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STATISTICAL MODELING · PYTHON ETL · STL TIME-SERIES FORECASTING · PANEL DASHBOARD·2024

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

PythonPandasHvplotPanelTime SeriesEpidemiology
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Public health analytical pipeline examining the reciprocal dynamics and seasonality shifts between respiratory pathogens following pandemic mitigation interventions.

01. The Problem

Fragmented healthcare data sources and differing reporting cadences made it difficult to assess true leading/lagging seasonal correlations across viral strains.

02. Architecture & Solution

Constructed automated Python ETL pipelines pulling from Johns Hopkins and CDC APIs. Applied STL (Seasonal and Trend decomposition using Loess) and cross-correlation to identify lead-lag dynamics, visualised via interactive Hvplot/Panel dashboards.

Key Architectural Highlights

  • ▹Automated multi-source data ingestion pipeline with automated imputation of missing reporting days
  • ▹STL decomposition separating seasonal oscillation from secular pandemic waves
  • ▹Interactive reactive dashboard rendered with Hvplot and HoloViews

03. Impact

Uncovered statistical leading indicators that help public health administrators anticipate pediatric ICU bed load surges weeks in advance.

Elgen Mar Arinasa © 2026

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