ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2018
Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Bayesian Hierarchical Modeling Approaches for Combining Information From Multiple Data Sources to Produce Annual Estimates of National Immunization Coverage.Biometrical journal. Biometrische Zeitschrift · 2026Article
- A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic.International journal of environmental research and public health · 2022Article
- A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates.Journal of geographical systems · 2022Article
- Are spatial models advantageous for predicting county-level HIV epidemiology across the United States?Spatial and spatio-temporal epidemiology · 2021Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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