Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh, Ammar H.; Saba, Amal I.; Elaziz, Mohamed Abd; Lu, Songfeng; Shanmugan, S.; Muthuramalingam, T.; Kumar, Ravinder; Mosleh, Ahmed O.; Essa, F.A.; Shehabeldeen, Taher A.

doi:10.1016/j.psep.2020.10.048

Cited by 118 publications

(78 citation statements)

References 48 publications

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“…Saudi Arabia is one of the biggest countries in Asia with a total area of about 2.15 million km2 and a total population of 34.77 million people ( 9 ). The healthcare system in the country relies on governmental healthcare services where the Ministry of Health (MoH) is responsible for providing healthcare services free of charge for citizens.…”

Section: Introductionmentioning

confidence: 99%

The Use of Digital Healthcare Platforms During the COVID-19 Pandemic: the Consumer Perspective

Alharbi

2021

Acta Inform Med

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Background: The spending on digital healthcare solutions is estimated to reach EUR 232 billion by 2025. Digital healthcare platforms are making transformative changes to conventional healthcare processes which can provide many beneficial improvements for both citizen and government provision to society. These benefits are obvious during pandemics such as Covid-19, when most healthcare services are offered through digital means. Objective: The objective of this study is to measure the role of trust and information quality when using digital healthcare platforms. These constructs are integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT) to provide a better understanding of the consumer perspective regarding the use of digital healthcare platforms. Methods: Online structured self-administered questionnaire was utilized to collect the data. A sample consisting of 249 respondents participated in the questionnaire. Descriptive analysis was used to characterize the attributes of participants, and other statistical tests were conducted to ensure the reliability and validity of the survey. The model of the study was evaluated using Structural Equation Modelling (SEM) to explain the extent of the relationship among latent variables. Results: The study determined that facilitating conditions (t=0.233, p=0.023) and trust (t=0.324, p=0.005) had a significant impact on consumers’ behavioral intention of using such platforms during Covid-19 pandemic. Conclusion: This study highlighted the importance of facilitating conditions and trust factors for healthcare consumers of digital healthcare platforms especially during the pandemic time.

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Section: Introductionmentioning

confidence: 99%

The Use of Digital Healthcare Platforms During the COVID-19 Pandemic: the Consumer Perspective

Alharbi

2021

Acta Inform Med

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“…Since this virus is spreading very fast, we require more strict policies and plans; thus, proper techniques are needed to foresee confirmed cases in upcoming days to make proper protection strategy. Here, prediction models play a key role to understand the duration of the epidemic [6][7][8]. Thus a highly accurate forecasting model is very much important to forecast the number of confirmed cases in upcoming days.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Saif

Das

Biswas

2021

J. Inst. Eng. India Ser. B

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In India, the first confirmed case of novel corona virus (COVID-19) was discovered on January 30, 2020. The number of confirmed cases is increasing day by day, and it crossed 21,53,010 on August 9, 2020. In this paper, a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed model is based on adaptive neuro-fuzzy inference system (ANFIS) and mutation-based Bees Algorithm (mBA). The meta-heuristic Bees Algorithm (BA) has been modified applying 4 types of mutation, and mutation-based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USA, and the number of confirmed cases in the next 10 days in India has been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results show that the proposed model has achieved better performance in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE). It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data. Similarly, for USA the values are 4468.72, 3082.07, 6.1, and 0.000952 for RMSE, MAE, MAPE, and NRMSE, respectively. Keywords Mutation-based Bees Algorithm (mBA) Á Adaptive neuro-fuzzy inference system (ANFIS) Á COVID-19 Á Forecast Á RMSE Á MAE Á MAPE Á NRMSE Á India & Suparna Biswas

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“…To solve this problem, the GA and ANN [27][28][29][30][31][32][33][34][35][36] hybrid method is proposed to optimize epidemic dynamics model and predict the COVID-19 spreading. Genetic algorithm is an adaptive global optimization search algorithm formed by simulating the genetic and evolutionary process of biological species in natural environment [37].…”

Section: Introductionmentioning

confidence: 99%

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

Zhang

Liu

2021

PLoS ONE

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The coronavirus (COVID-19) is a highly infectious disease that emerged in the late December 2019 in Wuhan, China. It caused a worldwide outbreak and a major threat to global health. It is important to design prediction and control strategies to restrain its exploding. In this study, a hybrid intelligent model is proposed to simulate the spreading of COVID-19. First, considering the effect of control measures, such as government investment, media publicity, medical treatment, and law enforcement in epidemic spreading. Then, the infection rates are optimized by genetic algorithm (GA) and a modified susceptible-infected-quarantined-recovered (SIQR) epidemic spreading model is proposed. In addition, the long short-term memory (LSTM) is imbedded into the SIQR model to design the hybrid intelligent model to further optimize other parameters of the system model, which can obtain the optimal predictive model and control measures. Simulation results show that the proposed hybrid intelligence algorithm has good predictive ability. This study provide a reliable model to predict cases of infection and death, and reasonable suggestion to control COVID-19.

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Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Abstract: Graphical abstract

Cited by 118 publications

References 48 publications

The Use of Digital Healthcare Platforms During the COVID-19 Pandemic: the Consumer Perspective

The Use of Digital Healthcare Platforms During the COVID-19 Pandemic: the Consumer Perspective

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Prediction and control of COVID-19 spreading based on a hybrid intelligent model

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