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.
Highlights
At the heart of disease surveillance and modeling are epidemiological data
Gathering, cleaning, and eliciting relevant data often require more time than the actual analysis itself. This paper discusses these three key technical challenges involving public health-related epidemiological data, in detail and with examples that were identified through detailed analysis of data deposition practices around the globe. Building from this analysis, we offer a framework of best practices comprised of modern standards that should be adhered to when releasing epidemiological data to the public
If one wants to download the latest influenza surveillance data weekly, instead of manually navigating an interactive web interface each week to export the data, the process could be automated by writing code that interacts with the application programming interfaces (APIs)
Summary
At the heart of disease surveillance and modeling are epidemiological data. These data are generally presented as a time series of cases, T, for a geographic region, G, and for a demographic, D. What is meant by “weekly” in one context may be different than another context (e.g., CDC epi weeks vs irregular reporting intervals in Poland, as described later) Together, these challenges make large-scale public health data analysis and modeling significantly more difficult and timeconsuming. Gathering, cleaning, and eliciting relevant data often require more time than the actual analysis itself This paper discusses these three key technical challenges involving public health-related epidemiological data, in detail and with examples that were identified through detailed analysis of data deposition practices around the globe. Building from this analysis, we offer a framework of best practices comprised of modern standards that should be adhered to when releasing epidemiological data to the public. Such a framework will enable a more robust future for accurate and high-confidence epidemiological data and analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
