Developing an HPV Infection Risk Prediction Model for Adult Females

According to the Centers for Disease Control and Prevention (CDC), nearly one in four people are currently infected with human papillomavirus (HPV) in the United States. Although most people with HPV never experience symptoms, there is a risk of developing different types of HPV-related cancers after infection. These cancers and other related diseases result in almost $8 billion spent annually for treatment. Currently, all boys and girls ages 11 or 12 years are recommended to receive HPV vaccination. Catch-up vaccines are recommended for males and females through the age of 21 and 26, respectively, if they did not get vaccinated previously. However, the uptake rates among young adult females remain low in the United States. This research seeks to create a risk prediction model with a focus on adult females that will assist these individuals to estimate the risk of HPV infection based on demographic, sexual behavior, and lifestyle factors. The focus of this thesis is on the impact diet and exercise have on risk of infection. A variety of predictive models were applied to the data collected to determine the best fit. These models include logistic regression, lasso regression, ridge regression, elastic net regression, and the random forest algorithm. Our results corroborate findings in other studies. Similar factors are recognized as significant such as sexual partners, age at first sexual activity, alcohol use, smoking habits, poverty level, and marital status. This study also found daily nutrition and sedentary activity has a significant role in HPV infection but was not able to show significance of daily exercise due to data constraints

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery

Report from a 2017 National Science Foundation workshop on promising research directions for applications of operations research and data analytics toward the disruption of illicit supply networks like human trafficking. The workshop was funded by the NSF’s Operations Engineering (ENG) and the Law & Social Sciences Program (SBE) under grant # CMMI-1726895. The report addresses the opportunity to apply advances from the fields of operations research, management science, analytics, machine learning, and data science toward the development of disruptive interventions against illicit networks. Such an extension of the current research agenda for trafficking would move understanding of such dynamic systems from descriptive characterization and predictive estimation toward improved dynamic operational control.Bureau of Business Researc