Exploring novel kinetics of automated H ₂ O ₂ nebulization: a breakthrough in SARS-CoV-2 elimination

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. To address this, we conducted a prospective environmental study in COVID-19 patient rooms, performing a tripartite assessment of automated H₂O₂ nebulization. We collected air and surface samples before and after treatment, quantifying viral RNA (RT-qPCR) and antigens (indirect ELISA), and evaluating infectivity in Vero E6 cell cultures. We then applied a piecewise exponential model to characterize the virucidal kinetics, successfully capturing both initial delay and subsequent decay phases. The treatment proved highly effective, with reductions in environmental SARS-CoV-2 RNA load expressed in log₁₀ units and uncertainty quantified by 95% confidence intervals. Results revealed a marked decrease in RT-qPCR positivity rates in air samples, from 55.6% to 22.2%, along with higher cycle threshold values indicative of lower viral loads, and substantially reduced cytopathic effects. Crucially, this suggests that the residual RNA detected corresponded to non-viable virus. Our findings demonstrate the non-linear dynamics of H₂O₂-mediated decontamination and highlight the influence of environmental variables. By integrating molecular, biological, and mathematical analyses, this study provides a robust framework for optimizing disinfection protocols. Future multi-centre studies should validate these models to enhance preparedness against emerging viral threats in diverse clinical environments.IMPORTANCEThis study provides a critical, multi-faceted validation of automated hydrogen peroxide (H₂O₂) nebulization using ductFIT photocatalysis systems as a tool for eliminating SARS-CoV-2 in hospital environments. Our work moves beyond simple pre- and post-treatment comparisons by integrating molecular detection, direct infectivity assays, and sophisticated kinetic modeling. This tripartite approach allows us to characterize the non-linear dynamics of viral decay and provides compelling evidence that residual viral RNA detected after decontamination corresponds to non-viable virus. These findings offer a robust, data-driven framework for optimizing and personalizing disinfection protocols to enhance patient and healthcare worker safety. Furthermore, by demonstrating the novel application of machine learning to augment sparse experimental data, we introduce a powerful method for improving the predictive accuracy of decontamination models. This research provides actionable insights for mitigating nosocomial transmission and strengthening preparedness against future airborne pathogens.