Sequence-informed surveillance is now recognized as an important extension to the monitoring of rapidly evolving pathogens [2]. This includes phylogeography, a field that studies the geographical lineages of species including viruses [3] by using sequence data (and relevant metadata such as sampling location). This work relies on bioinformatics knowledge. For example, the user first needs to find a relevant sequence database, navigate through it, and use proper search parameters to obtain the desired data.

Legionellosis is a respiratory illness that is mostly (80-90%) caused by the bacterium Legionella pneumophila. It is associated with a mild febrile illness, Pontiac fever, or Legionnaires'™ disease (1), a source of severe, community-acquired pneumonia. Legionella bacteria mostly affect elderly persons specifically those with underlying debilitating illnesses and with lowered immune systems.

Traditionally, public health agencies (PHAs) wait for hospital, laboratory or clinic staff to initiate case reports. However, this passive approach is burdensome for reporters and produces incomplete and delayed reports, which can hinder assessment of disease in the community and potentially delay recognition of patterns and outbreaks. Modern surveillance practice is shifting toward greater use of electronically transmitted disease information.

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).