# Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India

arXiv: https://arxiv.org/abs/2108.13823 · HTML: https://mlsanity.com/paper/2108.13823/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video](https://mlsanity.com/paper/1605.00894/) (2016)
- [Deep learning via LSTM models for COVID-19 infection forecasting in India](https://mlsanity.com/paper/2101.11881/) (2021)
- [Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series](https://mlsanity.com/paper/2009.12325/) (2020)
- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [Analysis of COVID-19 cases in India through Machine Learning: A Study of Intervention](https://mlsanity.com/paper/2008.10450/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case study](https://mlsanity.com/paper/2207.05753/) (2022)
- [Machine Learning for Infectious Disease Risk Prediction: A Survey](https://mlsanity.com/paper/2308.03037/) (2023)

## Abstract (arXiv)

To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are in search of new approaches to predict the COVID-19 outbreak dynamic trends that may slow down or stop the pandemic. Epidemiological models like Susceptible-Infected-Recovered (SIR) and its variants are helpful to understand the dynamics trend of pandemic that may be used in decision making to optimize possible controls from the infectious disease. But these epidemiological models based on mathematical assumptions may not predict the real pandemic situation. Recently the new machine learning approaches are being used to understand the dynamic trend of COVID-19 spread. In this paper, we designed the recurrent and convolutional neural network models: vanilla LSTM, stacked LSTM, ED-LSTM, Bi-LSTM, CNN, and hybrid CNN+LSTM model to capture the complex trend of COVID-19 outbreak and perform the forecasting of COVID-19 daily confirmed cases of 7, 14, 21 days for India and its four most affected states (Maharashtra, Kerala, Karnataka, and Tamil Nadu). The root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metric are computed on the testing data to demonstrate the relative performance of these models. The results show that the stacked LSTM and hybrid CNN+LSTM models perform best relative to other models.