Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal.

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the 'zero by 30' global mission.

Using natural language processing and patient journey clustering for temporal phenotyping of antimicrobial therapies for cat bite abscesses.

BACKGROUND: Temporal phenotyping of patient journeys, which capture the common sequence patterns of interventions in the treatment of a specific condition, is useful to support understanding of antimicrobial usage in veterinary patients. Identifying and describing these phenotypes can inform antimicrobial stewardship programs designed to fight antimicrobial resistance, a major health crisis affecting both humans and animals, in which veterinarians have an important role to play. OBJECTIVE: This research proposes a framework for extracting temporal phenotypes of patient journeys from clinical practice data through the application of natural language processing (NLP) and unsupervised machine learning (ML) techniques, using cat bite abscesses as a model condition. By constructing temporal phenotypes from key events, the relationship between antimicrobial administration and surgical interventions can be described, and similar treatment patterns can be grouped together to describe outcomes associated with specific antimicrobial selection. METHODS: Cases identified as having a cat bite abscess as a diagnosis were extracted from VetCompass Australia, a database of veterinary clinical records. A classifier was trained and used to label the most clinically relevant event features in each record as chosen by a group of veterinarians. The labeled records were processed into coded character strings, where each letter represents a summary of specific types of treatments performed at a given visit. The sequences of letters representing the cases were clustered based on weighted Levenshtein edit distances with KMeans+ + to identify the main variations of the patient treatment journeys, including the antimicrobials used and their duration of administration. RESULTS: A total of 13,744 records that met the selection criteria was extracted and grouped into 8436 cases. There were 9 clinically distinct event sequence patterns (temporal phenotypes) of patient journeys identified, representing the main sequences in which surgery and antimicrobial interventions are performed. Patients receiving amoxicillin and surgery had the shortest duration of antimicrobial administration (median of 3.4 days) and patients receiving cefovecin with no surgical intervention had the longest antimicrobial treatment duration (median of 27 days). CONCLUSION: Our study demonstrates methods to extract and provide an overview of temporal phenotypes of patient journeys, which can be applied to text-based clinical records for multiple species or clinical conditions. We demonstrate the effectiveness of this approach to derive real-world evidence of treatment impacts using cat bite abscesses as a model condition to describe patterns of antimicrobial therapy prescriptions and their outcomes.

Estimation of a Within-Herd Transmission Rate for African Swine Fever in Vietnam.

We describe results from a panel study in which pigs from a 17-sow African swine fever (ASF) positive herd in Thái Bình province, Vietnam, were followed over time to record the date of onset of ASF signs and the date of death from ASF. Our objectives were to (1) fit a susceptible-exposed-infectious-removed disease model to the data with transmission coefficients estimated using approximate Bayesian computation; (2) provide commentary on how a model of this type might be used to provide decision support for disease control authorities. For the outbreak in this herd, the median of the average latent period was 10 days (95% HPD (highest posterior density interval): 2 to 19 days), and the median of the average duration of infectiousness was 3 days (95% HPD: 2 to 4 days). The estimated median for the transmission coefficient was 3.3 (95% HPD: 0.4 to 8.9) infectious contacts per ASF-infectious pig per day. The estimated median for the basic reproductive number, R0, was 10 (95% HPD: 1.1 to 30). Our estimates of the basic reproductive number R0 were greater than estimates of R0 for ASF reported previously. The results presented in this study may be used to estimate the number of pigs expected to be showing clinical signs at a given number of days following an estimated incursion date. This will allow sample size calculations, with or without adjustment to account for less than perfect sensitivity of clinical examination, to be used to determine the appropriate number of pigs to examine to detect at least one with the disease. A second use of the results of this study would be to inform the equation-based within-herd spread components of stochastic agent-based and hybrid simulation models of ASF.

Bushfire exposure is associated with increased pH and dark-cutting in beef longissimus thoracis at grading.

To investigate if bushfire exposure is associated with increased loin pH, this study analysed temporal and geospatial data on fire incidence in South-Eastern Australia together with beef carcase quality and production records for fire affected animals (n = 451,299). Two outcomes were modelled: 1) loin pH at time of grading, and 2) the incidence of "high pH" defects (pH > 5.70). For both models, decreasing "time since closest fire" and "distance of property from closest fire" were associated with increasing loin pH and increased incidence of high pH carcases(p < 0.05 for all); interactions for "distance from the closest fire" with feed type (grain vs grass) and "days of fire exposure" with HGP (hormonal growth promotant) treatment (yes vs no) (p < 0.05 for both) indicate high pH outcomes were exacerbated in grass-fed and HGP treated cattle. It is concluded that exposure to bushfire is associated with increasing pH but the extent and magnitude of these increases are modulated by production factors.

