Title
Challenges of developing a global wildlife health information management system part 1: Lessons learned from a daya analysts' perspective
Author(s)
O'Rourke, Tammie; Joly, Damien O.; Palmer, Jonathan; Olson, Sarah; Mollard, Debbie; Dohm, Michelle; Rabinowitz, Peter; Vesgo, Sally; Goldstein, Tracey; Kreuder Johnson, Christine; Greig, Denise; Kelly, Terra; Karesh, William; Mazet, Jonna
Published
2013
Abstract
The USAID Emerging Pandemic Threats - PREDICT project is a multi-country endeavour led by the University of California - Davis One Health Institute to develop the global capacity to detect emerging zoonotic diseases of wildlife origin. Standardized data collection of surveillance data and test results from 5 organizations and 20 countries has been an essential aspect of this initiative. The 5-year project is now mid way through the 4th year and data analysis efforts to uncover patterns and trends in the surveillance and test data set are now underway. As in any large dataset, as data increases in volume, complexity and value, the preservation and maintenance of the data, also known as data curation, becomes increasingly important, especially when integrating heterogeneous data sources. As a result of these curation efforts, the Global Animal Information System (GAINS) information management team has gained insight and critical skill sets that could be considered when developing surveillance and test results data collection systems. Some examples of these are: the complexity of working with multiple organizations, countries and languages; developing a common language of database concepts between Information Management personnel and field staff; developing data tools in both connected and nonconnected environments; and developing standard animal taxonomy to handle sampling of multiple animal taxa across geographical regions. These challenges have been mitigated with development of standard approaches to data entry and data summaries, including use of controlled vocabularies and existing taxonomic databases, regular data quality checks and reviews, careful analysis, and adaptive management.

Access Full Text

A full-text copy of this article may be available. Please email the WCS Library to request.



Back

PUB27092