A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events.

Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.

Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context.

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.

Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study.

BACKGROUND: Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. METHODS: Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. RESULTS: The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3-4 of 2018. Estimates for R0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. CONCLUSIONS: This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

Evaluating the effect of direct-acting antiviral agent treatment scale-up on Hepatitis C virus prevalence among people who inject drugs in UK.

BACKGROUND: There is limited empirical work assessing the effectiveness of treatment as prevention (TasP) in reducing HCV prevalence among people who inject drugs (PWID). Here, we used survey data from the UK during 2010-2020, to evaluate the impact of direct-acting antiviral agent (DAA) treatment scale-up, which started in 2015, on HCV prevalence among PWID. METHODS: We fitted a logistic regression to time/location specific data on prevalence from the Needle Exchange Surveillance Initiative in Scotland and Unlinked Anonymous Monitoring programme in England. For each post-intervention year and location, we quantified the effect of TasP as the difference between estimated prevalence and its counterfactual (prevalence in the absence of scale-up). Progress to elimination was assessed by comparing most recent prevalence against one in 2015. RESULTS: In 2015, prevalence ranged from 0.44 to 0.71 across the 23 locations (3 Scottish, 20 English). Compared to counterfactuals, there was an absolute reduction of 46% (95% credible interval [32%,59%]) in Tayside in 2020, 35% ([24%,44%]) in Glasgow in 2019, and 25% ([10%,39%]) in the Rest of Scotland in 2020. The English sites with highest estimated absolute reductions in 2021 were South Yorkshire (45%, [29%,58%]), Thames Valley (49%, [34%,59%]) and West London (41%, [14%,59%]). Compared to 2015, there was 80% probability that prevalence had fallen by 65% in Tayside, 53% in Glasgow and 36% in the Rest of Scotland. The English sites with highest % prevalence decrease compared to 2015, achieved with probability 80%, were Chesire & Merseyside (70%), South Yorkshire (65%) and Thames Valley (71%). Higher treatment intensity was associated with higher reductions in prevalence. CONCLUSION: Conclusion. Real-world evidence showing substantial reductions in chronic HCV associated with increase of HCV treatment scale-up in the community thus supporting the effectiveness of HCV treatmen as prevention in people who inject drugs.

Mixture-of-exponentials models to explain heterogeneity in studies of the duration of Chlamydia trachomatis infection.

Published studies of the duration of asymptomatic Chlamydia trachomatis infection in women have produced diverse estimates, and most reviewers have not attempted an evidence synthesis. We review the designs of duration studies, distinguishing between the incident cases presenting soon after infection in clinic-based studies and prevalent cases ascertained in population screening studies. We combine evidence from all studies under fixed-effect (single clearance rate), random-effect (study-specific clearance rate), and mixture-of-exponentials models, in which there are either two or three classes of infection that clear at different rates. We can identify classes as 'passive' infection and fast-clearing and slow-clearing infections. We estimate models by Bayesian MCMC and compared them using posterior mean residual deviance and the deviance information criterion. The single fixed-effect clearance rate model fitted very poorly. The random-effect model was adequate but inferior to the two-class and three-class mixture of exponentials. According to the two-class model, the proportion in the first class was 23% (95% CI: 16-31%), and the mean duration of C. trachomatis infection is 1.36 years (95% CI: 1.13-1.63 years). With the three-rate model, duration was similar, but identification of the proportions in each class (19%, 31%, and 49%) was poor. Although the random-effect model was descriptively adequate, the extreme degree of between-study variation in the clearance rate it predicted lacked biological plausibility. Differences in study recruitment and sampling mechanisms, acting through a mixture-of-exponentials model, better explains the apparent heterogeneity in duration.

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission.

Understanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.

Trends in undiagnosed HIV prevalence in England and implications for eliminating HIV transmission by 2030: an evidence synthesis model.

BACKGROUND: A target to eliminate HIV transmission in England by 2030 was set in early 2019. This study aimed to estimate trends from 2013 to 2019 in HIV prevalence, particularly the number of people living with undiagnosed HIV, by exposure group, ethnicity, gender, age group, and region. These estimates are essential to monitor progress towards elimination. METHODS: A Bayesian synthesis of evidence from multiple surveillance, demographic, and survey datasets relevant to HIV in England was used to estimate trends in the number of people living with HIV, the proportion of people unaware of their HIV infection, and the corresponding prevalence of undiagnosed HIV. All estimates were stratified by exposure group, ethnicity, gender, age group (15-34, 35-44, 45-59, or 60-74 years), region (London, or outside of London) and year (2013-19). FINDINGS: The total number of people living with HIV aged 15-74 years in England increased from 83 500 (95% credible interval 80 200-89 600) in 2013 to 92 800 (91 000-95 600) in 2019. The proportion diagnosed steadily increased from 86% (80-90%) to 94% (91-95%) during the same time period, corresponding to a halving in the number of undiagnosed infections from 11 600 (8300-17 700) to 5900 (4400-8700) and in undiagnosed prevalence from 0·29 (0·21-0·44) to 0·14 (0·11-0·21) per 1000 population. Similar steep declines were estimated in all subgroups of gay, bisexual, and other men who have sex with men and in most subgroups of Black African heterosexuals. The pace of reduction was less pronounced for heterosexuals in other ethnic groups and people who inject drugs, particularly outside London; however, undiagnosed prevalence in these groups has remained very low. INTERPRETATION: The UNAIDS target of diagnosing 90% of people living with HIV by 2020 was reached by 2016 in England, with the country on track to achieve the new target of 95% diagnosed by 2025. Reductions in transmission and undiagnosed prevalence have corresponded to large scale-up of testing in key populations and early diagnosis and treatment. Additional and intensified prevention measures are required to eliminate transmission of HIV among the communities that have experienced slower declines than other subgroups, despite having very low prevalences of HIV. FUNDING: UK Medical Research Council and Public Health England.