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COVID-19 India Rt Dashboard

Python, Pandas, NumPy, Plotly, GitHub Actions, Bayesian Inference
Published Jan 15, 2026
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Project Overview

This open-source repository hosted a fully automated pipeline that calculated and visualized the effective reproduction number (Rt) for every Indian state and union territory. Inspired by Kevin Systrom’s Rt methodology, I adapted the Bayesian updating process to localized datasets and published the output as an interactive Plotly dashboard.

Data & Methodology

  • Pulled state-wise case counts from Thejesh GN’s scraped feeds of the Ministry of Health and Family Welfare, validating for missing or sparse regions.
  • Applied a modified Bettencourt & Ribeiro (2008) Bayesian approach with Gaussian process noise to estimate daily Rt values and credible intervals per state.
  • Highlighted model caveats, such as unmodeled states with insufficient data and the sensitivity of Rt to reporting anomalies.

Automation & Delivery

  • Scheduled GitHub Actions to run every six hours (UTC), refresh datasets, recompute Rt, and regenerate static assets.
  • Deployed artifacts to a public dashboard with a net-new historical chart, summary badges, and explanatory copy for policymakers and citizens.
  • Archived the project when upstream data feeds became unreliable, keeping the methodology and codebase available for future outbreaks.

Impact

  • Cited by outlets such as Deccan Herald and The Print when reporting on localized transmission trends.
  • Earned endorsements from epidemiologists and public health leaders including Dr. Giridhar R. Babu and Lipika Nanda for bringing transparent Rt tracking to the Indian context.
  • Demonstrated how small civic-tech efforts can combine open data, reproducible modeling, and automation to inform crisis response.

News Coverage

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Credits

Technologies & Concepts