Theory-Guided Bayesian Analysis for Modeling Impact of COVID-19 on Gross Domestic Product

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Conference Article

Publication Title

IEEE Region 10 Annual International Conference, Proceedings/TENCON


GDP or Gross Domestic Product is a key indicator of economic status, which provides an omni-comprehensive measure of the wealth of a country or a state. With the sudden proliferation of novel coronavirus disease (COVID-19), there has been increasing interest in forecasting GDP, since this may be severely impacted by the various pandemic control measures imposed in recent days. An accurate forecast of GDP can extensively help in putting forth right administrative measures while ensuring minimum disruption in economy. Though the recent researches focus on various machine learning-based data-driven models for this purpose, these primarily analyze the change in observed GDP data without explicitly modeling the pandemic impact. We address this issue by proposing a novel approach that incorporates epidemiological insights into Bayesian network-based predictive analytics to account for the influence of COVID-19 development on the GDP. Rigorous experimentation on state-level and country-level datasets of India demonstrates that a judicious combination of theoretical and data-driven models can substantially improve GDP forecast performance. Our model produces an average prediction error of 0.002% and outperforms several state-of-the-art techniques with a large margin.



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