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Quantifying how many people are or will be sick, and where, is a criticalingredient in reducing the burden of disease because it helps the public healthsystem plan and implement effective outbreak response. This process of diseasesurveillance is currently based on data gathering using clinical and laboratorymethods; this distributed human contact and resulting bureaucratic dataaggregation yield expensive procedures that lag real time by weeks or months.The promise of new surveillance approaches using internet data, such as webevent logs or social media messages, is to achieve the same goal but faster andcheaper. However, prior work in this area lacks a rigorous model of informationflow, making it difficult to assess the reliability of both specific approachesand the body of work as a whole.  We model disease surveillance as a Shannon communication. This new frameworklets any two disease surveillance approaches be compared using a unifiedvocabulary and conceptual model. Using it, we describe and compare thedeficiencies suffered by traditional and internet-based surveillance, introducea new risk metric called deceptiveness, and offer mitigations for some of thesedeficiencies. This framework also makes the rich tools of information theoryapplicable to disease surveillance. This better understanding will improve thedecision-making of public health practitioners by helping to leverageinternet-based surveillance in a way complementary to the strengths oftraditional surveillance.

Deceptiveness of internet data for disease surveillance