Imperial College London

Professor Christl Donnelly CBE FMedSci FRS

Faculty of MedicineSchool of Public Health

Professor of Statistical Epidemiology
 
 
 
//

Contact

 

+44 (0)20 7594 3394c.donnelly Website

 
 
//

Location

 

UGNorfolk PlaceSt Mary's Campus

//

Summary

 

Publications

Publication Type
Year
to

476 results found

Chadeau-Hyam M, Tang D, Eales O, Bodinier B, Wang H, Jonnerby J, Whitaker M, Elliott J, Haw D, Walters CE, Atchison C, Diggle PJ, Page AJ, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Donnelly CA, Elliott Pet al., 2022, Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys., Lancet Reg Health Eur, Vol: 21

Background: The Omicron wave of COVID-19 in England peaked in January 2022 resulting from the rapid transmission of the Omicron BA.1 variant. We investigate the spread and dynamics of the SARS-CoV-2 epidemic in the population of England during February 2022, by region, age and main SARS-CoV-2 sub-lineage. Methods: In the REal-time Assessment of Community Transmission-1 (REACT-1) study we obtained data from a random sample of 94,950 participants with valid throat and nose swab results by RT-PCR during round 18 (8 February to 1 March 2022). Findings: We estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credible interval [CrI] 2.76-3.00), with a within-round effective reproduction number (R) overall of 0.94 (0·91-0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) and adults aged 18 to 54 years, we observed a level or increasing weighted prevalence among those aged 55 years and older with an R of 1.04 (1.00-1.09). Among 1,616 positive samples with sublineages determined, one (0.1% [0.0-0.3]) corresponded to XE BA.1/BA.2 recombinant and the remainder were Omicron: N=1047, 64.8% (62.4-67.2) were BA.1; N=568, 35.2% (32.8-37.6) were BA.2. We estimated an R additive advantage for BA.2 (vs BA.1) of 0.38 (0.34-0.41). The highest proportion of BA.2 among positives was found in London. Interpretation: In February 2022, infection prevalence in England remained high with level or increasing rates of infection in older people and an uptick in hospitalisations. Ongoing surveillance of both survey and hospitalisations data is required. Funding: Department of Health and Social Care, England.

Journal article

Eales O, Ainslie KEC, Walters CE, Wang H, Atchison C, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P, Riley Set al., 2022, Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number, EPIDEMICS, Vol: 40, ISSN: 1755-4365

Journal article

Elliott P, Eales O, Bodinier B, Tang D, Wang H, Jonnerby LJA, Haw D, Elliott J, Whitaker M, Walters C, Atchison C, Diggle P, Page A, Trotter A, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke G, Chadeau M, Donnelly Cet al., 2022, Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England, Nature Communications, ISSN: 2041-1723

Rapid transmission of the SARS-CoV-2 Omicron variant has led to record-breaking case incidence rates around the world. Since May 2020, the REal-time Assessment of Community Transmission-1 (REACT-1) study tracked the spread of SARS-CoV-2 infection in England through RT-PCR of self-administered throat and nose swabs from randomly-selected participants aged 5 years and over. In January 2022, we found an overall weighted prevalence of 4.41% (n=102,174), three-fold higher than in November to December 2021; we sequenced 2,374 (99.2%) Omicron infections (19 BA.2), and only 19 (0.79%) Delta, with a growth rate advantage for BA.2 compared to BA.1 or BA.1.1. Prevalence was decreasing overall (reproduction number R=0.95, 95% credible interval [CrI], 0.93, 0.97), but increasing in children aged 5 to 17 years (R=1.13, 95% CrI, 1.09, 1.18). In England during January 2022, we observed unprecedented levels of SARS-CoV-2 infection, especially among children, driven by almost complete replacement of Delta by Omicron.

Journal article

Atchison C, Moshe M, Brown J, Whitaker M, Wong N, Bharath A, Mckendry R, Darzi A, Ashby D, Donnelly C, Riley S, Elliott P, Barclay W, Cooke G, Ward Het al., 2022, Validity of self-testing at home with rapid SARS-CoV-2 antibody detection by lateral flow immunoassay, Clinical Infectious Diseases, ISSN: 1058-4838

Background: We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based ELISA and live virus neutralisation. Methods: In July 2021, 3758 participants performed, at home, a self-administered LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of IgG antibodies using the Roche Elecsys® Anti-SARS-CoV-2 assay. We compared the self-reported LFIA result to the quantitative Roche assay and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralisation. Results: Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on Roche (using the manufacturer reference standard threshold for positivity of 0.8 U ml−1). Live virus neutralisation was detected in 169 of 250 randomly selected samples (67.6%); 133/169 were positive with self-reported LFIA (sensitivity 78.7%; 95% CI 71.8, 84.6), 142/155 (91.6%; 86.1, 95.5) with ALFA, and 169 (100%; 97.8, 100.0) with Roche. There were 81 samples with no detectable virus neutralisation; 47/81 were negative with self-reported LFIA (specificity 58.0%; 95% CI 46.5, 68.9), 34/75 (45.3%; 33.8, 57.3) with ALFA, and 0/81 (0%; 0.0, 4.5) with Roche. Conclusions: Self-administered LFIA is less sensitive than a quantitative antibody test, but the positivity in LFIA correlates better than the quantitative ELISA with virus neutralisation.

