Griffiths, Jasmina, Pellis, Lorenzo, Probert, Will, Shea, Katriona, Villela, Daniel, and Vernon, Ian
Published: 2022
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27. Evidence synthesis for stochastic epidemic models

Author: Birrell, Paul J, De Angelis, Daniela, and Presanis, Anne M
Subjects: Statistics - Methodology, Quantitative Biology - Populations and Evolution
Abstract: In recent years the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.
Published: 2017

28. Quantifying the recency of HIV infection using multiple longitudinal biomarkers

Author: Koulai, Loumpiana, Presanis, Anne, Murphy, Gary, Suligoi, Barbara, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: Knowledge of the time at which an HIV-infected individual seroconverts, when the immune system starts responding to HIV infection, plays a vital role in the design and implementation of interventions to reduce the impact of the HIV epidemic. A number of biomarkers have been developed to distinguish between recent and long-term HIV infection, based on the antibody response to HIV. To quantify the recency of infection at an individual level, we propose characterising the growth of such biomarkers from observations from a panel of individuals with known seroconversion time, using Bayesian mixed effect models. We combine this knowledge of the growth patterns with observations from a newly diagnosed individual, to estimate the probability seroconversion occurred in the X months prior to diagnosis. We explore, through a simulation study, the characteristics of different biomarkers that affect our ability to estimate recency, such as the growth rate. In particular, we find that predictive ability is improved by using joint models of two biomarkers, accounting for their correlation, rather than univariate models of single biomarkers.
Published: 2017

29. Exploiting routinely collected severe case data to monitor and predict influenza outbreaks

Author: Corbella, Alice, Zhang, Xu-Sheng, Birrell, Paul J., Boddington, Nicky, Presanis, Anne M., Pebody, Richard G., and De Angelis, Daniela
Subjects: Statistics - Applications, Physics - Physics and Society, Quantitative Biology - Populations and Evolution
Abstract: Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza. We propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admission to intensive care is possible. Our model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of Christmas school holiday on disease spread during season 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved. Readily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak., Comment: 17 pages, 6 Figures, 3 tables and one ancillary file (Additional_File_1) Replacement of "Monitoring and predicting influenza epidemics from routinely collected severe case data" with the following changes: we used a continuous-time system to describe transmission; we used a time varying transmission; we improved the analysis of the waiting time from symptoms to ICU admissions
Published: 2017

30. MultiBUGS: A parallel implementation of the BUGS modelling framework for faster Bayesian inference

Author: Goudie, Robert J. B., Turner, Rebecca M., De Angelis, Daniela, and Thomas, Andrew
Subjects: Statistics - Computation
Abstract: MultiBUGS (https://www.multibugs.org) is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelises evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelises sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelise the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational cores., Comment: 20 pages, 4 figures
Published: 2017
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31. Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

Author: Jackson, Christopher, Presanis, Anne, Conti, Stefano, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: Suppose we have a Bayesian model which combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore we want to prioritise what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modelling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from several surveys, registers and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and provides the expected improvements in precision from collecting specific amounts of additional data.
Published: 2017
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32. Conflict diagnostics for evidence synthesis in a multiple testing framework

Author: Presanis, Anne M., Ohlssen, David, Cui, Kai, Rosinska, Magdalena, and De Angelis, Daniela
Subjects: Statistics - Methodology
Abstract: Evidence synthesis models that combine multiple datasets of varying design, to estimate quantities that cannot be directly observed, require the formulation of complex probabilistic models that can be expressed as graphical models. An assessment of whether the different datasets synthesised contribute information that is consistent with each other, and in a Bayesian context, with the prior distribution, is a crucial component of the model criticism process. However, a systematic assessment of conflict suffers from the multiple testing problem, through testing for conflict at multiple locations in a model. We demonstrate the systematic use of conflict diagnostics, while accounting for the multiple hypothesis tests of no conflict at each location in the graphical model. The method is illustrated by a network meta-analysis to estimate treatment effects in smoking cessation programs and an evidence synthesis to estimate HIV prevalence in Poland.
Published: 2017

33. Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study

Author: Koshy, Cherian, Ash, Amy, Wise, Emma, Moore, Nathan, Mori, Matilde, Cortes, Nick, Lynch, Jessica, Kidd, Stephen, Fairley, Derek, Curran, Tanya, McKenna, James, Adams, Helen, Fraser, Christophe, Golubchik, Tanya, Bonsall, David, Hassan-Ibrahim, Mohammed, Malone, Cassandra, Cogger, Benjamin, 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, Fuller, William, Bresner, Catherine, Snell, Luke, Patel, Amita, Charalampous, Themoula, Nebbia, Gaia, Batra, Rahul, Edgeworth, Jonathan, Robson, Samuel, Beckett, Angela, Aanensen, David, Underwood, Anthony, Yeats, Corin, Abudahab, Khalil, Taylor, Ben, Menegazzo, Mirko, Clark, Gemma, Smith, Wendy, Khakh, Manjinder, Fleming, Vicki, Lister, Michelle, Howson-Wells, Hannah, 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, Holmes, Nadine, Moore, Christopher, Carlile, Matthew, Wright, Victoria, Sang, Fei, Debebe, Johnny, Coll, Francesc, Signell, Adrian, Betancor, Gilberto, Wilson, Harry, Eldirdiri, Sahar, Kenyon, Anita, Davis, Thomas, Pybus, Oliver, du Plessis, Louis, Zarebski, Alex, Raghwani, Jayna, Kraemer, Moritz, Francois, Sarah, Attwood, Stephen, Vasylyeva, Tetyana, Escalera Zamudio, Marina, Gutierrez, Bernardo, Torok, M. Estee, Hamilton, William, Goodfellow, Ian, Hall, Grant, Jahun, Aminu, Chaudhry, Yasmin, Hosmillo, Myra, Pinckert, Malte, Georgana, Iliana, Moses, Samuel, Lowe, Hannah, Bedford, Luke, Moore, Jonathan, Stonehouse, Susanne, Fisher, Chloe, Awan, Ali, BoYes, John, Breuer, Judith, Harris, Kathryn, Brown, Julianne, Shah, Divya, Atkinson, Laura, Lee, Jack, Storey, Nathaniel, Flaviani, Flavia, Alcolea-Medina, Adela, Williams, Rebecca, Vernet, Gabrielle, Chapman, Michael, Levett, Lisa, Heaney, Judith, Chatterton, Wendy, Pusok, Monika, Xu-McCrae, Li, Smith, Darren, Bashton, Matthew, Young, Gregory, Holmes, Alison, Randell, Paul, Cox, Alison, Madona, Pinglawathee, Bolt, Frances, Price, James, Mookerjee, Siddharth, Ragonnet-Cronin, Manon, F. Nascimento, Fabricia, Jorgensen, David, Siveroni, Igor, Johnson, Rob, Boyd, Olivia, Geidelberg, Lily, Volz, Erik, Rowan, Aileen, Taylor, Graham, Smollett, Katherine, Loman, Nicholas, Quick, Joshua, McMurray, Claire, Stockton, Joanne, Nicholls, Sam, Rowe, Will, Poplawski, Radoslaw, McNally, Alan, Martinez Nunez, Rocio, Mason, Jenifer, Robinson, Trevor, O'Toole, Elaine, Watts, Joanne, Breen, Cassie, Cowell, Angela, Sluga, Graciela, Machin, Nicholas, Ahmad, Shazaad, George, Ryan, Halstead, Fenella, Sivaprakasam, Venkat, Hogsden, Wendy, Illingworth, Chris, Jackson, Chris, Thomson, Emma, Shepherd, James, Asamaphan, Patawee, Niebel, Marc, Li, Kathy, Shah, Rajiv, Jesudason, Natasha, Tong, Lily, Broos, Alice, Mair, Daniel, Nichols, Jenna, Carmichael, Stephen, Nomikou, Kyriaki, Aranday-Cortes, Elihu, Johnson, Natasha, Starinskij, Igor, da Silva Filipe, Ana, Robertson, David, Orton, Richard, Hughes, Joseph, Vattipally, Sreenu, Singer, Joshua, Nickbakhsh, Seema, Hale, Antony, Macfarlane-Smith, Louissa, Harper, Katherine, Carden, Holli, Taha, Yusri, Payne, Brendan, Burton-Fanning, Shirelle, Waugh, Sheila, Collins, Jennifer, Eltringham, Gary, Rushton, Steven, O'Brien, Sarah, Bradley, Amanda, Maclean, Alasdair, Mollett, Guy, Blacow, Rachel, Templeton, Kate, McHugh, Martin, Dewar, Rebecca, Wastenge, Elizabeth, Dervisevic, Samir, Stanley, Rachael, Meader, Emma, Coupland, Lindsay, Smith, Louise, Graham, Clive, Barton, Edward, Padgett, Debra, Scott, Garren, Swindells, Emma, Greenaway, Jane, Nelson, Andrew, McCann, Clare, Yew, Wen, Andersson, Monique, Peto, Timothy, Justice, Anita, Eyre, David, Crook, Derrick, Sloan, Tim, Duckworth, Nichola, Walsh, Sarah, Chauhan, Anoop, Glaysher, Sharon, Bicknell, Kelly, Wyllie, Sarah, Elliott, Scott, Lloyd, Allyson, Impey, Robert, Levene, Nick, Monaghan, Lynn, Bradley, Declan, Wyatt, Tim, Allara, Elias, Pearson, Clare, Osman, Husam, Bosworth, Andrew, Robinson, Esther, Muir, Peter, Vipond, Ian, Hopes, Richard, Pymont, Hannah, Hutchings, Stephanie, Curran, Martin, 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, Holden, Matthew, 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, 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, Mather, Alison, Baker, David, Rudder, Steven, Aydin, Alp, Kay, Gemma, Trotter, Alexander, Alikhan, Nabil-Fareed, de Oliveira Martins, Leonardo, Le-Viet, Thanh, Meadows, Lizzie, Casey, Anna, Ratcliffe, Liz, Simpson, David, Molnar, Zoltan, Thompson, Thomas, Acheson, Erwan, Masoli, Jane, Knight, Bridget, Ellard, Sian, Auckland, Cressida, Jones, Christopher, Mahungu, Tabitha, Irish-Tavares, Dianne, Haque, Tanzina, Hart, Jennifer, Witele, Eric, Fenton, Melisa, Dadrah, Ashok, Symmonds, Amanda, Saluja, Tranprit, Bourgeois, Yann, Scarlett, Garry, Loveson, Katie, Goudarzi, Salman, Fearn, Christopher, Cook, Kate, Dent, Hannah, Paul, Hannah, Partridge, David, Raza, Mohammad, Evans, Cariad, Johnson, Kate, Liggett, Steven, Baker, Paul, Bonner, Stephen, Essex, Sarah, Lyons, Ronan, Saeed, Kordo, Mahanama, Adhyana, Samaraweera, Buddhini, Silveira, Siona, Pelosi, Emanuela, Wilson-Davies, Eleri, Williams, Rachel, Kristiansen, Mark, Roy, Sunando, Williams, Charlotte, Cotic, Marius, Bayzid, Nadua, Westhorpe, Adam, Hartley, John, Jannoo, Riaz, Lowe, Helen, Karamani, Angeliki, Ensell, Leah, Prieto, Jacqui, 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, Bucca, Giselda, Hesketh, Andrew, Blane, Beth, Girgis, Sophia, Leek, Danielle, Sridhar, Sushmita, Forrest, Sally, Cormie, Claire, Gill, Harmeet, Dias, Joana, Higginson, Ellen, Maes, Mailis, Young, Jamie, Kermack, Leanne, Gupta, Ravi, Ludden, Catherine, Peacock, Sharon, Palmer, Sophie, Churcher, Carol, Hadjirin, Nazreen, Carabelli, Alessandro, Brooks, Ellena, Smith, Kim, Galai, Katerina, McManus, Georgina, Ruis, Chris, Davidson, Rose, Rambaut, Andrew, Williams, Thomas, Balcazar, Carlos, Gallagher, Michael, O'Toole, Áine, Rooke, Stefan, Hill, Verity, Williamson, Kathleen, Stanton, Thomas, Michell, Stephen, Bewshea, Claire, Temperton, Ben, Michelsen, Michelle, Warwick-Dugdale, Joanna, Manley, Robin, Farbos, Audrey, Harrison, James, Sambles, Christine, Studholme, David, Jeffries, Aaron, Jackson, Leigh, Darby, Alistair, Hiscox, Julian, Paterson, Steve, Iturriza-Gomara, Miren, Jackson, Kathryn, Lucaci, Anita, Vamos, Edith, Hughes, Margaret, Rainbow, Lucille, Eccles, Richard, Nelson, Charlotte, Whitehead, Mark, Turtle, Lance, Haldenby, Sam, Gregory, Richard, Gemmell, Matthew, Wierzbicki, Claudia, Webster, Hermione, de Silva, Thushan, Smith, Nikki, Angyal, Adrienn, Lindsey, Benjamin, Groves, Danielle, Green, Luke, Wang, Dennis, Freeman, Timothy, Parker, Matthew, Keeley, Alexander, Parsons, Paul, Tucker, Rachel, Brown, Rebecca, Wyles, Matthew, Whiteley, Max, Zhang, Peijun, Gallis, Marta, Louka, Stavroula, Constantinidou, Chrystala, Unnikrishnan, Meera, Ott, Sascha, Cheng, Jeffrey, Bridgewater, Hannah, Frost, Lucy, Taylor-Joyce, Grace, Stark, Richard, Baxter, Laura, Alam, Mohammad, Brown, Paul, Aggarwal, Dinesh, Cerda, Alberto, Merrill, Tammy, Wilson, Rebekah, McClure, Patrick, Chappell, Joseph, Tsoleridis, Theocharis, Ball, Jonathan, Buck, David, Todd, John, Green, Angie, Trebes, Amy, MacIntyre-Cockett, George, de Cesare, Mariateresa, Alderton, Alex, Amato, Roberto, Ariani, Cristina, Beale, Mathew, Beaver, Charlotte, Bellis, Katherine, Betteridge, Emma, Bonfield, James, Danesh, John, Dorman, Matthew, Drury, Eleanor, Farr, Ben, Foulser, Luke, Goncalves, Sonia, Goodwin, Scott, Gourtovaia, Marina, Harrison, Ewan, Jackson, David, Jamrozy, Dorota, Johnston, Ian, Kane, Leanne, Kay, Sally, Keatley, Jon-Paul, Kwiatkowski, Dominic, Langford, Cordelia, Lawniczak, Mara, Letchford, Laura, Livett, Rich, Lo, Stephanie, Martincorena, Inigo, McGuigan, Samantha, Nelson, Rachel, Palmer, Steve, Park, Naomi, Patel, Minal, Prestwood, Liam, Puethe, Christoph, Quail, Michael, Rajatileka, Shavanthi, Scott, Carol, Shirley, Lesley, Sillitoe, John, Spencer Chapman, Michael, Thurston, Scott, Tonkin-Hill, Gerry, Weldon, Danni, Rajan, Diana, Bronner, Iraad, Aigrain, Louise, Redshaw, Nicholas, Lensing, Stefanie, Davies, Robert, Whitwham, Andrew, Liddle, Jennifier, Lewis, Kevin, Tovar-Corona, Jaime, Leonard, Steven, Durham, Jillian, Bassett, Andrew, McCarthy, Shane, Moll, Robin, James, Keith, Oliver, Karen, Makunin, Alex, Barrett, Jeff, Gunson, Rory, Twohig, Katherine A, Nyberg, Tommy, Zaidi, Asad, Thelwall, Simon, Sinnathamby, Mary A, Aliabadi, Shirin, Seaman, Shaun R, Harris, Ross J, Hope, Russell, Lopez-Bernal, Jamie, Charlett, Andre, De Angelis, Daniela, and Presanis, Anne M
Published: 2022
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34. Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England

Author: Manley, Harrison, primary, Bayley, Thomas, additional, Danelian, Gabriel, additional, Burton, Lucy, additional, Finnie, Thomas, additional, Charlett, Andre, additional, Watkins, Nicholas A., additional, Birrell, Paul, additional, De Angelis, Daniela, additional, Keeling, Matt, additional, Funk, Sebastian, additional, Medley, Graham, additional, Pellis, Lorenzo, additional, Baguelin, Marc, additional, Ackland, Graeme J., additional, Hutchinson, Johanna, additional, Riley, Steven, additional, and Panovska-Griffiths, Jasmina, additional
Published: 2024
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35. Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context

Author: Grosso Francesca Maria, Presanis Anne Margaret, Kunzmann Kevin, Jackson Chris, Corbella Alice, Grasselli Giacomo, Andreassi Aida, Bodina Annalisa, Gramegna Maria, Castaldi Silvana, Cereda Danilo, De Angelis Daniela, and Covid-19 Lombardy Working Group
Subjects: Public aspects of medicine, RA1-1270
Abstract: Abstract Background The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February–June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February–June 2020, with non-missing hospital of admission and non-missing admission date. Results The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56–80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1–28.0%); and steadily decreased from 34.6% (32.5–36.6%) in February to 7.6% (6.3–10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6–12.3) days, compared to 8.1 (7.8–8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5–22.8) days in February to 5.2 (4.7–5.8) days in June. Conclusions The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i.
Published: 2021
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36. The impact of viral mutations on recognition by SARS-CoV-2 specific T cells

