Beta-Poisson dose-response model is a popular parametric dose response model which is extensively applied in Microbial risk area. The advantage of Beta-Poisson dose-response model is that it is a model which can consider the change in the risk. The change is possibly happened because of human responses diversity, pathogen competence diversity, or the interaction between them. In our project, we divide it by two parts. The rst part is the Theoretical Part. We will nd we cannot use the Method of Moments and Maximum Likelihood Estimation directly to create the Con dence Ellipse for simultaneous estimation of Beta-Poisson dose-response model parameters. Therefore, we need to discover a suitable approximate distribution function for the Beta-Poisson dose-response model rst. Then, we will infer the Maximum Likelihood Estimators for the approximation. Afterwards, we will construct Fisher information matrix. In the nal, we are going to structure a normal approximation that gives con dence interval for parameters of Beta-Poisson dose-response approximation. The second part is the simulation part. Since we cannot nd that large number of real pathogen data, we are going to use R programming to simulate 10; 000 iterations base on 8 groups of the pathogen parameters, such as the parameters of Shigella spp., S.typhi and so on. Maximum Likelihood Estimates, and , for parameters will be calculated after the simulation with the sample size as n = 100; 500, and 1000. As the result of simulation, rst of all, we are going to calculate errors from the Monte-Carlo estimations. Then, we will use Scatter Plots to present the sets of parameters, and apply the 95% con dence ellipse from R to check the approximate model. After that, the Histograms shows the errors of parameters will be presented for each value of and . Finally, we will summarize the Maximum Likelihood Estimates of and , For comparing the results of the simulation, we will calculate the Error of Estimation, the Mean Square Error and the coverage of the probability. The method is quite useful for the future study in Epidemiology area.