Journal article

Eales O, Martins LDO, Page AJ, Wang H, Bodinier B, Tang D, Haw D, Jonnerby J, Atchison C, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Elliott P, Donnelly CA, Chadeau-Hyam Met al., 2022, Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England, NATURE COMMUNICATIONS, Vol: 13

Journal article

Eales O, Wang H, Bodinier B, Haw D, Jonnerby J, Atchison C, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Chadeau M, Donnelly C, Elliott Pet al., 2022, SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2, BMC Infectious Diseases, ISSN: 1471-2334

Background: Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape. Methods: We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September - 27 September 2021) and 15 (19 October - 5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month.Results: We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI, 8%-23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England.Conclusions: As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals.

Journal article

Atchison C, Moshe M, Brown J, Whitaker M, Wong N, Bharath A, McKendry R, Darzi A, Ashby D, Donnelly C, Riley S, Elliott P, Barclay W, Cooke G, Ward Het al., 2022, Validity of self-testing at home with rapid SARS-CoV-2 antibody detection by lateral flow immunoassay, Publisher: medRxiv

<h4>ABSTRACT</h4> <h4>Background</h4> Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassays (LFIA) can be carried out in the home and have been used as an affordable and practical approach to large-scale antibody prevalence studies. However, assay performance differs from that of high-throughput laboratory-based assays which can be highly sensitive. We explore LFIA performance under field conditions compared to laboratory-based ELISA and assess the potential of LFIAs to identify people who lack functional antibodies following infection or vaccination. <h4>Methods</h4> Field evaluation of a self-administered LFIA test (Fortress, NI) among 3758 participants from the REal-time Assessment of Community Transmission-2 (REACT-2) study in England selected based on vaccination history and previous LFIA result to ensure a range of antibody titres. In July 2021, participants performed, at home, a self-administered LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample (Tasso-SST) for serological assessment of IgG antibodies to the spike protein using the Roche Elecsys® Anti-SARS-CoV-2 assay. We compared the self-administered and reported LFIA result to the quantitative Roche assay and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralisation. <h4>Results</h4> Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection, with most having received one (862, 22.9%) or two (2430, 64.7%) COVID-19 vaccine doses. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on Roche anti-S (using the manufacturer reference standard threshold for positivity of 0.8 U ml -1 ). Live virus neutra

Working paper

Longini IM, Yang Y, Fleming TR, Munoz-Fontela C, Wang R, Ellenberg SS, Qian G, Halloran ME, Nason M, De Gruttola V, Mulangu S, Huang Y, Donnelly C, Henao Restrepo A-Met al., 2022, A platform trial design for preventive vaccines against Marburg virus and other emerging infectious disease threats, Clinical Trials, ISSN: 1740-7745

Background:The threat of a possible Marburg virus disease outbreak in Central and Western Africa is growing. While no Marburg virus vaccines are currently available for use, several candidates are in the pipeline. Building on knowledge and experiences in the designs of vaccine efficacy trials against other pathogens including SARS-CoV-2, we develop designs of randomized phase 3 vaccine efficacy trials for Marburg virus vaccines. Methods:A core protocol approach will be used, allowing multiple vaccine candidates to be tested against controls. The primary objective of the trial will be to evaluate the effect of each vaccine on the rate of virologically confirmed Marburg virus disease, although Marburg infection, assessed via seroconversion could be the primary objective in some cases. The overall trial design will be a mixture of individually and cluster randomized designs, with individual randomization done whenever possible. Clusters will consist of either contacts and contacts of contacts of index cases, i.e., ring vaccination, or other transmission units. Results:The primary efficacy endpoint will be analysed as a time-to-event outcome. A vaccine will be considered successful if its estimated efficacy is greater than 50% and has sufficient precision to rule out that true efficacy is less than 30%. This will require approximately 150 total endpoints, i.e., cases of confirmed Marburg virus disease, per vaccine/comparator combination Interim analyses will be conducted after 50 and after 100 events. Statistical analysis of the trial will be blended across the different types of designs. Under the assumption of a 6-month attack rate of 1% of the of the participants in the placebo arm for both the individually and cluster randomize populations, the most likely sample size is about 20,000 participants per armConclusions:This event-driven design takes into the account the potentially sporadic spread of Marburg virus. The proposed trial design may be applica

Journal article

Eales O, de Oliveira Martins L, Page A, Wang H, Bodinier B, Tang D, Haw D, Jonnerby LJA, Atchison C, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Elliott P, Donnelly C, Chadeau Met al., 2022, Dynamics and scale of the SARS-CoV-2 variant Omicron epidemic in England, Nature Communications, ISSN: 2041-1723

Journal article

Chadeau M, Tang D, Eales O, Bodinier B, Wang H, Jonnerby LJA, Whitaker M, Elliott J, Haw D, Walters C, Atchison C, Diggle P, Page A, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Donnelly C, Elliott Pet al., 2022, Cross-sectional community surveys to monitor the Omicron SARS-CoV-2 epidemic in England during February 2022, The Lancet Regional Health Europe, ISSN: 2666-7762