Author: Aanensen, David M., Abudahab, Khalil, Adams, Helen, Adams, Alexander, Afifi, Safiah, Aggarwal, Dinesh, Ahmad, Shazaad S.Y., Aigrain, Louise, Alcolea-Medina, Adela, Alikhan, Nabil-Fareed, Allara, Elias, Amato, Roberto, Annett, Tara, Aplin, Stephen, Ariani, Cristina V., Asad, Hibo, Ash, Amy, Ashfield, Paula, Ashford, Fiona, Atkinson, Laura, Attwood, Stephen W., Auckland, Cressida, Aydin, Alp, Baker, David J., Baker, Paul, Balcazar, Carlos E., Ball, Jonathan, Barrett, Jeffrey C., Barrow, Magdalena, Barton, Edward, Bashton, Matthew, Bassett, Andrew R., Batra, Rahul, Baxter, Chris, Bayzid, Nadua, Beaver, Charlotte, Beckett, Angela H., Beckwith, Shaun M., Bedford, Luke, Beer, Robert, Beggs, Andrew, Bellis, Katherine L., Berry, Louise, Bertolusso, Beatrice, Best, Angus, Betteridge, Emma, Bibby, David, Bicknell, Kelly, Binns, Debbie, Birchley, Alec, Bird, Paul W., Bishop, Chloe, Blacow, Rachel, Blakey, Victoria, Blane, Beth, Bolt, Frances, Bonfield, James, Bonner, Stephen, Bonsall, David, Boswell, Tim, Bosworth, Andrew, Bourgeois, Yann, Boyd, Olivia, Bradley, Declan T., Breen, Cassie, Bresner, Catherine, Breuer, Judith, Bridgett, Stephen, Bronner, Iraad F., Brooks, Ellena, Broos, Alice, Brown, Julianne R., Bucca, Giselda, Buchan, Sarah L., Buck, David, Bull, Matthew, Burns, Phillipa J., Burton-Fanning, Shirelle, Byaruhanga, Timothy, Byott, Matthew, Campbell, Sharon, Carabelli, Alessandro M., Cargill, James S., Carlile, Matthew, Carvalho, Silvia F., Casey, Anna, Castigador, Anibolina, Catalan, Jana, Chalker, Vicki, Chaloner, Nicola J., Chand, Meera, Chappell, Joseph G., Charalampous, Themoula, Chatterton, Wendy, Chaudhry, Yasmin, Churcher, Carol M., Clark, Gemma, Clarke, Phillip, Cogger, Benjamin J., Cole, Kevin, Collins, Jennifer, Colquhoun, Rachel, Connor, Thomas R., Cook, Kate F., Coombes, Jason, Corden, Sally, Cormie, Claire, Cortes, Nicholas, Cotic, Marius, Cotton, Seb, Cottrell, Simon, Coupland, Lindsay, Cox, MacGregor, Cox, Alison, Craine, Noel, Crawford, Liam, Cross, Aidan, Crown, Matthew R., Crudgington, Dorian, Cumley, Nicola, Curran, Tanya, Curran, Martin D., da Silva Filipe, Ana, Dabrera, Gavin, Darby, Alistair C., Davidson, Rose K., Davies, Alisha, Davies, Robert M., Davis, Thomas, de Angelis, Daniela, De Lacy, Elen, de Oliveira Martins, Leonardo, Debebe, Johnny, Denton-Smith, Rebecca, Dervisevic, Samir, Dewar, Rebecca, Dey, Jayasree, Dias, Joana, Dobie, Donald, Dorman, Matthew J., Downing, Fatima, Driscoll, Megan, du Plessis, Louis, Duckworth, Nichola, Durham, Jillian, Eastick, Kirstine, Easton, Lisa J., Eccles, Richard, Edgeworth, Jonathan, Edwards, Sue, El Bouzidi, Kate, Eldirdiri, Sahar, Ellaby, Nicholas, Elliott, Scott, Eltringham, Gary, Ensell, Leah, Erkiert, Michelle J., Zamudio, Marina Escalera, Essex, Sarah, Evans, Johnathan M., Evans, Cariad, Everson, William, Fairley, Derek J., Fallon, Karlie, Fanaie, Arezou, Farr, Ben W., Fearn, Christopher, Feltwell, Theresa, Ferguson, Lynne, Fina, Laia, Flaviani, Flavia, Fleming, Vicki M., Forrest, Sally, Foster-Nyarko, Ebenezer, Foulkes, Benjamin H., Foulser, Luke, Fragakis, Mireille, Frampton, Dan, Francois, Sarah, Fraser, Christophe, Freeman, Timothy M., Fryer, Helen, Fuchs, Marc, Fuller, William, Gajee, Kavitha, Galai, Katerina, Gallagher, Abbie, Gallagher, Eileen, Gallagher, Michael D., Gallis, Marta, Gaskin, Amy, Gatica-Wilcox, Bree, Geidelberg, Lily, Gemmell, Matthew, Georgana, Iliana, George, Ryan P., Gifford, Laura, Gilbert, Lauren, Girgis, Sophia T., Glaysher, Sharon, Goldstein, Emily J., Golubchik, Tanya, Gomes, Andrea N., Gonçalves, Sónia, Goodfellow, Ian G., Goodwin, Scott, Goudarzi, Salman, Gourtovaia, Marina, Graham, Clive, Graham, Lee, Grant, Paul R., Green, Luke R., Green, Angie, Greenaway, Jane, Gregory, Richard, Guest, Martyn, Gunson, Rory N., Gupta, Ravi K., Gutierrez, Bernardo, Haldenby, Sam T., Hamilton, William L., Hansford, Samantha E., Haque, Tanzina, Harris, Kathryn A., Harrison, Ian, Harrison, Ewan M., Hart, Jennifer, Hartley, John A., Harvey, William T., Harvey, Matthew, Hassan-Ibrahim, Mohammed O., Heaney, Judith, Helmer, Thomas, Henderson, John H., Hesketh, Andrew R., Hey, Jessica, Heyburn, David, Higginson, Ellen E., Hill, Verity, Hill, Jack D., Hilson, Rachel A., Hilvers, Ember, Holden, Matthew T.G., Hollis, Amy, Holmes, Christopher W., Holmes, Nadine, Holmes, Alison H., Hopes, Richard, Hornsby, Hailey R., Hosmillo, Myra, Houlihan, Catherine, Howson-Wells, Hannah C., Hubb, Jonathan, Huckson, Hannah, Hughes, Warwick, Hughes, Joseph, Hughes, Margaret, Hutchings, Stephanie, Idle, Giles, Illingworth, Chris J., Impey, Robert, Irish-Tavares, Dianne, Iturriza-Gomara, Miren, Izuagbe, Rhys, Jackson, Chris, Jackson, Ben, Jackson, Leigh M., Jackson, Kathryn A., Jackson, David K., Jahun, Aminu S., James, Victoria, James, Keith, Jeanes, Christopher, Jeffries, Aaron R., Jeremiah, Sarah, Jermy, Andrew, John, Michaela, Johnson, Rob, Johnson, Kate, Johnston, Ian, Jones, Owen, Jones, Sophie, Jones, Hannah, Jones, Christopher R., Jones, Neil, Joseph, Amelia, Judges, Sarah, Kay, Gemma L., Kay, Sally, Keatley, Jon-Paul, Keeley, Alexander J., Kenyon, Anita, Kermack, Leanne M., Khakh, Manjinder, Kidd, Stephen P., Kimuli, Maimuna, Kirk, Stuart, Kitchen, Christine, Kitchman, Katie, Knight, Bridget A., Koshy, Cherian, Kraemer, Moritz U.G., Kumziene-Summerhayes, Sara, Kwiatkowski, Dominic, Lackenby, Angie, Laing, Kenneth G., Lampejo, Temi, Langford, Cordelia F., Lavin, Deborah, Lawton, Andrew I., Lee, Jack, Lee, David, Lensing, Stefanie V., Leonard, Steven, Levett, Lisa J., Le-Viet, Thanh, Lewis, Jonathan, Lewis, Kevin, Liddle, Jennifier, Liggett, Steven, Lillie, Patrick J., Lister, Michelle M., Livett, Rich, Lo, Stephanie, Loman, Nicholas J., Loose, Matthew W., Louka, Stavroula F., Loveson, Katie F., Lowdon, Sarah, Lowe, Hannah, Lowe, Helen L., Lucaci, Anita O., Ludden, Catherine, Lynch, Jessica, Lyons, Ronan A., Lythgoe, Katrina, Machin, Nicholas W., MacIntyre-Cockett, George, Mack, Andrew, Macklin, Ben, Maclean, Alasdair, Macnaughton, Emily, Madona, Pinglawathee, Maes, Mailis, Maftei, Laurentiu, Mahanama, Adhyana I.K., Mahungu, Tabitha W., Mair, Daniel, Maksimovic, Joshua, Malone, Cassandra S., Maloney, Daniel, Manesis, Nikos, Manley, Robin, Mantzouratou, Anna, Marchbank, Angela, Mariappan, Arun, Martincorena, Inigo, Martinez Nunez, Rocio T., Mather, Alison E., Maxwell, Patrick, Mayhew, Megan, Mbisa, Tamyo, McCann, Clare M., McCarthy, Shane A., McCluggage, Kathryn, McClure, Patrick C., McCrone, J.T., McHugh, Martin P., McKenna, James P., McKerr, Caoimhe, McManus, Georgina M., McMurray, Claire L., McMurray, Claire, McNally, Alan, Meadows, Lizzie, Medd, Nathan, Megram, Oliver, Menegazzo, Mirko, Merrick, Ian, Michell, Stephen L., Michelsen, Michelle L., Mirfenderesky, Mariyam, Mirza, Jeremy, Miskelly, Julia, Moles-Garcia, Emma, Moll, Robin J., Molnar, Zoltan, Monahan, Irene M., Mondani, Matteo, Mookerjee, Siddharth, Moore, Christopher, Moore, Jonathan, Moore, Nathan, Moore, Catherine, Morcrette, Helen, Morgan, Sian, Morgan, Mari, Mori, Matilde, Morriss, Arthur, Moses, Samuel, Mower, Craig, Muir, Peter, Mukaddas, Afrida, Munemo, Florence, Munn, Robert, Murray, Abigail, Murray, Leanne J., Murray, Darren R., Mutingwende, Manasa, Myers, Richard, Nastouli, Eleni, Nebbia, Gaia, Nelson, Andrew, Nelson, Charlotte, Nicholls, Sam, Nichols, Jenna, Nicodemi, Roberto, Nomikou, Kyriaki, O’Grady, Justin, O'Brien, Sarah, Odedra, Mina, Ohemeng-Kumi, Natasha, Oliver, Karen, Orton, Richard J., Osman, Husam, xeine O'Toole, Pacchiarini, Nicole, Padgett, Debra, Page, Andrew J., Park, Emily J., Park, Naomi R., Parmar, Surendra, Partridge, David G., Pascall, David, Patel, Amita, Patel, Bindi, Paterson, Steve, Payne, Brendan A.I., Peacock, Sharon J., Pearson, Clare, Pelosi, Emanuela, Percival, Benita, Perkins, Jon, Perry, Malorie, Pinckert, Malte L., Platt, Steven, Podplomyk, Olga, Pohare, Manoj, Pond, Marcus, Pope, Cassie F., Poplawski, Radoslaw, Powell, Jessica, Poyner, Jennifer, Prestwood, Liam, Price, Anna, Price, James R., Prieto, Jacqui A., Pritchard, David T., Prosolek, Sophie J., Pugh, Georgia, Pusok, Monika, Pybus, Oliver G., Pymont, Hannah M., Quail, Michael A., Quick, Joshua, Radulescu, Clara, Raghwani, Jayna, Ragonnet-Cronin, Manon, Rainbow, Lucille, Rajan, Diana, Rajatileka, Shavanthi, Ramadan, Newara A., Rambaut, Andrew, Ramble, John, Randell, Paul A., Randell, Paul, Ratcliffe, Liz, Raviprakash, Veena, Raza, Mohammad, Redshaw, Nicholas M., Rey, Sara, Reynolds, Nicola, Richter, Alex, Robertson, David L., Robinson, Esther, Robson, Samuel C., Rogan, Fiona, Rooke, Stefan, Rowe, Will, Roy, Sunando, Rudder, Steven, Ruis, Chris, Rushton, Steven, Ryan, Felicity, Saeed, Kordo, Samaraweera, Buddhini, Sambles, Christine