A quantitative risk assessment for the incursion of lumpy skin disease virus into Australia via long-distance windborne dispersal of arthropod vectors.

Lumpy skin disease (LSD) is an infectious disease of cattle and water buffalo caused by lumpy skin disease virus (LSDV). It is primarily transmitted mechanically by biting insects. LSDV has spread from Africa to the Middle-East, the Balkans, Caucasus, Russia, Kazakhstan, China, Asia and India, suggesting that a wide variety of arthropod vectors are capable of mechanical transmission. In 2022, LSD was detected in Indonesia, heightening awareness for Australia's livestock industries. To better understand the risk of LSDV incursion to Australia we undertook a quantitative risk assessment (QRA) looking at windborne dispersal of arthropod vectors, assuming a hypothetical situation where LSD is endemic in south-east Asia and Papua New Guinea. We estimated the risk of LSDV incursion to be low, with a median incursion rate of one incursion every 403 years, based on a model where several infectious insects (i.e. a 'small batch' of 3-5) must bite a single bovine to transmit infection. The incursion risk increases substantially to one incursion every 7-8 years if a bite from a single insect is sufficient for transmission. The risk becomes negligible (one incursion every 20,706 years) if bites from many insects (i.e. a 'large batch' of 30-50 insects) are necessary. Critically, several of our parameter estimates were highly uncertain during sensitivity analyses. Thus, a key outcome of this QRA was to better prioritise surveillance activities and to understand the key research gaps associated with LSDV in the Australasian context. The current literature shows that multiple vectors are required for successful bovine-to-vector transmission of LSDV, suggesting that our estimate of one outbreak every 403 years more accurately represents the risk to Australia; however, the role of single insects in transmission has not yet been evaluated. Similarly, attempts to transmit LSDV between bovines by Culicoides have not been successful, although midges were the highest risk vector category in our model due to the high vector-to-host ratio for midges compared to other vector categories. Our findings provide further insight into the risk of LSD to Australian cattle industries and identify the Tiwi Islands and areas east of Darwin as priority regions for LSDV surveillance, especially between December and March.

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.

Use of scenario tree modelling to plan freedom from infection surveillance: Mycoplasma bovis in New Zealand.

Since mid-2018, the New Zealand (NZ) Ministry for Primary Industries (MPI) has been operating an eradication program for an incursion of Mycoplasma bovis. Although NZ is still delimiting the outbreak, consideration is being given to how freedom from M. bovis will be demonstrated. Rapid demonstration of freedom will minimise the length of the program, significantly reducing its financial burden. This collaborative research was undertaken to help inform planning of surveillance to demonstrate freedom after M. bovis is believed eradicated. Scenario tree modelling (STM) involves assimilating multiple surveillance system components to determine whether disease is absent. STM has infrequently been used to plan appropriate surveillance but this was the approach used here. A stochastic simulation model was implemented in R. The model represented the NZ commercial dairy and non-dairy cattle industries and the current surveillance components that are also planned to be used to gather evidence of absence of M. bovis once it is eradicated. Different surveillance intensities and risk based versus random surveillance were simulated and compared for probability of freedom, financial cost of sampling and testing and the time to demonstrate freedom. The results indicate that the current surveillance components will enable demonstration of freedom. Surveillance components included bulk tank milk testing, herd testing and testing at meat processing plants, predominantly using an imperfect ELISA. Several combinations of surveillance components appeared most efficient achieving >95 % confidence of freedom over 2-4 years, whilst sampling 4-7 % of the non-dairy herds and less than 25 % of dairy herds annually. The results indicate that surveillance intensity can be lower than is currently occurring to support the delimiting phase, thereby saving significant resources in the post eradication phase (proof of freedom phases). Further consideration is required to enable the assumption of 100 % herd specificity made in the model to be achieved. The ELISA used is very specific, but will yield some false positives that must be resolved to their true status. This may occur for example through modified diagnostic test interpretation (e.g. cut point optimisation at individual and herd level) or resolution of putative false positive herds with epidemiological investigation. In conclusion this research demonstrates the utility of STM for planning surveillance programs, and in this instance has highlighted efficient and effective surveillance components for demonstrating freedom from M. bovis in NZ. It also highlights the need to achieve 100 % specificity for M. bovis in herds tested during the proof of freedom phases.

