Public health practitioners rely on timely surveillance data for planning and decision-making; however, surveillance data are often subject to delays. Epidemic trend categories, based on time-varying effective reproductive number (R(t)) estimates that use nowcasting methods, can mitigate reporting lags in surveillance data and detect changes in community transmission before reporting is completed. CDC analyzed the performance of epidemic trend categories for COVID-19 during summer 2024 in the United States and at the state level in New Mexico. COVID-19 epidemic trend categories were estimated and released in real time based on preliminary data, then retrospectively compared with final emergency department (ED) visit data to determine their ability to detect or confirm real-time changes in subsequent ED visits. Across the United States and in New Mexico, epidemic trend categories were an early indicator of increases in COVID-19 community transmission, signifying increases in COVID-19 community transmission in May, and a confirmatory indicator that decreasing COVID-19 ED visits reflected actual decreases in COVID-19 community transmission in September, rather than incomplete reporting. Public health decision-makers can use epidemic trend categories, in combination with other surveillance indicators, to understand whether COVID-19 community transmission and subsequent ED visits are increasing, decreasing, or not changing; this information can guide communications decisions.

Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

The controversy surrounding 'gain-of-function' experiments on high-consequence avian influenza viruses has highlighted the role of ferret transmission experiments in studying the transmission potential of novel influenza strains. However, the mapping between influenza transmission in ferrets and in humans is unsubstantiated. We address this gap by compiling and analyzing 240 estimates of influenza transmission in ferrets and humans. We demonstrate that estimates of ferret secondary attack rate (SAR) explain 66% of the variation in human SAR estimates at the subtype level. Further analysis shows that ferret transmission experiments have potential to identify influenza viruses of concern for epidemic spread in humans, though small sample sizes and biological uncertainties prevent definitive classification of human transmissibility. Thus, ferret transmission experiments provide valid predictions of pandemic potential of novel influenza strains, though results should continue to be corroborated by targeted virological and epidemiological research.