High ambient temperature and humidity associated with early embryonic loss after embryo transfer in mares.

Increased ambient temperatures has been identified to contribute to reproductive outcomes for several domesticated species, but its impact on equine reproduction has not been previously investigated. Therefore, this study aimed to examine the relationship between ambient climatic conditions, as measured by temperature-humidity index (THI) between day 7 and day 14 of gestation, and early embryonic loss (EEL) in recipient mares undergoing embryo transfer. The study examined records from 834 embryo transfers at an equine breeding facility in Victoria, Australia. Early embryonic loss was defined as a negative transrectal ultrasound on day 14 of gestation after an embryo was transferred on day 7. Both maximum THI on the day of transfer (day 7) and mean THI between day 7 and day 14 were investigated for association with the outcome of EEL using multivariable logistic regression, controlling for confounders including embryo age and quality, recipient mare quality and embryo transfer quality. EEL was observed in 21% of embryo transfers. A five-unit increase in maximum THI on the day of transfer was associated with an 18% increase in the odds of EEL (p = 0.01). Similarly, the odds of EEL increased by 25% for each five-unit increase in mean THI between day 7 and 14 (p = 0.003). As both single and cumulative episodes of high THI were associated with increased EEL in embryo transfer mares from this equine breeding facility, further studies are warranted to identify similar effects in a broader population, establish causality and evaluate possible mitigation strategies in anticipation of heat waves.

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.

The Australian 2019/2020 Black Summer Bushfires: Analysis of the Pathology, Treatment Strategies and Decision Making About Burnt Livestock.

In 2019/2020, Australia experienced a severe bushfire event, with many tens of thousands of livestock killed or euthanized. Little systematic research has occurred to understand livestock bushfire injuries, risk factors for injury, or how to make decisions about management of bushfire-injured livestock. Addressing this research gap is important as there is an increasing bushfire incidence globally. This paper presents qualitative research findings about bushfire-injured and killed livestock in the south-east of Australia after the 2019/2020 Australian bushfires. We describe observed pathology, treatments used, and risk factors for injury, then use thematic analysis to understand decision making about managing fire-injured livestock. Livestock injured by the fires showed pathology predominantly associated with the common integument (feet, hooves and skin) and signs of acute respiratory damage. It could take several days for the full extent of burns to become apparent, leaving prognostic doubt. Treatment strategies included immediate euthanasia, salvage slaughter, retention for later culling, treatment and recovery on farm, hospitalization and intensive treatment, or no intervention. Risk factors reported for livestock injury included lack of warnings about an impending fire, the type and amount of vegetation around livestock and the weather conditions on the day the fire reached livestock. Moving stock to an area with little vegetation before fire arrived was seen as protective. Decision making regarding injured livestock appeared influenced by three main themes: (1) observations on the severity of pathology, clinical signs and level of prognostic doubt, (2) pre-existing beliefs about animal welfare (responsibility to minimize unnecessary suffering) and (3) assumptions about the future. The management of livestock was largely appropriate due to the rapid provision of veterinary expertise. However, it is likely that some injured livestock were euthanized due to conservative veterinary advice driven by a lack of opportunity to re-assess stock, with impacts on farmers. In future, resourcing regular revisits of injured livestock to manage risks of gradual progression of burn pathology may facilitate more accurate prognostic assessment, provided injured animals can receive appropriate pain relief. In addition, a more comprehensive burns classification system linked to prognosis that can be rapidly applied in the field may assist assessments.

Using farmer observations for animal health syndromic surveillance: Participation and performance of an online enhanced passive surveillance system.

