Influenza Insights

Spotting Trends. Saving Lives. Guiding Smarter Strategies.

Fast Facts for Busy Reviewers

Goal:
Identify patterns in flu mortality and vaccination behavior to support targeted public health responses and smarter resource planning.

Tools Used:
Excel • Tableau • SQL (pgAdmin 4)

Key Wins:

📉 Mortality peaked in 2013 yet many high-mortality states had low vaccination coverage
📍 States like Texas and Mississippi consistently show low flu shot rates + high mortality
📊 Some regions (like Colorado) defy trends, hinting at other factors worth investigating

Strategic Takeaways:
→ Prioritize states with low vaccination rates and high mortality
→ Use forecasts to plan ahead for seasonal staffing and vaccine needs
→ Investigate persistent outliers to improve public health equity

GitHub

The Challenge

Influenza continues to strain public health systems across the U.S., especially in vulnerable regions. I wanted to explore:

Where is flu mortality hitting hardest? Are vaccinations helping? And what can the data tell us about how to prevent future spikes?

Problem

Despite national vaccination efforts, certain states still see high flu death rates year after year. I wanted to dig into the data to understand what’s driving those patterns and figure out how to respond more effectively.

Tools Used

To clean, process, and analyze the data, the following tools were used:

Excel
Tableau
SQL (pgAdmin 4)

Key Insights

Flu patterns revealed both expected and surprising trends:

Some states (like Texas and Mississippi) show high mortality and low flu shot rates across multiple years
Outliers like Colorado and Washington had strong vaccine coverage but still saw high death rates
Flu deaths consistently spiked in years like 2013, pointing to seasonal planning needs

Business Suggestions

Prioritize high-risk states for vaccination and staffing campaigns
Investigate healthcare access issues in outlier regions
Launch targeted outreach efforts before flu season begins

Data Cleaning Highlights

Before diving into analysis, I made sure the dataset was in good shape.
Here’s what I cleaned up:

Combined multiple sources: CDC mortality data, NIS Flu Survey, U.S. Census
Standardized column names and cleaned inconsistent formatting
Checked for nulls, removed duplicates, and verified value consistency
Reformatted and converted numeric fields for flu deaths, populations, and shot rates
Merged by state and year to support clean joins across datasets

🧪 Why it matters:

Cleaning the data made it possible to compare trends across states and years, revealing patterns in flu-related mortality and vaccination coverage that would’ve been harder to detect in messy or inconsistent files.

🧪 This before/after table shows how I handled common issues like inconsistent naming, missing values, and duplicated entries across Excel and SQL tools.

🧪 Visual Tip

Preview snippets like the ones shown here highlight what changed—so reviewers can follow my process without digging into every raw file.

Snapshot of Key Cleaning Improvements

Key Visuals & What They Show

🧪 Influenza Deaths by State:

This choropleth map shows total flu deaths from 2009 to 2017 by state. Darker colors indicate higher death counts.

California and Texas consistently appear in the highest mortality range.
Insight: These hotspots can guide where to focus prevention efforts and vaccine access.

🧪 Dual-Axis Map: Flu Deaths + Vaccination Rates

The layered map uses state color to represent mortality and bubble size/color to reflect flu shot coverage.

Some states with low vaccination rates experienced high death counts, while others (like Colorado) defied the trend.
Insight: Cross-comparing vaccination and mortality rates reveals potential gaps in flu prevention efforts.

🧪 Deaths by Age Group (Histogram)

This histogram breaks down influenza-related deaths across age brackets.

Seniors show the sharpest mortality spikes, especially in years like 2013.
Insight: Supports the need for age-targeted flu prevention and resource allocation.

🧪 Flu Shot vs. Mortality by Risk Category

A bubble chart shows flu shot rates and mortality by demographic groups.

Older adults and young children had higher death rates despite decent vaccine coverage.
Insight: Some groups may require enhanced protection beyond standard vaccination outreach. convenience and flexibility.

Main Insights (Recap)

💉 Low Vaccination Rates ≠ Low Risk

States like Texas and Mississippi consistently had high flu mortality but low flu shot coverage over several years. This disconnect highlights the need for targeted campaigns where risk is high but prevention is low.

🫧 Vaccination Efforts Need Nuance

The bubble chart comparing shot rates and mortality revealed that coverage doesn’t always equal protection especially in high-risk or underserved communities. Outreach strategies need to go beyond awareness and address access gaps.

📍 Certain Regions Break the Pattern

States like Colorado showed high vaccination rates but still had elevated death counts. This suggests that other factors like healthcare infrastructure or demographic vulnerabilities may be influencing outcomes.

📈 2013 Was a Spike Year

Across multiple states, 2013 saw a sharp rise in flu deaths, with some states doubling their usual yearly totals. This points to a seasonal surge and underscores the importance of early forecasting and preparedness.

👵 Seniors Are at Highest Risk

The 85+ age group stood out in every analysis, accounting for the largest share of deaths by far. Prevention efforts should focus heavily on this group through early outreach, access, and awareness.

From Data to Decisions

This project focused on uncovering meaningful flu trends using a combination of SQL, Excel, and Tableau. The goal wasn’t just to find insights, but to surface patterns that could lead to smarter, equity-focused public health actions.

Hands-on techniques included:

Cleaning and merging multi-source datasets (CDC, Census, NIS)
Analyzing trends in mortality and vaccination across states and age groups
Building relational models and visualizing patterns in Tableau
Identifying outliers and gaps for targeted public health strategies

01: Prioritize Undervaccinated High-Mortality States

Focus flu prevention funding and outreach in places like Texas and Mississippi, where flu shot rates are low but mortality remains high year after year.

Business Recommendations:

02: Investigate Outliers with High Coverage + High Mortality

States like Colorado may benefit from deeper investigation. Strong vaccine access didn’t translate to lower death counts, pointing to potential gaps in care or other contributing factors.

03: Prepare for Seasonal Spikes

Historical spikes in years like 2013 suggest flu seasons vary in intensity. Use data trends to allocate vaccines and staff earlier in high-risk years.

04: Protect the Most Vulnerable

The 85+ population remains at the greatest risk across all years. Launch early flu shot campaigns and support systems for senior communities.

05: Go Beyond Awareness and Improve Access

In areas with consistent underperformance, public health efforts should address not just education but logistical access to vaccines, especially in rural and underserved regions.

Explore the Code Behind the Insights

Want to dive into the full analysis, see how tables were joined, or explore how the visualizations were built? The entire project from data cleaning in Excel to SQL queries and final Tableau dashboards is available on GitHub.