Exploring Novel Kinetics of Automated H2O2Nebulization: A Breakthrough in SARS-CoV-2 Elimination
Abstract
Although hydrogen peroxide (H2O2) nebulization has shown promise for reducing SARS-CoV-2 loads in healthcare settings, its precise kinetics and real-world efficacy remain incompletely understood. In this prospective environmental sampling study, we collected air and surface samples from COVID-19 patient rooms before and after automated H2O2nebulization, quantifying viral RNA by RT-qPCR and viral antigens by indirect ELISA, while assessing infectivity via Vero E6 cell cultures. A piecewise exponential model characterized the kinetics of viral load reduction, capturing both initial delays and subsequent decay phases. Results revealed a marked decrease in RT-qPCR positivity rates, higher cycle threshold values indicative of lower viral loads, and substantially reduced cytopathic effects, suggesting that residual viral RNA was largely non-viable. These findings underscore the non-linear nature of H2O2-mediated decontamination and the influence of environmental variables such as airflow, humidity, and surface composition. By integrating molecular diagnostics, infectivity assays, and mathematical modeling, our study offers a comprehensive framework for refining decontamination protocols. Future investigations should explore larger, multi-institutional cohorts and evaluate the applicability of these insights to emerging viral threats in diverse clinical environments.
Importance
This study represents a significant advance in environmental decontamination by demonstrating that automated hydrogen peroxide (H2O2) nebulization markedly reduces SARS-CoV-2 contamination in hospital settings. By integrating rigorous molecular diagnostics with infectivity assays and sophisticated kinetic modeling, the research delineates the non-linear decay of viral loads, providing robust evidence that residual viral RNA post-treatment is largely non-infectious. Such insights are invaluable for refining disinfection protocols, ensuring patient and healthcare worker safety, and mitigating nosocomial transmission. Incorporating advanced machine learning techniques further improves the predictive accuracy of decontamination outcomes. Ultimately, this work lays a strong foundation for future studies to optimize and personalize disinfection strategies across diverse clinical environments, thereby bolstering public health efforts to combat current and emerging infectious diseases.
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