Background
Novel influenza viruses pose a potential pandemic risk. Rapid detection of novel influenza virus infection in humans is critical to characterizing the virus and facilitating the implementation of public health response measures.
Methods
We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected under different community and healthcare (urgent care, emergency department, hospital admission, and intensive care unit) testing strategies while at low frequencies in the United States. Model parameters were informed by data on seasonal influenza virus activity and existing testing practices.
Results
In a baseline scenario reflecting the presence of 100 infections of a novel virus with similar severity to seasonal influenza, the probability of detecting at least one infection per month was highest in urgent care settings (72%) and when random testing was conducted in the community (77%). However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, probabilities of detection increased across all healthcare settings, with testing in hospital and ICU settings being most efficient.
Conclusions
Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection.