Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment.

It is long perceived that the more data collection, the more knowledge emerges about the real disease progression. During emergencies like the H1N1 and the severe acute respiratory syndrome coronavirus 2 pandemics, public health surveillance requested increased testing to address the exacerbated demand. However, it is currently unknown how accurately surveillance portrays disease progression through incidence and confirmed case trends. State surveillance, unlike commercial testing, can process specimens based on the upcoming demand (e.g., with testing restrictions). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure. Using the H1N1 pandemic experience, we developed a simulation that models the true unobserved influenza incidence trend in the State of Michigan, as well as trends observed at different data collection points of the surveillance system. We calculated the growth rate, or speed at which each trend increases during the pandemic growth phase, and we performed statistical experiments to assess the biases (or differences) between growth rates of unobserved and observed trends. We highlight the following results: 1) emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates; 2) the best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of health care providers; and 3) under several criteria to queue specimens for viral subtyping with limited capacity, the best-performing criterion was to queue first-come, first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should investigate additional restrictions to the queue.

Pulse-number discrimination by Cope's gray treefrog (Hyla chrysoscelis) in modulated and unmodulated noise.

In Cope's gray treefrog (Hyla chrysoscelis), thresholds for recognizing conspecific calls are lower in temporally modulated noise backgrounds compared with unmodulated noise. The effect of modulated noise on discrimination among different conspecific calls is unknown. In quiet, females prefer calls with relatively more pulses. This study tested the hypotheses that noise impairs selectivity for longer calls and that processes akin to dip listening in modulated noise can ameliorate this impairment. In two-stimulus choice tests, female subjects were allowed to choose between an average-length call and a shorter or longer alternative. Tests were replicated at two signal levels in quiet and in the presence of chorus-shaped noise that was unmodulated, modulated by a sinusoid, or modulated by envelopes resembling natural choruses. When subjects showed a preference, it was always for the relatively longer call. Noise reduced preferences for longer calls, but the magnitude of this reduction was unrelated to whether the noise envelope was modulated or unmodulated. Together, the results are inconsistent with the hypothesis that dip listening improves a female gray treefrog's ability to select longer calls in modulated compared with unmodulated noise.