Big Data: Blessing or Curse? Part 2

Source: Vishal Krishna/ (27 June 2016)

Big Data are mostly discussed for two reasons. The first reason is whether those data that include personal information are safe or not. The second one is about how the data that are provided are used.

To begin with, I need to clarify what I am talking about when I use the term Big Data. CERN also uses Big Data but with another meaning for example. In this post I refer to the specific kind of Big Data that include private information and are mostly generated by humans through social media platforms or records of mobile phone lines.

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Infodemiology in the Battle Against Ebola: Mining the Web for Public Health Surveillance

“Infodemiology includes the analysis of queries from Internet search engines to predict disease outbreaks; monitoring people’s’ status updates on microblogs such as Twitter for syndromic surveillance; detecting and quantifying disparities in health information availability; identifying and monitoring of public health relevant publications on the Internet” (Eysenbach, 2009)

Internet data, especially search engine queries and social media postings, have shown promise in contributing to syndromic surveillance for several communicable diseases, including Ebola. Much has been written about the global response to the 2014 Ebola outbreak in West Africa, “lessons learned” have often focused on operational reasons why health systems faltered and why the humanitarian response came late, often taking donors and international aid agencies like the World Health Organisation (WHO) to task for mishandling the crisis.

A systematic review published in 2014 by Nuti and his colleagues, highlighted that in recent years, researchers have been increasingly utilising online search data for a diversity of health topics with some successful applications in the field of infectious disease surveillance, especially in countries with high Internet penetration levels (Nuti et al., 2014).

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Spatial Big Data, Impact, Sharing and Ethics

The term ‘born digital data’ was coined by Taylor and Schroeder in 2015 to denote “data that are digital from the start rather than starting out in non-digital form”. ‘Born digital data’ can be ‘consciously volunteered data’ or ‘data in the wild’ (pp. 504-505).

In my post published on February 22, 2017, I already wrote about ‘consciously volunteered data’ that are ‘born digital data’, namely crowdsourced data. Crowdsourcing has been proved to be particularly useful for humanitarian response (Meier, 2015). One of the first, and most emblematic, example of the power of crowdsourcing is the digital humanitarian response to the 2010 Haiti earthquake, in which the Ushahidi crisis-mapping platform played a critical role.

The Haitian humanitarian crisis that followed the 2010 earthquake also highlighted the fact that real time data could now feature in humanitarian responses. In their 2011 Haitian study, Bengtsson and his colleagues demonstrate that data could, in principle, be obtained for continuous and extended periods and in near real time, and that data were readily available.

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Big Data: Blessing or Curse? Part 1

Source: Vishal Krishna/ (27 June 2016)

Due to their various applications, Big Data are very often demonized. Being a technological product, Big Data are however neutral. It depends on us, on the use that we choose to apply on them that eventually defines the outcome of the practice. The aspect of Big Data that is undeniable, is the power that they convey (Mayer-Schönberger & Cukier, 2013). This power can be used both for good and for evil.

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Health Equity: Role of Social Determinants of Health Data in Improving Health in Africa


During the 2011 World Conference on Social Determinants of Health, the Rio Political Declaration on Social Determinants of Health was adopted. The declaration expressed a global political commitment for the implementation of a social determinants of health (SDH) approach to reduce health inequities. Social determinants of health are defined by the World Health Organisation (WHO) as the conditions in which people are born, grow up, live, work and age. These conditions influence a person’s opportunity to be healthy, risk of illness and life expectancy. Social inequities in health – the unfair and avoidable differences in health status across groups in society – are those that result from the uneven distribution of social determinants. All of these drive health inequity – systematic disparities in health between social groups who have different levels of underlying social advantage or disadvantage such as food, shelter, clean water, sanitation, proper clothing and have limited access to medical care, education and finance.

Video: Dr Hans Rosling’s 200 Countries, 200 Years, 4 Minutes – use of data to visualize social determinants of health across the globe.

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