  The main goal of syndromic surveillance systems is early detection of an
outbreak in a society using available data sources. In this paper, we discuss
what are the challenges of syndromic surveillance systems and how continuous
Artificial Prediction Market [Jahedpari et al., 2017] can effectively be
applied to the problem of syndromic surveillance.
  We use two well-known models of (i) Google Flu Trends, and (ii) the latest
improvement of Google Flu Trends model, named as GP [Lampos et al., 2015], as
our case study and we show how c-APM can improve upon their performance. Our
results demonstrate that c-APM typically has a lower MAE to that of Google Flu
Trends in each year. Though this difference is relatively small in some years
like 2004 and 2007, it is relatively large in most years and very large between
2011 and 2013.
