EFSA has a tool for Monte Carlo simulation which offers a demonstration on a risk assessment model

https://zenodo.org/records/4590513

The principles and methods behind EFSA's Guidance on Uncertainty Analysis in Scientific Assessment, describe different methods to characterise uncertainty. Section 11.4.6 covers Monte Carlo simulation for uncertainty analysis. We have pasted the text here for convenience.

Monte Carlo simulation can be used for: (i) combining uncertainty about inputs to a deterministic or probabilistic quantitative model by numerical simulation when analytical solutions are not available; (ii) carrying out certain kinds of sensitivity analysis. Random samples from probability distributions representing uncertainty for non-variable quantities and variability for variables, are used as approximations to those distributions. Monte Carlo calculations are governed by the laws of probability. Distinction is often made between 2D Monte Carlo (2D-MC) and 1D Monte Carlo (1D-MC) (see below).

Potential role in main elements of uncertainty analysis: provides a way to combine uncertainties, expressed as probability distributions, about inputs to a model in order to obtain a probability distribution representing combined uncertainty about the model output. Also useful as part of a method for quantifying contributions of individual sources of uncertainty to combined uncertainty.

2D-MC separates distributions representing uncertainty from distributions representing variability and allows the calculation of combined uncertainty about any summary of interest of variability (e.g. a specified percentile of interest to decision-makers). The output from 2D-MC has two parts. The first is a random sample of values for all non-variable quantities, including parameters in statistical models, drawn from the joint distribution expressing uncertainty about them. The second part is, for each value of the non-variable quantities, a random sample of values for all variables, including the output of the model and any intermediate values arising from calculations. The first part of the output represents combined uncertainty about the non-variable quantities. The second part represents variability conditional on the parameter values. From the second part of the output, for each variability sample, one can calculate any summary statistic of interest such as the mean, standard deviation, specified percentile, fraction exceeding a specified threshold. The result is a sample of values representing uncertainty about the summary. More than one summary can be considered simultaneously if dependence is of interest.

1D-MC does not distinguish uncertainty from variability and is most useful if confined to either variability or uncertainty alone. In the context of uncertainty analysis, it is most likely to be helpful when variability is not part of the model. It then provides a random sample of values for all parameters, representing combined uncertainty about the output of the model.