This project is developing a standalone software suite to capture complex network and contextual data crucial for understanding population dynamics in disease control for HIV and Drug Research.

Overview

A thorough understanding of population dynamics is vital for reducing the spread of infectious and for informing disease control. This is particularly true with HIV, due to its high transmission dependence on drug and sexual network dynamics. For stigmatised populations with large disparities in the prevalence of HIV, structural determinants (e.g. poverty, access to care, stigma – the context surrounding the population) also drive disease. Therefore, the collection of network and contextual data is a high priority for researchers, but one that unfortunately poses enormous methodological challenges. In particular, the nature of these data has introduced new complexities in data collection, processing, and storage, limiting accessibility to only those who possess the strong technical expertise and resources necessary to create bespoke solutions.

Given these challenges, and fuelled by lower costs and easier access to ‘big data,’ some health and HIV researchers have turned to trace data (e.g. Twitter, Facebook) to facilitate their work. Yet, these approaches cannot replace data collected directly from individuals, since the network structure through which HIV is transmitted, the relevant behavioural data (e.g. condomless anal sex, injection drug use), and an individual’s disease status (e.g. HIV, gonorrhoea) are not readily obtainable from social media sites.

Benefiting from recent convergences in technologies, and responding to the clear need within the research community, the project researchers have already developed a software tool (netCanvas-R) that can quickly and accurately capture complex network and contextual data directly from a participant. This project seeks to build upon this work by evolving this novel and highly effective data collection and management tool (which was originally optimised for a single study) into a standalone software suite. This suite will ensure accessibility to all researchers, particularly HIV and drug researchers, who are interested in the capture of complex network and contextual data.

Project Aims

  1.  To extend and harden existing capabilities and build a standalone Network Canvas software suite. This will provide researchers with a user-friendly, generalizable, and customizable tool that will facilitate the efficient capture, storage, monitoring, and export of complex network, longitudinal, geospatial, contextual and behavioural data.
  2. To ensure the sustainability of the Network Canvas software suite. Promotional work, engagement activities, and the production of strong documentation, tutorials, and training materials will build a community that will sustain the open source and freely available software suite.

People