Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Band, Shahab S.; Várkonyi-Kóczy, Annamária R.

doi:10.1101/2020.10.22.20217604

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained with the dataset. A hybrid approach is demonstrated by Ardabili et al [ 152 ] who applied a Gray Wolf Optimizer (GWO) algorithm to optimize the weights of a Feed Forward (FF) ANN. The authors applied the proposed methodology on a global dataset and have evaluated the achieved models using MAPE, achieving an error of 11.4% on the validation dataset.…”

Section: Modeling Of Covid-19 Using Ec Methodsmentioning

confidence: 99%

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

Musulin

Šegota

Štifanić

et al. 2021

IJERPH

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COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.

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Section: Modeling Of Covid-19 Using Ec Methodsmentioning

confidence: 99%

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

Musulin

Šegota

Štifanić

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Song et al [63] first extracted the main regions of the lungs and filled the blank of lung segmentation with the lung itself to avoid noise caused by different lung contours. Then, they extracted the top-K details in the CT images and obtained image-level predictions.…”

Section: Number Of Coronavirus Patchesmentioning

confidence: 99%

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

Zaeri¹

2022

Simulation Modeling

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The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

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“…Furthermore, in [8], Anastassopouloua et al used the Susceptible-Infected-Recovered-Dead (SIRD) model to calculate the basic reproduction number, infection, and per day recovery rate for the data generated from China. Recent approaches in dealing with infectious diseases such as COVID-19 using data-driven methods such as machine learning and deep learning, hybrid artificial intelligence, and principal component analysis can be found in [12][13][14][15][16][17]. However, COVID-19 is rare, complex, and many things are yet unknown, which set limitations to what known models (especially the integer-order models) could capture.…”

Section: Introductionmentioning

confidence: 99%

System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

et al. 2021

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The emergence of the COVID-19 outbreak has caused a pandemic situation in over 210 countries. Controlling the spread of this disease has proven difficult despite several resources employed. Millions of hospitalizations and deaths have been observed, with thousands of cases occurring daily with many measures in place. Due to the complex nature of COVID-19, we proposed a system of time-fractional equations to better understand the transmission of the disease. Non-locality in the model has made fractional differential equations appropriate for modeling. Solving these types of models is computationally demanding. Our proposed generalized compartmental COVID-19 model incorporates effective contact rate, transition rate, quarantine rate, disease-induced death rate, natural death rate, natural recovery rate, and recovery rate of quarantine infected for a holistic study of the coronavirus disease. A detailed analysis of the proposed model is carried out, including the existence and uniqueness of solutions, local and global stability analysis of the disease-free equilibrium (symmetry), and sensitivity analysis. Furthermore, numerical solutions of the proposed model are obtained with the generalized Adam–Bashforth–Moulton method developed for the fractional-order model. Our analysis and solutions profile show that each of these incorporated parameters is very important in controlling the spread of COVID-19. Based on the results with different fractional-order, we observe that there seems to be a third or even fourth wave of the spike in cases of COVID-19, which is currently occurring in many countries.

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Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

Cited by 5 publications

References 15 publications

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

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