Application of Big Data in Digital Epidemiology

Application of Big Data in Digital Epidemiology

Sameena Naaz (Jamia Hamdard, India) and Farheen Siddiqui (Jamia Hamdard, India)
Copyright: © 2019 |Pages: 21
DOI: 10.4018/978-1-5225-7071-4.ch013

Abstract

Epidemiology is the study of dynamics of health and disease in human population. It aims to identify the occurrence, pattern, and etiology of human diseases so that the causes of these diseases can be understood, which in turn will help in preventing their spread. In traditional epidemiology, the data is collected by various public health agencies through various means. Many times, the actual figures vary a lot from the one reported. Sometimes this difference is due to human errors, but most of the time, it is due to intentional underreporting. Big data techniques can be used to analyze this huge amount of data so as to extract useful information from it. The electronic health data is so large and complex that it cannot be processed using traditional software and hardware. It is also not possible to manage this data using traditional data management tools. This data is huge in terms of volume as well as diversity and the speed at which it is being generated. The ability to combine and analyze these different sources of data has huge impact on epidemic tracking.
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Big Data In Health Care

Epidemic tracking has gone through a huge change as it can combine and analyze various sources of data. Big data initiative in the health care sector is being supported by governments of many countries. Some of the instances are: The government of Denmark is providing anonymously health as well as hospitalization data for carrying out research in the field of Big Data (Szlezak et al., 2014). They have a program which helps in better care of patients with diabetes and heart diseases by employing data analytics on the medical data available (IBM, 2013).

Swedish government is also trying to help research in Big Data analytics. Huge amount of data from lab results, healthcare centers and from ambulances carrying critically ill people or accident cases can be merged for the analytics purposes. A lot of research is going on for developing tools and techniques which can support decision making. One important area of research is the analysis of structured and unstructured data to study the effect of various drugs (Network, T.S.B.D.A., 2013).

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