Estimating the generation time for influenza transmission using household data in the United States.

The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially since the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023.

Highly Pathogenic Avian Influenza A(H5N1) Virus Infections in Humans.

Background: Highly pathogenic avian influenza A(H5N1) viruses have caused widespread infections in dairy cows and poultry in the United States, with sporadic human cases. We describe characteristics of human A(H5N1) cases identified from March through October 2024 in the United States.

Methods: We analyzed data from persons with laboratory-confirmed A(H5N1) virus infection using a standardized case-report form linked to laboratory results from the Centers for Disease Control and Prevention influenza A/H5 subtyping kit.

Asymptomatic and mildly symptomatic influenza virus infections by season --Case-ascertained household transmission studies, United States, 2017-2023.

Asymptomatic influenza virus infection occurs but may vary by factors such as age, vaccination status, or season. We examined the frequency of influenza virus infection and symptoms using data from two case-ascertained household transmission studies (2017-2023) with prospective, systematic collection of respiratory specimens and symptoms. From the 426 influenza virus infected household contacts that met our inclusion criteria, 8% were asymptomatic, 6% had non-respiratory symptoms, 23% had acute respiratory symptoms, and 62% had influenza-like illness symptoms.

Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis.

Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias.