Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya
Covariates can substantially reduce the uncertainty of the predictive inference generated from geostatistical models. Using covariates can thus contribute to the design of more effective STH control strategies by reducing sample sizes without compromising the predictive performance of geostatistical models.
Authors
- Florence Wakesho
- Amitha Puranik (ORCID: https://orcid.org/0000-0003-2047-9453)
- Collins Okoyo (ORCID: https://orcid.org/0000-0002-2647-1359)
- Stella Kepha (ORCID: https://orcid.org/0000-0003-4035-6485)
- Katherine Gass (ORCID: https://orcid.org/0000-0002-2084-8039)
- Charles Mwandawiro (ORCID: https://orcid.org/0000-0001-8175-3749)
- Peter J. Diggle (ORCID: https://orcid.org/0000-0003-3521-5020)
- Maurice R. Odiere (ORCID: https://orcid.org/0000-0001-7561-4873)
- Emanuele Giorgi (ORCID: https://orcid.org/0000-0003-0640-181X)
- Wycliff Omondi
- Hadley Matendechero Sultani
Institutions
- Kenya Medical Research Institute (KE)
- Ministry of Health (KE)
- The Task Force for Global Health (US)
- Lancaster University (GB)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1186/s12874-024-02420-1
- Citations
- 1
- Primary Topic
- Parasites and Host Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- United States Agency for International Development
- Bill and Melinda Gates Foundation