A plethora of works exist in the literature dealing with applications of machine learning in intelligent fish aquaculture. In [1], a review and analysis of the developments of machine learning in fish-intelligent aquaculture was conducted; the work emphasized the application and use of deep learning and neural network that has expanded the scope for intelligent application in fish aquaculture and improved the efficiency and benefit of breeding. The applications included fish biomass detection, identification, and classification of fish, behavior analysis, and water quality parameter prediction, while the machine learning models were mainly CNN models and then SVM, BPNN, BM, KNN, RF, AdaBoost, YOLOv3, RNN, and XGBoost. In [2], the authors reviewed the application of deep learning in aquaculture and categorize research by aquatic products (i.e., fish, shrimp, scallops, coral, jellyfish, aquatic macroinvertebrates, phytoplankton, and water quality), presenting examples of current research such as (among others) fish behavior monitoring, fish fillets defect detection, shrimp disease research, activity monitoring of cold water coral polyps, classification, phytoplankton classification, trend prediction of red tide biomass, dissolved oxygen content prediction, temperature prediction, and marine floating raft aquaculture monitoring. Of particular interest in the specific paper is the reference to works for water quality prediction, with various of its applications: Dissolved Oxygen prediction, Chlorophyll-a Content Prediction, Temperature Prediction.

Navigating disease transmission in aquaculture is challenging, given the multifactorial nature of fish diseases and their sensitivity to environmental conditions. The aquaculture industry, responsible for cultivating entities such as fish, shellfish, and aquatic plants, is integral to the global food system. However, the inherent conditions of aquaculture, characterized by close confinement and high densities, accentuate the potential for expedited disease spread. Consequently, a thorough understanding and precise prediction of disease trajectories are imperative to mitigate the associated economic and ecological impact.

The importance of forecasting disease transmission in aquaculture is emphasized by its pivotal role in ensuring the industry’s economic sustainability. Disease outbreaks can lead to substantial losses for aquaculture farmers, resulting in decreased production, elevated mortality rates, and financial instability. Swift and accurate prediction models enable the timely implementation of intervention strategies, including quarantine protocols, vaccination initiatives, and enhanced biosecurity measures. These proactive measures can considerably reduce the risk and severity of disease outbreaks, thereby safeguarding the livelihoods of aquaculture producers and fostering sustainable industry growth.

Furthermore, accurate disease transmission prediction holds broader implications for food security and public health. As aquaculture continues to play a pivotal role in global seafood supply, ensuring the health and well-being of farmed aquatic organisms is paramount. Diseases affecting aquaculture species not only impact the availability and affordability of seafood but may also pose risks to human health if zoonotic diseases are transmitted through the consumption of infected fish. Consequently, precise disease transmission models contribute to the production of safe and wholesome seafood, addressing worldwide food security concerns and minimizing public health hazards.

Addressing these challenges in disease transmission prediction requires interdisciplinary efforts and innovative approaches. Notable research studies contribute valuable insights. Dunstan et al. proposed utilizing trophic ecology to underpin ecosystem approaches for disease transmission prediction, emphasizing the importance of understanding species interactions ^[3][11]. Peeler and Taylor discussed risk-based approaches for aquatic animal health management and their application to disease transmission prediction ^[4][12]. Murray and Peeler provided a framework for understanding emerging diseases in aquaculture, emphasizing the need for proactive surveillance ^[5][13]. Moreover, Georgiadis et al. highlighted the role of epidemiology in disease control in wild and cultured fish populations ^[6][14].

On the other hand, Baud et al. demonstrated that disease transmission prediction is crucial in aquaculture to mitigate the impact of outbreaks, and discussed that accurate estimation of mortality rates is essential for effective management strategies ^[7][15]. Murray et al. highlighted that disease burden is a significant global issue, and accurate prediction and management of diseases is crucial for reducing mortality and disability rates across different industries, including aquaculture ^[8][16]. Bondad-Reantaso et al. addressed disease and health management in Asian aquaculture, and examined that the implementation of effective disease prediction and control measures is necessary to ensure sustainable and profitable aquaculture production ^[9][17]. Peeler et al. investigated the role of non-native aquatic animal introductions in driving disease emergence in Europe, and demonstrated that controls are essential to prevent further spread and protect aquaculture systems ^[10][18].

In addition, recent research by Alaliyat et al. introduced an agent-based model for predicting pathogen transmission patterns in Norwegian fjords ^[11][19]. Liao et al. developed a mechanistic population dynamics model to predict the effect of heavy-metal stresses on susceptibility to pollution-associated infectious diseases ^[12][20]. Stärk et al. emphasized the role of molecular and genomic data for disease surveillance ^[13][21]. Pérez-Sánchez et al. discussed biological approaches for bacterial disease prevention and control ^[14][22].

