Modeling Surface Disinfection Needs To Meet Microbial Risk Reduction Targets.

Area of Science:

• Infection Control
• Environmental Microbiology
• Stochastic Modeling

Background:

• Nosocomial viral infections are a significant concern in healthcare settings, with fomites playing a key role in transmission.
• Effective surface disinfection is crucial for mitigating healthcare-acquired infections but lacks standardized contamination level targets.

Purpose of the Study:

• To predict the impact of surface disinfection on viral infection risks in healthcare environments.
• To determine the necessary viral reductions on surfaces to achieve specific infection risk targets.

Main Methods:

• Stochastic modeling was employed to quantify the effects of surface disinfection on pathogen exposure and infection risk.
• Modeled infection risks for rotavirus, rhinovirus, and influenza A virus under different contact scenarios.
• Utilized measured viral reduction data (94.1%) from an EPA-registered disinfectant in an urgent care facility.

Main Results:

• A 94.1% surface viral reduction decreased infection risks by 94.1% under single fomite contact conditions.
• Under prolonged contact, the same reduction lowered median viral infection risks by 92.96-94.1%, with influenza A risk falling below one in a million.
• Achieving a one-in-a-million risk target for rotavirus and rhinovirus necessitates >99.99% viral surface reduction.

Conclusions:

• Surface disinfection effectively reduces viral infection risk, but the required efficacy varies by virus and contact scenario.
• Low-infectious-dose viruses present greater challenges in achieving stringent risk targets.
• Findings highlight the need for optimized cleaning protocols, potentially involving multiple disinfection applications, and further research into contamination-surface-behavior-risk relationships.