SWIFT Spatial Analyses

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This repository includes the statistical analysis plan for the following paper: Tedijanto C, Aragie S, Tadesse Z, Haile M, Zeru T, Nash SD, Wittberg DM, Gwyn S, Martin DL, Sturrock HJ, Lietman TM, Keenan JD, Arnold BF. Predicting future community-level ocular *Chlamydia trachomatis* infection prevalence using serological, clinical, molecular, and geospatial data. PLoS neglected tropical diseases. 2022 Mar 11;16(3):e0010273. https://pubmed.ncbi.nlm.nih.gov/35275911/ https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010273 The deidentified data and code for this analysis can be found at the following Github repository: https://github.com/ctedijanto/swift-spatial-prediction Should you have any questions about the files in this repository, please contact Christine Tedijanto at UCSF (christine.tedijanto@ucsf.edu).

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