An assessment of the association between soil pH and ovine Johne's disease using Australian abattoir surveillance data.

There has long been discussion in the literature about the role of soil on ovine Johnes disease (OJD). This is especially true of soil pH, however there is very little research to support an association between pH and OJD prevalence. The primary objective of this study was to examine the hypothesis that there is an association between soil pH and OJD. Several additional hypotheses were also assessed. Sheep properties that were surveyed by the Australian National Sheep Health Monitoring Project where classified as OJD reactor positive or otherwise. A variety of explanatory variables such as soil (especially soil pH), environmental and management factors were examined. Spatial regression models were assessed using information theory to examine support for various hypotheses and to examine associations; especially that soil pH is associated with OJD. A total of 1213 properties from 10,578 were classified as OJD positive (11.5%, 95% CI: 10.9-12.1). Within the limitations of the study, only modest support was found for an association between soil pH and the presence or absence of OJD. Instead, OJD prevalence was affected by several factors concurrently, a so called multi-factorial model (hypothesis). In this supported multifactorial hypothesis soil pH was marginally associated with OJD (p=0.04) and had a relatively weak effect (OR 0.91, 95% CI 0.82 to 1.00). OJD was strongly associated with a number of biosecurity and environmental factors such as the time since infection arrived in a region, absence of biosecurity programs (such as regional biosecurity programs or state based programs) and, to a lesser extent, solar irradiation. Soil pH may play a relatively small role in explaining OJD prevalence when evaluated as part of a multifactorial model. Biosecurity and other environmental factors appear to be more strongly associated with the presence of OJD in Australia.

Salmonella infection in a remote, isolated wild pig population.

Although wild pig populations are known to sometimes be infected by Salmonella, the situation in Australia has received little attention and few population-based, planned studies have been conducted. Understanding the distribution of Salmonella infections within wild pig populations allows the potential hazard posed to co-grazing livestock to be assessed. We sampled a remote and isolated wild pig population in northwestern Australia. Faecal and mesenteric lymph node samples were collected from 651 wild pigs at 93 locations and cultured for Salmonella. The population sampled was typical of wild pig populations in tropical areas of Australia, and sampling occurred approximately halfway through the population's breeding season (38% of the 229 adult females were pregnant and 35% were lactating). Overall, the prevalence of Salmonella infection based on culture of 546 freshly collected faecal samples was 36.3% (95% CI 32.1-40.7%), and based on culture of mesenteric lymph nodes was 11.9% (95% CI, 9.4-15.0%). A total of 39 serovars (139 isolates) were identified--29 in faecal samples and 24 in lymph node samples--however neither Salmonella enterica serovar Typhimurium nor Salmonella Cholerasuis were isolated. There was a significant (p<0.0001) disagreement between faecal and lymph node samples with respect to Salmonella isolation, with isolation more likely from faecal samples. Prevalence differed between age classes, with piglets being less likely to be faecal-positive but more likely to be lymph node positive than adults. The distribution of faecal-positive pigs was spatially structured, with spatial clusters being identified. Study results suggest that this population of wild pigs is highly endemic for Salmonella, and that Salmonella is transmitted from older to younger pigs, perhaps associated with landscape features such as water features. This might have implications for infection of co-grazing livestock within this environment.

Integrating survey and molecular approaches to better understand wildlife disease ecology.

