Deep learning via LSTM models for COVID-19 infection forecasting in India

TL;DR

This paper applies LSTM-based deep learning models to forecast COVID-19 infections in Indian states, demonstrating promising short-term prediction accuracy despite data and modeling challenges.

Contribution

It introduces the use of various LSTM models for multi-step COVID-19 forecasting in India, addressing data limitations and capturing infection waves.

Findings

Predicted low likelihood of new infection waves in late 2021.

Models achieved promising short-term forecasting accuracy.

Highlights challenges due to data reliability and social factors.

Abstract

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.