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Global Optimization for Estimation of On/Off Seasonality in Infectious Disease Spread Using Pyomo

Global Optimization for Estimation of On/Off Seasonality in Infectious Disease Spread Using Pyomo. Gabriel Hackebeil Chemical Engineering Dept. Texas A&M University. Carl Laird Chemical Engineering Dept. Texas A&M University. Introduction. Infectious Diseases Remain Important

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Global Optimization for Estimation of On/Off Seasonality in Infectious Disease Spread Using Pyomo

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  1. Global Optimization for Estimation of On/Off Seasonality in Infectious Disease Spread Using Pyomo Gabriel Hackebeil Chemical Engineering Dept. Texas A&M University Carl LairdChemical Engineering Dept.Texas A&M University

  2. Introduction • Infectious Diseases Remain Important • Understanding Disease Dynamics • Public Health Program Implementation • Childhood Diseases Useful for Study • Clear Temporal Dynamics • Annual and Biennial Drivers Dependent on Birthrate • Seasonal Patterns • Not Purely Random

  3. Compartment Models • Many Compartment Models Possible • Compartments Reflect Disease Progression • SIR Compartment Model • Suitable for Childhood Infectious Diseases R E I S S M (t) (t) S I R B(t) D(t) σ

  4. Introduction • Seasonality in Transmission Parameter • Previous Results Show Seasonality in Beta Corresponds With School Terms

  5. Outline • Problem Formulation • High-level Solution Strategy • Pyomo Implementation • Results • Pyomo Experiences • Acknowledgements

  6. Disease Model • TSIR Model – Measles Data

  7. Problem Formulation • Log Transform

  8. Problem Formulation • Multiplicative Noise in Data Measurement

  9. Problem Formulation • First-Order Taylor Series Approximation

  10. MIP Formulation

  11. NLP Formulation

  12. Global Optimization Algorithm

  13. Refining the Convex Relaxation

  14. Disease Model File

  15. Run File

  16. Results – New York Data • Global Solution – Two Years of Case Data

  17. Results – New York Data • Global Solution – On/Off Behavior Matches School Terms Summer Break

  18. Pyomo Experiences • Convenience of Python • plotting/saving results • lists/dictionaries for data analysis • functions, classes • Data files • previous models built in AMPL

  19. Acknowledgements • Sandia National Laboratories • National Science Foundation Faculty Early Career Development (CAREER) Award • Research Group

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