M., Sanderson, Roy, Sanderson, Theo, Sang, Fei, Sass, Thea, Scher, Emily, Scott, Garren, Scott, Carol, Sehmi, Jasveen, Shaaban, Sharif, Shah, Divya, Shaw, Jessica, Shelest, Ekaterina, Shepherd, James G., Sheridan, Liz A., Sheriff, Nicola, Shirley, Lesley, Sillitoe, John, Silviera, Siona, Simpson, David A., Singh, Aditi, Singleton, Dawn, Skvortsov, Timofey, Sloan, Tim J., Sluga, Graciela, Smith, Ken, Smith, Kim S., Smith, Perminder, Smith, Darren L., Smith, Louise, Smith, Colin P., Smith, Nikki, Smollett, Katherine L., Snell, Luke B., Somassa, Thomas, Southgate, Joel, Spellman, Karla, Spencer Chapman, Michael H., Spurgin, Lewis G., Spyer, Moira J., Stanley, Rachael, Stanley, William, Stanton, Thomas D., Starinskij, Igor, Stockton, Joanne, Stonehouse, Susanne, Storey, Nathaniel, Studholme, David J., Sudhanva, Malur, Swindells, Emma, Taha, Yusri, Tan, Ngee Keong, Tang, Julian W., Tang, Miao, Taylor, Ben E.W., Taylor, Joshua F., Taylor, Sarah, Temperton, Ben, Templeton, Kate E., Thomas, Claire, Thomson, Laura, Thomson, Emma C., Thornton, Alicia, Thurston, Scott A.J., Todd, John A., Tomb, Rachael, Tong, Lily, Tonkin-Hill, Gerry, Torok, M. Estee, Tovar-Corona, Jaime M., Trebes, Amy, Trotter, Alexander J., Tsatsani, Ioulia, Turnbull, Robyn, Twohig, Katherine A., Umpleby, Helen, Underwood, Anthony P., Vamos, Edith E., Vasylyeva, Tetyana I., Vattipally, Sreenu, Vernet, Gabrielle, Vipond, Barry B., Volz, Erik M., Walsh, Sarah, Wang, Dennis, Warne, Ben, Warwick-Dugdale, Joanna, Wastnedge, Elizabeth, Watkins, Joanne, Watson, Louisa K., Waugh, Sheila, Webster, Hermione J., Weldon, Danni, Westwick, Elaine, Whalley, Thomas, Wheeler, Helen, Whitehead, Mark, Whiteley, Max, Whitwham, Andrew, Wierzbicki, Claudia, Willford, Nicholas J., Williams, Lesley-Anne, Williams, Rebecca, Williams, Cheryl, Williams, Chris, Williams, Charlotte A., Williams, Rachel J., Williams, Thomas, Williams, Catryn, Williamson, Kathleen A., Wilson-Davies, Eleri, Witele, Eric, Withell, Karen T., Witney, Adam A., Wolverson, Paige, Wong, Nick, Workman, Trudy, Wright, Victoria, Wright, Derek W., Wyatt, Tim, Wyllie, Sarah, Xu-McCrae, Li, Yavus, Mehmet, Yaze, Geraldine, Yeats, Corin A., Yebra, Gonzalo, Yew, Wen C., Young, Gregory R., Young, Jamie, Zarebski, Alex E., Zhang, Peijun, Baillie, J. Kenneth, Semple, Malcolm G., Openshaw, Peter J.M., Carson, Gail, Alex, Beatrice, Andrikopoulos, Petros, Bach, Benjamin, Barclay, Wendy S., Bogaert, Debby, Chechi, Kanta, Cooke, Graham S., Docherty, Annemarie B., Correia, Gonçalo dos Santos, Dumas, Marc-Emmanuel, Dunning, Jake, Fletcher, Tom, Green, Christopher A., Greenhalf, William, Griffin, Julian L., Gupta, Rishi K., Harrison, Ewen M., Hiscox, Julian A., Wai Ho, Antonia Ying, Horby, Peter W., Ijaz, Samreen, Khoo, Saye, Klenerman, Paul, Law, Andrew, Lewis, Matthew R., Liggi, Sonia, Lim, Wei Shen, Maslen, Lynn, Mentzer, Alexander J., Merson, Laura, Meynert, Alison M., Noursadeghi, Mahdad, Olanipekun, Michael, Osagie, Anthonia, Palmarini, Massimo, Palmieri, Carlo, Paxton, William A., Pollakis, Georgios, Price, Nicholas, Russell, Clark D., Sancho-Shimizu, Vanessa, Sands, Caroline J., Scott, Janet T., Sigfrid, Louise, Solomon, Tom, Sriskandan, Shiranee, Stuart, David, Summers, Charlotte, Swann, Olivia V., Takats, Zoltan, Takis, Panteleimon, Tedder, Richard S., Thompson, A.A. Roger, Thwaites, Ryan S., Zambon, Maria, Hardwick, Hayley, Donohue, Chloe, Griffiths, Fiona, Oosthuyzen, Wilna, Donegan, Cara, Spencer, Rebecca G., Dalton, Jo, Girvan, Michelle, Saviciute, Egle, Roberts, Stephanie, Harrison, Janet, Marsh, Laura, Connor, Marie, Halpin, Sophie, Jackson, Clare, Gamble, Carrol, Plotkin, Daniel, Lee, James, Leeming, Gary, Wham, Murray, Clohisey, Sara, Hendry, Ross, Scott-Brown, James, Shaw, Victoria, McDonald, Sarah E., Keating, Seán, Ahmed, Katie A., Armstrong, Jane A., Ashworth, Milton, Asiimwe, Innocent G., Bakshi, Siddharth, Barlow, Samantha L., Booth, Laura, Brennan, Benjamin, Bullock, Katie, Catterall, Benjamin W.A., Clark, Jordan J., Clarke, Emily A., Cole, Sarah, Cooper, Louise, Cox, Helen, Davis, Christopher, Dincarslan, Oslem, Dunn, Chris, Dyer, Philip, Elliott, Angela, Evans, Anthony, Finch, Lorna, Fisher, Lewis W.S., Foster, Terry, Garcia-Dorival, Isabel, Gunning, Philip, Hartley, Catherine, Jensen, Rebecca L., Jones, Christopher B., Jones, Trevor R., Khandaker, Shadia, King, Katharine, Kiy, Robyn T., Koukorava, Chrysa, Lake, Annette, Lant, Suzannah, Latawiec, Diane, Lavelle-Langham, Lara, Lefteri, Daniella, Lett, Lauren, Livoti, Lucia A., Mancini, Maria, McDonald, Sarah, McEvoy, Laurence, McLauchlan, John, Metelmann, Soeren, Miah, Nahida S., Middleton, Joanna, Mitchell, Joyce, Moore, Shona C., Murphy, Ellen G., Penrice-Randal, Rebekah, Pilgrim, Jack, Prince, Tessa, Reynolds, Will, Ridley, P. Matthew, Sales, Debby, Shaw, Victoria E., Shears, Rebecca K., Small, Benjamin, Subramaniam, Krishanthi S., Szemiel, Agnieska, Taggart, Aislynn, Tanianis-Hughes, Jolanta, Thomas, Jordan, Trochu, Erwan, van Tonder, Libby, Wilcock, Eve, Zhang, J. Eunice, Flaherty, Lisa, Maziere, Nicole, Cass, Emily, Carracedo, Alejandra Doce, Carlucci, Nicola, Holmes, Anthony, Massey, Hannah, Murphy, Lee, Wrobel, Nicola, McCafferty, Sarah, Morrice, Kirstie, MacLean, Alan, Adeniji, Kayode, Agranoff, Daniel, Agwuh, Ken, Ail, Dhiraj, Aldera, Erin L., Alegria, Ana, Allen, Sam, Angus, Brian, Ashish, Abdul, Atkinson, Dougal, Bari, Shahedal, Barlow, Gavin, Barnass, Stella, Barrett, Nicholas, Bassford, Christopher, Basude, Sneha, Baxter, David, Beadsworth, Michael, Bernatoniene, Jolanta, Berridge, John, Berry, Colin, Best, Nicola, Bothma, Pieter, Chadwick, David, Brittain-Long, Robin, Bulteel, Naomi, Burden, Tom, Burtenshaw, Andrew, Caruth, Vikki, Chambler, Duncan, Chee, Nigel, Child, Jenny, Chukkambotla, Srikanth, Clark, Tom, Collini, Paul, Cosgrove, Catherine, Cupitt, Jason, Cutino-Moguel, Maria-Teresa, Dark, Paul, Dawson, Chris, Donnison, Phil, Douthwaite, Sam, Drummond, Andrew, DuRand, Ingrid, Dushianthan, Ahilanadan, Dyer, Tristan, Eziefula, Chi, Fegan, Chrisopher, Finn, Adam, Fullerton, Duncan, Garg, Sanjeev, Garg, Atul, Gkrania-Klotsas, Effrossyni, Godden, Jo, Goldsmith, Arthur, Hardy, Elaine, Hartshorn, Stuart, Harvey, Daniel, Havalda, Peter, Hawcutt, Daniel B., Hobrok, Maria, Hodgson, Luke, Hormis, Anil, Jacobs, Michael, Jain, Susan, Jennings, Paul, Kaliappan, Agilan, Kasipandian, Vidya, Kegg, Stephen, Kelsey, Michael, Kendall, Jason, Kerrison, Caroline, Kerslake, Ian, Koch, Oliver, Koduri, Gouri, Koshy, George, Laha, Shondipon, Laird, Steven, Larkin, Susan, Leiner, Tamas, Lillie, Patrick, Limb, James, Linnett, Vanessa, Little, Jeff, Lyttle, Mark, MacMahon, Michael, MacNaughton, Emily, Mankregod, Ravish, Masson, Huw, Matovu, Elijah, McCullough, Katherine, McEwen, Ruth, Meda, Manjula, Mills, Gary, Minton, Jane, Mohandas, Kavya, Mok, Quen, Moon, James, Moore, Elinoor, Morgan, Patrick, Morris, Craig, Mortimore, Katherine, Mpenge, Mbiye, Mulla, Rohinton, Murphy, Michael, Nagel, Megan, Nagarajan, Thapas, Nelson, Mark, Norris, Lillian, O'Shea, Matthew K., Otahal, Igor, Ostermann, Marlies, Pais, Mark, Panchatsharam, Selva, Papakonstantinou, Danai, Paraiso, Hassan, Patel, Brij, Pattison, Natalie, Pepperell, Justin, Peters, Mark, Phull, Mandeep, Pintus, Stefania, Pooni, Jagtur Singh, Planche, Tim, Post, Frank, Price, David, Prout, Rachel, Rae, Nikolas, Reschreiter, Henrik, Reynolds, Tim, Richardson, Neil, Roberts, Mark, Roberts, Devender, Rose, Alistair, Rousseau, Guy, Ruge, Bobby, Ryan, Brendan, Saluja, Taranprit, Schmid, Matthias L., Shah, Aarti, Shanmuga, Prad, Sharma, Anil, Shawcross, Anna, Sizer, Jeremy, Shankar-Hari, Manu, Smith, Richard, Snelson, Catherine, Spittle, Nick, Staines, Nikki, Stambach, Tom, Stewart, Richard, Subudhi, Pradeep, Szakmany, Tamas, Tatham, Kate, Thomas, Jo, Thompson, Chris, Thompson, Robert, Tridente, Ascanio, Tupper-Carey, Darell, Twagira, Mary, Vallotton, Nick, Vancheeswaran, Rama, Vincent-Smith, Lisa, Visuvanathan, Shico, Vuylsteke, Alan, Waddy, Sam, Wake, Rachel, Walden, Andrew, Welters, Ingeborg, Whitehouse, Tony, Whittaker, Paul, Whittington, Ashley, Papineni, Padmasayee, Wijesinghe, Meme, Williams, Martin, Wilson, Lawrence, Winchester, Stephen, Wiselka, Martin, Wolverson, Adam, Wootton, Daniel G., Workman, Andrew, Yates, Bryan, Young, Peter, de Silva, Thushan I., Liu, Guihai, Lindsey, Benjamin B., Dong, Danning, Hsu, Nienyun Sharon, Shah, Dhruv, Wellington, Dannielle, Angyal, Adrienn, Brown, Rebecca, Parker, Matthew D., Ying, Zixi, Yao, Xuan, Turtle, Lance, Dunachie, Susanna, Maini, Mala K., Ogg, Graham, Knight, Julian C., Peng, Yanchun, Rowland-Jones, Sarah L., and Dong, Tao
Published: 2021
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37. EFFICIENT REAL-TIME MONITORING OF AN EMERGING INFLUENZA PANDEMIC : HOW FEASIBLE?

