Mathematical models of disease spreading are a key factor of ensuring that we are prepared to deal with the next epidemic. They allow us to predict how an infection will spread throughout a population, thereby allowing us to make intelligent choices when attempting to contain the disease. Whether due to a lack of empirical data, a lack of computational power, a lack of biological understanding, or some combination thereof, traditional models must make sweeping assumptions about the behavior of a population during an epidemic.
In this thesis, we implement granular epidemic simulations using a rich social network constructed from real-world interactions. We develop computational models for three diseases, and we use these simulations to demonstrate the effects of twelve potential intervention strategies, both before and during a hypothetical epidemic. We show how representing a population as a temporal graph and applying existing graph metrics can lead to more effective interventions.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3440 |
| Date | 01 June 2019 |
| Creators | Siu, Christopher E |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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