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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.

Traditional influenza surveillance informs control strategies but can lag behind outbreak onset and undercount cases. Wastewater surveillance is effective for monitoring near real-time dynamics of outbreaks but has not been attempted for influenza. We quantified Influenza A virus RNA in wastewater during two active outbreaks on university campuses in different parts of the United States and during different times of year using case data from an outbreak investigation and high-quality surveillance data from student athletes. In both cases, the IAV RNA concentrations were strongly associated with reported IAV incidence rate (Kendall’s tau = 0.58 and 0.67 for University of Michigan and Stanford University, respectively). Furthermore, the RNA concentrations reflected outbreak patterns and magnitudes. For the University of Michigan outbreak, evidence from sequencing IAV RNA from wastewater indicated the same circulating strain identified in cases during the outbreak. The results demonstrate that wastewater surveillance can effectively detect influenza outbreaks and will therefore be a valuable supplement to traditional forms of influenza surveillance.SynopsisThis study provides evidence that detection of Influenza A RNA in wastewater settled solids can be effectively used to track dynamics of influenza A outbreaks in a community.

On November 10, 2021, the Michigan Department of Health and Human Services (MDHHS) was notified of a rapid increase in influenza A(H3N2) cases by the University Health Service (UHS) at the University of Michigan in Ann Arbor. Because this outbreak represented some of the first substantial influenza activity during the COVID-19 pandemic, CDC, in collaboration with the university, MDHHS, and local partners conducted an investigation to characterize and help control the outbreak. Beginning August 1, 2021, persons with COVID-19-like* or influenza-like illness evaluated at UHS received testing for SARS-CoV-2, influenza, and respiratory syncytial viruses by rapid multiplex molecular assay.

Background Coronavirus disease 2019 (COVID-19) has had high incidence rates at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are poorly understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities. Methods We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan and the surrounding community during the Fall 2020 semester (August 16–November 24). We sequenced complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total cases in Washtenaw County over the study interval. Results Phylogenetic analysis identified >200 introductions into the student population, most of which were not related to other student cases. There were 2 prolonged student transmission clusters, of 115 and 73 individuals, that spanned multiple on-campus residences. Remarkably, Conclusions The largest outbreaks among students at the University of Michigan did not significantly contribute to the rise in community cases in Fall 2020. These results provide valuable insights into SARS-CoV-2 transmission dynamics at the regional level.

COVID-19 has had high incidence at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are not well understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities. We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan-Ann Arbor and the surrounding community during the Fall 2020 semester (August 16th –November 24th). We sequenced complete SARS-CoV-2 genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total confirmed cases in Washtenaw County over the study interval. Phylogenetic analysis identified over 200 introductions into the student population, most of which were not related to other student cases. There were two prolonged transmission clusters among students that spanned across multiple on-campus residences. However, there were very few genetic descendants of student clusters among non-students during a subsequent November wave of infections in the community. We conclude that outbreaks at the University of Michigan did not significantly contribute to the rise in Washtenaw County COVID-19 incidence during November 2020. These results provide valuable insights into the distinct transmission dynamics of SARS-CoV-2 among IHE populations and surrounding communities.

The systems and models of care for delivering psychiatric services at institutions of higher education (IHEs) are as diverse as college campuses and students themselves. The major determinants of college mental health services—and nonacademic student services in general—are (1) student demographics; (2) geography; (3) institutional size and setting; (4) budget and funding models; and (5) organizational structure. For mental health services in particular, affiliation with an academic or community medical center and the availability of off-campus resources are also significant determinants. In short, context is everything.