Health of Aquatic Organisms

Risk assessment and modeling of infectious salmon diseases in Ireland

Organic Salmon farming industry in Ireland:

Social Network Analysis + Biosecurity Characterization + Risk assessment + Modeling + Disease BioPortal Integration

ere
salmon mapsalmon dna

Salmon farming has been carried out in Ireland for more than 30 years and is a significant contributor to the Irish economy, particularly along disadvantaged areas of the western seaboard. Since 2009 more than 65% of Irish farmed salmon has been sold as certified Organic (in 2012 was over 80%) and, as a consequence, sales prices have been relatively high and stable. Considering Ireland’s relatively low production and the potential growth of its market, because of its condition as an organic farmed salmon producer and the growing interest in this kind of niche products, there are projects being considered for increasing national salmon production output. One of the most prominent is the construction of two salmon farms in the Aran islands at Co. Galway, developed by the Irish sea fisheries state agency, which is expected to double Irelands farmed salmon production. Currently, Ireland imports a relatively low amount of live salmon (fertilized eggs) for domestic production; however, assuming this and other growing farming projects are approved, live salmon imports could increase substantially, which might in turn increase the risk for importing disease agents exotic to Irelands industry, such as infectious salmon anemia (ISA) and heart and This multi-institutional project aims to assess the risk for introduction and spread of pathogens in the Irish salmon farming industry, considering the specific imports and trade contact structure and the management and biosecurity measures implemented on farm. All of this will be evaluated in the context of the current productive and sanitary situation of Ireland’s salmon farming industry and the prospects for its growth. Outputs will provide valuable information to stakeholders for a better prevention and control of diseases that may impact not only the salmon industry, but the entire economy of the country.

Characterization of the Norwegian vessel movement network and its vulnerability for disease spread and development of decision support system for better prevention and control of infectious diseases in Norway 

Salmon farming industry in Norway:

Social Network Analysis + Risk factor analysis + Modeling + Disease BioPortal Integration

vetinstitute
Norwegian fish

This WP will describe spatial and temporal dynamics of fish transportation and characterize the contact network patterns among the fish holdings based on the transportations, characterize infection-inducing contact patterns, identify the highly connected sites, and elucidate implications of the contact pattern on controlling disease spread.

Social network analysis (SNA) will be the approach used to characterize the contact pattern on the basis of fish transportation, and then identify highly connected sites and areas to be targeted for surveillance and control programs. The degree centrality (number of incoming and outgoing contacts that a site has) and closeness centrality (how closely connected each site is to all other sites within the network) will be estimated for each site in the contact network (Koschutzki et al., 2005; Dube et al 2009; Martinez-Lopez et al., 2009b). The centrality measures will subsequently be used to identify the sites at potential highest risk of receiving and/or transmitting infections within the network, and which sites, areas and time periods may play a key role for disease introduction and spread in Norwegian fish farming industry.

The working hypothesis is that “central” Norwegian fish farms (i.e., farms with high values for centrality measures) and network structure (i.e., relationship between different farms or groups of farms) have a strong influence on the vulnerability or risk on introduction and spread of diseases.

Additionally, we will use exponential random graph models (ERGM) to identify which node attributes and network structural properties influence the formation of an observed contact or, in other words, what is the probability that a movement between two sites occur given the properties and characteristics of those sites. This is useful for prediction of “future” contact patterns and ultimately, will allow the better prevention and control of diseases and the implementation of risk-reduction measures on a farming site.”

