Bo Li

University of Illinois at Urbana-Champaign

“Spatially Varying Autoregressive Models for Prediction of New HIV Diagnoses”

Date: Thursday, September 20, 2018

In demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space-time interactions and are naturally suitable for predicting HIV cases and other spatio-temporal disease data that feature a similar data structure. We apply the proposed models to HIV data over Florida, California and New England states and compare them to a range of linear mixed models that have been recently popular for modeling spatio-temporal disease data. The results show that for such data our proposed models outperform the others in terms of prediction.

Important Dates

November 13 – November 16: 2018 Fall Term Break (No classes)

November 19: Fall Term Classes Voluntary Withdrawal (VW) Deadline

Where are they now?

David Bellhouse, M.Sc. (1972)

Wan-Chen Lee, Ph.D. (2014)