Infectious wildlife diseases have enormous global impacts, leading to human pandemics, global biodiversity declines and socio-economic hardship. Understanding how infection persists and is transmitted in wildlife is critical for managing diseases, but our understanding is limited. Our study aim was to better understand how infectious disease persists in wildlife populations by integrating genetics, ecology and epidemiology approaches. Specifically, we aimed to determine whether environmental or host factors were stronger drivers of Salmonella persistence or transmission within a remote and isolated wild pig (Sus scrofa) population. We determined the Salmonella infection status of wild pigs. Salmonella isolates were genotyped and a range of data was collected on putative risk factors for Salmonella transmission. We a priori identified several plausible biological hypotheses for Salmonella prevalence (cross sectional study design) versus transmission (molecular case series study design) and fit the data to these models. There were 543 wild pig Salmonella observations, sampled at 93 unique locations. Salmonella prevalence was 41% (95% confidence interval [CI]: 37-45%). The median Salmonella DICE coefficient (or Salmonella genetic similarity) was 52% (interquartile range [IQR]: 42-62%). Using the traditional cross sectional prevalence study design, the only supported model was based on the hypothesis that abundance of available ecological resources determines Salmonella prevalence in wild pigs. In the molecular study design, spatial proximity and herd membership as well as some individual risk factors (sex, condition score and relative density) determined transmission between pigs. Traditional cross sectional surveys and molecular epidemiological approaches are complementary and together can enhance understanding of disease ecology: abundance of ecological resources critical for wildlife influences Salmonella prevalence, whereas Salmonella transmission is driven by local spatial, social, density and individual factors, rather than resources. This enhanced understanding has implications for the control of diseases in wildlife populations. Attempts to manage wildlife disease using simplistic density approaches do not acknowledge the complexity of disease ecology.

Controlling disease outbreaks in wildlife using limited culling: modelling classical swine fever incursions in wild pigs in Australia.

Disease modelling is one approach for providing new insights into wildlife disease epidemiology. This paper describes a spatio-temporal, stochastic, susceptible- exposed-infected-recovered process model that simulates the potential spread of classical swine fever through a documented, large and free living wild pig population following a simulated incursion. The study area (300 000 km2) was in northern Australia. Published data on wild pig ecology from Australia, and international Classical Swine Fever data was used to parameterise the model. Sensitivity analyses revealed that herd density (best estimate 1-3 pigs km-2), daily herd movement distances (best estimate approximately 1 km), probability of infection transmission between herds (best estimate 0.75) and disease related herd mortality (best estimate 42%) were highly influential on epidemic size but that extraordinary movements of pigs and the yearly home range size of a pig herd were not. CSF generally established (98% of simulations) following a single point introduction. CSF spread at approximately 9 km2 per day with low incidence rates (< 2 herds per day) in an epidemic wave along contiguous habitat for several years, before dying out (when the epidemic arrived at the end of a contiguous sub-population or at a low density wild pig area). The low incidence rate indicates that surveillance for wildlife disease epidemics caused by short lived infections will be most efficient when surveillance is based on detection and investigation of clinical events, although this may not always be practical. Epidemics could be contained and eradicated with culling (aerial shooting) or vaccination when these were adequately implemented. It was apparent that the spatial structure, ecology and behaviour of wild populations must be accounted for during disease management in wildlife. An important finding was that it may only be necessary to cull or vaccinate relatively small proportions of a population to successfully contain and eradicate some wildlife disease epidemics.

Field evaluation of an equine influenza ELISA used in New South Wales during the 2007 Australian outbreak response.

During the Australian epidemic of equine influenza in 2007, tens of thousands of horses were infected. From the resulting field data, 475 known infected and 1323 uninfected horses were identified to allow a post outbreak evaluation of the performance of the commonly used bELISA for influenza A under field conditions. A variety of techniques, such as ROC plots, area under the curve and hypothesis testing were used to assess the overall performance of the test. The test was deemed to be accurate (area under curve=0.993+/-0.003 standard error) and significantly informative (z=-32.0; p<0.0001). Sensitivity and specificity of the test as used in the response (cut-point percentage inhibition> or =50) were 0.992 (95% CI: 0.979-0.997) and 0.967 (95% CI: 0.957-0.976), respectively.