Data-Driven Network Modeling as a Framework to Evaluate the Transmission of Piscine Myocarditis Virus (PMCV) in the Irish Farmed Atlantic Salmon Population and the Impact of Different Mitigation Measures

Diseases of marine fish and shellfish in an age of rapid climate change

A recurring trend in evidence scrutinized over the past few decades is that disease outbreaks will become more frequent, intense, and widespread on land and in water, due to climate change. Pathogens and the diseases they inflict represent a major constraint on seafood production and yield, and by extension, food security. The risk(s) for fish and shellfish from disease is a function of pathogen characteristics, biological species identity, and the ambient environmental conditions. A changing climate can adversely influence the host and environment, while augmenting pathogen characteristics simultaneously, thereby favoring disease outbreaks. Herein, we use a series of case studies covering some of the world's most cultured aquatic species (e.g., salmonids, penaeid shrimp, and oysters), and the pathogens (viral, fungal, bacterial, and parasitic) that afflict them, to illustrate the magnitude of disease-related problems linked to climate change.

DTU-DADS-Aqua: A simulation framework for modelling waterborne spread of highly infectious pathogens in marine aquaculture

Simulation models are useful tools to predict and elucidate the effects of factors influencing the occurrence and spread of epidemics in animal populations, evaluate the effectiveness of different control strategies and ultimately inform decision-makers about mitigations to reduce risk. There is a paucity of simulation models to study waterborne transmission of viral and bacterial pathogens in marine environments. We developed a stochastic, spatiotemporal hybrid simulation model (DTU-DADS-Aqua) that incorporates a compartmental model for infection spread within net-pens, an agent-based model for infection spread between net-pens within and between sites and uses seaway distance to inform farm-site hydroconnectivity. The model includes processes to simulate infection transmission and control over surveillance, detection and depopulation measures. Different what-if scenarios can be explored according to the input data provided and user-defined parameter values, such as daily surveillance and depopulation capacities or increased animal mortality that triggers diagnostic testing to detect infection. The latter can be easily defined in a software application, in which results are summarized after each simulation. To demonstrate capabilities of the model, we simulated the spread of infectious salmon anaemia virus (ISAv) for realistic scenarios in a transboundary population of farmed Atlantic salmon (Salmo salar L.) in New Brunswick, Canada and Maine, United States. We assessed the progression of infection in the different simulated outbreak scenarios, allowing for variation in the control strategies adopted for ISAv. Model results showed that improved disease detection, coupled with increasing surveillance visits to farm-sites and increased culling capacity for depopulation of infected net-pens reduced the number of infected net-pens and outbreak duration but the number of ISA-infected farm sites was minimally affected. DTU-DADS-Aqua is a flexible modelling framework, which can be applied to study different infectious diseases in the aquatic environment, allowing the incorporation of alternative transmission and control dynamics. The framework is open-source and available at https://github.com/upei-aqua/DTU-DADS-Aqua.