Background: The Omicron wave of COVID-19 in England peaked in January 2022 resulting from the rapid transmission of the Omicron BA.1 variant. We investigate the spread and dynamics of the SARS-CoV-2 epidemic in the population of England during February 2022, by region, age and main SARS-CoV-2 sub-lineage.Methods: In the REal-time Assessment of Community Transmission-1 (REACT-1) study we obtained data from a random sample of 94,950 participants with valid throat and nose swab results by RT-PCR during round 18 (8 February to 1 March 2022).Findings: We estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credible interval [CrI] 2.76–3.00), with a within-round effective reproduction number (R) overall of 0.94 (0·91–0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) and adults aged 18 to 54 years, we observed a level or increasing weighted prevalence among those aged 55 years and older with an R of 1.04 (1.00–1.09). Among 1,616 positive samples with sublineages determined, one (0.1% [0.0–0.3]) corresponded to XE BA.1/BA.2 recombinant and the remainder were Omicron: N=1,047, 64.8% (62.4–67.2) were BA.1; N=568, 35.2% (32.8–37.6) were BA.2. We estimated an R additive advantage for BA.2 (vs BA.1) of 0.38 (0.34–0.41). The highest proportion of BA.2 among positives was found in London. Interpretation: In February 2022, infection prevalence in England remained high with level or increasing rates of infection in older people and an uptick in hospitalisations. Ongoing surveillance of both survey and hospitalisations data is required.Funding Department of Health and Social Care, England.

Journal article

Penn MJ, Donnelly CA, 2022, Asymptotic analysis of optimal vaccination policies

<jats:title>Abstract</jats:title><jats:p>Targeted vaccination policies can have a significant impact on the number of infections and deaths in an epidemic. However, optimising such policies is complicated and the resultant solution may be difficult to explain to policy-makers and to the public. The key novelty of this paper is a derivation of the leading order optimal vaccination policy under multi-group SIR (Susceptible-Infected-Recovered) dynamics in two different cases. Firstly, it considers the case of a small vulnerable subgroup in a population and shows that (in the asymptotic limit) it is optimal to vaccinate this group first, regardless of the properties of the other groups. Then, it considers the case of a small vaccine supply and transforms the optimal vaccination problem into a simple knapsack problem by linearising the final size equations. Both of these cases are then explored further through numerical examples which show that these solutions are also directly useful for realistic parameter values. Moreover, the findings of this paper give some general principles for optimal vaccination policies which will help policy-makers and the public to understand the reasoning behind optimal vaccination programs in more generic cases.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>The COVID-19 pandemic has illustrated the importance of vaccination programs in preventing infections and deaths from an epidemic. A common feature of vaccination programs across the world has been a prioritisation of different groups within each country’s population, particularly those who are more vulnerable to the disease. Finding the best priority order is crucial, but may be complicated and difficult to justify to policy-makers and the public. In this paper, we consider two extreme cases where the best prioritisation order can be mathematically derived. Firstly, we consider the case of a population with a very

Journal article

Eales O, Wang H, Haw D, Ainslie KEC, Walters CE, Atchison C, Cooke G, Barclay W, Ward H, Darzi A, Ashby D, Donnelly CA, Elliott P, Riley Set al., 2022, Trends in SARS-CoV-2 infection prevalence during England's roadmap out of lockdown, January to July 2021

<jats:p>Background: Following rapidly rising COVID-19 case numbers, England entered a national lockdown on 6 January 2021, with staged relaxations of restrictions from 8 March 2021 onwards.Aim: We characterise how the lockdown and subsequent easing of restrictions affected trends in SARS-CoV-2 infection prevalence.Methods: On average, risk of infection is proportional to infection prevalence. The REal-time Assessment of Community Transmission-1 (REACT-1) study is a repeat cross-sectional study of over 98,000 people every round (rounds approximately monthly) that estimates infection prevalence in England. We used Bayesian P-splines to estimate prevalence and the time-varying reproduction number (Rt) nationally, regionally and by age group from round 8 (beginning 6 January 2021) to round 13 (ending 12 July 2021) of REACT-1. As a comparator, a separate segmented-exponential model was used to quantify the impact on Rt of each relaxation of restrictions.Results: Following an initial plateau of 1.54% until mid-January, infection prevalence decreased until 13 May when it reached a minimum of 0.09%, before increasing until the end of the study to 0.76%. Following the first easing of restrictions, which included schools reopening, the reproduction number Rt increased by 82% (55%, 108%), but then decreased by 61% (82%, 53%) at the second easing of restrictions, which was timed to match the Easter school holidays. Following further relaxations of restrictions, the observed Rt increased steadily, though the increase due to these restrictions being relaxed was masked by the effects of vaccination and the rapid rise of Delta. There was a high degree of synchrony in the temporal patterns of prevalence between regions and age groups. Conclusion: High-resolution prevalence data fitted to P-splines allowed us to show that the lockdown was highly effective at reducing risk of infection with school holidays/closures playing a significant part.</jats:p>

Journal article

Chadeau M, Eales O, Bodinier B, Wang H, Haw D, Whitaker M, Elliott J, Walters C, Jonnerby LJA, Atchison C, Diggle P, Page A, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Donnelly C, Elliott Pet al., 2022, Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study, EClinicalMedicine, Vol: 48, Pages: 1-14, ISSN: 2589-5370

Background: Prevalence of SARS-CoV-2 infection with Delta variant was increasing in England in late summer 2021 among children aged 5 to 17 years, and adults who had received two vaccine doses. In September 2021, a third (booster) dose was offered to vaccinated adults aged 50 years and over, vulnerable adults and healthcare/care-home workers, and a single vaccine dose already offered to 16 and 17 year-olds was extended to children aged 12 to 15 years. Methods: SARS-CoV-2 community prevalence in England was available from self-administered throat and nose swabs using reverse transcriptase polymerase chain reaction (RT-PCR) in round 13 (24 June to 12 July 2021, N= 98,233), round 14 (9 to 27 September 2021, N = 100,527) and round 15 (19 October to 5 November 2021, N = 100,112) from the REACT-1 study randomised community surveys. Linking to National Health Service (NHS) vaccination data for consenting participants, we estimated vaccine effectiveness in children aged 12 to 17 years and compared swab-positivity rates in adults who received a third dose with those who received two doses. Findings: Weighted SARS-CoV-2 prevalence was 1.57% (1.48%, 1.66%) in round 15 compared with 0.83% (0.76%, 0.89%) in round 14, and the previously observed link between infections and hospitalisations and deaths had weakened. Vaccine effectiveness against infection in children aged 12 to 17 years was estimated (round 15) at 64.0% (50.9%, 70.6%) and 67.7% (53.8%, 77.5%) for symptomatic infections. Adults who received a third vaccine dose were less likely to test positive compared to those who received two doses, with adjusted odds ratio of 0.36 (0.25, 0.53). Interpretation: Vaccination of children aged 12 to 17 years and third (booster) doses in adults were effective at reducing infection risk. High rates of vaccination, including booster doses, are a key part of the strategy to reduce infection rates in the community.

Journal article

Parag K, Thompson R, Donnelly C, 2022, Are epidemic growth rates more informative than reproduction numbers?, Journal of the Royal Statistical Society Series A: Statistics in Society, ISSN: 0964-1998

Summary statistics, often derived from simplified modelsof epidemic spread, inform public health policy in real time.The instantaneous reproduction number, Rt, is predominantamong these statistics, measuring the average ability of aninfection to multiply. However, Rt encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate,rt, i.e., the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnosticpolicy guide. We examine this assertion, identifying if andwhen estimates of rt are more informative than those of Rt.We assess their relative strengths both for learning aboutpathogen transmission mechanisms and for guiding publichealth interventions in real time.

Journal article

Whitaker M, Elliott J, Bodinier B, Barclay W, Ward H, Cooke G, Donnelly CA, Chadeau-Hyam M, Elliott Pet al., 2022, Variant-specific symptoms of COVID-19 among 1,542,510 people in England

<jats:title>Abstract</jats:title><jats:p>Infection with SARS-CoV-2 virus is associated with a wide range of symptoms. The REal-time Assessment of Community Transmission -1 (REACT-1) study has been monitoring the spread and clinical manifestation of SARS-CoV-2 among random samples of the population in England from 1 May 2020 to 31 March 2022. We show changing symptom profiles associated with the different variants over that period, with lower reporting of loss of sense of smell and taste for Omicron compared to previous variants, and higher reporting of cold-like and influenza-like symptoms, controlling for vaccination status. Contrary to the perception that recent variants have become successively milder, Omicron BA.2 was associated with reporting more symptoms, with greater disruption to daily activities, than BA.1. With restrictions lifted and routine testing limited in many countries, monitoring the changing symptom profiles associated with SARS-CoV-2 infection and induced changes in daily activities will become increasingly important.</jats:p>

Journal article

Penn MJ, Donnelly C, 2022, Analysis of a double Poisson model for predicting football results in Euro 2020, PLoS One, Vol: 17, ISSN: 1932-6203

First developed in 1982, the double Poisson model, where goals scored by each team areassumed to be Poisson distributed with a mean depending on attacking and defensivestrengths, remains a popular choice for predicting football scores, despite the multitudeof newer methods that have been developed. This paper examines the pre-tournamentpredictions made using this model for the Euro 2020 football tournament. Thesepredictions won the Royal Statistical Society’s prediction competition, demonstratingthat even this simple model can produce high-quality results. Moreover, the paper alsopresents a range of novel analytic results which exactly quantify the conditions for theexistence and uniqueness of the solution to the equations for the model parameters.After deriving these results, it provides a novel examination of a potential problem withthe model - the over-weighting of the results of weaker teams - and illustrates theeffectiveness of ignoring results against the weakest opposition. It also compares thepredictions with the actual results of Euro 2020, showing that they were extremelyaccurate in predicting the number of goals scored. Finally, it considers the choice ofstart date for the dataset, and illustrates that the choice made by the authors (whichwas to start the dataset just after the previous major international tournament) wasclose to optimal, at least in this case. The findings of this study give a betterunderstanding of the mathematical behaviour of the double Poisson model and provideevidence for its effectiveness as a match prediction tool.

