Synthetic data generation of health and demographic surveillance systems data: a case study in a low- and middle-income country
Synthetic data generation of tabular data using HDSS data, particularly via CTGAN, may enhance the accessibility of datasets in LMICs by creating synthetic datasets that preserve the characteristics and statistical properties of the original data, while upholding privacy and confidentiality.
Authors
- Dorcas Gicuku Mwigereri (ORCID: https://orcid.org/0009-0006-1142-1574)
- Ryan Rego (ORCID: https://orcid.org/0000-0003-1361-9366)
- Anthony Ngugi (ORCID: https://orcid.org/0000-0001-5415-8717)
- Stephen Wong
- Eileen M Weinheimer-Haus (ORCID: https://orcid.org/0000-0001-5408-0024)
- W. Nicholson Price (ORCID: https://orcid.org/0000-0003-0729-290X)
- Geoffrey Siwo (ORCID: https://orcid.org/0000-0003-0726-997X)
- Akbar K. Waljee (ORCID: https://orcid.org/0000-0003-1964-8790)
- Farhana Alarakhiya
- Ji Zhu (ORCID: https://orcid.org/0000-0002-7812-5378)
- Nigel T Kamotho
Institutions
- University of Michigan (US)
- Aga Khan University Hospital Nairobi (KE)
- Clinton Health Access Initiative (US)
- Aga Khan University Nairobi (KE)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf137
- Primary Topic
- ICT in Developing Communities
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Novo Nordisk
- Novo Nordisk Fonden
- National Institutes of Health
- National Institute of Mental Health
- Fogarty International Center
- National Institute of Biomedical Imaging and Bioengineering