Flu vs. COVID-19 Trends (2020–2024)Flu vs. COVID-19 Trends (2020–2024)Flu vs. COVID-19 Trends (2020–2024)

Comparative time-series analysis of influenza vs. COVID-19 incidence across New Jersey (2020–2024), leveraging Python-powered ETL pipelines, interactive dashboards, and rigorous statistical modeling.

01. The Problem

Public health data is often fragmented across disparate sources and formats, making it difficult to visualize seasonality overlays or perform direct per-capita comparisons between different viral outbreaks.

02. Architecture & Solution

Built automated ETL pipelines to harmonize date formats and normalize county-level counts from Johns Hopkins and WHO datasets. Developed interactive dashboards with Hvplot and Panel for drill-down analysis. Applied STL trend decomposition and correlation analysis to uncover leading indicators and seasonal patterns.

03. Impact

Delivered a high-fidelity, reproducible research notebook that highlights the distinct behavioral patterns of Flu vs. COVID-19, providing a template for future infectious disease time-series modeling.