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

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 6 th 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 2 , 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.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

“…Machine learning, including ANN models, has found many different applications in studies of the COVID-19 pandemic, including the clinical diagnosis using blood test and chest X-ray ( Brinati et al, 2020 ; Brunese et al, 2020 ; Mohammad-Rahimi et al, 2021 ), interactions of human mobility (transportation), air quality and COVID-19 transmission ( Asad et al, 2021 ; Rahman et al, 2021 ), the forecast or early detection of outbreaks and pandemic dynamics ( Allam et al, 2020 ; Braga et al, 2021 ; Shawaqfah and Almomani, 2021 ; Wieczorek et al, 2020 ). The applications of machine learning and artificial intelligent for the COVID-19 pandemic were reviewed for their potentials in treatment, medication, screening, prediction, forecasting, contact tracing, clinical trials, and drug/vaccination process ( Lalmuanawma et al, 2020 ; Mottaqi et al, 2021 ).…”

A critical assessment of SARS-CoV-2 in aqueous environment: Existence, detection, survival, wastewater-based surveillance, inactivation methods, and effective management of COVID-19

“…An artificial neural network (ANN) can model complex systems ( Park and Lek, 2016 ). Recently, researchers utilized neural networks in combination with genetic algorithms to model and estimated the number of infected/death cases without the assumptions required for epidemiological models ( Shawaqfah and Almomani, 2021 ). The ANN model can be used to predict the long-term spread of the pandemic by taking short-term data inputs.…”

“…7 ). For instance, Shawaqfah and Almomani (2021) developed an ANN algorithm for forecasting the COVID-19 infection/death cases in three countries, namely Spain, Italy, and Qatar. The obtained forecasted COVID-19 values (infected/death cases) were close to the real reported cases on specific dates with a high regression coefficient ( 0.99).…”

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

“…Because of these abilities, ANN has been applied to different machine learning tasks such as time-series prediction [ 64 ] and computer vision task [ 65 , 66 ], and has produced reliable results. In the COVID-19 prediction task, several works using the ANN structure have yielded excellent results [ 67 , 68 , 69 ]. Furthermore, Hayat Khaloufi et at.…”