Creating a framework for the prioritization of biosecurity risks to the New Zealand dairy industry

The New Zealand dairy sector relies on robust biosecurity measures to control and mitigate a wide range of threats to the industry. To optimize the prioritization of organisms and manage the risk they pose to the sector in a transparent and credible way, the Dairy Biosecurity Risk Evaluation Framework (D-BRiEF) was developed. This comprehensive framework was specifically designed for decision support, using a standardized approach to address the full spectrum of biosecurity threats to the sector, including exotic and endemic animal disease organisms, pest plants and insects. D-BRiEF is underpinned by three main processes, namely (i) hazard identification; (ii) multicriteria risk assessment; and (iii) communication for risk management. Expert knowledge and empirical data, including associated uncertainty, are harnessed in a standardized format. Results feed into a probability-impact model that was developed in close collaboration with dairy sector economists to provide overall comparative 10-year quantitative economic impact estimates for each assessed risk organism. A description of the overarching framework, which applies to diverse organism groups, is presented with detailed methodology on both endemic and exotic animal disease risk organisms. Examples of visual outputs are included, although actual ranking results are not reported due to industry confidentiality. D-BRiEF can provide a decision advantage to DairyNZ biosecurity risk managers and sector stakeholders by creating a transparent process that can be interrogated and updated at multiple levels to fully understand the layers of risk posed by different organisms.

Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota

Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state.

Sources of Klebsiella and Raoultella species on dairy farms: be careful where you walk

Klebsiella spp. are a common cause of mastitis, milk loss, and culling on dairy farms. Control of Klebsiella mastitis is largely based on prevention of exposure of the udder to the pathogen. To identify critical control points for mastitis prevention, potential Klebsiella sources and transmission cycles in the farm environment were investigated, including oro-fecal transmission, transmission via the indoor environment, and transmission via the outdoor environment. A total of 305 samples was collected from 3 dairy farms in upstate New York in the summer of 2007, and included soil, feed crops, feed, water, rumen content, feces, bedding, and manure from alleyways and holding pens. Klebsiella spp. were detected in 100% of rumen samples, 89% of water samples, and approximately 64% of soil, feces, bedding, alleyway, and holding pen samples. Detection of Klebsiella spp. in feed crops and feed was less common. Genotypic identification of species using rpoB sequence data showed that Klebsiella pneumoniae was the most common species in rumen content, feces, and alleyways, whereas Klebsiella oxytoca, Klebsiella variicola, and Raoultella planticola were the most frequent species among isolates from soil and feed crops. Random amplified polymorphic DNA-based strain typing showed heterogeneity of Klebsiella spp. in rumen content and feces, with a median of 4 strains per 5 isolates. Observational and bacteriological data support the existence of an oro-fecal transmission cycle, which is primarily maintained through direct contact with fecal contamination or through ingestion of contaminated drinking water. Fecal shedding of Klebsiella spp. contributes to pathogen loads in the environment, including bedding, alleyways, and holding pens. Hygiene of alleyways and holding pens is an important component of Klebsiella control on dairy farms.

A respiration monitor based on electrocardiographic and photoplethysmographic sensor fusion

Respiratory variations are present in the pulse wave transit time (PTT) and, as a consequence of respiratory sinus arrhythmia, in the electrocardiogram (ECG) and the photoplethysmogram (PPG). The aim of this study was to investigate these variations in healthy subjects during rest and invoked blood pressure changes. The primary goal was to develop a non-invasive respiration monitor. The error rates for breath detection during rest were 14%, 11% and 10% for PTT, ECG and PPG respectively. Significantly higher error rates were found in hypotension and hypertension. To improve accuracy and robustness, the signals were merged in a neural network resulting in an error rate of 9%.

Pulse wave transit time for monitoring respiration rate

In this study, we investigate the beat-to-beat respiratory fluctuations in pulse wave transit time (PTT) and its subcomponents, the cardiac pre-ejection period (PEP) and the vessel transit time (VTT) in ten healthy subjects. The three transit times were found to fluctuate in pace with respiration. When applying a simple breath detecting algorithm, 88% of the breaths seen in a respiration air-flow reference could be detected correctly in PTT. Corresponding numbers for PEP and VTT were 76 and 81%, respectively. The performance during hypo- and hypertension was investigated by invoking blood pressure changes. In these situations, the error rates in breath detection were significantly higher. PTT can be derived from signals already present in most standard monitoring set-ups. The transit time technology thus has prospects to become an interesting alternative for respiration rate monitoring.