The effect of slurry storage and anaerobic-digestion on survival of pathogenic bacteria

The decline in viable numbers of Salmonella typhimurium, Yersinia enterocolitica and Listeria monocytogenes in beef cattle slurry is temperature-dependent; they decline more rapidly at 17-degrees-C than at 4-degrees-C. Mesophilic anaerobic digestion caused an initial rapid decline in the viable numbers of Escherichia coli, Salm. typhimurium, Y. enterocolitica and L. monocytogenes. This was followed by a period in which the viable numbers were not reduced by 90%. The T90 values of E. coli, Salm. typhimurium and Y. enterocolitica ranged from 0.7 to 0.9 d during batch digestion and 1.1 to 2-5 d during semi-continuous digestion. Listeria monocytogenes had a significantly higher mean T90 value during semi-continuous digestion (35.7 d) than batch digestion (12.3 d). Anaerobic digestion had little effect in reducing the viable numbers of Campylobacter jejuni.

Survival of pathogenic bacteria during mesophilic anaerobic-digestion of animal waste

The survival of pathogenic bacteria was investigated during the operation of a full-scale anaerobic digester which was fed daily and operated at 28-degrees-C. The digester had a mean hydraulic retention time of 24 d. The viable numbers of Escherichia coli, Salmonella typhimurium, Yersinia enterocolitica, Listeria monocytogenes and Campylobacter jejuni were reduced during mesophilic anaerobic digestion. Escherichia coli had the smallest mean viable numbers at each stage of the digestion process. Its mean T90 value was 76-9 d. Yersinia enterocolitica was the least resistant to the anaerobic digester environment; its mean T90 value was 18.2 d. Campylobacter jejuni was the most resistant bacterium; its mean T90 value was 438.6 d. Regression analysis showed that there were no direct relationships between the slurry input and performance of the digester and the decline of pathogen numbers during the 140 d experimental period.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A Bayesian network hybrid model for representing accident and emergency waiting times

The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.