The Network Labeling Optimization for Hidden Population Size Estimation: A Case Solution for the Bangladesh kidney Sellers Problem
DC Field | Value | Language |
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dc.contributor.author | Lee, Narae | - |
dc.contributor.author | Siddique, Abu Bakkar | - |
dc.contributor.author | Li, Meng-Hao | - |
dc.contributor.author | Ahmad, Manzur | - |
dc.contributor.author | Lutfay, Tariq | - |
dc.contributor.author | Haque, Reaz | - |
dc.contributor.author | El-Amine, Hadi | - |
dc.contributor.author | Koizumi, Naoru | - |
dc.date.accessioned | 2022-06-05T10:32:53Z | - |
dc.date.available | 2022-06-05T10:32:53Z | - |
dc.date.issued | 2021-12 | - |
dc.identifier.uri | https://archives.kdischool.ac.kr/handle/11125/43187 | - |
dc.description.abstract | Estimating the prevalence of hidden population is a challenging but important task for policymakers. Without knowing the precise scale of the problem, it is difficult to design a sharp remedy. Existing tools such as facility-based sentinel surveillance, snowball sampling, respondent-driven sampling, and network scale-up methods are prone to respondents' misinformation, false responses, and sample misrepresentation. Therefore, this paper proposes a novel analytical framework to overcome such weaknesses and derive better estimates. Specifically, our optimization-based mathematical model employs the Integer Programming (IP) and Social Network Analysis (SNA) to directly remove double-counting from the survey of more accessible subjects of the general public. To validate the model, the study implemented a survey on kidney trafficking in the kidney selling hotspot of Bangladesh. Reflecting the survey responses of 400 residents in a Ward of one Union in Kalai Upazila, we simulated an Exponential Random Graph Models (ERGMs) driven network. Although the model validation using the simulated network showed some signs of over-representation, a secondary validation using other data showed that the model estimates are fairly accurate. | - |
dc.format.extent | 33 | - |
dc.language | eng | en_US |
dc.publisher | KDI School of Public Policy and Management | - |
dc.relation.isPartOfSeries | Development Studies Series DP 21-14 | - |
dc.subject | Hidden Population | - |
dc.subject | Social Network | - |
dc.subject | Network Density | - |
dc.subject | Sampling | - |
dc.subject | Illegal Behavior | - |
dc.subject | Integer programing Optimization | - |
dc.title | The Network Labeling Optimization for Hidden Population Size Estimation: A Case Solution for the Bangladesh kidney Sellers Problem | en_US |
dc.type | Working Paper | - |
dc.type.docType | Development Studies Series | - |
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