ArticleSpatial and spatio-temporal epidemiology2021
Are spatial models advantageous for predicting county-level HIV epidemiology across the United States?
Article in Spatial and spatio-temporal epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Graph-aware spatio-temporal attention for forecasting HIV/AIDS case counts in public health surveillance.Frontiers in public health · 2026Article
- Ending the HIV Epidemic: Development and Evaluation of CyberGIS-HIV, a Web-Based Prediction Application.American journal of public health · 2025Article
- HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact.Journal of AIDS and HIV treatment · 2025Article
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5 authors.
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Abstract
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.
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