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.

Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals.

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

Decision-making for foot-and-mouth disease control: Objectives matter.

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Assessing the risk of highly pathogenic avian influenza H5N1 transmission through poultry movements in Bali, Indonesia.

Indonesia continues to report the highest number of human and poultry cases of highly pathogenic avian influenza H5N1. The disease is considered to be endemic on the island of Bali. Live bird markets are integral in the poultry supply chain on Bali and are important, nutritionally and culturally, for the rural and urban human populations. Due to the lack of biosecurity practiced along the supply chain from producer to live bird markets, there is a need to understand the risks associated with the spread of H5N1 through live bird movements for effective control. Resources to control H5N1 in Indonesia are very limited and cost effective strategies are needed. We assessed the probability a live bird market is infected through live poultry movements and assessed the effects of implementing two simple and low cost control measures on this risk. Results suggest there is a high risk a live bird market is infected (0.78), and risk mitigation strategies such as detecting and removing infected poultry from markets reduce this risk somewhat (range 0.67-0.76). The study demonstrates the key role live poultry movements play in transmitting H5N1 and the need to implement a variety of control measures to reduce disease spread.

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.

Simulating disease spread within a geographic information system environment.

Simulation modelling is a tool that can be used to investigate the effectiveness and efficiency of exotic disease control, eradication and surveillance strategies. The Australian Government Department of Agriculture, Fisheries and Forestry (DAFF) has been involved with disease simulation modelling for more than 10 years. Although the focus has been on foot and mouth disease, models are now being developed for avian influenza, classical swine fever and other diseases. Recent models are spatially explicit, and incorporate a range of animal species and production types. The models also encompass a range of disease transmission pathways, including farm-to-farm animal movements, movements through saleyards, windborne spread, spread by feral animals and the less well-defined phenomenon of local spread. The DAFF spatial models are unique in that they are developed within the environment of a geographic information system (GIS) - MapBasic/MapInfo. This simplifies the spatial elements of their code and improves their ability to handle spatial data layers. Such layers vary, but may include the following: farm locations or boundaries; masks identifying grazing; cropping and non-agricultural land; water bodies and waterways; population centres, administrative boundaries and roadways; vegetation and other land cover masks; and, where relevant, elevation. The GIS environment also provides immediate access to sophisticated maps and tabular outputs.

Evaluating alternative approaches to managing animal disease outbreaks--the role of modelling in policy formulation.

Modelling is a powerful tool for informing development of policies for the control of animal diseases. By permitting the study of 'what if' scenarios, this tool can be used to help identify and evaluate strategies to reduce the number of animals destroyed to eradicate diseases. To be useful, models need to be fit for purpose and appropriately verified and validated. For informing disease control policy, modelling will be most useful when used before an outbreak, particularly in the areas of retrospective analysis of previous outbreaks, contingency planning, resource planning, risk assessments and training. Recent experience suggests that predictive modelling during actual outbreaks needs to be viewed and used with caution. It is important to recognise that models are just one tool for providing scientific advice and should not be considered in isolation from experimental studies and collection and analysis of epidemiological data. Collaborative studies and international cooperation can help address validation issues and improve the utility of models for emergency disease management. One such initiative, involving the 'Quadrilateral countries' (Australia, Canada, New Zealand and the United States), Ireland and the United Kingdom is discussed.