Recent studies emphasize on the complex challenges inherent in predicting disease transmission within aquaculture contexts. Mugimba et al. highlighted the rising complexities associated with emerging viral diseases, wild reservoirs, and diagnostic limitations ^[15][23]. Sabo-Attwood et al. examined the nuances of managing aquatic disease agents, exploring nanotechnology’s potential to enhance disinfection and early warning systems ^[16][24]. Ahmed et al. introduced a promising machine-learning technique for timely fish disease identification ^[17][25]. Romero et al. contributed a simulation framework for modeling waterborne pathogen spread in marine aquaculture, a tool valuable for assessing control strategies ^[18][26].

Ultimately, these studies collectively underscore the necessity of a multifaceted approach in predicting disease transmission within aquaculture. This demands cohesive strategies, including biosecurity policies, rigorous implementation of measures, vaccine advancements, strategic medication and probiotic use, selective breeding, advanced diagnostics, vigilant surveillance, and prudent husbandry. Such a holistic strategy fortifies aquaculture’s resilience against disease complexities, securing its future sustainability. As disease transmission intricacies persist, accurate prediction models are imperative. Addressing challenges and enhancing predictive capabilities are pivotal to sustaining the aquaculture industry’s profitability, ensuring food security, and safeguarding public health.

Aquaculture, a critical domain in sustainable food production, is increasingly relying on Geographic Information Systems (GIS) for disease transmission analysis. GIS, an advanced digital mapping and analytical tool, not only helps visualize the geographical spread of diseases but also discerns patterns and offers predictive insights. By integrating a variety of datasets, from environmental to biological, GIS enables comprehensive analysis of disease risk zones, facilitating preemptive measures and efficient resource allocation. As the literature suggests, this technology’s value in aquaculture disease management is significant, evolving, and is indispensable for ensuring a healthy and sustainable future in this sector. Studies have used GIS for site selection of aquaculture, mapping groundwater quality, predicting marine fish production ^[19][20][27,28], and spatial analysis ^[21][22][29,30].

The literature underscores the efficacy of Geographic Information Systems (GIS) in the sector of aquaculture, particularly concerning the analysis of disease dissemination. Evidently, works by McLeod et al. ^[23][31], Ross et al. ^[24][32], Falconer et al. ^[25][33], and Nath et al. ^[26][34] all combine the promising capabilities of GIS as a mechanism for detailed aquaculture planning and robust decision-making support. Such systems facilitate the pinpointing of areas optimal for aquaculture activities while equipping practitioners with tools to tackle multifaceted spatial management dilemmas. Significantly, Nath et al. ^[26][34] offered an immersive user-centric approach to GIS applications, underscoring the essential importance of harmonizing the perspectives of end users, domain experts, and analysts throughout the project lifecycle.

Norstrøm posits that the utilization of GIS facilitates an enhanced spatial representation of farms and their associated infrastructures, thereby optimizing responses to disease outbreaks and establishing preventive strategies against infectious pathogens ^[27][35]. In a parallel investigation, Simms underscores the role of GIS in monitoring soft-shell clam habitats, with an emphasis on its capability to rigorously evaluate water quality and sustainability metrics of the resource ^[28][36]. Expanding on this discussion, Kapetsky’s research et al. in ^[29][30][37,38] provided a comprehensive assessment of the extent to which GIS has been embedded within the developmental and managerial landscapes of aquaculture. Kapetsky et al. highlighted the numerous potential benefits of this integration, particularly in tackling pressing sustainability challenges.

Concluding from the examination of research studies, GIS is recognized not merely as a tool but as an integral component in the academic discourse of disease analysis within aquaculture. Its ability to offer a detailed spatial database combined with its capacity for virtual modeling underlines its importance. Collectively, these capabilities enable more precise assessments and interventions concerning disease outbreak patterns in aquaculture.

