Predictive Modeling of Virus Inactivation by UV

UV254 disinfection strategies
are commonly applied to
inactivate pathogenic viruses in water, food, air, and on surfaces.
There is a need for methods that rapidly predict the kinetics of virus
inactivation by UV254, particularly for emerging and difficult-to-culture
viruses. We conducted a systematic literature review of inactivation
rate constants for a wide range of viruses. Using these data and virus
characteristics, we developed and evaluated linear and nonlinear models
for predicting inactivation rate constants. Multiple linear regressions
performed best for predicting the inactivation kinetics of (+) ssRNA
and dsDNA viruses, with cross-validated root mean squared relative
prediction errors similar to those associated with experimental rate
constants. We tested the models by predicting and measuring inactivation
rate constants of a (+) ssRNA mouse coronavirus and a dsDNA marine
bacteriophage; the predicted rate constants were within 7% and 71%
of the experimental rate constants, respectively, indicating that
the prediction was more accurate for the (+) ssRNA virus than the
dsDNA virus. Finally, we applied our models to predict the UV254 rate constants of several viruses for which high-quality
UV254 inactivation data are not available. Our models will
be valuable for predicting inactivation kinetics of emerging or difficult-to-culture
viruses.