The Isospin Distribution of Fragments in Reactions 96Ru+96Ru, 96Ru+96Zr, 96Zr+96Ru, and 96Zr+96Zr at Beam Energy 400 AMeV

The isospin distribution of particles and fragments in collisions 96Ru+96Ru, 96Ru+96Zr, 96Zr+96Ru, and 96Zr+96Zr at beam energy 400 AMeV is studied with isospin dependent QMD model. We find that the rapidity distribution of differential neutron-proton counting in neutron rich nucleus-nucleus collisions at intermediate energies is sensitive to the isospin dependent part of nuclear potential. The study of the N/Z ratio of nucleons, light charged particles (LCP) and intermediate mass fragments (IMF) shows that the isospin dependent part of nuclear potential drives IMF to be more isospin symmetric and emitted nucleons to be more neutron rich. From the study of the time evolution of the isospin distribution in emitted nucleons, LCP and IMF we find that neutrons diffuse much faster than protons at beginning and the final isospin distribution is a result of dynamical balance of symmetry potential and Coulomb force under the charge conservation.Comment: 10 pages, 5 figure

Case-Control Study of Risk Factors for Sporadic Giardiasis and Parasite Assemblages in North West England

Giardia duodenalis is a major cause of infectious gastroenteritis worldwide, and it is diversified into eight genetic assemblages (A to H), which are distinguishable only by molecular typing. There is some evidence that the assemblages infecting humans (assemblages A and B) may have different transmission routes, but systematically acquired data, combining epidemiological and molecular findings, are required. We undertook a case-control study with Giardia genotyping in North West England, to determine general and parasite assemblage-specific risk factors. For people without a history of foreign travel, swimming in swimming pools and changing diapers were the most important risk factors for the disease. People infected with assemblage B reported a greater number of symptoms and higher frequencies of vomiting, abdominal pain, swollen stomach, and loss of appetite, compared with people infected with assemblage A. More importantly, keeping a dog was associated only with assemblage A infections, suggesting the presence of a potential zoonotic reservoir for this assemblage. This is the first case-control study to combine epidemiological data with Giardia genotyping, and it shows the importance of integrating these two levels of information for better understanding of the epidemiology of this pathogen

A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes

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

National observational study to evaluate the "cleanyourhands" campaign (NOSEC): a questionnaire based study of national implementation

INTRODUCTION: The number of national hand-hygiene campaigns has increased recently, following the World Health Organisation's (WHO) "Save Lives: clean your hands" initiative (2009), which offers hospitals a multi-component hand-hygiene intervention. The number of campaigns to be evaluated remains small. Most evaluations focus on consumption of alcohol hand rub (AHR). We are not aware of any evaluation reporting implementation of all campaign components. In a previously published report, we evaluated the effects of the English and Welsh cleanyourhands campaign (2004-8) on procurement of AHR and soap, and on selected healthcare associated infections. We now report on the implementation of each individual campaign component: provision of bedside AHR, ward posters, patient empowerment materials, audit and feedback, and guidance to secure institutional engagement. METHOD: SETTING: all 189 acute National Health Service (NHS) hospitals in England and Wales (December 2005-June 2008). Six postal questionnaires (five voluntary, one mandatory) were distributed to infection control teams six-monthly from 6 to 36 months post roll-out. Selection and attrition bias were measured. RESULTS: Response rates fell from 134 (71 %) at 6 months to 82 (44 %) at 30 months, rising to 167 (90 %) for the final mandatory one (36 months). There was no evidence of attrition or selection bias. Hospitals reported widespread early implementation of bedside AHR and posters and a gradual rise in audit. At 36 months, 90 % of respondents reported the campaign to be a top hospital priority, with implementation of AHR, posters and audit reported by 96 %, 97 % and 91 % respectively. Patient empowerment was less successful. CONCLUSIONS: The study suggests that all campaign components, apart from patient empowerment, were widely implemented and sustained. It supports previous work suggesting that adequate piloting, strong governmental support, refreshment of campaigns, and sufficient time to engage institutions help secure sustained implementation of a campaign's key components. The results should encourage countries wishing to launch coordinated national campaigns for hospitals to participate in the WHO's "Save Lives" initiative, which offers hospitals a similar multi-component intervention

Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020.

Severe acute respiratory syndrome coronavirus 2 viral load in the upper respiratory tract peaks around symptom onset and infectious virus persists for 10 days in mild-to-moderate coronavirus disease (n = 324 samples analysed). RT-PCR cycle threshold (Ct) values correlate strongly with cultivable virus. Probability of culturing virus declines to 8% in samples with Ct > 35 and to 6% 10 days after onset; it is similar in asymptomatic and symptomatic persons. Asymptomatic persons represent a source of transmissible virus

Can syndromic surveillance help forecast winter hospital bed pressures in England?

BACKGROUND: Health care planners need to predict demand for hospital beds to avoid deterioration in health care. Seasonal demand can be affected by respiratory illnesses which in England are monitored using syndromic surveillance systems. Therefore, we investigated the relationship between syndromic data and daily emergency hospital admissions. METHODS: We compared the timing of peaks in syndromic respiratory indicators and emergency hospital admissions, between 2013 and 2018. Furthermore, we created forecasts for daily admissions and investigated their accuracy when real-time syndromic data were included. RESULTS: We found that syndromic indicators were sensitive to changes in the timing of peaks in seasonal disease, especially influenza. However, each year, peak demand for hospital beds occurred on either 29th or 30th December, irrespective of the timing of syndromic peaks. Most forecast models using syndromic indicators explained over 70% of the seasonal variation in admissions (adjusted R square value). Forecast errors were reduced when syndromic data were included. For example, peak admissions for December 2014 and 2017 were underestimated when syndromic data were not used in models. CONCLUSION: Due to the lack of variability in the timing of the highest seasonal peak in hospital admissions, syndromic surveillance data do not provide additional early warning of timing. However, during atypical seasons syndromic data did improve the accuracy of forecast intensity