Real-time modelling of a pandemic influenza outbreak.

BACKGROUND: Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible. OBJECTIVES: To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software. METHODS: Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with 'gold-standard' MCMC-derived inferences in terms of estimation quality and computational burden. RESULTS: The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R0 is consistently estimated to be 1.76-1.80, dropping by 43-50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden 'shock' in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use. LIMITATIONS: The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance. CONCLUSIONS: Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation. FUTURE WORK RECOMMENDATIONS: Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling. TRIAL REGISTRATION: Current Controlled Trials ISRCTN40334843. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly

Sporadic Cryptosporidiosis Decline after Membrane Filtration of Public Water Supplies, England, 1996–2002

Sporadic cryptosporidiosis and associated hospital admissions of children declined after membrane filtration of public drinking water supplies was introduced

Blood profile holds clues to role of infection in a premonitory state for idiopathic parkinsonism and of gastrointestinal infection in established disease

The two-stage neuroinflammatory process, containment and progression, proposed to underlie neurodegeneration may predicate on systemic inflammation arising from the gastrointestinal tract. Helicobacter infection has been described as one switch in the pathogenic-circuitry of idiopathic parkinsonism (IP): eradication modifies disease progression and marked deterioration accompanies eradication-failure. Moreover, serum Helicobacter-antibody-profile predicts presence, severity and progression of IP. Slow gastrointestinal-transit precedes IP-diagnosis and becomes increasingly-apparent after, predisposing to small-intestinal bacterial-overgrowth (SIBO). Although IP is well-described as a systemic illness with a long prodrome, there has been no comprehensive overview of the blood profile. Here, it is examined in relation to Helicobacter status and lactulose-hydrogen-breath-testing for SIBO

Factors associated with four atypical cases of congenital syphilis in England, 2016 to 2017: an ecological analysis.

Four isolated cases of congenital syphilis born to mothers who screened syphilis negative in the first trimester were identified between March 2016 and January 2017 compared with three cases between 2010 and 2015. The mothers were United Kingdom-born and had no syphilis risk factors. Cases occurred in areas with recent increases in sexually-transmitted syphilis among women and men who have sex with men, some behaviourally bisexual, which may have facilitated bridging between sexual networks

Comparative transmission of SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) variants and the impact of vaccination: national cohort study, England

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

Interactions made easy

The creation and testing of interaction terms in regression models can be very cumbersome, even in Stata 8. We propose a simple wrapping command, -fitint-, that fits any generalised linear model and tests any twoway interactions, as well as all main effects. There is no need to use -xi- because categorical variables are identified with the option . Appropriate tests are chosen depending upon the fitted model.

FITINT: Stata module to fit generalized linear model and test two-way interactions

The creation and testing of interaction terms in regression models can be very cumbersome, even in Stata 8. We propose a simple wrapping command, -fitint-, that fits any generalised linear model and tests any twoway interactions, as well as all main effects. There is no need to use -xi- because categorical variables are identified with the option. Appropriate tests are chosen depending upon the fitted model.generalized linear model, two-way interactions

Comparison of Nine Commercially Available Clostridium difficile Toxin Detection Assays, a Real-Time PCR Assay for C. difficile tcdB, and a Glutamate Dehydrogenase Detection Assay to Cytotoxin Testing and Cytotoxigenic Culture Methods▿

The continuing rise in the incidence of Clostridium difficile infection is a cause for concern, with implications for patients and health care systems. Laboratory diagnosis largely relies on rapid toxin detection kits, although assays detecting alternative targets, including glutamate dehydrogenase (GDH) and toxin genes, are now available. Six hundred routine diagnostic diarrheal samples were tested prospectively using nine commercial toxin detection assays, cytotoxin assay (CYT), and cytotoxigenic culture (CYTGC) and retrospectively using a GDH detection assay and PCR for the toxin B gene. The mean sensitivity and specificity for toxin detection assays were 82.8% (range, 66.7 to 91.7%) and 95.4% (range, 90.9 to 98.8%), respectively, in comparison with CYT and 75.0% (range, 60.0 to 86.4%) and 96.1% (91.4 to 99.4%), respectively, in comparison with CYTGC. The sensitivity and specificity of the GDH assay were 90.1% and 92.9%, respectively, compared to CYT and 87.6% and 94.3%, respectively, compared to CYTGC. The PCR assay had the highest sensitivity of all the tests in comparison with CYT (92.2%) and CYTGC (88.5%), and the specificities of the PCR assay were 94.0% and 95.4% compared to CYT and CYTGC, respectively. All kits had low positive predictive values (range, 48.6 to 86.8%) compared with CYT, assuming a positive sample prevalence of 10% (representing the hospital setting), which compromises the clinical utility of single tests for the laboratory diagnosis of C. difficile infection. The optimum rapid single test was PCR for toxin B gene, as this had the highest negative predictive value. Diagnostic algorithms that optimize test combinations for the laboratory diagnosis of C. difficile infection need to be defined