Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran

The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.