CS1.1.2 Predict aquatic disease emergence

CS 1.1.2 Predict disease emergence

Emerging diseases

• New or previously unkown disease

• Known diseases appearing in a new location (expanding geographic range)

• known diseases with a new presentation (different signs) or higher virulence

• Known diseases appearing for first time in a new species (expanding host range)

• Outbreak timing (seasonal and long-term trends)

Drivers

• Environmental drivers: climate change, habitat destruction, pollution.

• Transmission: from animals to humans, between wildlife, crop and ornamentals

• Evolution: non-pathogenic micro-organisms

• Human pathways: seafood imports, ornamentals, aquaculture movements, shipping.

• Antibiotic resistance: overuse in aquaculture

Pathways

The disease triangle

Environmental drivers

Approaches for predicting new diseases

• Combine risk-analysis methods and virulence theory with historical data to identify disease-emergence risk.
  • Hazard identification
  • Release (introduction) and exposure assessment (establishment)
  • Consequence assessment (impact)

Aquaculture as a study case

Glocal records over time

Global distribution

Fish farming risk scores

Species distribution modelling

Could an algorithm predict the next disease?

::: callout-note # Carlson et al. 2021 Philos. Trans. R. Soc. Lond.B 376 (1837) :::

Epidemiological approaches

• The Distribution Fitting
• Time series regression modelling
• Disease ecology modelling
• Epidemiological modelling, e.g. SIRS.
  • Basic reproduction number R₀
  • Effective reproductive number
  • Herd immunity

Aquaculture example

Some freshwater examples

• Comprehensive habitat and land use databases available, e.g. FENZ and LANDUSE

  • Lakes380 bacterial communities as indicator of lake health

  • Toxic cyanobacteria forecasting

Challenges

• Models are only as good as the data they are fed
• Inadequate knowledge of the present geographic range
• No understanding of critical epidemiological factors (replication cycle, mode of transmission, reservoirs, vectors, stability)
• Species-specific effects impede generalisations
• Pathogen and parasite evolution in response to environmental change
• Microbes and other factors render predictions impossible

Thanks!

“Models are not going to have the exact time, place and species that will lead to the next disease; they might help us to understand what’s happening around us and prioritise.”