Model Overview
SLIM is a probabilistic, statistical epidemiological simulator. Compared to IBM approaches we simulate overall populations and perform statistical operations on top of them rather than emulating individual louse or fish.
At the same time, SLIM is also a game simulator where each agent (farmers) seeks to maximise their own profit and may choose to collaborate or defect (more on this later), which treatment to apply and so on.
So where does this agent differ from the others? The difference lies in the explicit encoding of genomic effects into the evolution of the pandemic. Treatments may encourage resistant traits to proliferate and thus make the former less and less effective at each cycle.
Note
This section assumes you have some basic knowledge of salmon aquaculture. If you don’t we suggest having a look at Rationale or 1 first.
The project models an environment in which an slim.simulation.simulator.Organisation of salmon farmers - which reside
and operate on the same loch - run their own slim.simulation.farm.Farm s which are in turn divided into
slim.simulation.cage.Cage s.
A cage is the physical location of salmons. Initially all cages within the same farm are filled with smolts which are then left to grow up and harvested after 1.5-2 years.
Sea lice stay in the reservoir and, either thanks to water currents or wild salmon, leak into salmon cages. Once they find a host to attach to they evolve from recruitment or copepopid to chalimus and beyond. SLIM models cage-specific, stage-specific lice populations and assumes concentration and pollution effects as farms are operative for long periods of time.
As lice attach to fish they cause a number of diseases which incur in fish death. If the lice aggregation ratio exceeds a given threshold treatment must be performed. However, other farmers not reaching that threshold may be faring better and opt to defect.
Whether egoism backfires or not is not an easy question as it depends on the current stage of the epidemics, lake pollution and how water currents work in the specific simulation environment.
Note that farmers are individual agents, meaning they have full freedom to open the cages in any season they prefer and whenever to treat, and also on which treatment to apply.
Note
The exact formulae behind this are documented in our paper. When it comes out please check it out!
Sea Lice Population
Our model is built on 3 main papers 2 3 4:
Just like in 2, we model the lice lifecycle as a compartmentalised model of 6 stages: recruitment (R),
copepopids (CO), chalimus (CH), preadults (PA) and adults, the latter divided in males (AM) and females (AF).
Differently from 2 we omit the explicit age encoding but rather explicitly group the population
by its genotype. Nevertheless, the age distribution can be simulated at will via
slim.simulation.cage.Cage.get_stage_ages_distrib().
This is modelled by the slim.simulation.lice_population.GenoDistrib class. A genotype distribution
is, ultimately, a discretised dictionary with where the keys are allele combinations (see
slim.simulation.lice_population.GenoDistrib.alleles) and the values are the actual number of lice in that group.
In a similar way to 2, each day we estimate how many lice die or evolve at every stage.
All stages are subject to the following:
background mortality;
treatment-induced mortality;
cleaner fish mortality (not yet implemented);
evolution (can be either additive from the previous stage or subtractive to the successive stage);
age mortality (i.e. lice that could not evolve will die).
In all stages but AM or AF lice can evolve. Evolution from PA will result in roughly 50% split between AM and AF.
Treatment
Reproduction
During mating those alleles are recombined according to a Mendelian approach. The number of reproduction events is calculated on the estimated number of matings that can happen on a single host. We assume a monogamist scenario in which one female lice can mate with only one male lice before being fecundated. As in 3 we estimate such number via a negative multinomial distribution.
The number of produced eggs is defined in a similar way to 2 and follows a power law parametrised on the (virtual) age distribution.
TODO: expand on this.