The purpose of this research is to assess whether integration of an aerosol biosensor and air quality risk assessment software that integrates into a building management system will improve indoor air quality metrics in hospital environments and reduces risk factors associated with respiratory disease transmission.

Executive Summary. The Hospital AIr QUality (HAIQU) team will develop a biosensor and risk assessment software to detect pathogens in indoor air, to be integrated with building management system (BMS), focusing on improving indoor air quality in hospitals to reduce disease transmission and enhance patient outcomes. The HAIQU biosensor will combine high-flow-rate aerosol collection highly sensitive and specific CRISPR detection, and digital droplet microfluidics to continuously detect multiple viral, bacterial, and fungal pathogens from indoor air. The HAIQU risk assessment software will combine multi-scale modeling into a 'digital twin', or virtual representation of an indoor space to model pathogen distribution and exposure risk to occupants. The HAIQU tools will be applied in clinical studies within the emergency departments (EDs) of Mayo Clinic campuses in MN, WI, AZ, and FL. Integrating with building control systems, the HAIQU biosensor and software will inform strategies to mitigate bioaerosol risks, enhance productivity, save energy and costs, and protect the health of staff and patients. By ensuring clean and safe indoor air, these tools will contribute to smarter, more resilient buildings that improve cognitive performance, wellness, and overall human health.

Goals and Impact. Although people spend the majority of our time indoors, indoor air quality (IAQ) is often overlooked, with more attention given to outdoor air quality. However, clean indoor air is essential for health and well-being. Monitoring of indoor air currently lags behind other 'big data' surveillance technologies, such as wearable health monitors and disease tracking. HAIQU aims to bridge this gap by developing rapid, real-time biosensing and risk assessment tools for healthier and safer hospital buildings. Current biosensing methods are slow, non-autonomous, and limited to detecting only a few pathogens at a time. Typically, aerosol samples are collected onto a filter over two to eight hours, then manually processed for detection, often using nucleic acid amplification. For example, researchers at Mayo performed clinical trials using the Thermo Scientific AerosolSense paired with the Renvo Rapid PCR Test to detect influenza A/B, SARS-CoV-2, and RSV. Though advanced microfluidic nucleic acid amplification methods exist that are more conductive to multiplexing, combining these methods with continuous or semi-continuous aerosol collection for rapid, autonomous detection is still lacking. To detect bioaerosols which are present at very low concentrations (<1 per L), high flow rate sampling must be paired with highly sensitive, multiplexed detection methods. Here, the investigators propose the HAIQU biosensor, a portable device that enables rapid, autonomous, and near real-time bioaerosol detection. It combines high flow rate virtual impactors and condensers for aerosol capture with drop-based microfluidics, CRISPR-Cas-based detection, and multiplexed barcoding. The healthcare environment provides an ideal use case for the proposed technologies. In EDs, patients often present with undiagnosed infectious diseases that can lead to significant exposures and outbreaks among staff, patients, and visitors, sometimes only recognized after diagnosis. Detection of airborne pathogens can prevent and reduce hospital-acquired infections, which are a leading cause of illness and lost worktime.

TECHNICAL AREA 1: The final HAIQU biosensor will be capable of detecting multiple pathogens (25 real-world, viral + bacterial + fungi; 100 lab-based) with >95% sensitivity and >99% specificity, once every 45 minutes.

TECHNICAL AREA 2: HAIQU risk assessment software will include a multiscale modeling framework to predict and assess infectious disease risk in indoor environments. The multi-scale modeling framework incorporates scales from individual events to room-level and system-level information, to yield outputs and predictions of bioaerosol dispersion, infection risk, number infected, intervention recommendations, and energy calculations.

TECHNICAL AREA 3: Multi-site field trials using the HAIQU biosensor and software in Mayo Clinic's emergency departments will be performed targeting a 25% decrease in facility-related incidence of each targeted respiratory illness relative to baseline data. Biosensor data will be combined with air quality sensing data and multi-system building controls and intervention.

Inclusion Criteria:

Target Group:

• Mayo Clinic Staff working in ED with a minimum of .7 FTE in Midwest (MN and WI), Phoenix (AZ), or Jacksonville (FL) and patient contact.

Reference Group:

• Mayo Clinic Staff working in primary care with a minimum of .7 FTE. in Rochester (MN), Phoenix (AZ), or Jacksonville (FL) and patient contact

Exclusion Criteria:

• Any participant who does not meet either of the inclusion criteria.

Individual participant data (IPD) will not be shared because the study collects detailed longitudinal health, occupational, and location-related information from Mayo Clinic employees. Although data will be coded for research purposes, the combination of respiratory health outcomes, workplace characteristics, and real-time location system (RTLS) data may increase the risk of participant re-identification. To protect participant privacy, confidentiality, and institutional data security requirements, individual-level datasets will not be made available to external researchers. Aggregate and de-identified study results will be reported through scientific publications and presentations in accordance with applicable regulations and institutional policies.

There is no masking in this study.