Synthetic data generation of health and demographic surveillance systems data: a case study in a low- and middle-income country.

ABSTRACT:

Objective

To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

Materials and methods

Three open-source models (CTGAN, TableGAN, and CopulaGAN) were used to generate synthetic data from the Kaloleni/Rabai HDSS dataset. To assess the quality of the synthetic datasets generated by each model, we performed fidelity, utility, and privacy tests.

Results

CTGAN outperformed the other models, producing synthetic data that closely mirrored the statistical properties of the real dataset while preserving privacy. Both CopulaGAN and TableGAN performed poorly, with TableGAN comple

SUBMITTER: Mwigereri DG 

PROVIDER: S-EPMC12628187 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature