This thesis aims to provide new insights on how viruses spread within people of the same nationality. Our central hypothesis states that viruses spread easily in people from the same nationality when they live close to each other. This paper creates and examines a network graph of patients where nodes are patients in different geographic locations fetched from the health dataset used and edges represent a relationship between them. A set of location-based graph attributes including Clustering Coefficient, Closeness, and Betweenness are discussed and used for analyzing the social context of a patient’s geographic location. The Clustering Coefficient measures the probability that the adjacent patients of a patient are connected. We utilize it to know how viruses spread from one patient to another in the same network. Closeness measures the steps required to reach every other patient from a given patient. We use it to analyze how quickly viruses can be spread among patients living in same region. Finally, we use Betweenness to find the patient mainly responsible for transmitting viruses among other patients, which provides the number of shortest paths passing through a patient. Our analysis extracts useful information to identify the factors behind virus transmission among people and across geographies. It also provides the most prevalent disease information by patient’s nationality. As a result of this analysis, it was found out that Indians are mostly infected by viruses of colds and these viruses spread easily in patients who live close to each other. This contextual information can be helpful in solving potential public health issues.
|Advisor:||Ponce, Oscar Morales|
|Commitee:||Englert, Burkhard, Murgolo, Frank|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 57/04M(E), Masters Abstracts International|
|Subjects:||Information science, Computer science|
|Keywords:||Data anlysis, Health, Health data, Health data predictions, Igraph, Python|
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