Background: Knowledge of the specific dynamics of influenza introduction and spread in university settings is limited. Methods: Persons with acute respiratory illness symptoms received influenza testing by molecular assay during October 6–November 23, 2022. Viral sequencing and phylogenetic analysis were conducted on nasal swab samples from case-patients. Case-control analysis of a voluntary survey of persons tested was used to identify factors associated with influenza; logistic regression was conducted to calculate odds ratios and 95% CIs. A subset of case-patients tested during the first month of the outbreak was interviewed to identify sources of introduction and early spread. Results: Among 3,268 persons tested, 788 (24.1%) tested positive for influenza; 744 (22.8%) were included in the survey analysis. All 380 sequenced specimens were influenza A (H3N2) virus clade 3C.2a1b.2a.2, suggesting rapid transmission. Influenza (OR [95% CI]) was associated with indoor congregate dining (1.43 [1.002–2.03]), attending large gatherings indoors (1.83 [1.26–2.66]) or outdoors (2.33 [1.64–3.31]), and varied by residence type (apartment with ≥1 roommate: 2.93 [1.21–7.11], residence hall room alone: 4.18 [1.31–13.31], or with roommate: 6.09 [2.46–15.06], or fraternity/sorority house: 15.13 [4.30–53.21], all compared with single-dwelling apartment). Odds of influenza were lower among persons who left campus for ≥1 day during the week before their influenza test (0.49 [0.32–0.75]). Almost all early cases reported attending large events. Conclusions: Congregate living and activity settings on university campuses can lead to rapid spread of influenza following introduction. Isolating following a positive influenza test or administering antiviral medications to exposed persons may help mitigate outbreaks.

Over 645 million people worldwide have been infected with SARS-CoV-2, the virus that causes COVID-19. Critical knowledge gaps regarding the characteristics, duration, and impact of symptoms due to “long COVID”. This study evaluated the persistence of symptoms at one year following hospitalization with COVID-19, comparing responses on standardized measurement tools to population norms. Adults hospitalized with COVID-19 March-October 2020 during the initial emergence period in Southeast Michigan were eligible. A detailed survey was conducted via telephone 9-15 months after hospital discharge. Questions assessed fatigue, dyspnea, and physical functioning related to their COVID-19 illness. The median World Health Organization Disability Assessment Schedule (WHODAS) polytomous score placed 50% (n=20/40) of participants in the 90 th percentile of disability based on population norms. The mean SF-36 physical functioning score was almost nine points below population norms (mean=70.6, standard deviation=27.4). Additionally, 47.5% of individuals (n=19) were severely emotionally affected by their health condition. Over half of participants surveyed were still impacted a year after initial infection, experiencing limited functional ability, fatigue, and emotional difficulties. Given the impact of these symptoms on day-to-day quality of life, further studies are needed to develop strategies for treatment and management of post-COVID conditions.

Background: Patients are admitted to the hospital for respiratory illness at different stages of their disease course. It is important to appropriately analyse this heterogeneity in surveillance data to accurately measure disease severity among those hospitalized. The purpose of this study was to determine if unique baseline clusters of influenza patients exist, and to examine the association between cluster membership and in-hospital outcomes. Methods: Patients hospitalized with influenza at two hospitals in Southeast Michigan during the 2017/2018 (n=242) and 2018/2019 (n=115) influenza seasons were included. Physiologic and laboratory variables were collected for the first 24 hours of the hospital stay. K-medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth’s logistic regression were used to examine the association between cluster membership and clinical outcomes. Results: Three clusters were selected for 2017/2018, mainly differentiated by blood glucose level. After adjustment, those in C171 had 5.6 times the odds of mechanical ventilator use than those in C172 (95%CI: 1.49,21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, 95%CI: 0.2,2.7) and C173 (mean 1.4 days longer, 95%CI: 0.3,2.5). Similar results were seen between the two clusters selected for 2018/2019. Conclusion: In this study of hospitalized influenza patients, we show that distinct clusters with higher disease acuity can be identified and could be targeted for evaluations of vaccine and influenza antiviral effectiveness against disease attenuation. The association of higher disease acuity with glucose level merits evaluation.