{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:37:13Z","timestamp":1787564233048,"version":"build-2736575974"},"reference-count":14,"publisher":"Wiley","issue":"20","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2023,9,10]]},"abstract":"<jats:p>Disease mapping is a research field to estimate spatial pattern of disease risks so that areas with elevated risk levels can be identified. The motivation of this article is from a study of dengue fever infection, which causes seasonal epidemics in almost every summer in Taiwan. For analysis of zero\u2010inflated data with spatial correlation and covariates, current methods would either cause a computational burden or miss associations between zero and non\u2010zero responses. In this article, we develop estimating equations for a mixture regression model that accommodates spatial dependence and zero inflation for study of disease propagation. Asymptotic properties for the proposed estimates are established. A simulation study is conducted to evaluate performance of the mixture estimating equations; and a dengue dataset from southern Taiwan is used to illustrate the proposed method.<\/jats:p>","DOI":"10.1002\/sim.9822","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T23:58:56Z","timestamp":1686787136000},"page":"3636-3648","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Disease mapping for spatially semi\u2010continuous data by estimating equations with application to dengue control"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3057-094X","authenticated-orcid":false,"given":"Pei\u2010Sheng","family":"Lin","sequence":"first","affiliation":[{"name":"Institute of Population Health Sciences National Health Research Institutes  Zhunan Taiwan"},{"name":"Department of Mathematics National Chung Cheng University  Minxiong Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yih\u2010Jeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Population Health Sciences National Health Research Institutes  Zhunan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Wisconsin\u2010Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420072884-c14"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119115151"},{"issue":"1","key":"e_1_2_8_4_1","first-page":"1","article-title":"Zero\u2010inflated Poisson regression, with an application to defects in manufacturing","volume":"34","author":"Lambert D","year":"1992","journal-title":"Dent Tech"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020910605990"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2010.08.001"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753168389"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420064254"},{"issue":"1","key":"e_1_2_8_9_1","first-page":"189","article-title":"Bayesian spatial\u2013temporal modeling of ecological zero\u2010inflated count data","volume":"25","author":"Wang X","year":"2015","journal-title":"Stat Sin"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7819"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2018.08.005"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1191\/1471082X04st076oa"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2021.100503"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0222668"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asn046"}],"container-title":["Statistics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sim.9822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T20:59:24Z","timestamp":1692651564000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/sim.9822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":14,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,9,10]]}},"alternative-id":["10.1002\/sim.9822"],"URL":"https:\/\/doi.org\/10.1002\/sim.9822","archive":["Portico"],"relation":{},"ISSN":["0277-6715","1097-0258"],"issn-type":[{"value":"0277-6715","type":"print"},{"value":"1097-0258","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,14]]},"assertion":[{"value":"2022-04-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-31","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}