Salmonid Rickettsial Septicaemia (SRS) disease is an endemic disease in the salmonid aquaculture industry in Chile that contributes to the loss of over 700 million USD annually. Despite the continuous effort in research for new vaccines and the implementation of evidence-based control policies, the industry has yet to successfully control the disease. With the growing number of reports of antimicrobial resistance cases, policy makers have sought to control the disease without increasing the use of antimicrobials. This makes modelling of outbreaks of SRS in Chile invaluable in planning and assessing the potential impact of different disease control strategies.

This thesis is comprised of five chapters, with the overaching aim to develop a disease-spread modelling tool for the prediction and evaluation of control strategies against SRS in the Aysén region of the Chilean salmonid aquaculture industry. This tool was developed using the spatial stochastic state-transitional model platform InterSpread Plus (ISP). The first chapter is a summary review of the latest literature on SRS (history, pathology, epidemiology), the control measurements against the pathogen Piscirickettsia salmonis, and a summary of the current simulation modelling strategy against the disease.

The second chapter describes the spatial and temporal patterns of the spread for SRS outbreaks in the Aysén region of Chile based on the industry surveillance-based database between 2011 – 2020. Descriptive statistics were generated from 1,053 complete production cycles with one of the three SRS-susceptible salmonid species (Oncorhynchus kisutch (Coho salmon), Salmo salar (Atlantic salmon), and Oncorhynchus mykiss (Rainbow trout)). During the 10-year study period, the disease was highly endemic in the region, with an estimated median weekly prevalence and incidence count of SRS at 58.6% (Interquartile range (IQR): 48.5 – 66.0%) and 3 (IQR: 1 – 4), respectively. The median duration of SRS, time-to-first SRS, and time-between-subsequent SRS outbreaks were 15 weeks (IQR: 7 – 34 weeks), 17 weeks (IQR: 11 – 26 weeks), and 8 weeks (IQR: 5 – 15 weeks), respectively. While disease-specific farm mortality percentages were generally low, occasional high-mortality events were observed, indicating the potential for significant disease impact. During an SRS outbreak, the weekly median SRS-specific mortality percentage was calculated to be 0.01% (IQR: 0.002 – 0.05%), and the mortality percentage appeared to peak at around 14 – 21 weeks into the outbreak. The median cumulative SRS-specific mortality percentage was 0.42% (IQR: 0.05 – 1.67%), with the highest recorded cumulative mortality percentage at 50.1%. These findings provide valuable insights into the epidemiology and risk factors of the disease, as well as identify knowledge gaps that can guide future research. These statistics also serve as baseline information for monitoring the disease, and facilitate the development and calibration of the InterSpread Plus SRS disease spread simulation model in Chapter 3.

The third chapter describes the development of an ISP simulation model for the transmission of SRS between saltwater salmonid farms in the Aysén region of Chile. This chapter builds on the preliminary study our team carried out in the neighbouring region Los Lagos. The final model (ISP-SRS-Aysén-Chile; hereby refered to as ISAC) was evaluated using sensitivity analysis on two outcomes of interest: prevalence and cumulative incidence of SRS outbreak. The model was validated, and the final model predicted the spread of SRS with an average 95% (range: 91.7 – 96.0%) regional level prevalence accuracy over six years of simulation (Sep 2013 – 2019).

The fourth chapter utilized the ISAC model to evaluate the impact of three types of control strategies in the Aysén region of Chile. The impact of these strategies was assessed based on the change in the weekly prevalence of SRS outbreaks, and both the annual incidence rate and cumulative incidence count of SRS outbreaks over six one-year simulation periods. The three types of strategies included: (i) stamping out infected farms, (ii) reducing farm density, and (iii) implementing different vaccination strategies. Several variations in each type of strategy were explored, and the three most effective strategies in reducing the incidence of SRS were: (i) immediate stamping out of infected farms, (ii) strategically lowering 30% of the farm density in the industry, and (iii) industry-wide implementation of a vaccine with at least 75% efficacy and immune protection lasting at least 6 months.

The final chapter provides a discussion of the overall applications of the tool, the limitations, and future research on SRS modelling for the Chilean salmonid aquaculture industry. The ISAC model offers epidemiologists and policy makers a tool for assessing large-scale, regional-level control measures against SRS. It is hoped that the tool may contribute to identify knowledge gaps in the understanding of the epidemiology of SRS in this region, and ultimately help to combat the highly prevalent disease that has been a primary cause of infectious-disease-related-mortality in the Chilean aquaculture industry over the past 15 years.