HIV Platform tool

Access

The tool can be accessed through the following options:

1. HIV Platform Tool is a web tool available online: https://shinyapps.ecdc.europa.eu/shiny/hivPlatform/

2. An offline Windows x64 deployment package with R environment embedded.

Deployment package includes all required software and R packages. Simply follow the steps:

a) Download the deployment package from here: https://www.nextpagesoft.net/hivPlatform/windows/ (195 MB download size)

b) Unpack the file to an arbitrary folder

c) After unpacking a new folder will appear called "hivPlatform". Browse inside and double-click file "hivPlatform.bat". This will open the tool in the default web browser. When done with working with it simply close the browser window.

This offline package can be run only on 64-bit versions of Microsoft Windows (7, 8, 10).

3. For experienced R users a CRAN-like repository is set up for installing the tool as R package

The repository of R packages is available here: https://www.nextpagesoft.net/hivPlatform/repo/

The tool can be installed using standard R commands executed in R console:

1) Type

install.packages(

  "hivPlatform",

  repo = "https://www.nextpagesoft.net/hivPlatform/repo"

)

and press ENTER. This will download and install latest version of the tool and all its dependencies.

2) Once R is done with installation the tool can be run with command:

hivPlatform::RunApp()

3) Periodically, the user can update the tool with the following command:

update.packages(repo = https://www.nextpagesoft.net/hivPlatform/repo")

Prerequisites

For option 1 only an active internet connection is required. It is advisable to use relatively recent versions of web browsers such as Chrome, Edge, Firefox, Safari with support for JavaScript enabled.

Options 2 and 3 are similar in terms of required and optional software:

Required software

a) R engine - https://www.r-project.org/ - performs all calculations

b) Pandoc - https://pandoc.org/ - converts Markdown documents (source of reports) to html, latex, Word. It is delivered with RStudio, so if one is using RStudio already, then there is no need to install it separately.

Optional software

c) Latex - various alternatives exist: TinyTex (https://yihui.name/tinytex/), Miktex (https://miktex.org/), TexLive (https://www.tug.org/texlive/) - generates pdf reports. If it is not installed, then outputting main report to pdf will fail.

Option 2 includes the required software embedded within the deployment package. The only missing piece of software is Latex. Option 3 requires a working installation of R and pandoc.

Background

Incidence and prevalence of HIV are not observable directly since the number of diagnosed cases largely depends on testing availability and patterns. Yet, these are the key quantities to measure in order to evaluate the effects of the public health efforts to control the epidemic. European HIV surveillance system relies on case-based reporting and the tool focuses on the methods that can be applied on such datasets. If the information on the clinical status, i.e., CD4 count and presence/absence of AIDS at diagnosis is available, then applying a mathematical model allows the recovery of the estimated numbers of incident cases, as well as the numbers of undiagnosed cases.

Missing data are a well-recognised problem within surveillance systems. When values for some variables are missing and cases with missing values are excluded from analysis, it may lead to biased and potentially less precise estimates. Reporting delay, the time from case diagnosis to notification, can lead to problems when analysing the most recent years, given that the information on some cases or variables may not have been collected yet because of national reporting process characteristics. These phenomena are common in disease surveillance and apply to HIV. 

The tool performs multiple imputations for the missing values for a set of variables (Age, Gender, CD4 count) using joint multivariate normal models (and extensions) or full conditional specification (also known as multiple imputation by chained equations, MICE). Additionally, the tool allows to correct for delays in reporting through reverse time hazard estimation. The adjustments may be used separately or in combination with the modelling module. This later module applies a compartmental model describing progression of infected individuals through categories of decreasing CD4 count levels and AIDS. The individual can be diagnosed at each stage of CD4 counts and thus the model is calibrated based on yearly numbers of diagnoses with CD4 counts falling into one of the CD4 count categories. The input data for modelling are prepared automatically by the tool from the surveillance data.

The outputs include results in the form of a report containing tables and graphs, and datasets in various file formats, in which the corrections have been incorporated and are ready for further analysis.

The ECDC HIV Platform Tool combines the functionalities of ECDC Modelling Tool (with exception of the London method) and ECDC Estimates Accuracy Tool.

Data preparation

The tool accepts case-based surveillance data for HIV, containing a minimum required set of variables. The tool also accepts aggregated data of the numbers of HIV diagnoses, AIDS cases and deaths, that can be used alone or in combination with case-based data in the modelling module. The support file types are: rds (R native file storage format), txt, csv, xls, xlsx Each can be compressed as zip archives for reducing upload time.

