Paper Info
Title
Network Analysis For Identifying And Characterizing Disease Outbreak Influence From Voluminous Epidemiology Data
Abstract
Planning for large-scale epidemiological outbreaks in livestock populations 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. In this study, we propose a distributed approach for analyzing voluminous epidemiology data to locate and classify the most influential entities in a disease outbreak. Using our disease transmission network (DTN), planners or analysts can isolate entities that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel. We use a representative dataset to verify our approach, including identification of influential entities and creation of machine learning models for accurate classifications that generalize to other datasets.
Year
Venue
Keywords
2016
2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Epidemiological network analysis, Distributed analytics, Disease spread classification, Super-Spreading Events
Field
DocType
Citations 
Data science,Data mining,Data modeling,Transmission network,Computer science,Epidemiology,Outbreak,Artificial intelligence,Network analysis,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
16
5
Name
Order
Citations
PageRank
Naman Shah101.01
Harshil Shah242.43
Matthew Malensek39310.44
Sangmi Lee Pallickara417024.46
Shrideep Pallickara583792.72