Background: Household transmission of SARS-CoV-2 is a driver of the ongoing COVID-19 pandemic. Understanding factors that contribute to secondary infection rates (SIR) can define changing trends and inform public health policies. Methods: The ORegon CHild Absenteeism due to Respiratory Disease Study (ORCHARDS) prospectively monitors respiratory viruses within the Oregon School District (OSD) in southcentral Wisconsin. Households with students who had ≥2 respiratory symptoms were eligible and opted to participate in ORCHARDS. Between October 28, 2020, and May 16, 2022, all household members provided self-collected nasal specimens on days 0, 7, and 14 for SARS-CoV-2 detection using real-time reverse-transcription-polymerase chain reaction. We used logistic regression to investigate individual- and household-level characteristics associated with SARS-CoV-2 transmission. Results: Overall, 127 households comprising 572 individuals (48% female; 52% male; 0.4% non-binary; 77% >18 years) had at least one detection of SARS-CoV-2. The overall SIR was 47% and decreased over time (pre-Delta=72% [95%CI: 58%-83%]; Delta=51% [40%-63%]; and Omicron=41% [36%-47%]. Odds of household transmission were 63% lower during the Omicron period compared to the pre-Delta period (OR=0.36 [95%CI: 0.13-0.94] P=0.037). Greater household density (members/bedroom) was significantly associated with household transmission during the Omicron period (OR=6.8, [2.19-21.37] P=0.001). Index case age, illness severity, and individual symptoms were not significantly associated with odds of household transmission. Conclusions: Greater household density was associated with higher risk of SARS-CoV-2 transmission, but the risk declined over time with subsequent variants. Interplay between variants, prior infection, and individual/household factors may identify modifiable factors (e.g., behavior, vaccination) to reduce future transmission risk.

Background School-aged children and school reopening dates have important roles in community influenza transmission. Although many studies evaluated the impact of reactive closures during seasonal and pandemic influenza outbreaks on medically attended influenza in surrounding communities, few assess the impact of planned breaks (i.e., school holidays) which coincide with influenza seasons, while accounting for differences in seasonal peak timing. Here, we analyze the effects of winter and spring breaks on influenza risk in school-aged children, measured by student absenteeism due to influenza-like illness (a-ILI). Methods We compared a-ILI counts in the two-week periods before and after each winter and spring break over five consecutive years in a single school district. We introduced a “pseudo-break” of 9 days’ duration between winter and spring break each year when school was still in session to serve as a control. The same analysis was applied to each pseudo-break to support any findings of true impact. Results We found strong associations between winter and spring breaks and a reduction in influenza risk, with a nearly 50% reduction in a-ILI counts post-break compared to the period before break, and the greatest impact when break coincided with increased local influenza activity. Conclusions These findings suggest that brief breaks of in-person schooling, such as planned breaks lasting 9-16 calendar days, can effectively reduce influenza in schools and community spread. Additional analyses investigating the impact of well-timed shorter breaks on a-ILI may determine an optimal duration for brief school closures to effectively suppress community transmission of influenza.

Rapid influenza diagnostic tests (RIDTs) have variable sensitivity. In a community-based population of kindergarten through 12th grade (K-12) students, we assessed factors that may influence RIDT performance using 2,368 paired results from Sofia® Influenza A + B fluorescent immunoassay and reverse transcription polymerase chain reaction (RT-PCR). RIDT sensitivity and specificity were 76.1% (95% CI: 72.8—79.1) and 97.2% (96.2—97.9), respectively. Factors associated with sensitivity included runny nose (OR=3.0, p<0.001), nasal congestion (1.59, p=0.045), days from symptom onset (per day; 0.75; p<0.001), myalgia (0.61; p=0.014), age (per 5 years; 0.55; p=0.001), and detection of another virus (0.50; p=0.043). Understanding these factors can aid in interpreting negative results.