Journal article

Parag KV, Donnelly CA, Zarebski AE, 2022, Quantifying the information in noisy epidemic curves

<jats:title>Abstract</jats:title><jats:p>Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters, such as the time-varying reproduction number, <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> at time <jats:italic>t</jats:italic>, are often inferred from incident time series, with the aim of informing policymakers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to those time series. While studies have proposed corrections for these issues, methodology for formally assessing how these noise sources degrade <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> estimate quality is lacking. By adapting Fisher information and experimental design theory, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections. This yields a novel metric, defined by the geometric means of reporting and cumulative delay probabilities, for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>: epidemic case and death curves. We show that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.</jats:p>

Journal article

Penn MJ, Donnelly CA, 2022, Optimality of Maximal-Effort Vaccination

<jats:title>Abstract</jats:title><jats:p>It is widely acknowledged that vaccinating at maximal effort in the face of an ongoing epidemic is the best strategy to minimise infections and deaths from the disease. Despite this, no one has proved that this is guaranteed to be true if the disease follows multi-group SIR (Susceptible-Infected-Recovered) dynamics. This paper provides a novel proof of this principle for the existing SIR framework, showing that the total number of deaths or infections from an epidemic is decreasing in vaccination effort. Furthermore, it presents a novel model for vaccination which assumes that vaccines are distributed randomly to the unvaccinated population and suggests, using COVID-19 data, that this more accurately captures vaccination dynamics than the model commonly found in the literature. However, as the novel model provides a strictly larger set of possible vaccination policies, the results presented in this paper hold for both models.</jats:p><jats:sec><jats:title>Highlights</jats:title><jats:list list-type="bullet"><jats:list-item><jats:p>It is proved that it is optimal to vaccinate at maximal effort</jats:p></jats:list-item><jats:list-item><jats:p>A novel model of vaccination is explored with COVID-19 vaccine data</jats:p></jats:list-item><jats:list-item><jats:p>Results are presented which hold for both the novel and classical vaccination models.</jats:p></jats:list-item></jats:list></jats:sec>

Journal article

Parag K, Donnelly C, 2022, Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers, PLoS Computational Biology, Vol: 18, ISSN: 1553-734X

We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.

Journal article

Elliott P, Eales O, Steyn N, Tang D, Bodinier B, Wang H, Elliott J, Whitaker M, Atchison C, Diggle P, Trotter A, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke G, Donnelly C, Chadeau-Hyam Met al., 2022, Twin peaks: the Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England

BACKGROUNDRapid transmission of the SARS-CoV-2 Omicron variant has led to record-breaking incidencerates around the world. Sub-lineages have been detected in many countries with BA.1replacing Delta and BA.2 replacing BA.1.METHODSThe REal-time Assessment of Community Transmission-1 (REACT-1) study has trackedSARS-CoV-2 infection in England using RT-PCR results from self-administered throat and noseswabs from randomly-selected participants aged 5+ years. Rounds of data collection wereapproximately monthly from May 2020 to March 2022.RESULTSIn March 2022, weighted prevalence was 6.37% (N=109,181), more than twice that inFebruary 2022 following an initial Omicron peak in January 2022. Of the lineagesdetermined by viral genome sequencing, 3,382 (99.97%) were Omicron, including 346(10.2%) BA.1, 3035 (89.7%) BA.2 and one (0.03%) BA.3 sub-lineage; the remainder (1, 0.03%)was Delta AY.4. The BA.2 Omicron sub-lineage had a growth rate advantage (compared toBA.1 and sub-lineages) of 0.11 (95% credible interval [CrI], 0.10, 0.11). Prevalence wasincreasing overall (reproduction number R=1.07, 95% CrI, 1.06, 1.09), with the greatestincrease in those aged 55+ years (R=1.12, 95% CrI, 1.09, 1.14) among whom estimatedprevalence on March 31, 2022 was 8.31%, nearly 20-fold the median prevalence since May1, 2020.CONCLUSIONSWe observed unprecedented levels of SARS-CoV-2 infection in England in March 2022 and analmost complete replacement of Omicron BA.1 by BA.2. The high and increasing prevalencein older adults may increase hospitalizations and deaths despite high levels of vaccination.(Funded by the Department of Health and Social Care in England.)

Journal article

Eales O, de Oliveira Martins L, Page AJ, Wang H, Bodinier B, Tang D, Haw D, Jonnerby J, Atchison C, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Elliott P, Donnelly CA, Chadeau-Hyam Met al., 2022, The new normal? Dynamics and scale of the SARS-CoV-2 variant Omicron epidemic in England

<jats:title>Summary</jats:title><jats:p>The SARS-CoV-2 pandemic has been characterised by the regular emergence of genomic variants which have led to substantial changes in the epidemiology of the virus. With natural and vaccine-induced population immunity at high levels, evolutionary pressure favours variants better able to evade SARS-CoV-2 neutralising antibodies. The Omicron variant was first detected in late November 2021 and exhibited a high degree of immune evasion, leading to increased infection rates in many countries. However, estimates of the magnitude of the Omicron wave have relied mainly on routine testing data, which are prone to several biases. Here we infer the dynamics of the Omicron wave in England using PCR testing and genomic sequencing obtained by the REal-time Assessment of Community Transmission-1 (REACT-1) study, a series of cross-sectional surveys testing random samples of the population of England. We estimate an initial peak in national Omicron prevalence of 6.89% (5.34%, 10.61%) during January 2022, followed by a resurgence in SARS-CoV-2 infections in England during February-March 2022 as the more transmissible Omicron sub-lineage, BA.2 replaced BA.1 and BA.1.1. Assuming the emergence of further distinct genomic variants, intermittent epidemics of similar magnitude as the Omicron wave may become the ‘new normal’.</jats:p>

Journal article

Unwin HJT, Hillis S, Cluver L, Flaxman S, Goldman PS, Butchart A, Bachman G, Rawlings L, Donnelly CA, Ratmann O, Green P, Nelson CA, Blenkinsop A, Bhatt S, Desmond C, Villaveces A, Sherr Let al., 2022, Global, regional, and national minimum estimates of children affected by COVID-19-associated orphanhood and caregiver death, by age and family circumstance up to Oct 31, 2021: an updated modelling study, The Lancet Child & Adolescent Health, Vol: 6, ISSN: 2352-4642

BACKGROUND: In the 6 months following our estimates from March 1, 2020, to April 30, 2021, the proliferation of new coronavirus variants, updated mortality data, and disparities in vaccine access increased the amount of children experiencing COVID-19-associated orphanhood. To inform responses, we aimed to model the increases in numbers of children affected by COVID-19-associated orphanhood and caregiver death, as well as the cumulative orphanhood age-group distribution and circumstance (maternal or paternal orphanhood). METHODS: We used updated excess mortality and fertility data to model increases in minimum estimates of COVID-19-associated orphanhood and caregiver deaths from our original study period of March 1, 2020-April 30, 2021, to include the new period of May 1-Oct 31, 2021, for 21 countries. Orphanhood was defined as the death of one or both parents; primary caregiver loss included parental death or the death of one or both custodial grandparents; and secondary caregiver loss included co-residing grandparents or kin. We used logistic regression and further incorporated a fixed effect for western European countries into our previous model to avoid over-predicting caregiver loss in that region. For the entire 20-month period, we grouped children by age (0-4 years, 5-9 years, and 10-17 years) and maternal or paternal orphanhood, using fertility contributions, and we modelled global and regional extrapolations of numbers of orphans. 95% credible intervals (CrIs) are given for all estimates. FINDINGS: The number of children affected by COVID-19-associated orphanhood and caregiver death is estimated to have increased by 90·0% (95% CrI 89·7-90·4) from April 30 to Oct 31, 2021, from 2 737 300 (95% CrI 1 976 100-2 987 000) to 5 200 300 (3 619 400-5 731 400). Between March 1, 2020, and Oct 31, 2021, 491 300 (95% CrI 485 100-497 900) children

Journal article

Chadeau-Hyam M, Wang H, Eales O, Haw D, Bodinier B, Whitaker M, Walters CE, Ainslie KEC, Atchison C, Fronterre C, Diggle PJ, Page AJ, Trotter AJ, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Riley S, Donnelly CA, Elliott Pet al., 2022, SARS-CoV-2 infection and vaccine effectiveness in England (REACT-1): a series of cross-sectional random community surveys, The Lancet Respiratory Medicine, Vol: 10, Pages: 355-366, ISSN: 2213-2600

SummaryBackground England has experienced a third wave of the COVID-19 epidemic since the end of May, 2021, coincidingwith the rapid spread of the delta (B.1.617.2) variant, despite high levels of vaccination among adults. Vaccinationrates (single dose) in England are lower among children aged 16–17 years and 12–15 years, whose vaccination inEngland commenced in August and September, 2021, respectively. We aimed to analyse the underlying dynamicsdriving patterns in SARS-CoV-2 prevalence during September, 2021, in England.Methods The REal-time Assessment of Community Transmission-1 (REACT-1) study, which commenced datacollection in May, 2020, involves a series of random cross-sectional surveys in the general population of Englandaged 5 years and older. Using RT-PCR swab positivity data from 100 527 participants with valid throat and noseswabs in round 14 of REACT-1 (Sept 9–27, 2021), we estimated community-based prevalence of SARS-CoV-2 andvaccine effectiveness against infection by combining round 14 data with data from round 13 (June 24 to July 12, 2021;n=172 862).Findings During September, 2021, we estimated a mean RT-PCR positivity rate of 0·83% (95% CrI 0·76–0·89), with areproduction number (R) overall of 1·03 (95% CrI 0·94–1·14). Among the 475 (62·2%) of 764 sequenced positiveswabs, all were of the delta variant; 22 (4·63%; 95% CI 3·07–6·91) included the Tyr145His mutation in the spikeprotein associated with the AY.4 sublineage, and there was one Glu484Lys mutation. Age, region, key worker status,and household size jointly contributed to the risk of swab positivity. The highest weighted prevalence was observedamong children aged 5–12 years, at 2·32% (95% CrI 1·96–2·73) and those aged 13–17 years, at 2·55% (2·11–3·08).The SARS-CoV-2 epidemic grew in those aged 5–11 years, with an R of 1&m

