Influenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns.

During an influenza pandemic, when hospitals and doctors'™ offices are or are perceived to be overwhelmed, many ill people may not seek medical care. People may also avoid medical facilities due to fear of contracting influenza or transmitting it to others. Therefore, syndromic methods for monitoring illness outside of health care settings are important adjuncts to traditional disease reporting. Monitoring absenteeism trends in schools and workplaces provide the archetypal examples for such approaches.

Although residents of LTCFs have high morbidity and mortality associated with ARIs, there is very limited information on the virology of ARI in LTCFs.[1,2] Moreover, most virological testing of LCTF residents is reactive and is triggered by a resident meeting selected surveillance criteria. We report on incidental findings from a prospective trial of introducing rapid influenza diagnostic testing (RIDT) in ten Wisconsin LTCFs over a two-year period with an approach of testing any resident with ARI.

Surveillance of severe influenza infections is lacking in the Netherlands. Ambulance dispatch (AD) data may provide information about severity of the influenza epidemic and its burden on emergency services. The current gold standard, primary care-based surveillance of influenza-like-illness (ILI), mainly captures mild to moderate influenza cases, and does not provide adequate information on severe disease.