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Epi Archive: Automated Synthesis of Global Notifiable Disease Data
Author(s) -
Hari Khalsa,
Sergio Cordova,
Nicholas Generous,
Prabhu Singh Khalsa,
Byron Tasseff,
James E. Arnold
Publication year - 2018
Publication title -
online journal of public health informatics
Language(s) - English
Resource type - Journals
ISSN - 1947-2579
DOI - 10.5210/ojphi.v10i1.8323
Subject(s) - computer science , publication , data science , disease surveillance , data mining , world wide web , information retrieval , disease , medicine , pathology , advertising , business
Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. While most nations likely store incident data in a machinereadable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1. A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language. The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors. LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.

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