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

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  • Middya, Asif Iqbal
  • Roy, Sarbani

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.

Suggested Citation

  • Middya, Asif Iqbal & Roy, Sarbani, 2022. "Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522004334
    DOI: 10.1016/j.techfore.2022.121911
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    References listed on IDEAS

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    Cited by:

    1. Lin, Weiran & He, Qiuqin & Xiao, Yuan & Yang, Jingwen, 2023. "Do city lockdowns effectively reduce air pollution?," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

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