The Network Labeling Optimization for Hidden Population Size Estimation : A Case Solution for the Bangladesh Kidney Sellers Problem
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
Year of publication: |
[2022]
|
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Authors: | Lee, Narae ; Siddique, Abu Bakkar ; Li, Meng-Hao ; Ahmad, Manzur ; Haque, Reaz ; El-Amine, Hadi ; Koizumi, Naoru |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (34 p) |
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Series: | |
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 4, 2022 erstellt |
Other identifiers: | 10.2139/ssrn.4074275 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013293838
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