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

arXiv: https://arxiv.org/abs/2101.11881 · HTML: https://mlsanity.com/paper/2101.11881/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 106 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Langevin-gradient parallel tempering for Bayesian neural learning](https://mlsanity.com/paper/1811.04343/) (2018)
- [Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables](https://mlsanity.com/paper/2007.10981/) (2020)
- [Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics](https://mlsanity.com/paper/1808.02763/) (2018)
- [India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling](https://mlsanity.com/paper/2005.06619/) (2020)

## Built on by (most cited first, 12 of 12)

- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
- [A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling](https://mlsanity.com/paper/2112.08618/) (2021)
- [Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study](https://mlsanity.com/paper/2305.07731/) (2023)
- [Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India](https://mlsanity.com/paper/2108.13823/) (2021)
- [Next Generation Equation-Free Multiscale Modelling of Crowd Dynamics via Machine Learning](https://mlsanity.com/paper/2508.03926/) (2025)
- [Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown](https://mlsanity.com/paper/2102.10551/) (2021)
- [Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data](https://mlsanity.com/paper/2310.14549/) (2023)
- [HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models](https://mlsanity.com/paper/2501.05482/) (2025)
- [Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach](https://mlsanity.com/paper/2109.13062/) (2021)
- [On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland](https://mlsanity.com/paper/2204.12346/) (2022)
- [Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models](https://mlsanity.com/paper/2210.03258/) (2022)
- [Extreme value forecasting using relevance-based data augmentation with deep learning models](https://mlsanity.com/paper/2510.02407/) (2025)

## Abstract (arXiv)

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.