Forum 5: Understanding Vaccine Efficacy and Effectiveness: A Statistician\u27s Perspective/Open Access STEM research

In the first part of this presentation, Jyotishka Datta discusses the concepts and calculations behind the efficacy and effectiveness for vaccines from a statistician’s perspective, as well as the different factors contributing to it. Prof. Datta also offers a brief tour through the history of vaccines in preventing infectious diseases and the development of statistical tools such as randomized controlled trials. Lastly, Prof. Datta discusses the direct and indirect effects, the inherent uncertainties involved with the efficacy and efficiency numbers and remark on their comparison. In the second part of this presentation, Angie Ohler discusses the vital role of open access in advancing research on COVID-19

Improving Spatial Visualization Abilities using 3D Printed Blocks

Spatial visualization abilities have been shown to be a key predictor of success in science, technology, engineering, and math fields. Past research has revealed that women and underrepresented minorities tend to lag behind in spatial visual abilities, however, research has also shown that these skills can be improved with guided practice. This study seeks to examine whether 3D printed aids help spatial visual retention in 6th graders. A modified Purdue spatial visualization test was used as the assessment standard. Students’ mental rotation abilities were assessed before and after the 3D printed aids were administered. Data was collected from five different schools in Northwest Arkansas to measure the effectiveness of the 3D aids and to examine the performance of students across various gender, ethnic, and socioeconomic backgrounds. A prospective power calculation was performed to ensure that the sample size for each group was sufficient enough for significant differences to be detected. A P-value of 8.2x10-16 was obtained for significant difference between the pre and post assessments. This indicates that the post scores were significantly higher than the pre scores, while adjusting for the other factors. The results suggest that the blocks are effective in improving scores on the Modified Purdue Visualization of Rotation test regardless of a student’s gender, socioeconomic background, or language

Quantifying the Effect of Socio-Economic Predictors and Built Environment on Mental Health Events in Little Rock, AR

Proper allocation of law enforcement resources remains a critical issue in crime prediction and prevention that operates by characterizing spatially aggregated crime activities and a multitude of predictor variables of interest. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework while extending the popular risk terrain modeling (Caplan et al., 2011, 2015; Drawve, 2016) approach. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear models, spatial regression models and a tree based method, viz., Poisson regression, spatial Durbin error model, Manski model and Random Forest. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources