Gravitational search algorithm-extreme learning machine for COVID-19 active cases forecasting

Abstract

Corona Virus disease 2019 (COVID-19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non-pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID-19 in some countries, it has been criticized as inefficient or ineffective, because of the time-delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA-ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID-19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID-19 dataset from Johns Hopkins University, the MAPE of the authors’ model is 7.79%, which corroborates the superiority of the model to state-of-the-art methods.