Suchergebnisse

AbstractAntimicrobial spray products are used by millions of people around the world for cleaning and disinfection of commonly touched surfaces. Influenza A is a pathogen of major concern, leading to up to 49,000 deaths and 114,000 hospitalizations per year in the United States alone. One of the recognized routes of transmission for influenza A is by transfer of viruses from surfaces to hands and subsequently to mucous membranes. Therefore, routine cleaning and disinfection of surfaces is an important part of the environmental management of influenza A. While the emphasis is generally on spraying hard surfaces and laundering cloth and linens with high temperature machine drying, not all surfaces can be treated in this manner. The quantitative microbial risk assessment (QMRA) approach was used to develop a stochastic risk model for estimating the risk of infection from indirect contact with porous fomite with and without surface treatment with an antimicrobial spray. The data collected from laboratory analysis combined with the risk model show that influenza A infection risk can be lowered by four logs after using an antimicrobial spray on a porous surface. Median risk associated with a single touch to a contaminated fabric was estimated to be 1.25 × 10−4 for the untreated surface, and 3.6 × 10−8 for the treated surface as a base case assumption. This single touch scenario was used to develop a generalizable model for estimating risks and comparing scenarios with and without treatment to more realistic multiple touch scenarios over time periods and with contact rates previously reported in the literature. The results of this study and understanding of product efficacy on risk reduction inform and broaden the range of risk management strategies for influenza A by demonstrating effective risk reduction associated with treating nonporous fomites that cannot be laundered at high temperatures.

AbstractMiddle Eastern respiratory syndrome, an emerging viral infection with a global case fatality rate of 35.5%, caused major outbreaks first in 2012 and 2015, though new cases are continuously reported around the world. Transmission is believed to mainly occur in healthcare settings through aerosolized particles. This study uses Quantitative Microbial Risk Assessment to develop a generalizable model that can assist with interpreting reported outbreak data or predict risk of infection with or without the recommended strategies. The exposure scenario includes a single index patient emitting virus‐containing aerosols into the air by coughing, leading to short‐ and long‐range airborne exposures for other patients in the same room, nurses, healthcare workers, and family visitors. Aerosol transport modeling was coupled with Monte Carlo simulation to evaluate the risk of MERS illness for the exposed population. Results from a typical scenario show the daily mean risk of infection to be the highest for the nurses and healthcare workers (8.49 × 10−4 and 7.91 × 10−4, respectively), and the lowest for family visitors and patients staying in the same room (3.12 × 10−4 and 1.29 × 10−4, respectively). Sensitivity analysis indicates that more than 90% of the uncertainty in the risk characterization is due to the viral concentration in saliva. Assessment of risk interventions showed that respiratory masks were found to have a greater effect in reducing the risks for all the groups evaluated (>90% risk reduction), while increasing the air exchange was effective for the other patients in the same room only (up to 58% risk reduction).