Predictive Modelling of COVID-19 New Cases in Algeria using An Extreme Learning Machines (ELM)
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Abstract
In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases in Algeria. In the present study, public health database from Algeria health ministry has been used to train and test the ELM models.
The input parameters for the predictive models include Cumulative Confirmed COVID-19 Cases (CCCC), Calculated COVID-19 New Cases (CCNC), and Index Day (ID).
The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912), and highest performance’s (NSE = 0.9999, IO = 0.9988, R 2 = 0.9999). Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS-19 cases in Algeria.
The proposed ELM model, it can be used as a decision support tool to manage public health medical efforts and facilities against the COVID-19 pandemic crisis.
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SciScore for 10.1101/2020.09.28.20203299: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank…
SciScore for 10.1101/2020.09.28.20203299: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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