Epidemiological evaluation of the spatio-temporal patterns and risk factors contributing to the incidence of White Spot Syndrome Virus and the Acute Necrosis of Hepatopancreas in whiteleg shrimp in Sinaloa state, Mexico

Shrimp farming industry in Mexico:

Spatial epidemiology + Survival analysis + Risk factor analysis + Disease BioPortal Integration

uc mexus
wsd_outbreak

Sinaloa is one of the Mexican states with the highest production of farmed shrimp. The appearance of the white spot (WSSV ) infection in Mexico in 1999 had a significant economic impact due to the high mortality rate. During the last decade different strategies have been implemented for production and health management by the Aquaculture Health Committees that have reduced the economic impact, however there are still factors that are contributing to the occurrence of WSSV outbreaks, which highlights the need to conduct further investigations to establish more effective control programs. Moreover,  in 2013 it has been introduced a new syndrome, early mortality syndrome (EMS), whose etiology is a specific p strain of Vibrio parahaemolyticus which has been devastating the shrimp productions and, as consequence, production have reach levels of 1999 . The Committee on Aquaculture Health of Sinaloa ( CESASIN ) has detailed production and health data from the last 8 years. We propose here to conduct an intensive epidemiological analysis of this data to identify spatiotemporal patterns of disease presentation and to identify potential risk factors for diseases affecting farmed shrimp in Mexico. We will use spatial analysis and geostatistical models for such purpose.

daysatrisk

Spatial and Temporal Distribution and the Implementation of a Near Real-Time Surveillance System for Infectious Hematopoietic Necrosis Virus (IHNV) Infection in California

ucd food health

ihnv image 1ihnv image 2

Aquaculture is rapidly growing in economic importance in the USA, where California (CA) is considered as an emergent leader aquaculture industry that worth an estimated $110 million [1]. Primary aquaculture species in the state are rainbow trout and various salmon species for which endemic infectious hematopoietic necrosis virus (IHNV), an acute systemic disease [2] and notifiable for the World Animal Health Organization (OIE), that causes high mortality and significant economic and social losses in hatchery stocks and wild salmonids in fresh waters [3]. IHNV spread in these species has been facilitated by a limited knowledge on the virus distribution, poor knowledge on risk factors, and the lack of an adequate surveillance system. In line with the priorities of the United States Aquatic Animal Health Plan, and with CFAH high priority issues, this project will address a key problem of national importance by analyzing historical monitoring data and by developing a prototype for a near real-time surveillance system for IHNV in CA.  During the project, monitoring data that is available and has been systematically compiled and organized during the past 20 years will be gathered with host and hatchery data, and selected environmental factors including water temperature, salinity, distance to urban settings, etc. Such database will be used to 1) describe the spatial and temporal patterns of IHNV detection in California, 2) to quantify the nature and extent to which environmental factors may be associated with the presence of the virus, and 3) to generate a series of IHNV risk maps varying in space and time using a user-friendly web-based platform

Network analysis to identify important factors for managing zones

sintef vet

This WP will describe spatial and temporal dynamics of fish transportation and characterize the contact network patterns among the fish holdings based on the transportations, characterize infection-inducing contact patterns, identify the highly connected sites, and elucidate implications of the contact pattern on controlling disease spread.

Social network analysis (SNA) will be the approach used to characterize the contact pattern on the basis of fish transportation, and then identify highly connected sites and areas to be targeted for surveillance and control programs. The degree centrality (number of incoming and outgoing contacts that a site has) and closeness centrality (how closely connected each site is to all other sites within the network) will be estimated for each site in the contact network (Koschutzki et al., 2005; Dube et al 2009; Martinez-Lopez et al., 2009b). The centrality measures will subsequently be used to identify the sites at potential highest risk of receiving and/or transmitting infections within the network, and which sites, areas and time periods may play a key role for disease introduction and spread in Norwegian fish farming industry.

The working hypothesis is that “central” Norwegian fish farms (i.e., farms with high values for centrality measures) and network structure (i.e., relationship between different farms or groups of farms) have a strong influence on the vulnerability or risk on introduction and spread of diseases.

Additionally, we will use exponential random graph models (ERGM) to identify which node attributes and network structural properties influence the formation of an observed contact or, in other words, what is the probability that a movement between two sites occur given the properties and characteristics of those sites. This is useful for prediction of “future” contact patterns and ultimately, will allow the better prevention and control of diseases and the implementation of risk-reduction measures on a farming site.”