A novel predictive model for bacterial growth on kitchen surfaces under thermal variations and user behaviors interactions

Residential kitchens are major reservoirs for foodborne pathogens that threaten public health. Effective hygiene management is often hampered by the absence of predictive models for microbial growth under real-world conditions. To this end, this study introduces a novel Gompertz model that incorporates cooking and cleaning effects (Gompertz-CC), developed through a year-long field investigation across eight Chinese cities and controlled laboratory experimentation. Statistical analyses of field data identified air temperature and user behaviors, specifically cooking and cleaning activities, as the most influential factors for bacterial growth. By incorporating cooking and cleaning correction factors that adjust to temperature variations, the new model outperforms the original Gompertz model across all tested conditions (Adj.R² > 0.902, RMSE < 0.127). It allows for estimation of the critical bacterial rebound times post-cooking and cleaning, which shorten as temperatures rise. In winter, Staphylococcus aureus and Salmonella enterica remain below the safe thresholds for 15–30 hours following disinfection, while during summer months these windows narrow to 3–6 hours. By bridging the gap between laboratory microbiology and real-world kitchen dynamics, the Gompertz-CC model provides a robust framework for adaptive cleaning protocols.