Description:

Speakers:

Albert Kabanda, PhD Candidate, Earth and Planetary Sciences, Northwestern University

Crustal Structure of the East African Rift System, in Uganda from Receiver Function Analysis
Abstract: We study crustal thickness heterogeneity beneath the western branch of the East African Rift System (EARS) using our temporary seismic array of 19 stations. We apply P-wave receiver function (RF) analysis to our data to estimate crustal thickness and velocity ratios along this part of EARS. Preliminary results from the H–k stacking method show that away from the rift branch the crust is generally 35-40 km thick, which is typical for tectonically stable continental crust. Along the rift the crust is significantly thinner, showing additional along-rift variation. Velocity ratio heterogeneity exists on similar scales.

Neelam Modi, PhD Candidate, Industrial Engineering & Management Sciences, Northwestern University

Modeling the “Who” and “How” of Social Influence in the Adoption of Health Practices
Abstract: Overpopulation in developing countries threatens the economy, environment, food supply, and more. The inadequate utilization of modern contraceptives (MCs) in these regions has prompted extensive exploration of supply-side barriers, but there is a crucial gap in understanding demand-side obstacles, such as personal or partner opposition. Our research addresses this gap by focusing on the sociocultural factors influencing contraceptive decision-making in communities with low modern Contraceptive Prevalence Rates (mCPR). Utilizing the novel Structured Influence Process (SIP) framework, we examine - and quantitatively assess - how an individual's social relations and exposure to persuasive messaging, either in favor of or against MC use, jointly influence their decision to adopt or reject contraceptives.

Maria Warns, PhD Candidate, Engineering Sciences & Applied Mathematics, Northwestern University

Identifiability Analysis of Wastewater Surveillance and Public Health Data
Abstract: Wastewater-based surveillance is an increasingly available data stream which may improve calibration of disease models. Unlike traditional public health measures, wastewater samples reflect the entire population in a sewershed community since individuals infected with SARS-CoV-2 shed viral RNA in their stool regardless of symptomology. But the utility of these measurements to inform models is unknown and depends on both functional characteristics of the chosen disease model and quality of measurements. We compare the utility of wastewater surveillance data with traditional public health data for the calibration of parameters in compartmental disease models using structural and practical identifiability analysis.

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Location:

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Remote option: https://northwestern.zoom.us/j/91878654083
Passcode: NICO24

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