Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1003-1022
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume67
Issue number4
DOIs
StatePublished - Aug 2018

Keywords

  • Bayesian hierarchical models
  • Conditional auto-regressive models
  • Copula
  • Spatiotemporal data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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