There is no formal guidance on target hand washing per employee hour (HW/EH) rates to achieve in quick service restaurant settings despite evidence that hand washing is important for both customer and staff health. The study objective was to evaluate how low vs. high norovirus infection prevalence among staff and/or customers impacts the ability for HW/EH to reduce staff and dine-in customer norovirus infection risks. We used a first order discrete time Markov chain compartment model to simulate the spread of norovirus in a quick service restaurant for 2 hours of simulated lunch time. Seven scenarios were explored, varying in whether staff and/or dine-in customers were assumed to be potentially infectious and whether restroom surfaces were contaminated with norovirus at the start of the simulation. HW/EH rates of 0 to 6 were explored, and risk reductions in estimated customer and staff infection risks were calculated. The greatest risk reductions were seen for improvements in HW/EH from 0 to 1, with risk reductions greater than 70%. Staff saw reduced risk across all scenarios while dine-in customers’ infection risks were only reduced when staff were the sole source of norovirus contamination in the restaurant. We demonstrate the importance of achieving at least 1 HW/EH to promote occupational health and reduce risks for customers when infected staff are the primary source of exposure. Beyond 2 to 3 HW/EH, there are increasingly diminishing returns in risk reduction for customers and staff. More behavioral data are needed to determine how varying conditions across restaurants change the effectiveness of increased HW/EH.
Antimicrobial resistance (AMR) carried through the food chain is among the most consequential One Health problems, yet the quantitative tools used to estimate the resulting human health risk remain fragmented across pathogens, commodities, and disciplines. This review appraises how quantitative microbial risk assessment (QMRA) has been applied to foodborne AMR and asks three questions: how published models are structured, where their parameters come from, and which data gaps most constrain reliable estimates. We synthesize farm-to-fork, transmission, and environmental exposure models spanning the main hazard and antimicrobial combinations, and map them against the four canonical risk-assessment components within a One Health structure that links animal, food, environmental, and human compartments. Most models adapt classical pathogen QMRA architectures but differ in how they represent resistance acquisition, horizontal gene transfer, and the additional burden of resistant infections. Parameterization is dominated by surveillance-derived prevalence, whereas dose-response relationships for resistant strains, transfer rates, and source-attribution fractions are frequently absent or borrowed from susceptible organisms. We argue that whole-genome sequencing, metagenomics, and machine learning offer a credible route to next-generation AMR-QMRA, provided genotype-to-phenotype uncertainty is represented explicitly rather than hidden. We close with a research agenda and a roadmap for harmonized, transparent modeling intended to orient risk assessors and food-safety authorities toward the priorities most likely to improve quantitative AMR risk estimation.
Buriganga River, which flows through Dhaka, Bangladesh, is heavily polluted and widely used for routine domestic activities, raising serious public health concerns. This study applied a quantitative microbial risk assessment (QMRA) framework to estimate the health burden associated with the exposure of Escherichia coli and Salmonella spp. as representative bacterial pathogens through the use of Buriganga River water without any treatment during bathing and washing activities. Disease burden was quantified in terms of Disability-Adjusted Life Years (DALYs) per person per year (DALY_pppy) to enable comparison with internationally accepted health-based targets. The results indicate that exposure to Buriganga River water poses non-negligible microbial health risks, with estimated DALY values exceeding the World Health Organization’s (WHO) tolerable risk benchmark of 10⁻⁶ DALY_pppy. Salmonella spp. contributed a substantially higher disease burden than E. coli, with DALY estimates on the order of 10⁻⁴ DALY_pppy. Sensitivity analysis identified pathogen concentration as the dominant driver of disease burden, while the infection-to-illness ratio was the second most influential parameter, indicating that assumptions regarding progression from infection to illness substantially affect the estimated health burden. This study applied a quantitative microbial risk assessment (QMRA) framework to estimate the health burden associated with exposure to E. coli and Salmonella spp. detected in the Buriganga River water during bathing and washing activities. In the absence of strain-specific characterization, the risk assessment was derived from measured bacterial concentrations combined with literature-based assumptions regarding the proportion of pathogenic strains.
