Statistics, Epidemiology, Team (Data) Science: Wastewater Surveillance (Virtual)

## Details

R-Ladies Irvine (https://www.rladiesirvine.org/) is very excited to host this virtual meeting on Tuesday, March 19, 2024 from 4:30-6 pm PDT!! The two speakers, Professor Katherine Ensor and Dr. Julia Schedler, are affiliated with Houston Wastewater Epidemiology (link https://hou-wastewater-epi.org/) and the Department of Statistics at Rice University. Professor Ensor will illustrate how Spatial-Temporal Modeling is applied to Public Health Surveillance through Wastewater and Dr. Schedler will share three stories about Reproducibility in Team Science.

Spatial-Temporal Modeling is applied to Public Health Surveillance through Wastewater
Wastewater surveillance has emerged as a strong tool for city, county, state, and national public health departments. By examining SARS-CoV-2 RNA viral load, wastewater provides a strong signal of the extent of the virus in a community. Measuring and modeling the evolution of the virus in a community is however fraught with many complexities. In this talk, I will explore the spatio-temporal modeling based on weekly observations of a mix of measurements across sewersheds of differing scales and localized lift stations. We will use adaptive temporal methods and the extended Hausdorff distance for spatial correlations of regions of vastly different shapes and serving different sized communities. Examining this case study in the context of statistical engineering and problem-solving, I will highlight some of the challenges and benefits of partnerships between universities and local governments.

Three Stories About Reproducibility in Team Science
Some statisticians and data scientists may imagine an ideal world where every scientific manuscript is accompanied by fully documented data sets and all code used to generate all included figures, tables, and numerical results. In the real world, there are many barriers to this ideal, for example, privacy concerns, time and staff constraints, differences in data science workflows, and statisticians getting excited about reproducibility and overwhelming their collaborators. In this talk, I will share three stories about reproducibility from my year working with the scientists and statisticians at Houston Wastewater Epidemiology. The first story will be our journey to learn Quarto to build a rendered vignette website to document the R code accompanying a recent paper and how we incorporated student research assistants into the publication process. The second story will tell the tale of two Shiny apps with the same functionality but drastically different implementations. Finally, I will share how we developed a reproducibility review process for providing feedback on R code written by our group's scientists, drawing on experiences from the business world.

## Schedule
4:30 - 4:45 pm PDT | Announcements
4:45 - 5:45 pm PDT | Speakers
5:45 - 6:00 pm PDT | Questions