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Earlier global detection of novel SARS-CoV-2 variants gives governments more time to respond. However, few countries can implement timely national surveillance, resulting in gaps in monitoring. The United Kingdom implemented large-scale community and hospital surveillance, but experience suggests it might be faster to detect new variants through testing England arrivals for surveillance. We developed simulations of emergence and importation of novel variants with a range of infection hospitalization rates to the United Kingdom. We compared time taken to detect the variant though testing arrivals at England borders, hospital admissions, and the general community. We found that sampling 10%–50% of arrivals at England borders could confer a speed advantage of 3.5–6 weeks over existing community surveillance and 1.5–5 weeks (depending on infection hospitalization rates) over hospital testing. Directing limited global capacity for surveillance to highly connected ports could speed up global detection of novel SARS-CoV-2 variants.

Background Peer support can enable patient engagement with healthcare services, particularly for marginalised populations. In this randomised controlled trial, the efficacy of a peer support intervention at promoting successful engagement with clinical services for chronic hepatitis C was assessed. Methods In London, UK, potential participants were approached through outreach services for problematic drug use and homelessness. Individuals positive for hepatitis C virus (HCV) after confirmatory testing were randomised using an online service to the intervention (peer support) or standard of care. The primary outcome of interest was successful engagement with clinical hepatitis services. The study was non-blinded. Absolute differences were calculated using a generalised linear model and the results compared to logistic regression. Results Three hundred sixty-four individuals consented to participate. One hundred one had chronic hepatitis C and were randomised, 63 to receive the intervention (peer support). A successful outcome was achieved by 23 individuals in this arm (36.5%) and seven (18.4%) receiving the standard of care, giving an absolute increase of 18.1% (95% confidence interval 1.0–35.2%, p value = 0.04). This was mirrored in the logistic regression (odds ratio 2.55 (0.97–6.70), p  = 0.06). No serious adverse events were reported. Conclusions Peer support can improve the engagement of patients with chronic HCV with healthcare services. Trial registration ISRCTN24707359 . Registered 19th October 2012.

A problem that is frequently encountered in many areas of scientific research is that of estimating the effect of a non-randomized binary intervention on an outcome of interest by using time series data on units that received the intervention (‘treated’) and units that did not (‘controls’). One popular estimation method in this setting is based on the factor analysis (FA) model. The FA model is fitted to the preintervention outcome data on treated units and all the outcome data on control units, and the counterfactual treatment-free post-intervention outcomes of the former are predicted from the fitted model. Intervention effects are estimated as the observed outcomes minus these predicted counterfactual outcomes. We propose a model that extends the FA model for estimating intervention effects by jointly modelling the multiple outcomes to exploit shared variability, and assuming an auto-regressive structure on factors to account for temporal correlations in the outcome. Using simulation studies, we show that the method proposed can improve the precision of the intervention effect estimates and achieve better control of the type I error rate (compared with the FA model), especially when either the number of preintervention measurements or the number of control units is small. We apply our method to estimate the effect of stricter alcohol licensing policies on alcohol-related harms.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) rapidly replaced Delta (B.1.617.2) to become dominant in England. Our study assessed differences in transmission between Omicron and Delta using two independent data sources and methods. Omicron and Delta cases were identified through genomic sequencing, genotyping and S-gene target failure in England from 5–11 December 2021. Secondary attack rates for named contacts were calculated in household and non-household settings using contact tracing data, while household clustering was identified using national surveillance data. Logistic regression models were applied to control for factors associated with transmission for both methods. For contact tracing data, higher secondary attack rates for Omicron vs. Delta were identified in households (15.0% vs. 10.8%) and non-households (8.2% vs. 3.7%). For both variants, in household settings, onward transmission was reduced from cases and named contacts who had three doses of vaccine compared to two, but this effect was less pronounced for Omicron (adjusted risk ratio, aRR 0.78 and 0.88) than Delta (aRR 0.62 and 0.68). In non-household settings, a similar reduction was observed only in contacts who had three doses vs. two doses for both Delta (aRR 0.51) and Omicron (aRR 0.76). For national surveillance data, the risk of household clustering, was increased 3.5-fold for Omicron compared to Delta (aRR 3.54 (3.29–3.81)). Our study identified increased risk of onward transmission of Omicron, consistent with its successful global displacement of Delta. We identified a reduced effectiveness of vaccination in lowering risk of transmission, a likely contributor for the rapid propagation of Omicron.

Development of gastrointestinal illness after animal contact at petting farms is well described, as are factors such as handwashing and facility design that may modify transmission risk. However, further field evidence on other behaviours and interventions in the context of Cryptosporidium outbreaks linked to animal contact events is needed. Here, we describe a large outbreak of Cryptosporidium parvum (C. parvum) associated with a multi-day lamb petting event in the south-west of England in 2023 and present findings from a cohort study undertaken to investigate factors associated with illness. Detailed exposure questionnaires were distributed to email addresses of 647 single or multiple ticket bookings, and 157 complete responses were received. The outbreak investigation identified 23 laboratory-confirmed primary C. parvum cases. Separately, the cohort study identified 83 cases of cryptosporidiosis-like illness. Associations between illness and entering a lamb petting pen (compared to observing from outside the pen; odds ratio (OR) = 2.28, 95 per cent confidence interval (95% CI) 1.17 to 4.53) and self-reported awareness of diarrhoeal and vomiting disease transmission risk on farm sites at the time of visit (OR = 0.40, 95% CI 0.19 to 0.84) were observed. In a multivariable model adjusted for household clustering, awareness of disease transmission risk remained a significant protective factor (adjusted OR (aOR) = 0.07, 95% CI 0.01 to 0.78). The study demonstrates the likely under-ascertainment of cryptosporidiosis through laboratory surveillance and provides evidence of the impact that public health messaging could have.

The tracking and projection of emerging epidemics is hindered by the disconnect between apparent epidemic dynamics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly observed, system. Behavior changes compound this, altering both true dynamics and reporting patterns, particularly for diseases with nonspecific symptoms, such as influenza. We disentangle these effects to unravel the hidden dynamics of the 2009 influenza A/H1N1pdm pandemic in London, where surveillance suggests an unusual dominant peak in the summer. We embed an age-structured model into a Bayesian synthesis of multiple evidence sources to reveal substantial changes in contact patterns and health-seeking behavior throughout the epidemic, uncovering two similar infection waves, despite large differences in the reported levels of disease. We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution.

Background. An urgent UK investigation was launched to assess risk of invasive Mycobacterium chimaera infection in cardiothoracic surgery and a possible association with cardiopulmonary bypass heater-cooler units following alerts in Switzerland and The Netherlands. Methods. Parallel investigations were pursued: (1) identification of cardiopulmonary bypass–associated M. chimaera infection through national laboratory and hospital admissions data linkage; (2) cohort study to assess patient risk; (3) microbiological and aerobiological investigations of heater-coolers in situ and under controlled laboratory conditions; and (4) whole-genome sequencing of clinical and environmental isolates. Results. Eighteen probable cases of cardiopulmonary bypass–associated M. chimaera infection were identified; all except one occurred in adults. Patients had undergone valve replacement in 11 hospitals between 2007 and 2015, a median of 19 months prior to onset (range, 3 months to 5 years). Risk to patients increased after 2010 from <0.2 to 1.65 per 10000 person-years in 2013, a 9-fold rise for infections within 2 years of surgery (rate ratio, 9.08 [95% CI, 1.81–87.76]). Endocarditis was the most common presentation (n = 11). To date, 9 patients have died. Investigations identified aerosol release through breaches in heater-cooler tanks. Mycobacterium chimaera and other pathogens were recovered from water and air samples. Phylogenetic analysis found close clustering of strains from probable cases. Conclusions. We identified low but escalating risk of severe M. chimaera infection associated with heater-coolers with cases in a quarter of cardiothoracic centers. Our investigations strengthen etiological evidence for the role of heater-coolers in transmission and raise the possibility of an ongoing, international point-source outbreak. Active management of heater-coolers and heightened clinical awareness are imperative given the consequences of infection.

The Flexible Farrington Algorithm (FFA) is widely used to detect infectious disease outbreaks at national/regional levels on a weekly basis. The rapid spread of SARS-CoV-2 alongside the speed at which diagnostic and public health interventions were introduced made the FFA of limited use. We describe how the methodology was adapted to provide a daily alert system to support local health protection teams (HPTs) working in the 316 English lower-tier local authorities. To minimize the impact of a rapidly changing epidemiological situation, the FFA was altered to use 8 weeks of data. The adapted algorithm was based on reported positive counts using total tests as an offset. Performance was assessed using the root mean square error (RMSE) over a period. Graphical reports were sent to local teams enabling targeted public health action. From 1 July 2020, results were routinely reported. Adaptions accommodated the impact on reporting because of changes in diagnostic strategy (introduction of lateral flow devices). RMSE values were relatively small compared to observed counts, increased during periods of increased reporting, and were relatively higher in the northern and western areas of the country. The exceedance reports were well received. This presentation should be considered as a successful proof-of-concept.

National foodborne outbreaks of gastrointestinal disease often require rapid case–control investigations to identify the source. Online market panels offer a potential alternative to traditional control recruitment. We compared market panel controls to traditional controls in case–control recruitment during a 2024 UK outbreak of STEC O145. Two case–control studies were conducted for two different control groups (a) Salmonella cases as case–controls and (b) online market panel members. Timeliness, cost, and resources were compared, and a logistic regression compared findings in a control–control analysis. In total, 43 cases of STEC O145, 63 Salmonella case–controls, and 93 panel controls were recruited. Neither control group reached the recruitment target for the younger adult age group. Salmonella case–controls had a ninefold greater staff time to recruit and cost five times more than panel controls (£25.82 vs. £4.99 per control), partially due to interviewer-administered questionnaires compared with self-completion by panel controls. Both analytical approaches identified the same outbreak source, with no significant differences in exposures between the control groups. The cost and resource savings associated with panel controls justify their use as a standard procedure in outbreak investigations. We recommend exploring engagement with age groups that are difficult to recruit and assessing alternative strategies to reach them.