Predictive Modeling of Virus Inactivation by UV

Area of Science:

• Environmental microbiology
• Virology
• Disinfection science

Background:

• Ultraviolet type 254 (UV254) disinfection is widely used to inactivate viruses in various settings.
• Rapid prediction of UV254 inactivation kinetics is needed, especially for emerging and hard-to-culture viruses.

Purpose of the Study:

• To develop and evaluate models for predicting virus inactivation rate constants using UV254.
• To assess the accuracy of these models for different virus types.

Main Methods:

• Systematic literature review of virus inactivation rate constants.
• Development and evaluation of linear and nonlinear regression models based on virus characteristics.
• Experimental validation using a (+) ssRNA mouse coronavirus and a dsDNA marine bacteriophage.

Main Results:

• Multiple linear regression models accurately predicted inactivation kinetics for (+) ssRNA and dsDNA viruses.
• Predicted rate constants for the mouse coronavirus and marine bacteriophage were within 7% and 71% of experimental values, respectively.
• Model accuracy was higher for (+) ssRNA viruses compared to dsDNA viruses.

Conclusions:

• Developed models provide a valuable tool for predicting UV254 inactivation kinetics.
• These models are particularly useful for emerging or difficult-to-culture viruses lacking experimental data.
• The findings support the enhanced application of UV254 disinfection strategies.