Journal article

Eales O, Walters CE, Wang H, Haw D, Ainslie KEC, Atchison CJ, Page AJ, Prosolek S, Trotter AJ, Le Viet T, Alikhan N-F, Jackson LM, Ludden C, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P, Riley Set al., 2022, Characterising the persistence of RT-PCR positivity and incidence in a community survey of SARS-CoV-2, Wellcome Open Research, Vol: 7, Pages: 102-102, ISSN: 2398-502X

Background: The REal-time Assessment of Community Transmission-1 (REACT-1) study has provided unbiased estimates of swab-positivity in England approximately monthly since May 2020 using RT-PCR testing of self-administered throat and nose swabs. However, estimating infection incidence requires an understanding of the persistence of RT-PCR swab-positivity in the community.Methods: During round 8 of REACT-1 from 6 January to 22 January 2021, we collected up to two additional swabs from 896 initially RT-PCR positive individuals approximately 6 and 9 days after their initial swab.Results: Test sensitivity and duration of positivity were estimated using an exponential decay model, for all participants and for subsets by initial N-gene cycle threshold (Ct) value, symptom status, lineage and age. A P-spline model was used to estimate infection incidence for the entire duration of the REACT-1 study. REACT-1 test sensitivity was estimated at 0.79 (0.77, 0.81) with median duration of positivity at 9.7 (8.9, 10.6) days. We found greater duration of positivity in those exhibiting symptoms, with low N-gene Ct values, or infected with the Alpha variant. Test sensitivity was found to be higher for those who were pre-symptomatic or with low N-gene Ct values. Compared to swab-positivity, our estimates of infection incidence included sharper features with evident transient increases around the time of changes in social distancing measures.Conclusions: These results validate previous efforts to estimate incidence of SARS-CoV-2 from swab-positivity data and provide a reliable means to obtain community infection estimates to inform policy response.

Journal article

Chadeau-Hyam M, Tang D, Eales O, Bodinier B, Wang H, Jonnerby J, Whitaker M, Elliott J, Haw D, Walters C, Atchison C, Diggle P, Page A, Ashby D, Barclay W, Taylor G, Cooke G, Ward H, Darzi A, Donnelly C, Elliott Pet al., 2022, The Omicron SARS-CoV-2 epidemic in England during February 2022

Background The third wave of COVID-19 in England peaked in January 2022 resulting fromthe rapid transmission of the Omicron variant. However, rates of hospitalisations and deathswere substantially lower than in the first and second wavesMethods In the REal-time Assessment of Community Transmission-1 (REACT-1) study weobtained data from a random sample of 94,950 participants with valid throat and nose swabresults by RT-PCR during round 18 (8 February to 1 March 2022).Findings We estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credibleinterval [CrI] 2.76–3.00), with a within-round reproduction number (R) overall of 0.94 (0·91–0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) andadults aged 18 to 54 years, we observed a level or increasing weighted prevalence amongthose aged 55 years and older with an R of 1.04 (1.00–1.09). Among 1,195 positive sampleswith sublineages determined, only one (0.1% [0.0–0.5]) corresponded to AY.39 Deltasublineage and the remainder were Omicron: N=390, 32.7% (30.0–35.4) were BA.1; N=473,39.6% (36.8–42.5) were BA.1.1; and N=331, 27.7% (25.2–30.4) were BA.2. We estimated anR additive advantage for BA.2 (vs BA.1 or BA.1.1) of 0.40 (0.36–0.43). The highest proportionof BA.2 among positives was found in London.Interpretation In February 2022, infection prevalence in England remained high with levelor increasing rates of infection in older people and an uptick in hospitalisations. Ongoingsurveillance of both survey and hospitalisations data is required.Funding Department of Health and Social Care, England.

Working paper

Ward H, Whittaker M, Flower B, Tang S, Atchison C, Darzi A, Donnelly C, Cann A, Diggle P, Ashby D, Riley S, Barclay W, Elliott P, Cooke Get al., 2022, Population antibody responses following COVID-19 vaccination in 212,102 individuals, Nature Communications, Vol: 13, ISSN: 2041-1723

Population antibody surveillance helps track immune responses to COVID-19 vaccinations at scale, and identify host factors that may affect antibody production. We analyse data from 212,102 vaccinated individuals within the REACT-2 programme in England, which uses self-administered lateral flow antibody tests in sequential cross-sectional community samples; 71,923 (33.9%) received at least one dose of BNT162b2 vaccine and 139,067 (65.6%) received ChAdOx1. For both vaccines, antibody positivity peaks 4-5 weeks after first dose and then declines. At least 21 days after second dose of BNT162b2, close to 100% of respondents test positive, while for ChAdOx1, this is significantly reduced, particularly in the oldest age groups (72.7% [70.9–74.4] at ages 75 years and above). For both vaccines, antibody positivity decreases with age, and is higher in females and those with previous infection. Antibody positivity is lower in transplant recipients, obese individuals, smokers and those with specific comorbidities. These groups will benefit from additional vaccine doses.

