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Ecosystem Modeling of College Drinking: Parameter Estimation and Comparing Models to Data.

Azmy S Ackleh¹, Ben G Fitzpatrick, Richard Scribner

¹Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA 70504.

Mathematical and Computer Modelling

|February 18, 2010

Summary

This summary is machine-generated.

We developed a novel method to estimate parameters for a model of college student drinking patterns. Reducing campus "wetness" effectively decreases binge drinking, unlike penalizing students.

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Area of Science:

Mathematical modeling
Epidemiology
Public health

Background:

College student drinking persists at epidemic levels.
Accurate predictive models require precise parameter estimation.
Standard parameter estimation methods were insufficient for this model.

Purpose of the Study:

To present an unconventional parameter estimation approach for a drinking behavior model.
To validate the model's fit with real-world survey data.
To evaluate the impact of hypothetical intervention strategies.

Main Methods:

Developed a model of college student drinking using five impulsive differential equations.
Employed an unconventional inverse problem approach for parameter estimation.
Utilized survey data from 32 college campuses for model fitting and validation.

Main Results:

The developed model demonstrated a good fit to survey data across 32 campuses.
Parameter estimation was successfully achieved using the novel approach.
Simulations indicated that reducing campus "wetness" significantly reduces binge drinking.

Conclusions:

The unconventional parameter estimation method is effective for this complex model.
Reducing environmental factors contributing to drinking ("wetness") is a promising intervention strategy.
Policies penalizing student drinkers are less effective than environmental interventions for reducing binge drinking.