Unbiased Estimator of Population Proportion for Hidden Populations Exposing High Risk Diseases

Since society health is endanger of high risk diseases, always populations exposing these diseases especially hidden populations are considered by researchers and policy makers in the field of public health. Conventional methods that are used by researchers for sampling and also computing estimation of population proportions, often lead to under or over estimations of these proportions in the interested populations. Despite the introduction of efficient sampling methods such as respondent driven sampling method for more than two decades ago, due to unfamiliarity of researchers in this field to the technique of calculating estimations for samples in this method, this sampling method is applied less for estimating proportions of hidden populations.
The main objective of current article is to identify estimators of population proportions for qualitative variables such as catching disease by the usage of estimating probability of inter- and cross-relationships, and social network size of respondents.
Findings: Existing theories and computer simulations have shown that estimators introduced for population proportions are asymptotically unbiased and have high rate of convergence.
Due to the lack of proper selection of sampling method and also computing method for estimating proportions of hidden populations, that generally exposing high risk diseases and are effective in health policies, acceptable results in achieving the objectives of this policy will not provide.