Author: Birrell, Paul J., Wernisch, Lorenz, Tom, Brian D. M., Held, Leonhard, Roberts, Gareth O., Pebody, Richard G., and De Angelis, Daniela
Published: 2020

38. Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

Author: Birrell, Paul J, Wernisch, Lorenz, Tom, Brian D M, Held, Leonhard, Roberts, Gareth O, Pebody, Richard G, and De Angelis, Daniela
Subjects: Statistics - Computation, Statistics - Applications
Abstract: A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased, and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter non-identifiability., Comment: 30 pages, 8 figures
Published: 2016

39. Joining and splitting models with Markov melding

Author: Goudie, Robert J. B., Presanis, Anne M., Lunn, David, De Angelis, Daniela, and Wernisch, Lorenz
Subjects: Statistics - Methodology, Statistics - Applications, Statistics - Computation
Abstract: Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. We propose a generic method for forming a suitable joint model when joining submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model. The approach also enables splitting of large joint models into smaller submodels, allowing inference for the original joint model to be conducted via our multi-stage algorithm. We motivate and demonstrate our approach through two examples: joining components of an evidence synthesis of A/H1N1 influenza, and splitting a large ecology model.
Published: 2016
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40. Tracking elimination of HIV transmission in men who have sex with men in England: a modelling study

Author: Brizzi, Francesco, Birrell, Paul J, Kirwan, Peter, Ogaz, Dana, Brown, Alison E, Delpech, Valerie C, Gill, O Noel, and De Angelis, Daniela
Published: 2021
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41. Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK

Author: Baillie, J Kenneth, Semple, Malcolm G, Openshaw, Peter JM, Carson, Gail, Alex, Beatrice, Bach, Benjamin, Barclay, Wendy S, Bogaert, Debby, Chand, Meera, Cooke, Graham S, Docherty, Annemarie B, Dunning, Jake, da Silva Filipe, Ana, Fletcher, Tom, Green, Christopher A, Harrison, Ewen M, Hiscox, Julian A, Ho, Antonia YW, Horby, Peter W, Ijaz, Samreen, Khoo, Say, Klenerman, Paul, Law, Andrew, Lim, Wei Shen, Mentzer, Alexander J, Merson, Laura, Meynert, Alison M, Noursadeghi, Mahdad, Moore, Shona C, Palmarini, Massimo, Paxton, William A, Pollakis, Georgios, Price, Nicholas, Rambaut, Andrew, Robertson, David L, Russell, Clark D, Sancho-Shimizu, Vanessa, Scott, Janet T, de Silva, Thushan, Sigfrid, Louise, Solomon, Tom, Sriskandan, Shiranee, Stuart, David, Summers, Charlotte, Tedder, Richard S, Thomson, Emma C, Thompson, AA Roger, Thwaites, Ryan S, Turtle, Lance CW, Gupta, Rishi K, Palmieri, Carlo, Zambon, Maria, Hardwick, Hayley, Donohue, Chloe, Lyons, Ruth, Griffiths, Fiona, Oosthuyzen, Wilna, Norman, Lisa, Pius, Riinu, Drake, Thomas M, Fairfield, Cameron J, Knight, Stephen R, Mclean, Kenneth A, Murphy, Derek, Shaw, Catherine A, Dalton, Jo, Girvan, Michelle, Saviciute, Egle, Roberts, Stephanie, Harrison, Janet, Marsh, Laura, Connor, Marie, Halpin, Sophie, Jackson, Clare, Gamble, Carrol, Leeming, Gary, Wham, Murray, Clohisey, Sara, Hendry, Ross, Scott-Brown, James, Greenhalf, William, Shaw, Victoria, McDonald, Sarah E, Keating, Seán, Ahmed, Katie A, Armstrong, Jane A, Ashworth, Milton, Asiimwe, Innocent G, Bakshi, Siddharth, Barlow, Samantha L, Booth, Laura, Brennan, Benjamin, Bullock, Katie, Catterall, Benjamin WA, Clark, Jordan J, Clarke, Emily A, Cole, Sarah, Cooper, Louise, Cox, Helen, Davis, Christopher, Dincarslan, Oslem, Dunn, Chris, Dyer, Philip, Elliott, Angela, Evans, Anthony, Finch, Lorna, Fisher, Lewis WS, Foster, Terry, Garcia-Dorival, Isabel, Gunning, Philip, Hartley, Catherine, Jensen, Rebecca L, Jones, Christopher B, Jones, Trevor R, Khandaker, Shadia, King, Katharine, Kiy, Robyn T, Koukorava, Chrysa, Lake, Annette, Lant, Suzannah, Latawiec, Diane, Lavelle-Langham, Lara, Lefteri, Daniella, Lett, Lauren, Livoti, Lucia A, Mancini, Maria, McDonald, Sarah, McEvoy, Laurence, McLauchlan, John, Metelmann, Soeren, Miah, Nahida S, Middleton, Joanna, Mitchell, Joyce, Murphy, Ellen G, Penrice-Randal, Rebekah, Pilgrim, Jack, Prince, Tessa, Reynolds, Will, Ridley, P. Matthew, Sales, Debby, Shaw, Victoria E, Shears, Rebecca K, Small, Benjamin, Subramaniam, Krishanthi S, Szemiel, Agnieska, Taggart, Aislynn, Tanianis-Hughes, Jolanta, Thomas, Jordan, Trochu, Erwan, van Tonder, Libby, Wilcock, Eve, Zhang, J. Eunice, Flaherty, Lisa, Maziere, Nicole, Cass, Emily, Doce Carracedo, Alejandra, Carlucci, Nicola, Holmes, Anthony, Massey, Hannah, Murphy, Lee, Wrobel, Nicola, McCafferty, Sarah, Morrice, Kirstie, MacLean, Alan, Adeniji, Kayode, Agranoff, Daniel, Agwuh, Ken, Ail, Dhiraj, Aldera, Erin L, Alegria, Ana, Angus, Brian, Ashish, Abdul, Atkinson, Dougal, Bari, Shahedal, Barlow, Gavin, Barnass, Stella, Barrett, Nicholas, Bassford, Christopher, Basude, Sneha, Baxter, David, Beadsworth, Michael, Bernatoniene, Jolanta, Berridge, John, Best, Nicola, Bothma, Pieter, Chadwick, David, Brittain-Long, Robin, Bulteel, Naomi, Burden, Tom, Burtenshaw, Andrew, Caruth, Vikki, Chambler, Duncan, Chee, Nigel, Child, Jenny, Chukkambotla, Srikanth, Clark, Tom, Collini, Paul, Cosgrove, Catherine, Cupitt, Jason, Cutino-Moguel, Maria-Teresa, Dark, Paul, Dawson, Chris, Dervisevic, Samir, Donnison, Phil, Douthwaite, Sam, DuRand, Ingrid, Dushianthan, Ahilanadan, Dyer, Tristan, Evans, Cariad, Eziefula, Chi, Fegan, Chrisopher, Finn, Adam, Fullerton, Duncan, Garg, Sanjeev, Garg, Atul, Gkrania-Klotsas, Effrossyni, Godden, Jo, Goldsmith, Arthur, Graham, Clive, Hardy, Elaine, Hartshorn, Stuart, Harvey, Daniel, Havalda, Peter, Hawcutt, Daniel B, Hobrok, Maria, Hodgson, Luke, Hormis, Anil, Jacobs, Michael, Jain, Susan, Jennings, Paul, Kaliappan, Agilan, Kasipandian, Vidya, Kegg, Stephen, Kelsey, Michael, Kendall, Jason, Kerrison, Caroline, Kerslake, Ian, Koch, Oliver, Koduri, Gouri, Koshy, George, Laha, Shondipon, Laird, Steven, Larkin, Susan, Leiner, Tamas, Lillie, Patrick, Limb, James, Linnett, Vanessa, Little, Jeff, Lyttle, Mark, MacMahon, Michael, MacNaughton, Emily, Mankregod, Ravish, Masson, Huw, Matovu, Elijah, McCullough, Katherine, McEwen, Ruth, Meda, Manjula, Mills, Gary, Minton, Jane, Mirfenderesky, Mariyam, Mohandas, Kavya, Mok, Quen, Moon, James, Moore, Elinoor, Morgan, Patrick, Morris, Craig, Mortimore, Katherine, Moses, Samuel, Mpenge, Mbiye, Mulla, Rohinton, Murphy, Michael, Nagel, Megan, Nagarajan, Thapas, Nelson, Mark, O'Shea, Matthew K, Otahal, Igor, Ostermann, Marlies, Pais, Mark, Panchatsharam, Selva, Papakonstantinou, Danai, Paraiso, Hassan, Patel, Brij, Pattison, Natalie, Pepperell, Justin, Peters, Mark, Phull, Mandeep, Pintus, Stefania, Singh Pooni, Jagtur, Post, Frank, Price, David, Prout, Rachel, Rae, Nikolas, Reschreiter, Henrik, Reynolds, Tim, Richardson, Neil, Roberts, Mark, Roberts, Devender, Rose, Alistair, Rousseau, Guy, Ryan, Brendan, Saluja, Taranprit, Shah, Aarti, Shanmuga, Prad, Sharma, Anil, Shawcross, Anna, Sizer, Jeremy, Shankar-Hari, Manu, Smith, Richard, Snelson, Catherine, Spittle, Nick, Staines, Nikki, Stambach, Tom, Stewart, Richard, Subudhi, Pradeep, Szakmany, Tamas, Tatham, Kate, Thomas, Jo, Thompson, Chris, Thompson, Robert, Tridente, Ascanio, Tupper-Carey, Darell, Twagira, Mary, Ustianowski, Andrew, Vallotton, Nick, Vincent-Smith, Lisa, Visuvanathan, Shico, Vuylsteke, Alan, Waddy, Sam, Wake, Rachel, Walden, Andrew, Welters, Ingeborg, Whitehouse, Tony, Whittaker, Paul, Whittington, Ashley, Papineni, Padmasayee, Wijesinghe, Meme, Williams, Martin, Wilson, Lawrence, Sarah, Sarah, Winchester, Stephen, Wiselka, Martin, Wolverson, Adam, Wooton, Daniel G, Workman, Andrew, Yates, Bryan, Young, Peter, Mulholland, Rachel H, Lone, Nazir I, Cheyne, Christopher P, De Angelis, Daniela, Diaz-Ordaz, Karla, Donegan, Cara, Funk, Sebastian, García-Fiñana, Marta, Hardwick, Hayley E, Ho, Antonia, Hughes, David M, Keogh, Ruth H, Kirwan, Peter D, Nguyen Van-Tam, Jonathan S, Spencer, Rebecca G, Tom, Brian DM, and Turtle, Lance
Published: 2021
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42. Stable population structure in Europe since the Iron Age, despite high mobility

