De-escalation strategies for non-pharmaceutical interventions following infectious disease outbreaks: a rapid review and a proposed dynamic de-escalation framework

Tracking SARS-CoV-2 RNA in the air: Lessons from a COVID-19 outbreak in an infirmary unit

BACKGROUND

The duration and magnitude of SARS-CoV-2 air dispersal during nosocomial outbreaks remain uncertain. This study evaluates the impact of mobile modular high-efficiency particulate air filter units (MMHUs) on SARS-CoV-2 air dispersal.

METHODS

We investigated a nosocomial COVID-19 outbreak in an infirmary unit. The viral load (VL) of SARS-CoV-2 in air samples was correlated with the VL in nasopharyngeal swabs with or without MMHU. These samples underwent whole-genome sequencing and phylogenetic analysis.

RESULTS

Upon outbreak declaration (August 2, 2024, day 0), 44 (69.8%) of 63 patients acquired COVID-19 in Ward 2B (19 male) and 2C (25 female) by day 4. The VL of SARS-CoV-2 remained detectable in air until day 11 (2B) and day 20 (2C). The VL of air samples was significantly correlated with the VL in nasopharyngeal swabs collected on days 5, 7, 10, and 13 in 2C (r = -0.975, P = .004). Using MMHU, the mean daily ratio of SARS-CoV-2 RNA (copies/1,000 L of air/patient) in 2B was 5 times lower than in 2C from days 5 to 10. Whole-genome sequencing revealed all 41 tested strains belonged to the Omicron variant, KP.3.1.1, phylogenetically related to the prevailing community strains.

CONCLUSIONS

Using MMHU mitigates the duration and magnitude of SARS-CoV-2 air dispersal during nosocomial outbreak.

Lessons Learned From Applying a Monitoring and Evaluation Framework to Economic, Social, and Other Health Impacts of the COVID-19 Pandemic

Individual and community-level COVID-19 mitigation policies can have effects beyond direct COVID-19 health outcomes, including social, behavioral, and economic outcomes. These social, behavioral, and economic outcomes can extend beyond the pandemic period and have disparate effects on populations. Public Health-Seattle & King County (PHSKC) built on the Centers for Disease Control and Prevention's community mitigation strategy framework to create a local project tracking near-real-time data to understand factors affected by mitigation approaches, inform decision-making, and monitor and evaluate community-level disparities during the pandemic. This case study describes the framework and lessons learned from PHSKC's collation, use, and dissemination of local data from 20 data sources to guide community and public health decision-making. Social, behavioral, economic, and health indicators were regularly updated and disseminated through interactive dashboards and products that examined data in the context of applicable policies. Data disaggregated by demographic characteristics and geography highlighted inequities, but not all datasets contained the same details; local surveys or qualitative data were used to fill gaps. Project outcomes included informing city and county emergency response planning related to implementation of financial and food assistance programs. Key lessons learned included the need to (1) build on existing processes and use automated processes and (2) partner with other sectors to use nontraditional public health data for active dissemination and data disaggregation and for real-time data contextualized by policy changes. This project provided programs and communities with timely, reliable data to understand where to invest recovery funding. A similar framework could position other health departments to examine social and economic effects during future public health emergencies.