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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Spatial Big Data, Crisis Response and International Development Policy

“Social media, data and development”… It didn’t take me long to choose a focus within that theme: spatial data and mapping will be my common thread in the next few weeks.

Gathering geographical data about a crisis area is considered a traditional data-gathering target (Read et al., 2016, p. 6). According to some experts, the most mentioned application of ‘big data’ in developing countries is the possibility of mapping problems, for instance tracking and modelling the spread of diseases, through novel ways (Hay et al., 2013 cited in Spratt & Baker, 2015, p. 14).

Before going any further, let’s start with a video from the Geospatial Revolution Project.

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