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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Spatial Big Data, Participation, Empowerment and Agency

What does ‘development’ mean to data scientists, and how does that determine what data science can achieve within the field of international development? This essential question has been raised, in relation to certain D4D (Data for Development) projects, by some experts who further state:

Data science conducted with the aim of informing development policy must, by definition, involve an understanding of the policy area in question, and importantly the analysis must be combined with understanding of the local context. Without these characteristics, research only informs the field of data science rather than development policy.

(Taylor & Schroeder, 2015, pp. 508 & 514)

‘Data science must involve an understanding of the policy area and the local context’. Here is an interesting statement to begin with. So, let’s start with a video from the Geospatial Revolution Project.

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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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