Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts

Tang, Xuan; Li, Zexuan; Hu, Xian; Xu, Zefeng; Peng, Linxi

doi:10.1016/j.scs.2021.103219

Cited by 22 publications

(19 citation statements)

References 39 publications

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“…Following the same motive, Srikanta et al have analyzed COVID-19 mortality and infectious diseases in Europe using spatial regression models. More specifically, they select thirty-one countries for modelling and consequent analysis [12]. Elbasi et al, aiming to discover vulnerable groups and to reduce the impact of the disease on particular groups, have deployed machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

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Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

et al. 2021

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Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.

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Section: Literature Reviewmentioning

confidence: 99%

“…We denote the constant value as a 0 , which constitutes the sensitive value of the National Identity Number, NIDN. Hence, we construct a (k − 1) degree polynomial as shown in the Equation (12).…”

Section: Personal and Health Information Protectionmentioning

confidence: 99%

Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

et al. 2021

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“…The Chinese consumer confidence index was analysed and forecasted using a DNN_CNN-LSTM model in [ 22 ]. Paper [ 23 ] suggested an autoregressive (AR) model for more accurate forecasting of energy demand and supply. Paper [ 24 ] created a dataset of 77 countries covering over 90% of world GDP and used cross-country statistics to anticipate US economic growth during downturns.…”

Section: Introductionmentioning

confidence: 99%

Regional Economic Prediction Model Using Backpropagation Integrated with Bayesian Vector Neural Network in Big Data Analytics

Zhang

Lei

et al. 2022

Computational Intelligence and Neuroscience

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Forecasting economic growth is critical for formulating national economic development policies. Neural Networks are a type of artificial intelligence that may be used to model complex target functions. ANN (Artificial Neural Networks) are one of the most effective learning approaches now available for specific sorts of tasks, such as learning to understand complex real-world sensor data. This paper proposes the regional economic prediction model based on neural networks techniques. Bayesian vector neural network (BVNN) is integrated with backpropagation (BP) model. The database has been collected based on the economics of particular region which has been extracted and classified using knowledge-based computer analysis by neural networks. Discretization, reduction, importance ranking, and prediction rule are attributes considered here. Then, as the input training sample, feed extracted important components into the NN. This strategy enhanced the training speed and prediction accuracy by reducing structure of NN. WEO, APDREO, and AFRREO are the dataset and FWA-SVR and LSTM are the existing method taken for comparison. For the WEO dataset, 97% of GDP and 98% of accuracy are produced. For APDREO dataset, 92% of accuracy and GDP of 97% are obtained. For AFRREO dataset, 98% of accuracy is produced. The neural network can tackle nonlinear problems, according to experimental data, and the technology has been proven to be successful and viable with high accuracy. For practical application, the model has a good reference value. The proposed model reduces error by increasing the convergence rate and accuracy for each dataset.

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“…Since the outbreak of the Covid-19 outbreak in the world, all components of the government and the world community seem to clean up and prepare for the worst scenario that will be obtained due to the rapid spread of the virus (Gunadi et al, 2021). This is of course also the main concern of the Indonesian government, but there are no significant points where the application of the law must be carried out for preventive action (Tirachini & Cats, 2020;Tang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Strategy of the Village Head in Handling Covid-19 in Palae Village South Sinjai District Sinjai Regency

Sani

Ernawati

Jusmiati

2022

pdr

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The Sinjai Regency Government in optimizing the handling of Covid-19 implemented a policy in five stages of handling the corona virus, namely the prevention stage, the handling stage, post-covid-19, stimulant assistance to affected SMEs and the fulfillment of social safety nets. The purpose of this study was to find out how the village head's strategy in handling covid-19 in Palae Village, South Sinjai District, Sinjai Regency. The research method used is qualitative research. Data collection techniques used are observation, interviews and documentation. The types of data used are primary and secondary data. The informants in this study were the Head of Palae Village; Village Consultative Body (BPD); Village officials; Covid Safe Village Volunteers; and Community Leaders. Data analysis techniques used are data collection, data reduction, data presentation, and drawing conclusions. The results of this study describe the Village Head's strategy in handling covid-19 through the application of PERBUP Sinjai Article 9 Number 27 of 2020. Then form a management called Covid-19 Safe Village Volunteers, conduct socialization related to handling Covid-19, and prepare village alert cars. Distribution of aid in the form of goods, namely the distribution of masks to residents, while cash assistance, namely Direct Cash Assistance (BLT).

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Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts

Cited by 22 publications

References 39 publications

Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Regional Economic Prediction Model Using Backpropagation Integrated with Bayesian Vector Neural Network in Big Data Analytics

Strategy of the Village Head in Handling Covid-19 in Palae Village South Sinjai District Sinjai Regency

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