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Related Experiment Video

Updated: Oct 20, 2025

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Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model.

Monidipa Das1, Akash Ghosh2, Soumya K Ghosh3

  • 1Machine Intelligence Unit (MIU), Indian Statistical Institute (ISI), Kolkata, India.

SN Computer Science
|September 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework to analyze how climate variability impacts COVID-19 spread, addressing data limitations. The semantically-enhanced approach improves probabilistic modeling for better understanding disease outbreaks.

Keywords:
COVID-19Climate variabilitySemantic Bayesian analysisTheory-guided approach

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Area of Science:

  • Environmental Science
  • Epidemiology
  • Data Science

Background:

  • The COVID-19 pandemic's spread is influenced by various factors, including climate.
  • Existing research on climate's impact on COVID-19 faces challenges due to data inadequacy and transparency issues, leading to inconsistent findings.
  • There is a need for robust analytical frameworks to understand the causal influence of climate variability on infectious disease outbreaks.

Purpose of the Study:

  • To develop and evaluate a semantics-driven probabilistic framework for analyzing the causal influence of climate variability on the COVID-19 outbreak.
  • To address data inadequacy and uncertainty by integrating probabilistic graphical analysis with domain-specific semantics from climatology.
  • To enhance the framework with regional semantic relatedness for improved analysis of diverse climate types within spatial regions.

Main Methods:

  • Employed a semantics-driven probabilistic framework incorporating domain semantics from climatology.
  • Utilized probabilistic graphical analysis to handle data inadequacy and uncertainty.
  • Integrated theoretical guidance from epidemiological models and an auxiliary module for regional semantic relatedness analysis.

Main Results:

  • The semantically-enhanced, theory-guided, data-driven approach demonstrated effectiveness in analyzing COVID-19 datasets across diverse climate regions in India.
  • The framework successfully accounted for multiple climate types within single regions through regional semantic relatedness.
  • Improved probabilistic analysis was achieved for modeling the climatological impact on the disease outbreak.

Conclusions:

  • The proposed framework provides an effective method for analyzing the impact of climate variability on COVID-19 spread, overcoming data limitations.
  • The integration of semantics and regional analysis enhances the accuracy and explainability of epidemiological impact assessments.
  • The framework is generic and adaptable for intelligent impact analysis in various other application domains.