Organization as a Multi-level Design Pattern for Agent-based Simulation of Complex Systems

Sicard, Vianney; Andraud, Mathieu; Picault, Sébastien

doi:10.5220/0010223202320241

Cited by 6 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EMULSION framework provides real readability (R) through its specific language (DSL) in a “no-code” approach and ensures, through its internal structure, reproducibility (R) (Picault et al, 2019). The add-on of our organizational design-pattern offers the opportunity to tackle population structures with fine-grained representations of their epidemiological consequences with great flexibility (F), keeping the above-mentioned (RRF) advantages (Sicard et al, 2021b). This was recently illustrated with a model representing the transmission of a swine influenza A virus in a pig herds with different spatial configurations (Sicard et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Pig herd management and infection transmission dynamics: a challenge for modellers

Sicard

Picault

Andraud³

2023

Preprint

Self Cite

View full text Add to dashboard Cite

The control of epidemics requires a thorough understanding of the complex interaction between pathogen transmission, disease impact and population dynamics and management. Mechanistic epidemiological modelling is an effective way to address this issue, but handling highly structured and dynamic systems, remains challenging. We therefore developed a novel approach that combines multi-level agent-based systems with spatial and temporal organization, allowing for a tuned representation of the transmission processes amongst the host population. We applied this method to model the spread of a PRRSv-like virus in pig farms, integrating the clinical consequences (conception and reproduction failures), in terms of animal husbandry practices. The results highlighted the importance to account for spatial and temporal structuring and herd management policies in epidemiological models. Indeed, disease-related abortions, inducing reassignments of sows in different batches, was shown to enhance the transmission process, favouring the persistence of the virus at the herd level. Supported by a domain-specific declarative language, our approach provides flexible and powerful solutions to address the issues of on-farm epidemics and broader public health concerns. The present application based on a simple SEIR model opens the way to the representation of more complex epidemiological systems, including more specific features such as maternally derived antibodies, vaccination, or dual infections, along with their respective clinical consequences on the management procedures.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The EMULSION framework, extended with its organizational component (Sicard et al, 2021b), was used to account for the complex herd structure in both space and time, including multilevel aspects. An organization is an entity made up of groups to which individuals belong.…”

Section: Methodsmentioning

confidence: 99%

Pig herd management and infection transmission dynamics: a challenge for modellers

Sicard

Picault

Andraud³

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since its first release (Picault et al, 2019), EMULSION has been extended to also account for complex population structuring (Sicard et al, 2021), which are quite common in veterinary epidemiology, such as the combination time, group and building constraints found in batch-rearing systems in the pig sector (Sicard et al, 2022a). This multi-level agent-based approach has been incorporated in the simulation engine and declarative DSL of EMULSION recently (Sicard et al, 2022b).…”

Section: Leveraging Ai For Mechanistic Epidemiological Modelsmentioning

confidence: 99%

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models

Picault,

Niang,

Sicard

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Epidemiological modelling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and decision-support tools becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modelling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The decision-support tool developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of decision-support tools based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.

show abstract

“…Livestock management clearly illustrates that problematic, obeying predefined rules ensuring a balance between animal welfare, good sanitary conditions, and productivity. The spread of pathogens in a pig farm, managed with batch-rearing procedures, therefore represents an ideal application for the integration of an innovative organizational pattern within a multi-level agent-based modelling framework dedicated to epidemiological modelling (Sicard et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

“…In a previous study, we first developed a model of swine influenza A in pig farms, highlighting the impact of the spatio-temporal structure of the herd on the transmission dynamics and its impact on virus spread and control based on EMULSION extended with an organizational pattern (Sicard et al, 2021a). However, in this study, influenza infections were not considered to have any consequence in terms of animal management.…”

Section: Introductionmentioning

confidence: 99%

Pig herd management and infection transmission dynamics: a challenge for modellers

Sicard,

Picault,

Andraud

2024

Peer Community Journal

Self Cite

View full text Add to dashboard Cite

The control of epidemics requires a thorough understanding of the complex interactions between pathogen transmission, disease impact, and population dynamics and management. Mechanistic epidemiological modelling is an effective way to address this issue, but handling highly structured and dynamic systems, remains challenging. We therefore developed a novel approach that combines Multi-Level Agent-Based Systems (MLABS) with spatial and temporal organization, allowing for a tuned representation of the transmission processes amongst the host population. We applied this method to model the spread of a PRRSv-like virus in pig farms, integrating the clinical consequences (conception and reproduction failures), in terms of animal husbandry practices. Results highlighted the importance to account for spatial and temporal structuring and herd management policies in epidemiological models. Indeed, disease-related abortions, inducing reassignments of sows in different batches, was shown to enhance the transmission process, favouring the persistence of the virus at the herd level. Supported by a declarative Domain-Specific Language (DSL), our approach provides flexible and powerful solutions to address the issues of on-farm epidemics and broader public health concerns. The present application, based on a simple Susceptible-Exposed-Infected-Recovered (SEIR) model, opens the way to the representation of more complex epidemiological systems, including more specific features such as maternally derived antibodies, vaccination, or dual infections, along with their respective clinical consequences on the management practices.

show abstract

Organization as a Multi-level Design Pattern for Agent-based Simulation of Complex Systems

Cited by 6 publications

References 0 publications

Pig herd management and infection transmission dynamics: a challenge for modellers

Pig herd management and infection transmission dynamics: a challenge for modellers

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models

Pig herd management and infection transmission dynamics: a challenge for modellers

Contact Info

Product

Resources

About