Journal article

Elliott P, Bodinier B, Eales O, Wang H, Haw D, Elliott J, Whitaker M, Jonnerby J, Tang D, Walters CE, Atchison C, Diggle PJ, Page AJ, Trotter AJ, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke GS, Chadeau-Hyam M, Donnelly CAet al., 2022, Rapid increase in Omicron infections in England during December 2021: REACT-1 study., Science, Vol: 375, Pages: eabn8347-eabn8347, ISSN: 0036-8075

The unprecedented rise in SARS-CoV-2 infections during December 2021 was concurrent with rapid spread of the Omicron variant in England and globally. We analyzed prevalence of SARS-CoV-2 and its dynamics in England from end November to mid-December 2021 among almost 100,000 participants from the REACT-1 study. Prevalence was high with rapid growth nationally and particularly in London during December 2021, and an increasing proportion of infections due to Omicron. We observed large falls in swab positivity among mostly vaccinated older children (12-17 years) compared with unvaccinated younger children (5-11 years), and in adults who received a third (booster) vaccine dose vs. two doses. Our results reinforce the importance of vaccination and booster campaigns, although additional measures have been needed to control the rapid growth of the Omicron variant.

Journal article

Eales O, Ainslie KEC, Walters CE, Wang H, Atchison C, Ashby D, Donnelly CA, Cooke G, Barclay W, Ward H, Darzi A, Elliott P, Riley Set al., 2022, Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number

<jats:title>Abstract</jats:title><jats:p>The time-varying reproduction number (<jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub>) can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> from case data. However, these are not easily adapted to point prevalence data nor can they infer <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020 – December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> over the period of two subsequent rounds (6-8 weeks) and single rounds (2-3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats

Journal article

Elliott P, Eales O, Bodinier B, Tang D, Wang H, Jonnerby J, Haw D, Elliott J, Whitaker M, Walters C, Atchison C, Diggle P, Page A, Trotter A, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke G, Chadeau-Hyam M, Donnelly Cet al., 2022, Post-peak dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022

Background: Rapid transmission of the SARS-CoV-2 Omicron variant has led to the highestever recorded case incidence levels in many countries around the world.Methods: The REal-time Assessment of Community Transmission-1 (REACT-1) study hasbeen characterising the transmission of the SARS-CoV-2 virus using RT-PCR test results fromself-administered throat and nose swabs from randomly-selected participants in England atages 5 years and over, approximately monthly since May 2020. Round 17 data were collectedbetween 5 and 20 January 2022 and provide data on the temporal, socio-demographic andgeographical spread of the virus, viral loads and viral genome sequence data for positiveswabs.Results: From 102,174 valid tests in round 17, weighted prevalence of swab positivity was4.41% (95% credible interval [CrI], 4.25% to 4.56%), which is over three-fold higher than inDecember 2021 in England. Of 3,028 sequenced positive swabs, 2,393 lineages weredetermined and 2,374 (99.2%) were Omicron including 19 (0.80% of all Omicron lineages)cases of BA.2 sub-lineage and one BA.3 (0.04% of all Omicron) detected on 17 January 2022,and only 19 (0.79%) were Delta. The growth of the BA.2 Omicron sub-lineage against BA.1and its sub-lineage BA.1.1 indicated a daily growth rate advantage of 0.14 (95% CrI, 0.03,0.28) for BA.2, which corresponds to an additive R advantage of 0.46 (95% CrI, 0.10, 0.92).Within round 17, prevalence was decreasing overall (R=0.95, 95% CrI, 0.93, 0.97) butincreasing in children aged 5 to 17 years (R=1.13, 95% CrI, 1.09, 1.18). Those 75 years andolder had a swab-positivity prevalence of 2.46% (95% CI, 2.16%, 2.80%) reflecting a highlevel of infection among a highly vulnerable group. Among the 3,613 swab-positiveindividuals reporting whether or not they had had previous infection, 2,334 (64.6%)reported previous confirmed COVID-19. Of these, 64.4% reported a positive test from 1 to30 days before their swab date. Risks of infection were increased among essential/keyworkers

Working paper

Menkir TF, Donnelly CA, 2022, The impact of repeated rapid test strategies on the effectiveness of at-home antiviral treatments for SARS-CoV-2

<jats:title>Abstract</jats:title><jats:p>As has been consistently demonstrated, rapid tests administered at regular intervals can offer significant benefits to both individuals and their communities at large by helping identify whether an individual is infected and potentially infectious. An additional advantage to the tested individuals is that positive tests may be provided sufficiently early enough during their infections that treatment with antiviral treatments can effectively inhibit development of severe disease, particularly when PCR uptake is limited and/or delays to receipt of results are substantial. Here, we provide a quantitative illustration of the extent to which rapid tests administered at various intervals can deliver benefits accrued from the novel Pfizer treatment (nirmatrelvir) among high-risk populations. We find that strategies in which tests are administered more frequently, i.e. every other day or every three days, are associated with greater reductions in the risk of hospitalization with weighted risk ratios ranging from 0.17 (95% CI: 0.11-0.28) to 0.77 (95% CI: 0.69-0.83) and correspondingly, higher proportions of the infected population benefiting from treatment, ranging from 0.26 (95% CI: 0.18-0.34) to 0.92 (95% CI: 0.80-0.98). We further observed that reduced positive-test-to-treatment delays and increased testing and treatment coverage have a critical influence on average treatment benefits, confirming the significance of access.</jats:p>

Journal article

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00308903&limit=30&person=true