Predictive modeling of virus inactivation by UV

Abstract

Disinfection strategies are commonly applied to inactivate pathogenic viruses in water, food, air, and on surfaces to prevent the spread of infectious diseases. Determining how quickly viruses are inactivated to mitigate health risks is not always feasible due to biosafety restrictions or difficulties with virus culturability. Therefore, methods that would rapidly predict kinetics of virus inactivation by UV254 would be valuable, particularly for emerging and difficult-to-culture viruses. We conducted a rapid systematic literature review to collect high-quality inactivation rate constants for a wide range of viruses. Using these data and basic virus information (e.g., genome sequence attributes), we developed and evaluated four different model classes, including linear and non-linear approaches, to find the top performing prediction model. For both the (+) ssRNA and dsDNA virus types, multiple linear regressions were the top performing model classes. In both cases, the cross-validated root mean squared relative prediction errors were similar to those associated with experimental rate constants. We tested the models by predicting and measuring inactivation rate constants for two viruses that were not identified in our systematic review, including a (+) ssRNA mouse coronavirus and a dsDNA marine bacteriophage; the predicted rate constants were within 7% and 71% of the experimental rate constants, respectively. 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.

Deep-ultraviolet photonics for the disinfection of SARS-CoV-2 and its variants (Delta and Omicron) in the cryogenic environment

“…Thanks to this transition, those electrons were able to conduct a relaxation and overcame the barrier, and further resulted in physicochemical reactions (such as the covalent bond formation of the cyclobutane pyrimidine dimers (CPDs) or the cross-linking between pyrimidine and proteins) 43,44 . Furthermore, the DUV disinfection was a continuous process involving multiple photons, and products from the physicochemical reactions were more stable on energy (because the formed covalent bond is significantly more stable than the initial hydrogen bonding) 45 . At the same time, the energy of the barrier would increase for the subsequent reactions when more physicochemical reactions happened.…”

“…6(a), the inactivation of (+) single-stranded RNA viruses were mainly caused by the formation of UU (uracil/uracil) and UC (uracil/cytosine) dimers after the UV radiation. The published literature has demonstrated that these formed CPDs were relatively stable and required much higher excitation energy to initiate the photo-reversibility process back to their original monomer pyrimidines 45 . The possibility of dimers formation was related to the number of Cs, Us, UUs, and UUUs in the gene sequence 46 .…”

SARS-CoV-2 Ultraviolet Radiation Dose-Response Behavior

“…Three coronaviruses have been identified that cause more serious, sometimes fatal diseases in humans: SARS-CoV, MERS-CoV (cause of Middle East respiratory syndrome, MERS), and SARS-CoV-2. The structural similarity of these viruses, including their relatively large genomes, suggests that they should all be susceptible to inactivation by exposure to UV-C radiation and that their UV-C dose-response behaviors should be similar [ 10 – 15 ].…”

“…Rockey et al . [ 15 ] conducted a review of the literature related to UV 254 dose-response behavior of viruses. For positive-sense, single-stranded RNA viruses, which include the coronaviruses, they developed a multiple linear regression model that yielded the lowest root-mean-squared relative prediction error (RMSrPE) based on the following variables: number of cytosines, uracils, uracil doublets, and uracil triplets.…”