Module L: New Models of Salmon Health...
Transcript of Module L: New Models of Salmon Health...
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Module L: New Models of
Salmon Health Management
Overview of Planned Research
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November 7, 2017
Dr. Ian Gardner
on behalf of the team
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Ocean Data & Technology
Sustainable Fisheries
Sustainable Aquaculture
Marine Safety
Ocean Change
Ocean Solutions
J
Atmosphere-Ocean
Interactions
J
Shifting
Ecosystems
A: Marine Atmospheric Composition & Visibility
B: Auditing the Northwest Atlantic Carbon Sink
C: Dynamic microbial communities
D: Impact of Warming on Groundfish
E: Ecosystem Indicators
F. Cooperative Physical Modelling
G: Future-proofing Marine Protected Area
Networks
H: Novel Stock Assessment Models
I. Informing Governance Responses
J: Improving Aquaculture Sustainability
K: Novel sensors for fish health and welfare
L: New Models of Salmon Health Management
M: Social License & Planning in Coastal
Communities
N: Safe Navigation & Environmental Protection
O: Transforming Ocean Observations
P: Research Data Management
MUN
Dal
UPEI
Dal
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Linkages of aquaculture modules
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Novel sensors
(Dal - Module K)
Improving sustainability
(MUN - Module J)
Module L
Projects
Social license
(Dal- Module M)
Data
Biocapacity estimates
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Project 1: Risk-based models to reduce spread of Infectious
Salmon Anaemia virus (ISAv)
Project 2: Improving antimicrobial treatment efficacy in Atlantic
salmon
Project 3: Modeling tools to investigate disease occurrences,
transmission patterns, and mitigation strategies, in the context
of biocapacity
Project 4: Interpretation of novel data streams from pen-level
sensors and microscale current patterns for fish health
monitoring and parasite control
Module L: New Models of
Salmon Health Management
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Project 1: Risk ranking of
Atlantic salmon farms to
minimize spread of ISAv
• PI:
– Ian Gardner
• Collaborators:
– Kim Klotins, Raju Gautum – CFIA
– Lori Gustafson – USDA:APHIS:VS
– Mike Beattie – Gas infusion systems
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Project 1
• Why?
– CFIA is responsible for management of
outbreaks caused by World Organisation of
Animal Health (OIE) listed diseases
– Needs science-based guidance on
prioritization of salmon farm surveillance
during outbreaks
• Outcomes and impacts
– Risk ranking tool (R software) in real time
– More efficient disease response
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Objectives and approach
• Compare seaway distance and a combination
of seaway distance and hydrodynamic
connectivity data for predicting risk after ISAv
spread from an “index” site
• Susceptible-Infected (SI) model coded in R
software
• Validation of model with monthly case data from
2002 -2004 ISAv outbreak in Bay of Fundy (24
farms in NB and 6 in Maine)
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Example
Model
Output
Infected sites
Highest risk
(non-infected) sites
Medium risk
(non-infected) sites
Low risk
(non-infected) sites
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Project 2 – Improving antibacterial
treatment efficacy
• PI:
– Sophie St-Hilaire
• Collaborators:
– Eastern Aquatic Veterinary Association
– Industry and provincial veterinarians
– Salmon companies
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Treatment failure may occur if fish
have antibiotic concentrations
lower than the MIC
~11% below the MIC90
~46% below the MIC90
1000 2000
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MIC90: Minimum Inhibitory Concentration for 90% of isolates
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Project 2
• Why?
– Early detection of bacterial disease is desirable
– More effective in-feed delivery systems are
needed
• Outcomes and impacts
– Reduced use of antibiotics while salmon are in
sea-water
– “Best Practices” guide developed
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Improving antibacterial treatment
efficacy
• Objective
– Determine ways to improve the distribution of
antibiotics in salmon aquaculture to achieve
better coverage within the population
• At different temperatures
• For different size fish
• For different antibiotic classes
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Approach: starting in 2019
• Industry focus group meeting
• Pilot study to assess post-treatment
antibiotic tissue concentrations
• Update focus group meeting on pilot &
make adjustment based on feedback
• Initiate larger-scale longitudinal study
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Project 3 – Modelling tools to
investigate disease occurrences,
transmission patterns, and mitigation
strategies
• PIs: – Raphael Vanderstichel
– Henrik Stryhn
– Crawford Revie
• Collaborators:
– Jon Grant – Dalhousie University;
– Tor Horsberg – Norwegian School Vet Sciences;
– Anja Kristoffersen, Peder Jansen - Norwegian
Veterinary Institute 14
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Background
• Why?
– Responding to external requests to improve
models that reduce and better manage
diseases (e.g. sea lice) in farm/zones
• Outcomes and impacts
– Utilize the more rich and complicated data
now available
– Expand current farm-level models to zone
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Modelling Tools
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Ocean circulation models
Observed Disease
Occurrence = Internal Expected External Factors + + error
Pathogen biological parameters
e.g. historical pathogen levels, farm size,
temperature, etc.
Hypothesized hydrological parameters
e.g. particle intensities weighted by
neighbouring farm pathogen levels
Mapping errors
Values from model residuals
describe transmission patterns
Epi-Statistical models
Informing
policy and
management
decisions
Epi-Simulation models
Farm A
Farm B
Farm C Self-infection
Unidirectional or bidirectional
farm-to-farm infection
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Extending the Models
• Unobserved disease status (Simulation
models)
– Current agent-based models exist for
‘individual farm’ simulations
• Evolution of chemotherapeutant resistance
– Future goal to expand this to represent a
larger area with multiple farms
• Build connectivity among farms
• Solve scalability issues
– Computational demands
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Extending the Models
• State-space models
– Apply a multivariate state-
space model for sea lice for
active sites in Grand Manan
area
• Quantify internal and external
infection pressure of sea lice
on salmon sites
• Evaluate the predictive
accuracy of the model
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BMA: Bay Management Area
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Project 4 – Interpretation of novel data
streams (pen sensors and microscale
current patterns) for fish health
monitoring and parasite control
• PI:
– Crawford Revie
• Collaborators:
– SINTEF Norway
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Project 4
• Why?
– Increased numbers/types of pen-level data
– Uncertainty as to how these can best be used
in the context of fish health
• Outcomes and impacts
– New methods to interpret data and better
target cage-level health interventions
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Novel data streams from pen-level
sensors
• Environmental data from each cage
• Water movement/flow patterns
within and
around cages
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SINTEF
LiceRisk
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Novel data streams from pen-level
sensors
• Video and other signals
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New methods for interpretation of
data
• Integration of data sources/types
• Multivariate statistics to filter/summarise
signals
• Machine learning
algorithms to detect
trends and associations
between signals and
fish health
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Project 1: Risk-based models to reduce spread of Infectious
Salmon Anaemia virus (ISAv)
Project 2: Improving antimicrobial treatment efficacy in Atlantic
salmon
Project 3: Modeling tools to investigate disease occurrences,
transmission patterns, and mitigation strategies, in the context
of biocapacity
Project 4: Interpretation of novel data streams from pen-level
sensors and microscale current patterns for fish health
monitoring and parasite control
Questions:
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