The challenge of animal health surveillance is to provide the information necessary to appropriately inform disease prevention and control activities within the constraints of available resources. Syndromic surveillance of farmers' disease observations can improve animal health data capture from extensive livestock farming systems, especially where data are not otherwise being systematically collected or when data on confirmed aetiological diagnoses are unavailable at the disease level. As it is rarely feasible to recruit a truly random sample of farmers to provide observational reports, directing farmer sampling to align with the surveillance objectives is a reasonable and practical approach. As long as potential bias is recognised and managed, farmers who will report reliably can be desirable participants in a surveillance system. Thus, one early objective of a surveillance program should be to identify characteristics associated with reporting behaviour. Knowledge of the demographic and managerial characteristics of good reporters can inform efforts to recruit additional farms into the system or aid understanding of potential bias of system reports. We describe the operation of a farmer syndromic surveillance system in Victoria, Australia, over its first two years from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with 'reliable' participation (low non-response rates in longitudinal reporting). Response rate and timeliness were not associated with whether farmers had disease to report, or with different months of the year. Farmers keeping only sheep were the most reliable and timely respondents. Farmers < 43 years of age had lower response rates than older farmers. Farmers with veterinary qualifications and those working full-time on-farm provided less timely reports than other educational backgrounds and farmers who worked part-time on-farm. These analyses provide a starting point to guide recruitment of participants for surveillance of farmers' observations using syndromic surveillance, and provide examples of strengths and weaknesses of syndromic surveillance systems for extensively-managed livestock. Once farm characteristics associated with reliable participation are known, they can be incorporated into surveillance system design in accordance with the objectives of the system.

Herd-Level Risk Factors for Swine Influenza (H1N1) Seropositivity in West Java and Banten Provinces of Indonesia (2016-2017).

Swine could play a role as a "mixing vessel" for avian and human influenza viruses and should, therefore, be thought of playing an intermediate role in the emergence of pandemic influenza strains. The aim of this study was to identify risk factors for Swine influenza virus (SIV) seropositivity at the farm level in West Java and Banten provinces, Indonesia. A total of 649 blood samples were collected from 175 pig farms, and at the time of sampling, a questionnaire about routine herd management was administered to participant herd managers. Swine influenza virus serological status for each of the sampled pigs was tested using the IDEXX ELISA-test (Maine, US). The apparent herd-level prevalence of SIV seropositivity was expressed as a true herd-level prevalence using the Rogan and Gladen method, modified to account for low and high prevalence herds using a Markov chain Monte Carlo Bayesian approach. The association between herd-level characteristics and SIV seropositivity status was assessed using binary logistic regression. The true prevalence of SIV seropositivity was 26% (95% CI = 20-33). The presence of animals apart from pigs on farm (odds ratio, OR = 2.51, 95% CI = 1.0-6.0), keeping breeding sows for <2 years (OR = 5.9, 95% Cl = 1.8-20), being <1 km from a poultry farm (OR = 2.4, 95% Cl = 1.0-5.7), and purchasing pigs only through pig collectors (OR = 11, 95% CI = 4.3-29) increased the risk of a herd being seropositive to SIV. Our results show that biosecurity to limit the introduction of SIV should be enhanced on farms located in areas of high pig and poultry farm density. While the role that pig collectors play in the transmission of SIV warrants further investigation, swine producers in West Java and Banten should be made aware of the enhanced risk of SIV associated with purchasing of replacements from collectors.

Adaptive e-Learning: Emerging Digital Tools for Teaching Parasitology.

Adaptive learning activities can respond to a learner's needs in real time, facilitating the development of higher-level skills including bringing together knowledge from different disciplines to solve real-world problems. Here we discuss the use of online adaptive learning activities designed to help veterinary students apply their knowledge to work through parasite case studies.

Sample size considerations for livestock movement network data.

The movement of animals between farms contributes to infectious disease spread in production animal populations, and is increasingly investigated with social network analysis methods. Tangible outcomes of this work include the identification of high-risk premises for targeting surveillance or control programs. However, knowledge of the effect of sampling or incomplete network enumeration on these studies is limited. In this study, a simulation algorithm is presented that provides an estimate of required sampling proportions based on predicted network size, density and degree value distribution. The algorithm may be applied a priori to ensure network analyses based on sampled or incomplete data provide population estimates of known precision. Results demonstrate that, for network degree measures, sample size requirements vary with sampling method. The repeatability of the algorithm output under constant network and sampling criteria was found to be consistent for networks with at least 1000 nodes (in this case, farms). Where simulated networks can be constructed to closely mimic the true network in a target population, this algorithm provides a straightforward approach to determining sample size under a given sampling procedure for a network measure of interest. It can be used to tailor study designs of known precision, for investigating specific livestock movement networks and their impact on disease dissemination within populations.