A fully GIS-integrated simulation approach for analyzing the spread of epidemics in urban areas

Haddad, Hedi; Moulin, Bernard; Thériault, Marius

doi:10.1145/2961028.2961034

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Wan et al proposed a multi-objective model-based reinforcement learning framework aimed at minimizing the overall long-term cost of implementing infectious disease control measures [7]. Haddad et al introduced a GIS-based spatialtemporal simulation approach and software that aids public health decision-making in the context of communicable diseases in urban areas [8]. Raissi et al emphasized the significance of determining model parameters for disease transmission prediction through optimization, deep learning, and statistical inference methods [9].…”

Section: Introductionmentioning

confidence: 99%

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

Karras,

Sioutas

et al. 2023

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This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further.

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Section: Introductionmentioning

confidence: 99%

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

Karras,

Sioutas

et al. 2023

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The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review

Pujante-Otalora,

Canovas-Segura,

Campos

et al. 2023

Journal of Biomedical Informatics

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A generic multi-level stochastic modelling framework in computational epidemiology

Picault¹,

Huang²,

Sicard³

et al. 2018

Preprint

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There is currently an overwhelming increased interest in predictive biology and computational modelling. The development of reliable, reproducible and revisable simulation models in computational life sciences is often pointed out as a challenging issue. Population dynamics, including epidemiology, has not yet developed a language to formalize complex models in a univocal and automatable way, hence hindering the capability to implement in short time reliable, revisable and expert-friendly models intended for realistic mechanistic simulations. In epidemiology specifically, models aim not only at understanding pathogen spread but also at assessing control measures at several scales. To achieve this goal efficiently, best software practices should be supported by Artificial Intelligence methods to handle experts' knowledge. The framework EMULSION presented here intends to both tackle multiple modelling paradigms in epidemiology and facilitate the automation of model design. We therefore built both a domain-specific language (DSL) for the modular description of complex epidemiological models, and a generic simulation engine designed to embed existing modelling paradigms within a homogeneous architecture based on adaptive software agents. The diversity of concerns (biology, economics, human activities) involved in real pathosystems requires an explicit, comprehensive and intelligible way to describe epidemiological models, to involve experts without computer science skills throughout the modelling, simulation and output analysis steps. This approach was applied to compare hypotheses in modelling a zoonosis (Q fever), to study its transmission dynamics within and between cattle herds at a regional scale, and to assess the contribution of transmission pathways. Separating model description from the simulation engine allowed epidemiologists to be involved in assumption revision, while guaranteeing very few code modifications. We assessed the added value of EMULSION by applying the DSL and the simulation engine to a concrete disease. Future extensions of EMULSION towards a broader range of epidemiological concerns will reduce significantly the time required to design and assess models and control measures against endemic and epidemic diseases. Ultimately, we believe this effort is a major lever to increase scientists' preparedness to face emerging threats for public health and provide rapid, reliable, and reasoned assessments of control measures.

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A fully GIS-integrated simulation approach for analyzing the spread of epidemics in urban areas

Cited by 6 publications

References 8 publications

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review

A generic multi-level stochastic modelling framework in computational epidemiology

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