Increasingly public health decision-makers are using syndromic surveillance for real-time reassurance and situational awareness in addition to early warning1. Decision-makers using intelligence, including syndromic data, need to understand what the systems are capable of detecting, what they cannot detect and specifically how much reassurance should be inferred when syndromic systems report nothing detected. In this study we quantify the detection capabilities of syndromic surveillance systems used by Public Health England (PHE). The key measures for detection capabilities are specificity and sensitivity (although timeliness is also very important for surveillance systems)2. However, measuring the specificity and sensitivity of syndromic surveillance systems is not straight forward. Firstly, syndromic systems are usually multi-purpose and may be better at identifying certain types of public health threat than others. Secondly, whilst it is easy to quantify statistical aberration detection algorithms, surveillance systems involve other stages, including data collection and human decision-making, which also affect detection capabilities. Here, we have taken a systems thinking approach to understand potential barriers to detection, and summarize what we know about detection capabilities of syndromic surveillance systems in England.
Objective: To communicate the detection capabilities of syndromic surveillance systems to public health decision makers.