2.3. Reinforcement Learning in Disease Control

Reinforcement learning, a specified domain within machine learning, engages agents in decision-making processes governed by received rewards or penalties from environmental interactions. Its applicability spans diverse fields, notably aquaculture and healthcare. Within aquaculture, research proposed in ^[31][54] underscores the efficacy of reinforcement learning in monitoring fish growth trajectories and optimizing feeding control mechanisms. Sources have advocated for the use of model-based reinforcement learning (MBRL) in infectious disease control to help reduce the overall cost of interventions. Additionally, RL emerges as a powerful tool in optimizing disease control strategies in aquaculture, as illuminated by the collective findings of several academic papers. Notably, Chahid et al. showcased the development and implementation of Q-learning algorithms tailored for optimizing feeding control policies, both in caged fish growth rate environments and tank-based fish growth rate settings with optimal temperature profiles. The simulation outcomes affirm the algorithms’ ability to achieve precise trajectory tracking while conserving feed. Similarly, Kuroki et al. presented the conception of an automatic feeding system for Sillago japonica using reinforcement learning, underscoring the growing influence of this technology ^[32][55]. Palaiokostas furthers this narrative by revealing the efficacy of machine learning models in anticipating disease resistance among aquaculture species ^[33][56]. While the prowess of reinforcement learning is evident, a broader scope of research accentuates the value of varied approaches in disease control. Chinabut et al. comprehensively detailed multiple strategies, spanning from antibiotics and probiotics to biocontrol, bioremediation, and vaccination ^[34][57]. Villena introduced the potential of in vitro methods in pioneering safe therapeutants and enhancing disease control methodologies ^[35][58]. Park gave an overview of Korean aquaculture practices, emphasizing chemotherapeutics, herbal immunostimulants, and vaccination ^[36][59]. In the same scope, Assefa et al. offered a comprehensive review of epidemiological methods, spotlighting vaccination and biosecurity measures as key components in combating infectious diseases ^[37][60]. Furthermore, Schryver et al. emphasized the importance of integrating ecological theory into disease management, proposing microbial management strategies for disease prevention in larviculture ^[38][61]. Pérez-Sánchez et al. presented a balanced overview of the triumphs and hurdles associated with biological methods for bacterial disease prevention in aquaculture. Xiong et al. delved into the utility of probiotics in shaping desired gut microbiota compositions in shrimp, identifying the larval stage as an opportune period for introducing specific probiotics into the rearing water ^[39][62]. In closing, Subasinghe accentuated the critical role vaccination plays in ensuring aquatic animal health in aquaculture settings ^[40][63]. The research studies generally demonstrate a multi-pronged approach to disease control in aquaculture, which combines cutting-edge technologies, ecological understanding, and biological interventions.

2.4. Integration of GIS and Reinforcement Learning

The integration of Geographic Information Systems (GIS) and Reinforcement Learning (RL) represents a promising avenue in the realm of disease transmission prediction and strategy formulation. GIS, fundamentally an assembly of tools designed to collate, analyze, and illustrate spatial or geographical data, provides a comprehensive landscape of disease spread patterns and affected regions. Meanwhile, RL, nested within the broader domain of machine learning, operates on a framework where agents are trained to make informed decisions by processing the consequences in terms of rewards or penalties. When the fields of GIS and RL converge, they create a robust and sophisticated mechanism that can be used to understand current disease spread, anticipate future transmission trajectories, and empower stakeholders to make proactive and informed interventions Several notable benefits arise from this integration:

• Precision Enhancement: The integration of GIS’s spatial data with RL’s decision-making abilities has the potential to improve disease spread predictions by providing a more complex understanding of the factors that influence disease transmission.
• Optimal Resource Deployment: RL is adept at optimizing the distribution of resources based on the data at hand. For instance, by analyzing spatial data, agents can determine the best locations for establishing vaccination centers.
• Agile Decision Processes: One of the strengths of reinforcement learning (RL) is its ability to make adaptive decisions. This means that RL agents can learn and improve their strategies over time, even as situations change. For example, if there is a sudden spike in cases in a particular region, an RL agent could recalibrate its strategy to focus more on that region.
• Cost-Efficiency: The combination of GIS’s spatial intelligence and RL’s optimization abilities can lead to more cost-effective strategies for health infrastructure planning. For example, by determining the most strategic locations for hospitals and clinics, we can save money on transportation, staffing, and other costs.

