Open AccessPopulation Size Estimation From Capture-Recapture Studies Using shinyrecap: Design and Implementation of a Web-Based Graphical User Interface
Author(s) -
Anne F McIntyre,
Ian E. Fellows,
Steve Gutreuter,
Wolfgang Hladik
Publication year - 2022
Publication title -
jmir public health and surveillance
Language(s) - English
Resource type - Journals
ISSN - 2369-2960
DOI - 10.2196/32645
Subject(s) - computer science , disk formatting , interface (matter) , user interface , population , sample size determination , graphical user interface , web application , bayesian probability , data mining , software , mark and recapture , information retrieval , data science , statistics , world wide web , artificial intelligence , medicine , parallel computing , programming language , mathematics , environmental health , bubble , maximum bubble pressure method , operating system
Background Population size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis. Objective This study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE. Methods shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). Additionally, the application may be accessed online or run locally on the user’s machine. Results The application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by the analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided. Conclusions Through a use case, we demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa.