Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks

TL;DR

This paper introduces a transfer-recursive-ensemble learning approach using gated recurrent units, pre-trained on multiple countries' COVID-19 data, to accurately predict multi-day cases and deaths in India, aiding resource planning.

Contribution

It presents a novel transfer learning ensemble method with recursive prediction for multi-day COVID-19 forecasting in India, leveraging diverse international data sources.

Findings

Ensemble of models from Spain and Brazil achieved best accuracy.

Transfer learning improved prediction performance over traditional models.

Method effectively predicts multi-day COVID-19 cases and deaths in India.

Abstract

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.