Journal:Epidemiological data challenges: Planning for a more robust future through data standards
| Full article title | Epidemiological data challenges: Planning for a more robust future through data standards |
|---|---|
| Journal | Frontiers in Public Health |
| Author(s) |
Fairchild, Geoffrey; Tasseff, Byron; Khalsa, Hari; Generous, Nicholas; Daughton, Ashlynn R.; Velappan, Nileena; Priedhorsky, Reid; Deshpande, Alina |
| Author affiliation(s) | Los Alamos National Laboratory |
| Primary contact | Email: gfairchild at lanl dot gov |
| Editors | Efird, Jimmy T. |
| Year published | 2018 |
| Volume and issue | 6 |
| Article # | 336 |
| DOI | 10.3389/fpubh.2018.00336 |
| ISSN | 2296-2565 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.frontiersin.org/articles/10.3389/fpubh.2018.00336/full |
| Download | https://www.frontiersin.org/articles/10.3389/fpubh.2018.00336/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.
Keywords: data, computational epidemiology, public health, disease modeling, informatics, disease surveillance
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
