  We present the first meta-analysis of co-evolutionary learning networks for
digital disease surveillance research over last 10 years. In doing so, we show
the co-evolution and dynamical changes that occurred in academic research
related to digital disease surveillance for improving accuracy, approach and
results. Using dynamic network analysis, we are able to show the incorporation
of social media-based analytics and algorithms which have been proposed and
later improved by other researchers as co-evolutionary learning networks. This
essentially demonstrates how we improve our research and increase accuracy
through feedback loop for correcting the behaviour of an open system and
perhaps infer learning patterns, reliability and validity using 10 years
scientific research in digital disease surveillance.
