ArticleResults in physics2021
Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy.
Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- A besyian regularisation neural network approach for hepatitis B virus spread prediction and immune system therapy model.Scientific reports · 2024Article
- Modelling the Unidentified Abortion Burden from Four Infectious Pathogenic Microorganisms (Animals : an open access journal from MDPI · 2023Article
- Review
- Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.Water research · 2022Article
- FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The present study illustrates the outbreak prediction and analysis on the growth and expansion of the COVID-19 pandemic using artificial neural network (ANN). The first wave of the pandemic outbreak of the novel Coronavirus (SARS-CoV-2) began in September 2019 and continued to March 2020. As declared by the World Health Organization (WHO), this virus affected populations all over the globe, and its accelerated spread is a universal concern. An ANN architecture was developed to predict the serious pandemic outbreak impact in Qatar, Spain, and Italy. Official statistical data gathered from each country until July 6th was used to validate and test the prediction model. The model sensitivity was analyzed using the root mean square error (RMSE), the mean absolute percentage error and the regression coefficient index R
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