Dynamic Models Augmented by Hierarchical Data
报告人：Le Bao (Penn State University)
时间：2024-03-07 14:00-15:00
地点：智华楼四元厅-225
Abstract:
Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. We propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.
About the Speaker:
Dr. Bao’s research is motivated by solving practically important real-world problems through developing and applying statistical methodologies. They cover the areas of public health, bioinformatics, and social science. His current research focus includes: 1. Small Area Estimation Models for Health Inequality Problems, 2. Bayesian Models for Integrating Multiple Data Sources, and 3. Diagnostics for Complicated Statistical Models. https://lebao0215.github.io/
