Determining disease outbreak influence from voluminous epidemiology data on enhanced distributed graph-parallel system
| dc.contributor.author | Shah, Naman, author | |
| dc.contributor.author | Pallickara, Sangmi Lee, advisor | |
| dc.contributor.author | Pallickara, Shrideep, committee member | |
| dc.contributor.author | Turk, Daniel E., committee member | |
| dc.date.accessioned | 2017-09-14T16:06:49Z | |
| dc.date.available | 2017-09-14T16:06:49Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Historically, catastrophe has resulted from large-scale epidemiological outbreaks in livestock populations. Efforts to prepare for these inevitable disasters are critical, and these efforts primarily involve the efficient use of limited available resources. Therefore, determining the relative influence of the entities involved in large-scale outbreaks is mandatory. Planning for outbreaks often involves executing compute-intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. This thesis proposes a distributed approach for aggregating and analyzing voluminous epidemiology data to determine the influential measure of the entities in a disease outbreak using the PageRank algorithm. Using the Disease Transmission Network (DTN) established in this research, planners or analysts can accomplish effective allocation of limited resources, such as vaccinations and field personnel, by observing the relative influential measure of the entities. To improve the performance of the analysis execution pipeline, an extension to the Apache Spark GraphX distributed graph-parallel system has been proposed. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Shah_colostate_0053N_14415.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/184038 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | distributed analytics | |
| dc.subject | epidemiological PageRank | |
| dc.subject | NAADSM influential analysis | |
| dc.subject | enhanced distributed graph-parallel system | |
| dc.subject | disease propagation network | |
| dc.subject | extended Apache Spark Graphx | |
| dc.title | Determining disease outbreak influence from voluminous epidemiology data on enhanced distributed graph-parallel system | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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