Use of contact structures for the control of infectious diseases in the British aquaculture industry

Infectious fish diseases present an ongoing threat to the British Aquaculture Industry. Britain has so far been fortunate in avoiding large-scale outbreaks of major infectious diseases such as Viral Haemorrhagic Septicaemia, Gyrodactylus salaris and Infectious Haematopoietic Necrosis. However, these diseases are prevalent in parts of Europe and could potentially have serious economic implications for British Aquaculture. This research project aims to develop the capacity to understand, prevent and control outbreaks of these and other infectious diseases.

Working with project partners at Marine Scotland and the Centre for Environment, Fisheries & Aquaculture Science (Cefas), we will assimilate existing detailed information from England, Scotland and Wales on the structure and operation of the British Aquaculture industry into the first single network of disease transmission routes between fish farms and fisheries throughout Britain. This structure will be analysed using modern methods from network theory to identify properties that will assist and inform the design of optimal strategies for the control and prevention of fish disease epidemics. In particular, by running computer simulations of large and small epidemics upon this realistic network, we will identify the full range of behaviours and test the efficacy of possible interventions.

Acting in a similar way to weather forecasting, running millions of simulations will enable us to test the full breadth of possible outbreak scenarios, and establish their general likelihoods, as well as profiling high-risk spreading dynamics originating at any specific site or site cluster we choose. In addition, this "numerical laboratory" lets us explore and fine-tune a broad range of existing and novel containment, mitigation, and eradication policies These measures range from site-specific controls, through local contact tracing, to nationwide measures (e.g., a stakeholder-targeted awareness campaign of specific symptoms to look out for), while simultaneously imposing realistic capacity constraints. Analysis of interventions will also have an associated economic costing, as it would clearly be counter-productive to engage in an intervention strategy that was as costly as the infectious disease itself. The overall aim here is not just exploratory and analytical, but, moreover, to create a practical, flexible expert system that can be fed the latest data on the (slowly changing) network architecture, pathogen properties, and any emerging outbreak, and be able to produce accurate real-time risk assessments of spreading dynamics, as well as being able to suggest the most effective counter-strategies given specific locations and circumstances.

Finally, an unusual property of fish pathogens is that their properties and effects are often temperature-dependent in terms of their infectivity, the extent infections cause recognisable symptoms, and fish mortality. An important goal of this work is therefore to include farm-specific temperature data into the representation of epidemic spread on the network. This is part of a wider strategy to model the system across multiple scales whereby other farm-specific data such as size, type and production can feed into the model. Such farm-level sub-models will be readily changeable to maximise adaptability of the simulator for future research. In this respect, we specifically aim to address the effects of climate change (over years to decades) in terms of higher water temperatures impacting fish disease epidemics. As far as we are aware, no-one has ever investigated the scale and severity of these future impacts on the British Aquaculture industry, and we need to be (better) prepared. This project thus aspires to create a durable foundation of field expertise, scientific insights, and data on the British Aquaculture Industry, to safeguard its future success in the face of ongoing epidemic threats.

This project aims to create a flexible expert system to quantify:
1) the likely impacts of specific fish pathogens upon the British Aquaculture Industry (BAI);
2) the most (cost-)effective countermeasures to any epidemic or endemic outbreak scenario;
3) the effects of seasonality and climate change in terms cyclical plus baseline changes in (water) temperature.

This system serves two objectives:
A) provide a tool for ensemble forecasting to generate risk profiles for various outbreak scenarios, to identify weaknesses and to optimise (in terms of efficiency, constraints, and cost) both existing and novel biosecurity controls for outbreak prevention, mitigation, and eradication;
B) enable rapid response, risk assessment, policy advice, and adaptation to changing circumstances during an actual fish disease outbreak.

Methodologically, we will combine practical stakeholder expertise (biosecurity measures and constraints, plus the economic costs of interventions), veterinary science (pathogen and disease properties, e.g., host susceptibility and transmission), high-throughput computing, extending a stochastic epidemiological fish disease simulator operational since 2009, complex network mathematics (network partitioning, hotspot identification, and centrality measures), and recent data on BAI network architecture and geography (live fish transports; river network contacts; coastal, local, and fomite transmissions), and local temperature (Met Office) over time.

Aside from the first-ever joining of English, Welsh and Scottish aquaculture networks, another novelty of our approach is the sub-model structure whereby specific models at individual fish-farm level can be easily devised and incorporated into the wider network model, e.g., the temperature-dependence part. From a methodological point of view, this modelling across scales represents a proof of principle for explicit cross-scale modelling in contact network-based epidemiological models.

The impacts from this project can be envisaged as an outward-radiating pattern encompassing the science, the government institutions involved, the British aquaculture Industry stakeholders, and society at large.

Starting with the science, cross-pollination of the latest insights from complex network theory and nonlinear statistics with the practical features and constraints from the British Aquaculture Industry (BAI) provides a fertile basis for improved understanding of spreading dynamics in general and for fish pathogens in the BAI in particular. Moreover, we provide not just an engine for analytical data and practical risk assessments to inform biosecurity policies, but also an example of the application of interdisciplinary knowledge that benefits both academic and stakeholder communities alike. In the longer term, the methodologies we develop, including network analysis and multi-scale models, will likely also influence epidemiological research (and implementations) in other areas of disease control in livestock (poultry, cattle, pig, sheep), in Britain and beyond.

The two collaborating government institutions represent our bridge to the BAI, and constitute direct liaisons to government-backed implementation of our findings. At the same time, we provide a bridge for them, as this is the first time that BAI network data for the whole of Britain will be combined. Through our discussions and meetings and a joint approach to studying and controlling fish pathogens - which do not respect national borders - we hope to foster increased exchange in communication of network data, the most effective policies, and early-warning whenever a new outbreak occurs. Furthermore, we generate a system able to assess the likelihoods of epidemic scenarios, yielding a comprehensive, quantitative, statistically robust basis upon which to make decisions on the types, extent, and duration of controls, producing optimised and cost-effective measures. It it is hoped that its usage and outputs may provide additional inputs for discussion between experts on both sides of the English/Scottish border.

These benefits naturally extend to other industry stakeholders and society as a whole. More (cost-) effective and targeted biosecurity measures make strategies for outbreak containment and mitigation more robust and efficient. This will positively impact not just animal welfare and productivity, but also the economic viability of the fish farming industry. As outbreaks of major infectious diseases in the BAI could have huge economic costs, prevention and effective mitigation represents a significant economic saving, one that may also be passed on to consumers in the price of fish. Additionally, since farmed fish can also act as a source for infecting wild fish, and the size and importance of this food industry is growing, there are additional benefits to the natural world, which are however, much harder to quantify.

Finally, the long-term future of the industry cannot ignore the effects of a warming climate. Water temperature directly affects many important fish pathogens' potential for harm, and although this has been studied in vitro, the actual outbreak dynamics resulting from the highly complex interplay of seasonality, weather fluctuations, and the gradual increase in the baseline temperature over the next few decades in Britain has to our knowledge never been adequately investigated using real-world data. A better understanding of this increased potential for large, devastating outbreaks may very well prove crucial for the industry, and yield both purely scientific and more generally relevant insights, that beckon to be investigated.