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For example, the association between infectious disease incidence and risk factors may vary according to climatic factors, such as time, season, and temperature. In this study, we aim to develop a spatial model that accounts simultaneously for the spatial and varying effects of covariates believed to modulate the spatial association. We employ a local linear estimation method to estimate covariate\u2010dependent coefficients in a model for excess zero counts. The local linear estimator effectively smooths covariate\u2010dependent coefficient estimation. Comprehensive simulation studies were conducted to evaluate the performance of the local linear estimators, and reported dengue cases in villages in Kaohsiung City from January 2014 to December 2015 are used to inform our proposed method for practical use in real\u2010world applications.<\/jats:p>","DOI":"10.1002\/sim.70713","type":"journal-article","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:45:48Z","timestamp":1787499948000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Local Linear Estimation for Covariate\u2010Dependent Coefficients Model in Disease Mapping"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5994-0525","authenticated-orcid":false,"given":"Yexuan","family":"Jiang","sequence":"first","affiliation":[{"name":"Division of Epidemiology and Biostatistics, School of Public Health University of Illinois Chicago  Chicago Illinois 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