Several studies in the academic area underscore the potential of GIS and RL in disease control. For example, Palaniyandi ^[41][64] and Bouwmeester et al. ^[42][65] showed that GIS can be used to monitor and predict the spread of diseases, and to identify critical intervention areas for disease control. Li et al. proposed an evolutionary ensemble model based on a Genetic Algorithm (GA) to predict the transmission trend of infectious diseases, which utilizes the strong global optimization capability of GA for tuning the ensemble structure ^[43][66]. Sadilek et al. proposed a probabilistic model that uses social networks to predict disease spread ^[44][67]. Chae et al. used deep learning algorithms and big data to predict infectious diseases, which can help eliminate reporting delays in existing surveillance systems ^[45][68]. Peng et al. described the design and development of an AIDS Transmission Management and Spatial Decision Support System based on GIS, which allows for the prediction of future trends of the AIDS epidemic ^[46][69]. More specifically, in the context of aquaculture, a plethora of studies have shown the potential of using GIS and related technologies to improve aquaculture practices. For example, Navas et al. showed that combining GIS with neuro-fuzzy modeling can be used to classify areas particularly vulnerable to pollutants, which can inform policy decisions for aquaculture site selection ^[47][70]. Martínez demonstrated that GIS-based models can be effective for helping the decision-making process of site selection in aquaculture planning and management ^[48][71]. Kriaridou et al. suggested that genomic selection can improve disease resistance in aquaculture breeding programs, and that this can be achieved at lower SNP densities and at lower cost ^[49][72]. Villanueva et al. showed that genome-wide evaluation of breeding values offers new opportunities for using variation within families when dealing with dichotomous traits such as resistance to disease, and that the threshold liability model used fits very well with the BayesB model of GWE ^[50][73]. The use of GIS and machine learning techniques to study disease transmission in aquaculture has been well-documented. However, the application of reinforcement learning (RL) and GIS to this field is still in its early stages. Khiem et al. used GIS and machine learning to predict the occurrence of three serious diseases of shrimp in Vietnam, and found that the neural network model outperformed other methods in predicting infection ^[51][74]. Greed developed a stochastic metapopulation model of infectious disease in aquaculture, and found that concentration of production into separate areas successfully slows the spread of simulated disease ^[52][75]. Silva et al. used GIS to predict fish yield in Sri Lankan reservoirs, and found that highly significant relationships existed between fish yield and different land-use patterns ^[53][76]. Finally, Zambrano et al. studied the use of machine learning techniques to model the dynamics of water quality variables in fish farming, and found that random forests can be used to forecast dissolved oxygen, pond temperature, pH, ammonia, and ammonium when the water pond variables are measured only twice per day ^[54][77]. The collaborative potential between GIS and RL stands out prominently in current research trends, offering promising avenues for enhancing predictive modeling and devising nuanced disease control strategies. However, within the research community, there is a noticeable lack of substantial studies or applications that integrate reinforcement learning with GIS, particularly within aquaculture. Moreover, the research landscape remains rather limited in terms of publications applying Q-learning and multi-armed bandit techniques to aquaculture. Such limitations extend to predicting disease spread in animals and, more broadly, to applications in agriculture and livestock farming.

2.5. Applications in Aquaculture

The intersection of Geographic Information Systems (GIS) and reinforcement learning (RL) offers a promising avenue for enhancing disease control strategies in aquaculture. The application of such integrative methodologies, however, remains in nascent stages, with only a few studies delving deep into this interdisciplinary approach. In this study ^[55][78], the researchers explore the use of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria. The study identified the most influential water quality parameters and reclassified the six water quality criteria into four major hazard groups. The results of the irrigation water quality suitability assessment for the Okurumutet-Iyamitet agricultural-mine district showed that the water quality was generally unsuitable for irrigation due to high levels of salinity, sodium adsorption ratio, and residual sodium carbonate. The study highlights the importance of resilient and sustainable water management in agriculture and provides insights into effective water quality prediction and management strategies. In the study by ^[56][79], a combination of machine learning, remote sensing, and GIS data was applied to enhance the mapping of landslide susceptibility in the Abbottabad district of Pakistan. This area is particularly vulnerable to landslides due to factors such as heavy monsoon rainfall, varied terrain, earthquake events, and human activities. The incorporation of machine learning models increased the accuracy of the susceptibility maps, offering essential data for disaster management teams, researchers, and government officials. These maps can guide land use planning efforts, aiming to minimize potential human and economic losses. The research stands out by addressing a specific need for improved landslide mapping in the Abbottabad region and underscores the benefits of using modern technological methods in predicting and managing landslide risks. In the study by ^[57][80], a novel framework is introduced which seamlessly merges IoT, ML, and GIS with geospatial methodologies to ensure the sustainable management of aquaculture resources. This approach prominently features the utilization of intelligent sensors for meticulous data gathering, coupled with cloud-supported analysis, paving the way for informed decision-making regarding feeding and monitoring fish health. Such integration with geospatial methods notably enhances the precision in pinpointing ideal fish habitats, culminating in a comprehensive database tailored for aquaculture resource management. Furthermore, the research elaborates on the synergy of GIS and GPS in sculpting a web-based platform for the fisheries domain. The outcomes of the research underscore the framework’s prowess in discerning potential fishing areas and resource management, thereby shedding light on the transformative power of technological and geospatial advancements in bolstering the fishing sector while championing environmental sustainability.