Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks

Abstract

Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.

SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD

“…In order to verify the effectiveness of the proposed SIRVD-DL model prediction, we compared it with the method of using deep learning to predict in existing studies. Among them, Stacked LSTM and bi-directional LSTM use the methods of Arora et al [ 32 ], Gru and vanilla LSTM use the methods of Nabi et al [ 13 ]. Table 5 shows the comparison of SIRVD-DL and Vanilla LSTM, Stacked LSTM, BiDirectional LSTM, and GRU four prediction models on single-day prediction.…”

“…Through the time-dependant SIRVD model, the measurement and evaluation values of the parameters can be obtained and arranged in time series. There is a problem that the curve of a time series of data - the number of infected people does not have a related sequence pattern [ 13 ]. To solve the problem, our model firstly predicts the estimated parameters to discover the development trend of the epidemic and then builds the time-dependant SIRVD model by using the values of predicted parameter.…”

“…Smail et al [ 12 ] used ARIMA(Autoregressive integrated moving average method), NARNN(Nonlinear autoregressive neural network), and LSTM methods to model confirmed cases of new coronary pneumonia, and the results of the study found that LSTM is the most accurate model. Khondoker et al [ 13 ] studied four deep learning models: LSTM, GRU(Gated recurrent unit), CNN(Convolutional neural network), and MCNN(Multivariate convolutional neural network), the results show that the CNN is superior to other deep learning models in terms of verification accuracy and prediction consistency. Jayanthi et al [ 14 ] used ARIMA, LSTM, Stacked LSTM, and prophet models to analyze and predict the global cumulative number of confirmed cases, death cases, and recovery cases.…”

“…However, this kind of methods have two problems. The first one is that these methods by using pure numerical fitting cannot correctly capture the trend of epidemics in the spreading process [ 13 ]. The another one is that the temporal change pattern of the number of infected people is very simple.…”

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

“…So, we chosen ADAM as the model optimizer. We initially determined the range of input time step 35 , then by the trial-and-error method, we chosen the best value of window and assigned each country with corresponding best time step. The prediction effects of different parameters are shown in Tables 1 and 2 .…”