A Binary Particle Swarm Optimizer With Priority Planning and Hierarchical Learning for Networked Epidemic Control

Zhao, Tian-Fang; Chen, Wei-Neng; Liew, Alan Wee‐Chung; Gu, Tianlong; Wu, Xiaoxia; Zhang, Mengjie

doi:10.1109/tsmc.2019.2945055

Cited by 24 publications

(11 citation statements)

References 59 publications

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“…Particle swarm optimization, a population-based method that was introduced by Kennedy and Eberhart [35] in 1995 simulates the social behavior of animals. Since then, PSO has been used efficiently in various fields including water demand prediction [36], electric peak-load forecasting [37], energy market forecasting [38] and networked epidemic control [39], to cite just a few. In PSO, each member (called a particle) is assigned a position vector 𝑋𝑋 = [𝐾𝐾𝛽𝛽] including the parameters of the SIR model to be optimized.…”

Section: Sir-pso Modelmentioning

confidence: 99%

Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches

Zrieq

Boubaker

Kamel

et al. 2022

J Infect Dev Ctries

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Introduction: COVID-19 has become a global concern because it has extensive damage to health, social and economic systems worldwide. Consequently, there is an urgent need to develop tools to understand, analyze, monitor and control further outbreaks of the disease. Methodology: The Susceptible Infected Recovered-Particle SwarmOptimization model and the feed-forward artificial neural network model were separately developed to model COVID-19 dynamics based on daily time-series data reported by the Saudi authorities from March 2, 2020 to February 21, 2021. The collected data were divided into training and validation datasets. The effectiveness of the investigated models was evaluated by using various performance metrics. The Susceptible-Infected-Recovered-Particle-Swarm-Optimization model was found to well predict the cumulative infected and recovered cases and to optimally tune the contact rate and the characteristic duration of the illness. The feed-forward artificial neural network model was found to be efficient in modeling daily new and cumulative infections, recoveries and deaths. Results: The forecasts provided by the investigated models had high coefficient of determination values of more than 0.97 and low mean absolute percentage errors (around 7% on average). Conclusions: Both the Susceptible-Infected-Recovered-Particle-Swarm-Optimization and feed-forward artificial neural network models were efficient in modeling COVID-19 dynamics in Saudi Arabia. The results produced by the models can help the Saudi health authorities to analyze the virus dynamics and prepare efficient measures to control any future occurrence of the epidemic.

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Section: Sir-pso Modelmentioning

confidence: 99%

Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches

Zrieq

Boubaker

Kamel

et al. 2022

J Infect Dev Ctries

View full text Add to dashboard Cite

show abstract

“…Computing methods are critical tools in dealing with the complexity of infectious disease dynamics [36,37]. However, most data in the medical field have the problems of fuzziness and uncertainties [38], due to substantial individual differences (e.g., personal health status, medical history, medical diagnosis, demographic characteristics, and other standard medical information) [39] and measurement errors [38].…”

Section: Fuzzy Cognitive Inferencementioning

confidence: 99%

Incorporating Fuzzy Cognitive Inference for Vaccine Hesitancy Measuring

Sun

Zhao

et al. 2022

Sustainability

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Vaccine hesitancy plays a key role in vaccine delay and refusal, but its measurement is still a challenge due to multiple intricacies and uncertainties in factors. This paper attempts to tackle this problem through fuzzy cognitive inference techniques. Firstly, we formulate a vaccine hesitancy determinants matrix containing multi-level factors. Relations between factors are formulated through group decision-making of domain experts, which results in a fuzzy cognitive map. The subjective uncertainty of linguistic variables is expressed by fuzzy numbers. A double-weighted method is designed to integrate the distinguished decisions, in which the subjective hesitancy is considered for each decision. Next, three typical scenarios are constructed to identify key and sensitive factors under different experimental conditions. The experimental results are further discussed, which enrich the approaches of vaccine hesitancy estimation for the post-pandemic global recovery.

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“…The final goal of this article is to prevent virus spread. Existing studies [79] have provided both the theoretical and experimental evidences that as long as the algorithm takes priority in comparison experiments, it would take advantage of epidemic control. Since NCD-CEA performs better than the other algorithms in comparison experiments, it should perform best in actual virus control theoretically.…”

Section: Effectiveness In Virus Controlmentioning

confidence: 99%

Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks

Zhao

Chen

et al. 2021

IEEE Trans. Cybern.

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The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA). It is characterized by the community-based dividing technique and cooperative coevolution conquering thought. First, to reduce the time complexity, NCD-CEA divides a network into multiple communities by a modified community detection method such that the most relevant variables in the solution space are clustered together. The problem and the global swarm are subsequently decomposed into subproblems and subswarms with low-dimensional embeddings. Second, to obtain high-quality solutions, an alternative evolutionary approach is designed by promoting the evolution of subswarms and the global swarm, in turn, with subsolutions evaluated by local fitness functions and global solutions evaluated by a global fitness function. Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.

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A Binary Particle Swarm Optimizer With Priority Planning and Hierarchical Learning for Networked Epidemic Control

Cited by 24 publications

References 59 publications

Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches

Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches

Incorporating Fuzzy Cognitive Inference for Vaccine Hesitancy Measuring

Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks

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