Term of Award
Fall 2018
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Divine Wanduku
Committee Member 1
Broderick Oluyede
Committee Member 2
stephen carden
Abstract
Influenza ranks among the top ten most important diseases in the USA. In fact, the CDC estimates that between 12 - 56 thousand deaths occur annually since 2010. In this study, we present a stochastic chain-binomial model for the dynamics of in-fluenza in a closed human population. We consider treatment for the disease in the form of vaccination, and incorporate the periods of effectiveness of the vaccine and infectiousness. Our model is an SVIR model, where all individuals who recover from the disease acquire natural immunity. The transition probabilities are presented for two scenarios depending on whether the events of infectiousness and vaccination are dependent. The parameters of the model are estimated using the maximum likeli- hood method and the EM algorithm. Some epidemiological assessment parameters, such as the basic reproduction number and probability of no spread, are computed. Numerical simulation examples are presented for the model.
OCLC Number
1085541980
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916218293602950
Recommended Citation
Newman, Charles C., "Stochastic Modeling and Parameter Estimation for Influenza Epidemics" (2018). Electronic Theses and Dissertations. 1839.
https://digitalcommons.georgiasouthern.edu/etd/1839
Research Data and Supplementary Material
No