Influenza mortality mapped for resource planning
An end-to-end Excel and Tableau analytics project that turned 700K+ CDC flu-death records (2009-2017) into an eight view interactive Tableau story: age group risk, state level concentration, seasonal peaks, and a regional forecast; telling a medical staffing agency where, when, and for whom to plan ahead.
From 700K CDC death records to a seasonal staffing plan
The project was built around a practical operational question: where is flu mortality highest, when do seasonal peaks occur, and which age groups need the most resource-planning attention?
Business problem
A medical staffing agency supports hospitals across all 50 states and must decide when and where to send temporary staff during flu season, particularly for vulnerable populations. Raw CDC mortality data is not decision ready. The goal was to turn it into clear, data driven guidance on the timing and geographic distribution of staffing needs.
Final output
The final deliverable was an eight view interactive Tableau story fed by a cleaned validated Excel dataset (CDC mortality) joined to US Census population, covering national totals, age and seasonality, state concentration, senior specific risk, a regional forecast, and a findings with next steps summary.
The story helps stakeholders see which states carry the highest burden, where seniors are most at risk per capita, the recurring November&-;January peak, and a clear set of staffing priorities.
How the analysis was built
The Excel work turned raw, inconsistent government health data into a profiled, validated, analysis-ready dataset, the foundation everything visual rests on, before a single chart was drawn in Tableau.
Per-capita senior risk, not just raw death counts
Large states will always post the biggest totals, so a raw count is misleading across states of very different sizes. Normalising each state’s senior deaths against its senior population makes the comparison fair and defensible.
How the senior-risk view works
The map shades each state by the share of flu deaths among people aged 65+, not the raw number, so population size does not dominate the picture, and it never claims a clinical or causal finding.
The single largest senior flu death total 2009-2017, ahead of New York (36.6K) and Texas (22.1K).
Darkest on the % senior-death map, a high share of senior deaths even where total numbers are modest.
Senior population and senior deaths track most tightly in these regions, pointing to population-driven demand.
The biggest total is not always the highest risk, a smaller state can need proportionally more support.
The data at a glance
Headline cuts of the 700K+ death records, all drawn from the cleaned CDC and US Census datasets.
Deaths by age group
Total flu deaths 2009-2017.
- 65+ Years412K
- 35&-;64 Years108K
- 05&-;34 Years83K
- 0&-;4 Years55K
Worst years
Highest annual death totals.
- 201579.5K
- 201379.2K
- 201777.9K
- 201176.3K
Peak season
Deaths by month; winter-dominant.
- January76.9K
- March66.1K
- December63.7K
- October52.6K
Top states (65+)
Senior flu deaths by state.
- California47.5K
- New York36.6K
- Texas22.1K
- Pennsylvania20.6K
An eight-view story built around stakeholder questions
The story moves from a national overview into seasonality, geography, senior-specific risk, and a forward-looking forecast with recommendations.
National Overview
KPI cards by age group and the human cost framing; who is most at risk from flu, and where, across all 50 states.
Seasonality & Age
Yearly and monthly death trends, the age-group breakdown, and a senior population-vs-deaths correlation scatter.
Geography & Senior Risk
A total-deaths state map, the top-10 states ranking, a compare-your-states tool, and the per-capita % senior-death map.
Forecast & Next Steps
A regional forecast with prediction bands, plus prioritised findings, recommendations, and next steps.
What the data made clear
The story turns 700K+ death records into a small number of decisions a public-health staffing team can actually act on.
Adults aged 65+ accounted for 412K of the flu deaths more than every other age group combined, making them the clear priority for staffing and vaccination outreach.
Mortality consistently peaks November-January (January highest at 76.9K), followed by a notable secondary wave in March (66.1K), a recurring pattern staffing can be planned around.
California, New York and Texas post the biggest totals, but Alaska, Wyoming and Vermont carry the highest share of senior deaths, so the busiest states are not always the most at risk per capita.
Years 2013, 2015 and 2017 were the worst, and a regional forecast projects continued seasonal recurrence every winter, confirming flu demand can be planned for, not just reacted to.
Cleaned, validated, and honest about its limits
A public-health dashboard only holds up if the cleaning is documented, the totals are checked, and the analysis is clear about what it can and cannot claim. All three were built in from the start.
Numbers you can defend
The dataset was not taken on trust; every transformation was documented and re validated before any chart was built.
CDC mortality and US Census population data, each profiled, cleaned, and re-validated in Excel before use.
A % senior-death metric separating per-capita risk from raw counts across all states.
National overview, seasonality, age, geography, senior risk, correlation, forecast, and recommendations.
Prioritised, plain-language actions tied to peak months, high-risk states, and vulnerable populations.
How a staffing agency could use the dashboard
Staff up for the winter peak: concentrate temporary staffing in high-risk states across the November&-;January peak, and account for the smaller March second wave.
Protect vulnerable seniors first: allocate extra support where the senior population is especially at risk per capita, even where total numbers are smaller.
Plan from the forecast: use the regional forecast and year-to-year patterns to anticipate surges rather than react to them.
Know the limits: the analysis guides the timing and geography of staffing; it deliberately stops short of clinical or hospital capacity claims the data cannot support.
What this project demonstrates
End-to-end data profiling and cleaning of messy government data, descriptive statistics and frequency analysis for quality assurance, population normalisation, multi-view Tableau storytelling, trend forecasting, and the ability to turn a large public-health dataset into prioritised, defensible recommendations.
Need the full portfolio or resume?
Download the PDF portfolio for a polished overview of the projects, or open the resume for the formal career summary, tools, and work history.