Sampling is an important method to investigate and understand the population. It has been widely applied in various disciplines such as natural resources, environmental pollution, and public health. With the sample data collected, various parameters of the population (for example, mean and sum) can be estimated using an appropriate model. Usually, a best and unbiased estimation is expected. However, if the samples are not carefully selected to match the stochastic features of the population, the estimated result could be biased with respect to the population's real value.

  Researchers from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences proposed a tool to obtain unbiased estimation of population from biased samples with the support of auxiliary parameters based on the B-SHADE (Biased Sentinel Hospital based Area Disease Estimation) model, which is a model-assisted and data-driven model aimed at dealing with bias correction for population estimation using biased samples.

  The B-SHADE model is originally designed for biased sentinel hospitals' patient number/incidence estimation. It is equally well suited to the population estimation of biased samples. Based on the earlier published paper (Wang et al., 2011), three extensions are made in this research. Firstly, it extends the original total population-oriented estimation method to population mean estimation, which is another important parameter in sampling. Secondly, historical sample instead of historical population is found to be applicable in population mean estimation. This is important in some practice, where there is no integrated historical population information but good historical samples. Finally, an efficient sampling optimization based on the simulated annealing algorithm is proposed and implemented in the software. It is useful in evaluating the efficiency of old samples and designing new samples.