RITA Incidence Estimation

Recent Infection Testing Algorithms (RITA) are used in cross-sectional surveys to identify individuals whose HIV infections are likely to have been recency aquired. The methods of Fellows (2022) are used to calculate incidence adjusting for the RITA process.

Launch Application


Authors: Ian E. Fellows
Github: https://github.com/fellstat/recent
Documentation: https://fellstat.github.io/recent/index.html
Technical Paper: https://arxiv.org/abs/2204.00048

Consensus Estimation

This tool assists in synthesizing multiple independent estimates of a quantity (e.g. population size or prevalence). Stakeholders may add additional information regarding the methodological quality of the studies and prior knowledge of the metric.

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Authors: Ian E. Fellows
Github: https://github.com/fellstat/combine_estimates

Multiple Source Capture Recapture

Implements user interfaces for log-linear models, Bayesian model averaging and Bayesian Dirichlet process mixture models.

Analysis Power Data Formatter

Authors: Ian E. Fellows
Video Tutorial: https://www.youtube.com/watch?v=PgmyUnFlo5Y&feature=youtu.be
Manual: https://fellstat.github.io/shinyrecap/
Github: https://github.com/fellstat/shinyrecap

Population Size Estimation Using Multiple Data Sources

Implements a user interface for a Bayesian hierarchical model used to estimate the size of local and national populations. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion

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Authors: Jacob Parsons using the algorithm developed by Le Bao, Adrian E. Raftery and Kyongwon Kim
CRAN Repository: https://CRAN.R-project.org/package=SizeEstimation
Reference: Bao, L., Raftery, A. E., & Reddy, A. (2015). Estimating the sizes of populations at risk of HIV infection from multiple data sources using a Bayesian hierarchical model. Statistics and its Interface, 8(2), 125-136.

Incidence Estimation In Cross-sectional Surveys Using Testing History

Utilizes crosssectional survey data containing information on participants' testing history and diagnosis to estimate incidence.

Launch Application

Authors: Ian E. Fellows
Video Tutorial: https://www.youtube.com/watch?v=YVPcLLs9zxc&t=08s
Manual: https://github.com/fellstat/TestingHistoryIncidence/wiki/
Shiny-App-Documentation
Github: https://github.com/fellstat/TestingHistoryIncidence
Example Data: https://raw.githubusercontent.com/fellstat/
TestingHistoryIncidence/master/inst/shiny_ui/tstdat.csv
Photo by JasonParis (Edited)

Design and Analysis of Time-Location Surveys

Time location sampling (TLS) is an important tool for surveying populations that attend venues. It is particularly useful in populations (such as injection drug users and men who have sex with men) that are difficult to reach via other methods.

Launch Application

Authors: Ian E. Fellows
Github: https://github.com/fellstat/shinytls