Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List

Full text release has been delayed at the author's request until August 03, 2025

ETD Abstract Container

Abstract Header

Nonlinear Model Predictive Control for Epidemic Mitigation Using a Spatio-temporal Dynamic Model

Street, Logan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721233246017435

Abstract Details

2024, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Within this thesis document we focus on the application of Nonlinear Model Predictive Control (NMPC) onto an epidemic compartmental model. The compartmental model is a partial differential equation (PDE) based Susceptible Latent Infected Recovered (SLIR) epidemic model. This model serves as the basis of the NMPC. In order to generate the necessary parameters for initializing and training the use of constrained optimization, a single-objective Genetic Algorithm (GA), and LSTM (Long-Short-Term-Memory) deep learning were explored. The spatial domains considered for the SLIR epidemic model includes Hamilton County, Ohio as well as the entire state of Ohio, USA. With respect to Hamilton County, Ohio three different time periods were evaluated in which varied levels of infection relating to COVID-19 were observed. At the state wide level only one time period was consider. The NMPC considers two control schemes. The first being control applied uniformly across the spatial domain of interest. While the second focuses on applying the control in a spatially targeted manner to specific geographical areas based on observed higher levels of infection. The NMPC also employs a cost function comprising the infection spread density and the associated cost of applied control measures. The latter of which in turn representing socioeconomic effects. Overall, the NMPC framework developed here is intended to aid in the evaluation of optimal Non-Pharmaceutical Interventions (NPI) towards spread mitigation of infectious diseases.
Manish Kumar, Ph.D. (Committee Chair)
Shelley Ehrlich, M.D. (Committee Member)
Subramanian Ramakrishnan, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
101 p.
Mechanical Engineering
Nonlinear Model Predictive Control; Partial Differential Equations; Mathematical Modeling; Epidemic Models; Numerical Analysis; Numerical Optimization

Recommended Citations

Citations

  • Street, L. (2024). Nonlinear Model Predictive Control for Epidemic Mitigation Using a Spatio-temporal Dynamic Model [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721233246017435

    APA Style (7th edition)

  • Street, Logan. Nonlinear Model Predictive Control for Epidemic Mitigation Using a Spatio-temporal Dynamic Model. 2024. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721233246017435.

    MLA Style (8th edition)

  • Street, Logan. "Nonlinear Model Predictive Control for Epidemic Mitigation Using a Spatio-temporal Dynamic Model." Master's thesis, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721233246017435

    Chicago Manual of Style (17th edition)

ucin1721233246017435
© 2024, some rights reserved.Creative Commons License Nonlinear Model Predictive Control for Epidemic Mitigation Using a Spatio-temporal Dynamic Model by Logan Street is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.