Harnessing big data for communicable tropical and sub-tropical disorders
Although with limitations of their own, novel data streams (NDS) appear to be promising for predicting the spread of infectious agents. Traditional surveillance systems often have drawbacks such as significant delays in releasing information, whereas NDS could offer a real-time way to track and monitor outbreak dynamics and capture relevant parameters regarding infection rates.
This systematic review aimed to assess how feasible the use of novel data streams (NDS) is in the context of capturing public reactions to epidemic outbreaks and for surveillance purposes. It comprised of 47 observational studies, of which 19 focused on Ebola. Most studies examined a single NDS, with Twitter being the most exploited tool (25 studies). An unbalanced coverage in research was found, such that most studies have focused on communicable tropical/sub-tropical diseases, and fewer focused on neglected tropical diseases. The main determinant of this unbalanced coverage seems to be media resonance and impact. Further research is required to address these limitations and more efforts should be made to integrate novel data streams.