We are living in a data-driven world. Every sector in our global economy is using data to make better management and policy decisions. The mission of the AU School of Public Affairs' new Center for Data Science is to provide technical, managerial, and institutional resources to aid research and grant development focused on the production, analysis, and storage of 21st century data.
Across public, private, and nonprofit sectors, big data is making a big difference. Those with the right skills will lead this change. In fact, Data Scientist has ranked the number one job in America for the last three consecutive years. And by 2020, it is predicted that there will be a 28 percent increase in data science jobs.
In 2018, SPA established its Center for Data Science to focus on the theoretical and practical research aspects of computer technology, software engineering, computer architecture, artificial intelligence, simulation, modeling, and much more.
Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual's true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia.
In January, 2020, the center will sponsor a 2-week Institute for Data Science with 3 graduate course credits. Professor Ryan Moore will be the instructor. Participants also receive a certificate of completion.