Afternoon Sessions: 2:00-3:15 pm

1. Hal West-Page: hwest10@charlotte.edu  (in-person)

Title: Optimizing critical care resources during health emergencies

2. Molly Duffy-Stermon: mduffyst@charlotte.edu (in-person)

Title: Using SINDy to model COVID-19 transmission dynamics in Florid

3. Shanshan Wang: swang47@charlotte.edu (in-person)

Title: Dimension Reduction for the Conditional Quantiles of Functional Data with Categorical Predictors 

1 Title: Optimizing critical care resources during health emergencies

Abstract: Queueing theory has been used to investigate the needs of healthcare systems for decades. Recent work has focused on analysing the flow of critical care patients and resources in the wake of the COVID-19 pandemic. In this talk, I will share a model of a simulated intensive care unit during a pandemic situation. In this model, trade-offs resulting from the use of limited resources such as beds and healthcare personnel are investigated.

2. Title: Using SINDy to model COVID-19 transmission dynamics in Florida

Abstract: In this study, I seek to apply the Sparse Identification of Nonlinear Dynamics (SINDy) to COVID-19 data gathered from Florida in the Spring of 2020 in order to model the dynamical system. To do this, I will adapt the SINDy python code for use with time-delay differential equations and then feed it the data grouped by metropolitan area. In order to reduce error and bias from the inhomogeneous data, I will implement maximin aggregation techniques. The goal is to create a robust data-driven model of the disease transmission dynamics that is more accurate than the traditional SIR model..

3. Title: Dimension Reduction for the Conditional Quantiles of Functional Data with Categorical Predictors 

Abstract: Functional data analysis has received significant attention due to its frequent occurrence in modern applications, such as in the medical field, where electrocardiograms or electroencephalograms can be used for better understanding of various medical conditions. Due to the infinite dimensional nature of functional elements, current work focuses on dimension reduction techniques. This study shifts its focus to modeling the conditional quantiles of functional data, noting that existing works are limited to quantitative predictors. Consequently, we introduce the first approach to partial dimension reduction for the conditional quantiles under the presence of both functional and categorical  predictors. We present the proposed algorithm and derive the convergence rates of the estimators. Moreover, we demonstrate the finite sample performance of the method using simulation examples and a real data set based on functional magnetic resonance imaging.