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May 20, 2026American Journal of Respiratory and Critical Care Medicine0 citations

C57-19 Optimizing Indoor Air Quality for Controlled Human Infection Model Trials: Equivalent Clean Airflow Strategies

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CCC CaglayanJWJ L WinklerJOJ R Ortiz

Key Points

  • Evaluate the effectiveness of air cleaning devices in reducing airborne infectious disease exposure during controlled trials.
  • Conducted airflow measurements and estimated equivalent clean airflow for various air cleaning devices.
  • Utilized multi-zone airflow and GUV modeling to analyze infection risk under different ventilation scenarios.
  • Calculated infection probabilities based on the Wells-Riley model for varying exposure durations.
  • Infection probability decreased from 64% without controls to 9% with air cleaning devices and effectively to 0.5% with N95 use, based on 24-hour exposure.
  • The engineering control strategy met and exceeded ASHRAE's minimum clean airflow standards for healthcare settings.

Abstract

Abstract Rationale Ventilation is a key component of indoor infection prevention, diluting and removing aerosolized pathogens and reducing transmission risk. Engineering controls such as air cleaning devices (ACDs), including high-efficiency particulate air filtration and germicidal ultraviolet (GUV) disinfection, augment ventilation and have a well-established role in healthcare environments. However, limited data exists on whether these interventions can achieve comparable effectiveness in low-ventilation buildings. Recent work by Sobhani et al.1 introduced a framework for evaluating air-cleaning strategies in a low-ventilation, 97-year-old hotel used for COVID-19 quarantine. Building on this, the present study supported a controlled human influenza virus challenge trial in the same setting. We deployed a smaller number of ACDs and fit existing in-room air-conditioning devices with filters. The equivalent clean airflow (ECA) and infectious exposure reduction were estimated. Methods Airflow rates were measured and ECA were calculated for each ACD type on a unit basis, using multi-zone airflow and GUV modeling results from Sobhani et al. and manufacturer’s specifications. Estimates of engineering control efficacy were developed for overall and situational infection probability over varying exposure durations, based on the Wells-Riley model, which estimates infectious dose as quanta – the number of virions associated with a 62.3% probability of infection. The influenza A shedding rate as quanta/hour was calculated (Bennett, J., personal communication, September 2025) using published studies and CDC web guidance. Results The present engineering control strategy exceeded the current ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) Standard 241 Infection Risk Management Mode minimum equivalent clean airflow rate of 40 cfm/person for healthcare settings. Modeled infection probabilities for both baseline building ventilation and our engineering control strategy are demonstrated in Table 1. Infection probability over 24 hours decreased from 64% without additional controls or an N95 respirator to 9% with our control strategy without N95 use and 0.5% with our strategy and N95 use. Conclusions Engineering controls can substantially reduce airborne infectious disease exposure in low-ventilation settings. These findings support the feasibility of conducting controlled human infection model trials outside clinical care environments when engineering controls are implemented. Limitations of this work include assumptions of static viral shedding and uniform infection risk across private and communal areas. This study extends prior findings on repurposed buildings and informs preparedness strategies for infection control in varied settings. References: 1.Sobhani H, Zhu S, Srebric J. Hotels as quarantine facilities with airborne virus controls. Build Environ. 2025 May 1;275:112765. This abstract is funded by: The authors would like to acknowledge the contributions of the NIAID Collaborative Influenza Vaccine Innovation Centers which provides support to the University of Maryland School of Medicine for influenza vaccine research (75N93019C00055).

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Caglayan et al. (2026) studied this question.

synapsesocial.com/papers/6a0d5078f03e14405aa9c35dhttps://doi.org/10.1093/ajrccm/aamag162.5274
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