Livestock exposure to bushfires and meat, offal and carcase quality: Is there an association?

The occurrence of bushfires (wildfires) is increasing with climate change in many areas of the world. In Australia, the 2019/20 bushfire season involved a particularly severe and widespread fire emergency (the 'Black Summer' bushfires). Understanding of how exposure to bushfires affects specific disease processes in livestock is limited. This research investigated spatiotemporal relationships between exposure to bushfires and observations of pneumonia and pleurisy in slaughtered sheep, and meat quality in slaughtered cattle. Two related cross-sectional studies were undertaken using historical abattoir monitoring data from the National Sheep Health Monitoring Project and the Meat Standards Australia Program. The study area involved the Australian states of New South Wales and Victoria, and the Australian Capital Territory, which were heavily affected by the 'Black Summer' bushfires. Carcase data were matched to fire occurrence data and to potential confounders including rainfall, pasture growth and pasture biomass indices for the farm of origin. The predictive approach to modelling included generalised additive mixed effects models and a generalised linear mixed model. Consistent though imprecise trends in pneumonia occurrence in sheep carcases were observed across time and distance since exposure to fire, with sheep slaughtered in the immediate aftermath of exposure to high intensity fires at a close distance having the highest occurrence (3.78 cases per 1000 sheep slaughtered 5 days after exposure to medium-to-high intensity fire at 0.5 km distance (95% CI 0.48, 30.02), compared to 0.387 cases per 1000 slaughtered sheep (95% CI 0.147, 1.02) across the study population). However, the economic implications of this for producers and processors are considered to be very limited. No such trends were observed in regards to pleurisy occurrence in sheep. Consistent trends were observed in meat quality in cattle carcases, with lower meat quality scores observed in cattle slaughtered after close proximity to fire (mean MSA index of 57.12 for cattle slaughtered 5 days after exposure to medium-to-high intensity fire at 0.5 km distance (95% CI 56.91, 57.34), compared to a mean of 57.65 (95% CI 57.60, 57.71) across the study population). In the aftermath of exposure to mid-to-high intensity fires, this may warrant consideration in withholding cattle from slaughter from an economic perspective, after decisions based on welfare grounds have been made. These observations will inform practical recommendations to improve health and productivity outcomes in management of bushfire-affected livestock. The observed trends may reflect causal relationships, but this requires further investigation with specific explanatory modelling studies.

A strategy for selection of reference materials in stable oxygen isotope analyses of solid materials.

The propagation of uncertainties associated with the stable oxygen isotope reference materials through a multi-point normalisation procedure was evaluated in this study using Monte Carlo (MC) simulation. We quantified the normalisation error for a particular selection of reference materials and their number of replicates, when the choice of standards is restricted to either nitrates, sulphates or organic reference materials alone, and in comparison with when this restriction was relaxed. A lower uncertainty in stable oxygen isotope analyses of solid materials performed using High-Temperature Pyrolysis (HTP) can be readily achieved through an optimal selection of reference materials. Among the currently available certified reference materials the best performing pairs minimising the normalisation errors are USGS35 and USGS34 for nitrates; IAEA-SO-6 and IAEA-SO-5 for sulphates; and IAEA-601 and IAEA-602 for organic materials. The normalisation error can be reduced further--by approximately half--if each of these two analysed reference materials is replicated four times. The overall optimal selection among all nine considered reference materials is the IAEA-602 and IAEA-SO-6 pair. If each of these two reference materials is replicated four times the maximum predicted normalisation error will equal 0.22‰, the minimum normalisation error 0.12‰, and the mean normalisation error 0.15‰ over the natural range of δ(18)O variability. We argue that the proposed approach provides useful insights into reference material selection and in assessing the propagation of analytical error through normalisation procedures in stable oxygen isotope studies.

Mycoplasma bovis outbreak in New Zealand cattle: An assessment of transmission trends using surveillance data.

Mycoplasma bovis most likely infected New Zealand cattle in the latter half of 2015. Infection was detected in mid-2017 after which control activities were implemented. An official eradication programme commenced in mid-2018, which is ongoing. We examined farm-level tracing and surveillance data to describe the outbreak, analyse transmission trends and make inference on progress towards eradication. Results indicate that cattle movements were the primary means of spread. Although case farms were distributed throughout both islands of New Zealand, most animal movements off infected farms did not result in newly infected farms, indicating Mycoplasma bovis is not highly transmissible between farms. To describe and analyse outbreak trends, we undertook a standard descriptive outbreak investigation, including construction of an epidemic curve and calculation of estimated dissemination ratios. We then employed three empirical models-a non-linear growth model, time series model and branching process model based on time-varying effective reproduction numbers-to further analyse transmission trends and provide short-term forecasts of farm-level incidence. Our analyses suggest that Mycoplasma bovis transmission in New Zealand has declined and progress towards eradication has been made. Few incident cases were forecast for the period between 8 September and 17 December 2019. To date, no case farms with an estimated infection date assigned to this period have been detected; however, case detection is ongoing, and these results need to be interpreted cautiously considering model validation and other important contextual information on performance of the eradication programme, such as the time between infection, detection and implementation of movement controls on case farms.

Error propagation in normalization of stable isotope data: a Monte Carlo analysis.

A higher analytical precision of a stable isotope ratio mass spectrometer does not automatically guarantee accurate determination of the true isotope composition (delta-value) of samples, since estimates of true delta-values are obtained from the normalization of raw isotope data. We performed both Monte Carlo simulations and laboratory experiments to investigate aspects of error propagation during the normalization of carbon stable isotope data. We found that increasing both the number of different reference standards and the number of repetitions of each of these standards reduces the normalization error. A 50% reduction in the normalization error can be achieved over the two-point normalization by either analyzing two standards four times each, or four standards two times each. If the true delta-value of a sample is approximately known a priori, the normalization error may then be reduced through a targeted choice of locally optimal standards. However, the difference in improvement is minimal and, therefore, a more practical strategy is to use two or more standards covering the whole stable isotope scale. The selection of different sets of standards by different laboratories or for different batches of samples in the same laboratory may lead to significant differences in the normalized delta-values of the same samples, leading to inconsistent results. Hence, the same set of standards should always be used for a particular element and a particular stable isotope analytical technique.

Evidence That Higher Temperatures Are Associated With a Marginally Lower Incidence of COVID-19 Cases.

Seasonal variations in COVID-19 incidence have been suggested as a potentially important factor in the future trajectory of the pandemic. Using global line-list data on COVID-19 cases reported until 17th of March 2020 and global gridded weather data, we assessed the effects of air temperature and relative humidity on the daily incidence of confirmed COVID-19 local cases at the subnational level (first-level administrative divisions). After adjusting for surveillance capacity and time since first imported case, average temperature had a statistically significant, negative association with COVID-19 incidence for temperatures of -15°C and above. However, temperature only explained a relatively modest amount of the total variation in COVID-19 cases. The effect of relative humidity was not statistically significant. These results suggest that warmer weather may modestly reduce the rate of spread of COVID-19, but anticipation of a substantial decline in transmission due to temperature alone with onset of summer in the northern hemisphere, or in tropical regions, is not warranted by these findings.