Genomic modeling as an approach to identify surrogates for use in experimental validation of SARS-CoV-2 and HuNoVs inactivation by UV-C treatment

Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) is responsible for the COVID-19 pandemic that continues to pose significant public health concerns. While research to deliver vaccines and antivirals are being pursued, various effective technologies to control its environmental spread are also being targeted. Ultraviolet light (UV-C) technologies are effective against a broad spectrum of microorganisms when used even on large surface areas. In this study, we developed a pyrimidine dinucleotide frequency based genomic model to predict the sensitivity of select enveloped and non-enveloped viruses to UV-C treatments in order to identify potential SARS-CoV-2 and human noroviruses surrogates. The results revealed that this model was best fitted using linear regression with r²=0.90. The predicted UV-C sensitivity (D₉₀- dose for 90% inactivation) for SARS-CoV-2 and MERS-CoV was found to be 21 and 28 J/m², respectively (with an estimated 18 J/m²as published for SARS-CoV-1), suggesting that coronaviruses are highly sensitive to UV-C light compared to other ssRNA viruses used in this modeling study. Murine hepatitis virus (MHV) A59 strain with a D₉₀of 21 J/m²close to that of SARS-CoV-2 was identified as a suitable surrogate to validate SARS-CoV-2 inactivation by UV-C treatment. Furthermore, the non-enveloped human noroviruses (HuNoVs), had predicted D₉₀values of 69.1, 89 and 77.6 J/m²for genogroups GI, GII and GIV, respectively. Murine norovirus (MNV-1) of GV with a D₉₀= 100 J/m²was identified as a potential conservative surrogate for UV-C inactivation of these HuNoVs. This study provides useful insights for the identification of potential nonpathogenic surrogates to understand inactivation kinetics and their use in experimental validation of UV-C disinfection systems. This approach can be used to narrow the number of surrogates used in testing UV-C inactivation of other human and animal ssRNA viral pathogens for experimental validation that can save cost, labor and time.