Objective: The study was aimed to analyze the microbiological quality of mixed vegetable salads and to understand the risk related with its consumption from different restaurants around Bangladesh Agricultural University campus in Mymensingh. Materials and Methods: Sixty (60) samples of mixed vegetable salads were taken from twelve (12) different restaurants in five different time points from each restaurant. In parallel, restaurant workers were asked for handling practices while the consumers were interviewed about their salad consumption pattern and whether they had experienced any health-related problems. Microbial risk assessment of Staphylococcus spp., Salmonella spp., and Escherichia coli was esti¬mated by Monte Carlo simulation (10,000 iterations), an exponential model. Results: Aerobic plate count was ranged from 7.73 ± 0.61 to 9.04 ± 0.26 log cfu/gm, Staphylococcus spp. from 4.64 ± 0.61 to 6.42 ± 0.53 log cfu/gm, Salmonella spp. from 4.75 ± 0.08 to 5.27 ± 0.53 log cfu/gm, and E. coli from 4.98 ± 0.20 to 6.66 ± 0.80 log cfu/gm. From the survey, it was found that total consumers had 18% chances where the male had 13% and the female had 30% chances of being infected with salads. Again frequent, average, and occasional consumers had 31%, 13%, and 0% chances, respectively, of being infected with those salads. From the Monte Carlo simulation, the calculated mean annual risks of Staphylococcus spp., Salmonella spp., and E. coli infection for the three exposure scenarios were found to be about 100%. Conclusion: The study actually revealed the potential microbial contamination in mixed vegetable salads which may impact on food safety and human health. So, the findings suggest that following hygienic measures during processing and handling the microbiological quality of mixed vegetables salads can be improved. [J Adv Vet Anim Res 2020; 7(1.000): 34-41]

The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution.

A stochastic simulation model incorporated with Reed-Frost approach was derived for evaluating diagnostic performance of a test used for a screening program of an infectious disease. The Reed-Frost model was used to characterize the within-herd spread of the disease using a hypothetical example. Specifically, simulation model was aimed to estimate the number infected animals in an infected herd, in which imperfect serologic tests are performed on samples taken from herds and to illustrate better interpreting survey results at herd-level when uncertainty inevitably exists.