ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.
- 2 Public
- Applied mathematics
- Ordinary Differential Equations
- 1 Combination Treatment
- 1 Dendritic Cell Vaccine
- 1 Epidemiology
- 1 Immunotherapy
- 1 Infectious Disease Epidemiology
- 1 Numerical analysis
- 1 Oncology
- 1 Oncolytic Virus
- 1 Parameter Estimation or inverse problem
- 1 Statistics
- 1 Stochastic modeling
- 1 Uncertainty and Sensitivity Analyses
- 1 Volterra Integral Equations
In the field of infectious disease epidemiology, the assessment of model robustness outcomes plays a significant role in the identification, reformulation, and evaluation of preparedness strategies aimed at limiting the impact of catastrophic events (pandemics or the deliberate release of biological agents) or used in the management of disease prevention strategies, or employed in the identification and evaluation of control or mitigation measures. The research work in this dissertation focuses on: The comparison and assessment of the role of exponentially distributed waiting times versus the use of generalized non-exponential parametric distributed waiting times of infectious periods on the quantitative and …
- Morale Butler, Emmanuel Jesús, Castillo-Chavez, Carlos, Aparicio, Juan P, et al.
- Created Date
Combination therapy has shown to improve success for cancer treatment. Oncolytic virotherapy is cancer treatment that uses engineered viruses to specifically infect and kill cancer cells, without harming healthy cells. Immunotherapy boosts the body's natural defenses towards cancer. The combination of oncolytic virotherapy and immunotherapy is explored through deterministic systems of nonlinear differential equations, constructed to match experimental data for murine melanoma. Mathematical analysis was done in order to gain insight on the relationship between cancer, viruses and immune response. One extension of the model focuses on clinical needs, with the underlying goal to seek optimal treatment regimens; for both …
- Summer, Ilyssa, Castillo-Chavez, Carlos, Nagy, John, et al.
- Created Date