Influenza viruses create emergencies almost every year, and existing surveillance systems are important for addressing uncertainty in the disease detection, monitoring and control. An essential tool for monitoring influenza activity is the trend of cases confirmed with an influenza virus type. Confirmed case trends are useful to estimate when the season starts and which viruses are circulating, as each flu virus targets different age groups and produces symptoms of different types and severity. During influenza emergencies, specimens from infected cases massively arrive at the testing laboratories, which leads to chaos and increased operational costs as the labs increase their capacity in response to the overwhelming testing demand. Moreover, confirmed case data produced under this chaotic environment is being used intently by decision makers and the research community. This award supports fundamental research to understand how conventional and non-conventional methods for specimen sampling minimize the publication delay and uncertainty in the daily number of influenza confirmed cases. The new findings and methods will not only improve sampling and testing practices, but will also guarantee quality of data to its users. The research promotes diversity, as female graduate students will be recruited for the project. In addition, the research promotes knowledge dissemination, as training materials will be prepared and delivered to public health policymakers, and graduate students in engineering and medicine. Analysts use the reported trend of confirmed influenza cases to identify the epidemiological features of a circulating virus. However, there is uncertainty about the extent to which confirmed cases are representative of the properties of the virus. To address this concern, several methods have been suggested but they present significant limitations. For example, the methods require information that is hard to estimate, they do not consider constrained lab capacity, and they do not account for the uncertainty in the outbreak evolution and the specimen collection process. The research team will develop a Method for Operations during Disease Emergencies in the Lab (MODEL). MODEL is an algorithm for real-time sampling and Bayesian learning of epidemic parameters that accounts for the uncertainties in the course of the disease and in the specimen collection process. It is expected that MODEL features will contribute to the theory of dynamic sampling. Research efforts will also focus on a framework to evaluate any type of specimen sampling criteria during an influenza emergency. The results derived from the framework will contribute to the theory of management and surveillance of viral infectious diseases.