Author: Antonio, Margaret L, Weiß, Clemens L, Gao, Ziyue, Sawyer, Susanna, Oberreiter, Victoria, Moots, Hannah M, Spence, Jeffrey P, Cheronet, Olivia, Zagorc, Brina, Praxmarer, Elisa, Özdoğan, Kadir Toykan, Demetz, Lea, Gelabert, Pere, Fernandes, Daniel, Lucci, Michaela, Alihodžić, Timka, Amrani, Selma, Avetisyan, Pavel, Baillif-Ducros, Christèle, Bedić, Željka, Bertrand, Audrey, Bilić, Maja, Bondioli, Luca, Borówka, Paulina, Botte, Emmanuel, Burmaz, Josip, Bužanić, Domagoj, Candilio, Francesca, Cvetko, Mirna, De Angelis, Daniela, Drnić, Ivan, Elschek, Kristián, Fantar, Mounir, Gaspari, Andrej, Gasperetti, Gabriella, Genchi, Francesco, Golubović, Snežana, Hukeľová, Zuzana, Jankauskas, Rimantas, Vučković, Kristina Jelinčić, Jeremić, Gordana, Kaić, Iva, Kazek, Kevin, Khachatryan, Hamazasp, Khudaverdyan, Anahit, Kirchengast, Sylvia, Korać, Miomir, Kozlowski, Valérie, Krošláková, Mária, Kušan Špalj, Dora, La Pastina, Francesco, Laguardia, Marie, Legrand, Sandra, Leleković, Tino, Leskovar, Tamara, Lorkiewicz, Wiesław, Los, Dženi, Silva, Ana Maria, Masaryk, Rene, Matijević, Vinka, Cherifi, Yahia Mehdi Seddik, Meyer, Nicolas, Mikić, Ilija, Miladinović-Radmilović, Nataša, Milošević Zakić, Branka, Nacouzi, Lina, Natuniewicz-Sekuła, Magdalena, Nava, Alessia, Neugebauer-Maresch, Christine, Nováček, Jan, Osterholtz, Anna, Paige, Julianne, Paraman, Lujana, Pieri, Dominique, Pieta, Karol, Pop-Lazić, Stefan, Ruttkay, Matej, Sanader, Mirjana, Sołtysiak, Arkadiusz, Sperduti, Alessandra, Stankovic Pesterac, Tijana, Teschler-Nicola, Maria, Teul, Iwona, Tončinić, Domagoj, Trapp, Julien, Vulović, Dragana, Waliszewski, Tomasz, Walter, Diethard, Živanović, Miloš, Filah, Mohamed el Mostefa, Čaušević-Bully, Morana, Šlaus, Mario, Borić, Dušan, Novak, Mario, Coppa, Alfredo, Pinhasi, Ron, Pritchard, Jonathan K, Antonio, Margaret L, Weiß, Clemens L, Gao, Ziyue, Sawyer, Susanna, Oberreiter, Victoria, Moots, Hannah M, Spence, Jeffrey P, Cheronet, Olivia, Zagorc, Brina, Praxmarer, Elisa, Özdoğan, Kadir Toykan, Demetz, Lea, Gelabert, Pere, Fernandes, Daniel, Lucci, Michaela, Alihodžić, Timka, Amrani, Selma, Avetisyan, Pavel, Baillif-Ducros, Christèle, Bedić, Željka, Bertrand, Audrey, Bilić, Maja, Bondioli, Luca, Borówka, Paulina, Botte, Emmanuel, Burmaz, Josip, Bužanić, Domagoj, Candilio, Francesca, Cvetko, Mirna, De Angelis, Daniela, Drnić, Ivan, Elschek, Kristián, Fantar, Mounir, Gaspari, Andrej, Gasperetti, Gabriella, Genchi, Francesco, Golubović, Snežana, Hukeľová, Zuzana, Jankauskas, Rimantas, Vučković, Kristina Jelinčić, Jeremić, Gordana, Kaić, Iva, Kazek, Kevin, Khachatryan, Hamazasp, Khudaverdyan, Anahit, Kirchengast, Sylvia, Korać, Miomir, Kozlowski, Valérie, Krošláková, Mária, Kušan Špalj, Dora, La Pastina, Francesco, Laguardia, Marie, Legrand, Sandra, Leleković, Tino, Leskovar, Tamara, Lorkiewicz, Wiesław, Los, Dženi, Silva, Ana Maria, Masaryk, Rene, Matijević, Vinka, Cherifi, Yahia Mehdi Seddik, Meyer, Nicolas, Mikić, Ilija, Miladinović-Radmilović, Nataša, Milošević Zakić, Branka, Nacouzi, Lina, Natuniewicz-Sekuła, Magdalena, Nava, Alessia, Neugebauer-Maresch, Christine, Nováček, Jan, Osterholtz, Anna, Paige, Julianne, Paraman, Lujana, Pieri, Dominique, Pieta, Karol, Pop-Lazić, Stefan, Ruttkay, Matej, Sanader, Mirjana, Sołtysiak, Arkadiusz, Sperduti, Alessandra, Stankovic Pesterac, Tijana, Teschler-Nicola, Maria, Teul, Iwona, Tončinić, Domagoj, Trapp, Julien, Vulović, Dragana, Waliszewski, Tomasz, Walter, Diethard, Živanović, Miloš, Filah, Mohamed el Mostefa, Čaušević-Bully, Morana, Šlaus, Mario, Borić, Dušan, Novak, Mario, Coppa, Alfredo, Pinhasi, Ron, and Pritchard, Jonathan K
Abstract: Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000–3000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire’s mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history.
Published: 2024

43. Effect of second booster vaccinations and prior infection against SARS-CoV-2 in the UK SIREN healthcare worker cohort

Author: Kirwan, Peter D., primary, Hall, Victoria J., additional, Foulkes, Sarah, additional, Otter, Ashley D., additional, Munro, Katie, additional, Sparkes, Dominic, additional, Howells, Anna, additional, Platt, Naomi, additional, Broad, Jonathan, additional, Crossman, David, additional, Norman, Chris, additional, Corrigan, Diane, additional, Jackson, Christopher H., additional, Cole, Michelle, additional, Brown, Colin S., additional, Atti, Ana, additional, Islam, Jasmin, additional, Presanis, Anne M., additional, Charlett, Andre, additional, De Angelis, Daniela, additional, Hopkins, Susan, additional, Lewis, Tracy, additional, Bain, Steve, additional, Thomas, Rebeccah, additional, Geen, John, additional, Pothecary, Carla, additional, Cutler, Sean, additional, Northfield, John, additional, Price, Cathy, additional, Tomlinson, Johanne, additional, Knight, Sarah, additional, Macnaughton, Emily, additional, Watson, Ekaterina, additional, Lazarus, Rajeka, additional, Sinclair, Aaran, additional, Galliford, Joanne, additional, Masunda, Bridgett, additional, Mahungu, Tabitha, additional, Rodger, Alison, additional, Hanison, Esther, additional, Warren, Simon, additional, Jain, Swati, additional, Mirfenderesky, Mariyam, additional, Mahabir, Natasha, additional, Pritchard-Jones, Rowan, additional, Wycherley, Diane, additional, Gabriel, Claire, additional, Matovu, Elijah, additional, Bakker, Philippa, additional, Guha, Simantee, additional, Gormley, S., additional, Pethick, James, additional, Butt, Georgina, additional, Pepper, Stacey, additional, Bedford, Luke, additional, Ridley, Paul, additional, Democratis, Jane, additional, Meda, Manjula, additional, Chawla, Anu, additional, Westwell, Fran, additional, Kalakonda, Nagesh, additional, Khanduri, Sheena, additional, Doel, Allison, additional, Pai, Sumita, additional, Hacon, Christian, additional, Nwaka, Davis, additional, Moro, Veronica Mendez, additional, Moody, A., additional, Auckland, Cressida, additional, Prince, Stephanie, additional, de Silva, Thushan, additional, Shulver, Helen, additional, Shah, A., additional, Jones, C., additional, Subhro-Osuji, Banerjee, additional, Houston, Angela, additional, Planche, Tim, additional, Booth, Martin, additional, Duff, Christopher, additional, Aeron-Thomas, Jonnie, additional, Chaudhuri, Ray, additional, Hilton, David, additional, Jory, Hannah, additional, Al-Khafaji, Zehra'a, additional, Kemsley, Philippa, additional, Longfellow, Ruth, additional, Boss, David, additional, Brake, Simon, additional, Coke, Louise, additional, Elumogo, Ngozi, additional, Latham, Scott, additional, Subudhi, Chinari, additional, Hoad, Ina, additional, Thomas, Claire, additional, Chitalia, Nihil, additional, Edmunds, Tracy, additional, Ashby, Helen, additional, Elliott, John, additional, Wilkinson, Beverley, additional, Rand, Abby, additional, Thompson, Catherine, additional, Agwuh, K., additional, Grice, Anna, additional, Moran, Kelly, additional, Waykar, Vijayendra, additional, Lester, Yvonne, additional, Sach, Lauren, additional, Court, Kathryn, additional, White, Nikki, additional, Favager, Clair, additional, Holliday, Kyra, additional, Harwood, Jayne, additional, Payne, Brendan, additional, Burns, Karen, additional, Fothergill, Lynda, additional, Arenas-Pinto, Alejandro, additional, Severn, Abigail, additional, Brown, Kerryanne, additional, Gray, Katherine, additional, Dare, Jane, additional, Zheng, Qi, additional, Hollinshead, Kathryn, additional, Shorten, Robert, additional, Roebuck, Alun, additional, Holmes, Christopher, additional, Wiselka, Martin, additional, Faris, Barzo, additional, Marsh, Liane, additional, McAdam, Clare, additional, Ditchfield, Lisa, additional, Qazzafi, Zaman, additional, Boyd, G., additional, Wong, N., additional, Brand, Sarah, additional, Squires, Jack, additional, Ashcroft, John, additional, Rosario, Ismaelette Del, additional, Howard, Joanne, additional, Ward, Emma, additional, Harrison, Gemma, additional, Morgan, Joely, additional, Corless, Claire, additional, Penn, Ruth, additional, Wong, Nick, additional, Bagary, Manny, additional, Starkova, Nadezda, additional, Beekes, Mandy, additional, Carnahan, Mandy, additional, Khan, Shivani, additional, Mackay, Shekoo, additional, Lewis, Keneisha, additional, Pickard, Graham, additional, Dawson, Joy, additional, Finlayson, Lauren, additional, Cameron, Euan, additional, Todd, Anne, additional, Fagegaltier, Sebastien, additional, Mavin, Sally, additional, Cochrane, Alexandra, additional, Gibson, Andrew, additional, Donaldson, Sam, additional, Templeton, Kate, additional, Malcolm, Martin, additional, Smith, Beth, additional, Dhasmana, Devesh, additional, Fowler, Susan, additional, Ho, Antonia, additional, Murphy, Michael, additional, Beith, Claire, additional, Patel, Manish, additional, Boyd, Elizabeth, additional, Irvine, Val, additional, Grant, Alison, additional, Temple-Purcell, Rebecca, additional, Loughrey, Clodagh, additional, Hanna, Elinor, additional, Johnston, Frances, additional, Boulos, Angel, additional, Thompson, Fiona, additional, Protaschik, Yuri, additional, Regan, Susan, additional, Donaghy, Tracy, additional, O'Kane, Maurice, additional, Akinbami, Omolola, additional, Barbero, Paola, additional, Brooks, Tim, additional, Chand, Meera, additional, Insalata, Ferdinando, additional, Joshi, Palak, additional, O'Connell, Anne-Marie, additional, Ramsay, Mary, additional, Saei, Ayoub, additional, Zambon, Maria, additional, Linley, Ezra, additional, Tonge, Simon, additional, Adaji, Enemona, additional, Adebiyi, Omoyeni, additional, Andrews, Nick, additional, Conneely, Joanna, additional, Conneely, Paul, additional, Dunne, Angela, additional, Dyer, Simone, additional, Emmett, Hannah, additional, Hettiarachchi, Nipunadi, additional, Kapirial, Nishanthan, additional, Khawam, Jameel, additional, Monk, Edward, additional, Russell, Sophie, additional, Taylor-Kerr, Andrew, additional, Timeyin, Jean, additional, D'Arcangelo, Silvia, additional, Rowe, Cathy, additional, Semper, Amanda, additional, Gallagher, Eileen, additional, Kyffin, Robert, additional, Cromey, Lisa, additional, Areghan, Desmond, additional, Bishop, Jennifer, additional, Dembinsky, Melanie, additional, Dobbie, Laura, additional, Evans, Josie, additional, Goldberg, David, additional, Haahr, Lynne, additional, Jorgenson, Annelysse, additional, Matuluko, Ayodeji, additional, Naismith, Laura, additional, Nuryunarsih, Desy, additional, Olaoye, Alexander, additional, Plank, Caitlin, additional, Price, Lesley, additional, Sergenson, Nicole, additional, Stewart, Sally, additional, Telfer, Andrew, additional, Weir, Jennifer, additional, De Lacy, Ellen, additional, Ellis, Yvette, additional, Froude, Susannah, additional, Stevens, Guy, additional, Tyson, Linda, additional, Dunachie, Susanna, additional, Klenerman, Paul, additional, Duncan, Chris, additional, Payne, Rebecca, additional, Turtle, Lance, additional, Richter, Alex, additional, De Silva, Thushan, additional, Barnes, Eleanor, additional, Wootton, Daniel, additional, Galgut, Oliver, additional, Heeney, Jonathan, additional, Baxendale, Helen, additional, Castillo-Olivares, Javier, additional, Beale, Rupert, additional, Carr, Edward, additional, Barclay, Wendy, additional, Moshe, Maya, additional, Palmarini, Massimo, additional, Willett, Brian, additional, Baillie, John Kenneth, additional, Evans, Jennie, additional, and Aquino, Erika, additional
Published: 2024
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44. A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes

Author: Samartsidis, Pantelis, primary, Seaman, Shaun R, additional, Harrison, Abbie, additional, Alexopoulos, Angelos, additional, Hughes, Gareth J, additional, Rawlinson, Christopher, additional, Anderson, Charlotte, additional, Charlett, André, additional, Oliver, Isabel, additional, and De Angelis, Daniela, additional
Published: 2023
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45. Synthesising evidence to estimate pandemic (2009) A/H1N1 influenza severity in 2009-2011

Author: Presanis, Anne M., Pebody, Richard G., Birrell, Paul J., Tom, Brian D. M., Green, Helen K., Durnall, Hayley, Fleming, Douglas, and De Angelis, Daniela
Subjects: Statistics - Applications
Abstract: Knowledge of the severity of an influenza outbreak is crucial for informing and monitoring appropriate public health responses, both during and after an epidemic. However, case-fatality, case-intensive care admission and case-hospitalisation risks are difficult to measure directly. Bayesian evidence synthesis methods have previously been employed to combine fragmented, under-ascertained and biased surveillance data coherently and consistently, to estimate case-severity risks in the first two waves of the 2009 A/H1N1 influenza pandemic experienced in England. We present in detail the complex probabilistic model underlying this evidence synthesis, and extend the analysis to also estimate severity in the third wave of the pandemic strain during the 2010/2011 influenza season. We adapt the model to account for changes in the surveillance data available over the three waves. We consider two approaches: (a) a two-stage approach using posterior distributions from the model for the first two waves to inform priors for the third wave model; and (b) a one-stage approach modelling all three waves simultaneously. Both approaches result in the same key conclusions: (1) that the age-distribution of the case-severity risks is "u"-shaped, with children and older adults having the highest severity; (2) that the age-distribution of the infection attack rate changes over waves, school-age children being most affected in the first two waves and the attack rate in adults over 25 increasing from the second to third waves; and (3) that when averaged over all age groups, case-severity appears to increase over the three waves. The extent to which the final conclusion is driven by the change in age-distribution of those infected over time is subject to discussion., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS775 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Published: 2014
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46. Estimation of HIV Burden through Bayesian Evidence Synthesis

Author: De Angelis, Daniela, Presanis, Anne M., Conti, Stefano, and Ades, A. E.
Subjects: Statistics - Methodology
Abstract: Planning, implementation and evaluation of public health policies to control the human immunodeficiency virus (HIV) epidemic require regular monitoring of disease burden. This includes the proportion living with HIV, whether diagnosed or not, and the rate of new infections in the general population and in specific risk groups and regions. Estimation of these quantities is not straightforward: data informing them directly are not typically available, but a wealth of indirect information from surveillance systems and ad hoc studies can inform functions of these quantities. In this paper we show how the estimation problem can be successfully solved through a Bayesian evidence synthesis approach, relaxing the focus on "best available" data to which classical methods are typically restricted. This more comprehensive and flexible use of evidence has led to the adoption of our proposed approach as the official method to estimate HIV prevalence in the United Kingdom since 2005., Comment: Published in at http://dx.doi.org/10.1214/13-STS428 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Published: 2014
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47. Reconstructing transmission trees for communicable diseases using densely sampled genetic data

Author: Worby, Colin J., O'Neill, Philip D., Kypraios, Theodore, Robotham, Julie V., De Angelis, Daniela, Cartwright, Edward J. P., Peacock, Sharon J., and Cooper, Ben S.
Subjects: Statistics - Applications, Quantitative Biology - Populations and Evolution
Abstract: Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen, and within-host genetic diversity, as well as allowing forward simulation.
Published: 2014
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48. Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Author: Samartsidis, Pantelis, Seaman, Shaun R., Presanis, Anne M., Hickman, Matthew, and De Angelis, Daniela
Published: 2019

49. Hepatitis C virus treatment as prevention in people who inject drugs

Author: Hickman, Matthew, De Angelis, Daniela, Vickerman, Peter, Hutchinson, Sharon, and Martin, Natasha Kaleta
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Digestive Diseases, Hepatitis - C, Hepatitis, HIV/AIDS, Liver Disease, Emerging Infectious Diseases, Drug Abuse (NIDA only), Clinical Research, Chronic Liver Disease and Cirrhosis, Substance Misuse, Infectious Diseases, Prevention, Infection, Good Health and Well Being, Antiviral Agents, Cost-Benefit Analysis, Evidence-Based Medicine, Hepacivirus, Hepatitis C, Humans, Interferons, Models, Theoretical, Practice Guidelines as Topic, Substance Abuse, Intravenous, Treatment Outcome, United Kingdom, Viral Load, evaluation, hepatitis C virus, injecting drug use, prevention, treatment, Microbiology, Medical Microbiology, Clinical sciences
Abstract: Purpose of reviewThe majority of hepatitis C virus (HCV) infections in the United Kingdom and many developing countries were acquired through injecting. New clinical guidance suggests that HCV treatment should be offered to people with a transmission risk - such as people who inject drugs (PWID) - irrespective of severity of liver disease. We consider the strength of the evidence base and potential problems in evaluating HCV treatment as prevention among PWID.Recent findingsThere is good theoretical evidence from dynamic models that HCV treatment for PWID could reduce HCV chronic prevalence and incidence among PWID. Economic evaluations from high-income settings have suggested HCV treatment for PWID is cost-effective, and that in many settings HCV treatment of PWID could be more cost-effective than treating those at an equivalent stage with no ongoing transmission risk. Epidemiological studies of older interferon treatments have suggested that PWID can achieve similar treatment outcomes to other patient groups treated for chronic HCV. Impact and cost-effectiveness of HCV treatment is driven by the potential 'prevention benefit' of treating PWID. Model projections suggest that more future infections, end stage liver disease, and HCV-related deaths will be averted than lost through reinfection of PWID treated successfully for HCV. However, there is to date no empirical evidence from trials or observational studies that test the model projections and 'prevention benefit' hypothesis. In part this is because of uncertainty in the evidence base but also there is unlikely to have been a change in HCV prevalence due to HCV treatment because PWID HCV treatment rates historically in most sites have been low, and any scale-up and switch to the new direct acting antiviral has not yet occurred. There are a number of key uncertainties in the data available on PWID that need to be improved and addressed to evaluate treatment as prevention. These include estimates of the prevalence of PWID, measurements of HCV chronic prevalence and incidence among PWID, and how to interpret reinfection rates as potential outcome measures.SummaryEliminating HCV through scaling up treatment is a theoretical possibility. But empirical data are required to demonstrate that HCV treatment can reduce HCV transmission, which will require an improved evidence base and analytic framework for measuring PWID and HCV prevalence.
Published: 2015

50. Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study

Author: Zhang, Xu-Sheng, Vynnycky, Emilia, Charlett, Andre, De Angelis, Daniela, Chen, Zhengji, and Liu, Wei
Published: 2021
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