Statistical framework for assessing heterogeneous sensitivity of viruses to ultraviolet, ozone, and free chlorine

Disinfection is key to controlling the infection risk caused by viral contaminations. Recent disinfection guidelines often refer to a single virus resistant to the disinfectant of interest, despite a large variation in sensitivity to disinfectants between viruses or even strains within the same species. Here, we show a statistical framework to integrate multiple experimental datasets and measure the variation in sensitivity to disinfectants across different virus species by a parametric distribution, termed the disinfectant sensitivity distribution. To illustrate this framework, we collected 37, 9, and 28 species-dependent inactivation rate constants for ultraviolet (UV), ozone, and free chlorine from systematic reviews, and constructed the sensitivity distributions for those disinfectants. Our approach incorporated the uncertainty in individual inactivation rate constants using a Bayesian framework. The estimated sensitivity distributions suggested that 93.0% (95% CrI: 84.4-97.6%), 99.4% (95% CrI: 86.7-100%), and 90.3% (95% CrI: 78.2-96.6%) of the examined virus species for UV, ozone, and free chlorine are expected to be inactivated by 4-log if the disinfectant dose complies with the values recommended by USEPA Guidelines for 4-log inactivation, respectively. The proposed approach provides a reasonable extrapolation of observed inactivation kinetics to untested viruses and tools for more transparent risk assessments. Graphic for Table of Contents (TOC)/Abstract Art O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/633964v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1215b39org.highwire.dtl.DTLVardef@ec1b90org.highwire.dtl.DTLVardef@1059ee6org.highwire.dtl.DTLVardef@33aac_HPS_FORMAT_FIGEXP M_FIG C_FIG One sentence summaryWe propose a statistical framework to quantify the variation in sensitivity to common disinfectants across virus species synthesizing disinfection data.