Local Linear Estimation for Covariate-Dependent Coefficients Model in Disease Mapping.
Statistics in medicine·
- DOI
- 10.1002/sim.70713
- PMID
- 42633632
- PMCID
- —
- OpenAlex
- —
- Study type
- Journal article
- Publisher
- Publisher unavailable
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The work is relevant to disease-mapping methods for dengue and to settings where spatial associations may vary with climate or other covariates. The article is indexed with dengue and climate/environment topics, which supports its contextual relevance for public-health literature review.
Structured evidence summary
Research question
The paper asks whether a disease-mapping model can estimate coefficients that vary with covariates while also accounting for spatial effects, using excess zero counts as the outcome structure.
Study design
Methodological statistics paper describing a local linear estimation approach, evaluated with simulation studies and illustrated with an applied dengue example.
Population and setting
The applied example uses reported dengue cases from villages in Kaohsiung City, Taiwan, from January 2014 through December 2015. The abstract also frames the work in disease mapping and climate-related covariates.
Main findings
The abstract states that the local linear estimator smooths estimation of covariate-dependent coefficients. It also reports that simulation studies were conducted to assess estimator performance.
Public-health relevance
The work is relevant to disease-mapping methods for dengue and to settings where spatial associations may vary with climate or other covariates. The article is indexed with dengue and climate/environment topics, which supports its contextual relevance for public-health literature review.
Important limitations
The abstract does not state explicit study limitations. This summary is limited to the supplied single-article abstract and metadata, so the original paper is needed for decision-grade interpretation.
GIDS interpretation
This article is discoverable as a methodological dengue disease-mapping study with climate-related context. The supplied evidence supports cataloging and screening, but not any live surveillance interpretation or signal confirmation.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 7 auditable classifier relationships to diseases, places, topics, and study design.