Are spatial models advantageous for predicting county-level HIV epidemiology across the United States?

Danielle Sass, Bita Fayaz Farkhad, Bo Li, Man Pui Sally Chan, Dolores Albarracín

Research output: Contribution to journalArticlepeer-review


Predicting human immunodeficiency virus (HIV) epidemiology is vital for achieving public health milestones. Incorporating spatial dependence when data varies by region can often provide better prediction results, at the cost of computational efficiency. However, with the growing number of covariates available that capture the data variability, the benefit of a spatial model could be less crucial. We investigate this conjecture by considering both non-spatial and spatial models for county-level HIV prediction over the US. Due to many counties with zero HIV incidences, we utilize a two-part model, with one part estimating the probability of positive HIV rates and the other estimating HIV rates of counties not classified as zero. Based on our data, the compound of logistic regression and a generalized estimating equation outperforms the candidate models in making predictions. The results suggest that considering spatial correlation for our data is not necessarily advantageous when the purpose is making predictions.

Original languageEnglish (US)
Pages (from-to)100436
Number of pages1
JournalSpatial and Spatio-temporal Epidemiology
StatePublished - Aug 1 2021
Externally publishedYes


  • Dynamic bayesian network
  • Generalized estimating equation
  • HIV Prediction
  • Quantile regression
  • Spatial autoregressive model
  • Two-part model

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Infectious Diseases
  • Health, Toxicology and Mutagenesis
  • Epidemiology


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