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

Pujante-Otalora, Lorena; Canovas-Segura, Bernardo; Campos, Manuel; Juarez, Jose M.

doi:10.1016/j.jbi.2023.104422

Cited by 5 publications

(1 citation statement)

References 126 publications

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“…The last few years have seen a significant increase in interest in epidemic mathematical models inside various formal frameworks [14]- [15]. Some of these models are expressed using dynamic systems, control theory, differential, difference, and hybrid equations [16]- [23], information theory, [24], etc.…”

Section: Mathematical Model Of Sirmentioning

confidence: 99%

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Abougarair,

Elwefati

2023

IJRCS

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Developing effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale.

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Section: Mathematical Model Of Sirmentioning

confidence: 99%

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Abougarair,

Elwefati

2023

IJRCS

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A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization

González-Parra,

Villanueva-Oller,

Navarro-González

et al. 2024

Chaos, Solitons & Fractals

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Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications

Rujas,

del Moral Herranz,

Fico

et al. 2024

Preprint

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The development of Artificial Intelligence (AI) in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality data due to issues in data collection and regulatory constraints, for which synthetic data is an emerging alternative. This Scoping review analyses reviews from the past 10 years from three different databases (i.e., PubMed, Scopus, and Web of Science) to identify the healthcare domains where synthetic data are currently generated, the motivations behind their creation, their future uses, limitations, and types of data. A total of 13 main domains were identified, with Oncology, Neurology, and Cardiology being the most frequently mentioned. Five types of motivations and three principal future uses were also identified. Furthermore, it was found that the predominant type of data generated is unstructured, particularly images. Finally, several future work directions were suggested, including exploring new domains and less commonly used data types (e.g., video and text), and developing an evaluation benchmark and standard generative models for specific domains.

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

Cited by 5 publications

References 126 publications

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization

Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications

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