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- All Subjects: Social networks--Mathematical models.
- Creators: Laubichler, Manfred
Description
Influenza has shown its potential to affect and even kill millions of people within an extremely short time frame, yet studies and surveys show that the general public is not well educated about the facts about influenza, including prevention and treatment. For this reason, public perception of influenza is extremely skewed, with people generally not taking the disease as seriously as they should given its severity. To investigate the inconsistencies between action and awareness of best available knowledge regarding influenza, this study conducted literature review and a survey of university students about their knowledge, perceptions, and action taken in relationship to influenza. Due to their dense living quarters, constant daily interactions, and mindset that they are "immune" to fairly common diseases like influenza, university students are a representative sample of urban populations. According to the World Health Organization (WHO), 54% of the world's population lived in cities as of 2014 (Urban population growth). Between 2015 and 2020, the global urban population is expected to grow 1.84% per year, 1.63% between 2020 and 2025, and 1.44% between 2025 and 2030 (Urban population growth). Similar projections estimate that by 2017, an overwhelming majority of the world's population, even in less developed countries, will be living in cities (Urban population growth). Results of this study suggest possible reasons for the large gap between best available knowledge and the perceptions and actions of individuals on the other hand. This may lead to better-oriented influenza education initiatives, more effective prevention and treatment plans, and generally raise excitement and awareness surrounding public health and scientific communication.
ContributorsGur-Arie, Rachel Ellen Haviva (Author) / Maienschein, Jane (Thesis director) / Laubichler, Manfred (Committee member) / Creath, Richard (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2014-12
Description
Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
ContributorsBliss, Nadya Travinin (Author) / Laubichler, Manfred (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2015