Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks
This addresses the challenge of hospital resource management during the COVID-19 pandemic in India, but it is incremental as it applies existing transfer and ensemble learning techniques to a new dataset.
The paper tackles the problem of predicting COVID-19 cases, deaths, and active cases multiple days in advance in India to aid resource allocation, achieving the best performance with an ensemble of models pre-trained on Spain and Brazil data compared to other methods.
The current COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With more and more people getting affected during the second wave, the hospitals were over-burdened, running out of supplies and oxygen. In this scenario, prediction of the number of COVID-19 cases beforehand might have helped in the better utilization of limited resources and supplies. This manuscript deals with the prediction of new COVID-19 cases, new deaths and total active cases for multiple days in advance. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models that are pre-trained on the data from four different countries (United States of America, Brazil, Spain and Bangladesh) and are fine-tuned or retrained on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then give a multiple days ahead predictions using recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the combination of different models. This method with two countries, Spain and Brazil, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.