Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy

• Published in: Results in physics Vol. 27; p. 104484
• Main Authors: Shawaqfah, Moayyad, Almomani, Fares
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-08-2021; The Author(s). Published by Elsevier B.V; Elsevier
• Subjects: COVID-19; Outbreak; Political; Prediction; The decision-maker; COVID-19; Outbreak; Political; Prediction; The decision-maker
• ISSN: 2211-3797; 2211-3797
• DOI: 10.1016/j.rinp.2021.104484

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 R2, which yielded highly accurate values of the predicted correlation for the infected and dead cases of 0.99 for the dates considered. The verified and validated growth model of COVID-19 for these countries showed the effects of the measures taken by the government and medical sectors to alleviate the pandemic effect and the effort to decrease the spread of the virus in order to reduce the death rate. The differences in the spread rate were related to different exogenous factors (such as social, political, and health factors, among others) that are difficult to measure. The simple and well-structured ANN model can be adapted to different propagation dynamics and could be useful for health managers and decision-makers to better control and prevent the occurrence of a pandemic.