  Quantifying how many people are or will be sick, and where, is a critical
ingredient in reducing the burden of disease because it helps the public health
system plan and implement effective outbreak response. This process of disease
surveillance is currently based on data gathering using clinical and laboratory
methods; this distributed human contact and resulting bureaucratic data
aggregation yield expensive procedures that lag real time by weeks or months.
The promise of new surveillance approaches using internet data, such as web
event logs or social media messages, is to achieve the same goal but faster and
cheaper. However, prior work in this area lacks a rigorous model of information
flow, making it difficult to assess the reliability of both specific approaches
and the body of work as a whole.
  We model disease surveillance as a Shannon communication. This new framework
lets any two disease surveillance approaches be compared using a unified
vocabulary and conceptual model. Using it, we describe and compare the
deficiencies suffered by traditional and internet-based surveillance, introduce
a new risk metric called deceptiveness, and offer mitigations for some of these
deficiencies. This framework also makes the rich tools of information theory
applicable to disease surveillance. This better understanding will improve the
decision-making of public health practitioners by helping to leverage
internet-based surveillance in a way complementary to the strengths of
traditional surveillance.
