What Can a Wolf Teach Us About Public Health Data?

by Jon Scaccia May 5, 2025

Ever feel overwhelmed by your own data?

Maybe you’re sitting on 200 survey variables, trying to figure out which ones actually matter. Or you’re evaluating a program with a rich but chaotic dataset—clients, services, outcomes, and community factors all jumbled together. You know there’s insight in there somewhere, but where do you start?

Enter: the wolf.

Okay, not an actual wolf. But a machine learning algorithm inspired by grey wolf hunting behavior—and yes, it might be exactly what your public health work needs.

Here’s the original study introducing the Grey Wolf Optimizer with Self-Repulsion Strategy (GWO-SRS), a cutting-edge approach to feature selection—that is, finding the most important variables in a big pile of data.

We broke down the science in our deep dive post, but today, let’s get practical. How can a tool like this actually help in the real world of community health, evaluation, and equity-driven work?

1. Cut Through the Clutter in Survey Data

If you’re using tools like BRFSS, PACEs, or your own community-developed surveys, you probably know this feeling:

“We asked 120 questions. Only 5 really told us something.”

GWO-SRS helps you find those 5—not just by hunch, but through rigorous testing. It figures out which variables best predict an outcome like depression, food insecurity, or service engagement—while filtering out redundant or noisy ones.

Use it for:

• Identifying key risk factors in community needs assessments
• Simplifying evaluation tools for quicker data collection
• Pinpointing what matters most in social determinant datasets

Build Leaner, Stronger Evaluation Models

When you’re working with small sample sizes—like in a peer recovery program or community health worker initiative—you need your model to be efficient. Too many variables = overfitting and confusion.

GWO-SRS helps you trim the fat and focus on the features that actually predict outcomes.

Use it for:

• Understanding what really drives success in your programs
• Creating justifiable, data-backed logic models
• Turning complex data into clear talking points for funders

Prioritize SDOH Factors for Place-Based Interventions

You’ve got ZIP-code-level data. You’ve got housing stats, education levels, air quality, income, walkability, grocery access…

Which ones matter most for diabetes rates? Or maternal health? Or opioid overdoses?

GWO-SRS lets you model these relationships efficiently, surfacing the most predictive variables without manually testing dozens of combinations.

Use it for:

• Informing policy recommendations
• Building equity dashboards
• Targeting neighborhoods for specific interventions

Create Short-Form Assessment Tools That Actually Work

Long surveys cause burnout—for both clients and staff. But if you shorten them, how do you know what to keep?

This method helps you build short-form versions of validated tools, selecting only the questions that offer real predictive power.

Use it for:

• Reducing intake burden for behavioral health or recovery services
• Making trauma screening more accessible
• Supporting “screen-and-refer” models in tight clinical workflows

Add Muscle to Grant Applications and Strategic Planning

Trying to tell a compelling story with your data? GWO-SRS gives you defensible, transparent ways to show what matters most.

No more, “Well, we think these variables are important.” Now you can say, “Across 100 factors, these 7 predicted overdose risk with 92% accuracy.”

Use it for:

• Data-driven grant justifications
• Priority setting with coalitions or funders
• Sharpening the focus of strategic plans

Bonus: It Plays Nice with Lived Experience

The real magic happens when you bring this data back to your community partners. Ask: “Do these results reflect your lived experience?” Use the output as a starting point for deeper conversation, not the final word.

Want to Try It?

We built an R template that applies GWO-SRS to a real-world dataset, complete with code annotations and public health framing. It’s not plug-and-play, but it’s a solid foundation for your next data deep dive. Reach out to us if you’d like it!

Let’s Explore Together

We’d love to know:

• What’s your biggest challenge when working with high-dimensional data?
• Have you ever trimmed a survey or dataset—what did you keep?
• What tool has unexpectedly helped your public health work?

Leave a comment, share with a fellow evaluator, or drop us a line. We’re building the future of community health—one data story at a time.

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