Machine Learning and Imputation to Characterize Human Norovirus Genotype Susceptibility to Sodium Hypochlorite

Area of Science:

• Environmental microbiology
• Food safety science
• Virology

Background:

• Human norovirus (HuNoV) is a primary cause of foodborne illness and global gastroenteritis.
• Effective disinfection is crucial due to HuNoV's low infectious dose and environmental persistence.
• Sodium hypochlorite (NaOCl) is a common disinfectant for HuNoV on surfaces.

Purpose of the Study:

• To assess the susceptibility of 11 HuNoV genotypes from genogroups I, II, and IV to NaOCl in suspension.
• To determine the impact of NaOCl concentration and exposure duration on HuNoV inactivation.
• To inform optimized disinfection strategies for public health and food safety.

Main Methods:

• HuNoV strains were incubated with varying NaOCl concentrations (50-150 ppm) at pH 7.0-7.5 for 1 and 5 minutes.
• Viral inactivation was quantified using RNase pre-treatment followed by RT-qPCR.
• Statistical analyses, including imputation and machine learning, were employed for data interpretation.

Main Results:

• NaOCl reduced viral loads across all tested HuNoV genotypes, with significant variation in efficacy.
• Genotypes GI.1, GII.4 New Orleans, and GII.4 Sydney showed the lowest susceptibility.
• GII.6 and GII.13 were the most susceptible; NaOCl concentrations above 0 ppm showed statistically similar inactivation, and exposure time had minimal impact.

Conclusions:

• HuNoV genotype significantly influences susceptibility to NaOCl disinfection.
• Optimizing NaOCl concentration and contact time based on specific genotypes is essential for effective control.
• These findings support enhanced public health and food safety measures against norovirus outbreaks.