SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD. (November 2021) - Liao, Zhifang; Lan, Peng; Fan, Xiaoping; Kelly, Benjamin; Innes, Aidan; Liao, Zhining
Abstract: COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries – India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust. Highlights: The time-dependent SIRVD can measure and evaluate the parameters in the epidemic. The combination of mathematical model of infectious diseases and deep learning is useful. The SIRVD-DL performs better than single deep learning method and is suitable for short and medium term forecasts. The moving average method is effective for the COVID-19 data smoothing.