Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model - Yanwen Shao; Tsz Kin Wan; Kei Hang Katie Chan (Scientific Reports)
Abstract Since December 2019, cases of COVID-19 have spread globally, caused millions of deaths and huge economic losses. To investigate the impact of different factors and predict the future trend, this study collects relevant data for 15 countries, containing 44 features in about 900 days, which can be classified into four groups: pandemic information, the characteristics of countries, climate, and prevention policies. Through the selection of several important features, we identified the factors that have stronger impact on the increase of new cases in different groups. Then, we use a long-time span data to predict the future COVID-19 new cases by training a long short-term memory (LSTM) model, a support vector regressor (SVR) and a temporal convolutional network (TCN), among which LSTM possessed the best performance and offered a good generalization ability. Under the metric of explained variance scores (EVS), the prediction performances were the most accurate for Germany (0.864), Italy (0.860) and the United States (0.766). Overall, the results of this study may provide insight for predictions of number of COVID-19 new cases in more countries/regions and offer some insightful recommendation for governments to carry out more effective policies to prevent COVID-19.