Date of Award:
8-2020
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Mathematics and Statistics
Committee Chair(s)
Kevin R. Moon
Committee
Kevin R. Moon
Committee
Kezia Manlove
Committee
David Brown
Abstract
When modeling the spread of disease, ecologists use ecological or contact networks to model how species interact with their environment and one another. The structure of these networks can vary widely depending on the study, where the nodes of a network can be defined as individuals, groups, or locations among other things. With this wide range of definition and with the difficulty of collecting samples, it is difficult to capture every factor of every population. Thus ecologists are limited to creating smaller networks that both fit their budget as well as what is reasonable within the population of interest. With smaller networks, there is a concern of information loss when generalizing collected results to the whole population. In this work, we use the von Neumann entropy as a measure of the amount of information contained in a given ecological network. We compute the von Neumann entropy of a simulated contact network over a variety of parameters. The goal is this will introduce a standard for ecologists when designing their studies to minimize information loss and thus reduce costs, reduce time, and minimize human error particularly during sample collection. We further demonstrate our approach on data measured from bighorn sheep.
Checksum
2fdd670e25761c1886db267797a8a2af
Recommended Citation
Brower, Thomas J., "Analyzing the von Neumann Entropy of Contact Networks" (2020). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 7845.
https://digitalcommons.usu.edu/etd/7845
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