There are several required attributes/variables by the tool to run the adjustments. The upload file must contain all these attributes/variables names. Different names of variables are accepted. However, please note that the variables must be coded in the specific way.

Manual 

A complete instruction manual will guide you through the basics of the ECDC HIV Platform tool 

including technical and methodological details, how to use the tool and to calculate the estimates. The manual can also be consulted to interpret the outputs of the tool and to aid in the selection of some parameters.

Disclaimer

ECDC accepts no responsibility or liability whatsoever (including but not limited to any direct or consequential loss or damage it might occur to you and/or any other third party) arising out of or in connection with the installation and/or usage of this software.

Copyright © European Centre for Disease Prevention and Control, 2021. 

Suggested citation

ECDC HIV Platform tool [software application]. Version 2.0.0 Stockholm: European Centre for Disease Prevention and Control; 2021. Available from: https://www.ecdc.europa.eu/en/publications-data/hiv-platform-tool

Suggested literature

Rosinska Magdalena, Pantazis Nikos, Janiec Janusz, Pharris Anastasia, Amato-Gauci Andrew J, Quinten Chantal, ECDC HIV/AIDS Surveillance Network. Potential adjustment methodology for missing data and reporting delay in the HIV Surveillance System, European Union/European Economic Area, 2015. EuroSurveillance 2018; 23(23).

https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2018.23.23.1700359

van Sighem A, Nakagawa F, De Angelis D, Quinten C, Bezemer D, de Coul EO, Egger M, de Wolf F, Fraser C, Phillips A. Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data. Epidemiology. 2015 Sep;26(5):653-60. doi: 10.1097/EDE.0000000000000324. PMID: 26214334; PMCID: PMC4521901.

Examples of application:

Whittaker R, Case KK, Nilsen Ø, Blystad H, Cowan S, Kløvstad H, van Sighem A. Monitoring progress towards the first UNAIDS 90-90-90 target in key populations living with HIV in Norway. BMC Infect Dis. 2020 Jun 26;20(1):451. doi: 10.1186/s12879-020-05178-1. PMID: 32590964; PMCID: PMC7318482.

Andersson E, Nakagawa F, van Sighem A, Axelsson M, Phillips AN, Sönnerborg A, Albert J. Challenges in modelling the proportion of undiagnosed HIV infections in Sweden. Euro Surveill. 2019 Apr;24(14):1800203. doi: 10.2807/1560-7917.ES.2019.24.14.1800203. PMID: 30968824; PMCID: PMC6462786.

van Sighem A, Pharris A, Quinten C, Noori T, Amato-Gauci AJ; The Ecdc HIv/Aids Surveillance And Dublin Declaration Monitoring Networks. Reduction in undiagnosed HIV infection in the European Union/European Economic Area, 2012 to 2016. Euro Surveill. 2017 Nov;22(48):17-00771. doi: 10.2807/1560-7917.ES.2017.22.48.17-00771. PMID: 29208159; PMCID: PMC5725787.

Pharris A, Quinten C, Noori T, Amato-Gauci AJ, van Sighem A; ECDC HIV/AIDS Surveillance and Dublin Declaration Monitoring Networks. Estimating HIV incidence and number of undiagnosed individuals living with HIV in the European Union/European Economic Area, 2015. Euro Surveill. 2016 Dec 1;21(48):30417. doi: 10.2807/1560-7917.ES.2016.21.48.30417. PMID: 27934585; PMCID: PMC5388115.

Nacher M, Adriouch L, Huber F, Vantilcke V, Djossou F, Elenga N, Adenis A, Couppié P. Modeling of the HIV epidemic and continuum of care in French Guiana. PLoS One. 2018 May 24;13(5):e0197990. doi: 10.1371/journal.pone.0197990. PMID: 29795698; PMCID: PMC5967714.

Reyes-Urueña JM, Campbell CNJ, Vives N, Esteve A, Ambrosioni J, Tural C, Ferrer E, Navarro G, Force L, García I, Masabeu À, Vilaró JM, García de Olalla P, Caylà JA, Miró JM, Casabona J; PISCIS investigators. Estimating the HIV undiagnosed population in Catalonia, Spain: descriptive and comparative data analysis to identify differences in MSM stratified by migrant and Spanish-born population. BMJ Open. 2018 Feb 28;8(2):e018533. doi: 10.1136/bmjopen-2017-018533. PMID: 29490955; PMCID: PMC5855442.

Support

For technical support and reporting problems please contact: HIV.Modelling@ecdc.europa.eu