Analysis, visualization and forecasting of COVID-19 outbreak using LSTM model

ABSTRACT

The COVID-19 outbreak has been treated as a pandemic disease by the World Health Organization (WHO). Severe diseases like Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) are caused by members of a large family of viruses called coronavirus (CoV). A new strain was identified in humans in December 2019, named coronavirus (COVID-19). The highest affected countries are unable to predict the pace of the outbreak of COVID-19. So, AI is helpful to analyze the COVID-19 outbreak in the world. We have used the LSTM model to predict the outbreak of COVID-19 all over the world with limited epidemiological data. A variant of recurrent neural network (RNN) which has the capability of learning long-term dependencies with feedback connections, also known as long short-term memory (LSTM), is used in resolving the problems related to time series in deep learning. LSTM is capable of processing a single data point and an entire sequence of data related to any field. We observe that the LSTM model is useful to predict the ongoing outbreak so that authorities can take preventive action earlier. The LSTM model result shows that the growth rate of infected cases of COVID-19 increased exponentially every week. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.