Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models

https://doi.org/10.1016/j.techfore.2022.121911Get rights and content

Highlights

  • Deep learning-based COVID-19 health outcome forecasting at the state level in India.

  • Explore spatio-temporal variability pattern in the performance of learning models.

  • Finding spatial distribution of the models for predicting COVID-19 mortality data.

  • Rigorous evaluation of the models based on COVID-19 mortality time-series data.

Abstract

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

Keywords

Covid-19
Deep learning
Spatio-temporal variation

Data availability

Data will be made available on request.

Cited by (0)

Asif Iqbal Middya is currently pursuing Ph.D in Computer Science and Engineering at Jadavpur University, India. His research interests include AI for social good, Data Science, Geoinformatics, and IoT-Cloud.

Sarbani Roy is a Professor in the Department of Computer Science and Engineering, Jadavpur University, India. She received her Ph.D. degree from Jadavpur University, Kolkata, India in July, 2008. Her research interests include AI for social good, IoT-Cloud, Social Network Analysis, Geoinformatics, and Data Science. She has received research project grants from DST-NGP, Govt. of India and DSTBT, Govt. of West Bengal.

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