Network modeling of BVD transmission

Endemic diseases of cattle, such as bovine viral diarrhea, have significant impact on production efficiency of food of animal origin with consequences for animal welfare and climate change reduction targets. Many modeling studies focus on the local scale, examining the on-farm dynamics of this infectious disease. However, insight into prevalence and control across a network of farms ultimately requires a network level approach. Here, we implement understanding of infection dynamics, gained through these detailed on-farm modeling studies, to produce a national scale model of bovine viral diarrhea virus transmission. The complex disease epidemiology and on-farm dynamics are approximated using SIS dynamics with each farm treated as a single unit. Using a top down approach, we estimate on-farm parameters associated with contraction and subsequent clearance from infection at herd level. We examine possible control strategies associated with animal movements between farms and find measures targeted at a small number of high-movement farms efficient for rapid and sustained prevalence reduction

Identifying environmental risk factors for louping Ill virus seroprevalence in sheep and the potential to inform wildlife management policy

Identifying the risk factors for disease is crucial for developing policy and strategies for controlling exposure to pathogens. However, this is often challenging, especially in complex disease systems, such as vector-borne diseases with multiple hosts and other environmental drivers. Here we combine seroprevalence data with GIS-based environmental variables to identify the environmental risk factors associated with an endemic tick-borne pathogen—louping ill virus—in sheep in Scotland. Higher seroprevalences were associated with (i) upland/moorland habitats, in accordance with what we predicted from the habitat preferences of alternative LIV transmission hosts (such as red grouse), (ii) areas of higher deer density, which supports predictions from previous theoretical models, since deer are the key Ixodes ricinus tick reproduction host in this system, and (iii) a warmer climate, concurring with our current knowledge of how temperature affects tick activity and development rates. The implications for policy include adopting increased disease management and awareness in high risk habitats and in the presence of alternative LIV hosts (e.g., grouse) and tick hosts (especially deer). These results can also inform deer management policy, especially where there may be conflict between contrasting upland management objectives, for example, revenue from deer hunting vs. sheep farmers

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.</p

Investigating hill sheep farmers and crofters’ experiences of blackloss in the Highlands and Islands of Scotland

Hill sheep farming is an important component of Scottish agriculture and comprises a significant land use in much of the Highlands and Islands. However it faces significant challenges due to the natural constraints of the landscape. Hill sheep farming uses hardy traditional breeds, such as the Scottish blackface and North Country Cheviot to graze extensive areas, where the sheep are not housed and tend to lamb on the open hill. Flocks are gathered several times a year for stock checks, husbandry, and health treatments. Between these handling events, stock will disappear and be unaccounted for. These unexplained losses are known as blackloss in the Highlands and Islands. Previously reported figures for annual lamb blackloss give an average of 18.6%. These losses are in addition to the known losses of lambs and represent a significant welfare and sustainability issue. High parasite burdens, predation, a photosensitisation disease known as plochteach or yellowses, and poor nutrition are often given as presumed reasons for blackloss. A questionnaire was developed to assess the experiences, impacts and understanding flock managers have of blackloss. Typology analysis using partitioning around medoids was used to cluster respondents into three distinct groups: 1- very large extensive farms and Sheep Stock Clubs, 2- medium sized farms, and 3- small-scale crofts. The responses of these groups were subsequently analysed to see if their experiences and perceptions of blackloss differed with relation to lamb health challenges and predation impacts. The groups reported similar health challenges, apart from Group 1 which had a significantly higher plochteach challenge. In terms of predators, Group 1 also perceived white-tailed eagles (Haliaeetus albicilla) as a much higher threat to their lambs than the other groups. It was observed that many of the respondents believed blackloss is inevitable and that predators pose a large threat to lambs. However, most agreed that reducing these losses is important and that understanding the causes would enable them to do so.</p

Text mining of veterinary forums for epidemiological surveillance supplementation

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns

Increased Toxoplasma gondii positivity relative to age in 125 Scottish sheep flocks; evidence of frequent acquired infection

Toxoplasma gondii seroprevalence was determined in 3333 sheep sera from 125 distinct sheep flocks in Scotland, with the majority of flocks being represented by 27 samples, which were collected between July 2006 and August 2008. The selected farms give a representative sample of 14 400 sheep holdings identified in the Scottish Government census data from 2004. Overall T. gondii seroprevalence, at individual sheep level, was determined to be 56.6%; each flock tested, had at least a single positive animal and in four flocks all ewes tested positive. The seroprevalence of sheep increased from 37.7% in one year old stock to 73.8% in ewes that were older than six years, showing that acquired infections during the life of the animals is frequent and that environmental contamination by T. gondii oocysts must be significant. The median within-flock seroprevalence varied significantly across Scotland, with the lowest seroprevalence of 42.3% in the South and the highest seroprevalence of 69.2% in the far North of Scotland and the Scottish Islands, while the central part of Scotland had a seroprevalence of 57.7%. This distribution disequilibrium may be due to the spread and survival of oocysts on pasture and lambing areas. A questionnaire accompanying sampling of flocks identified farms that used Toxovax®, a commercial vaccine that protects sheep from abortion due to T. gondii infection. Only 24.7% of farmers used the vaccine and the vaccine did not significantly affect the within flock seroprevalence for T. gondii. The implications for food safety and human infection are discussed

Evaluations of the Disease Surveillance Centre network in Scotland: What parts has it reached?

Regular evaluation is a prerequisite for systems that provide surveillance of animal populations. Scotland's Rural College Veterinary Services' Disease Surveillance Centre (DSC) network plays an integral part in surveillance to detect new and re-emerging threats within animal populations, predominantly livestock. In response to surveillance reviews and proposed changes to the network, an initial evaluation of diagnostic submissions data in 2010 to mid-2012 established a baseline “footprint,” while highlighting challenges with the data. In this recent evaluation for the period 2013–2018, we developed a new denominator using a combination of agricultural census and movement data, to identify relevant holdings more accurately. Iterative discussions between those processing submissions data and those involved in collection at source took place to understand the intricacies of the data, establish the most appropriate dataset, and develop the processes required to optimise the data extraction and cleansing. The subsequent descriptive analysis identifies the number of diagnostic submissions, the number of unique holdings making submissions to the network and shows that both the surrounding geographic region of, and maximum distance to the closest DSC vary greatly between centres. Analysis of those submissions classed as farm animal post-mortems also highlights the effect of distance to the closest DSC. Whether specific differences between the time periods are due to changes in the behavior of the submitting holdings or the data extraction and cleaning processes was difficult to disentangle. However, with the improved techniques producing better data to work with, a new baseline footprint for the network has been created. This provides information that can help policy makers and surveillance providers make decisions about service provision and evaluate the impact of future changes. Additionally, the outputs of these analyses can provide feedback to those employed in the service, providing evidence of what they are achieving and why changes to data collection processes and ways of working are being made. In a different setting, other data will be available and different challenges may arise. However, the fundamental principles highlighted in these evaluations and the solutions developed should be of interest to any surveillance providers generating similar diagnostic data