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In disease mapping models, zero inflation is an important issue, which often occurs in disease incidence datasets with high proportions of zero disease count. It is originated from limited survey coverage or unadvanced testing equipment, which makes some regions have no observed patients. Then excessive zeros recorded in the disease incidence dataset would mess up the true distributions of disease incidence and lead to inaccurate estimates. To address this issue, a zero\u2010inflated disease mapping model is developed in this work. In this model, a zero\u2010inflated process using Bernoulli indicators is assumed to characterize whether the zero inflation occurs for each region. For regions without zero inflation, a coherent and generative disease mapping model is applied for mapping the spatially correlated disease incidence. Independent spatial random effects are incorporated in both processes to account for the spatial patterns of zero inflation and disease incidence. External covariates are also considered in both processes to better explain the disease count data. To estimate the model, a Markov chain Monte Carlo algorithm is proposed. We evaluate model performance via a variety of simulation experiments. Finally, a Lyme disease dataset of Virginia is analyzed to illustrate the application of the proposed\u00a0model.<\/jats:p>","DOI":"10.1002\/bimj.202200090","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T05:05:40Z","timestamp":1675400740000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spatial correlated incidence modeling with zero inflation"],"prefix":"10.1002","volume":"65","author":[{"given":"Feifei","family":"Wang","sequence":"first","affiliation":[{"name":"Center for Applied Statistics Renmin University of China Beijing China"},{"name":"School of Statistics Renmin University of China Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haofeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Statistics Renmin University of China Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Wang","sequence":"additional","affiliation":[{"name":"Chengdu Center for Disease Prevention and Control Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6287-5094","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics Renmin University of China Beijing China"},{"name":"School of Statistics Renmin University of China Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16224460"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/b17115"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116466"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1191\/0962280205sm388oa"},{"key":"e_1_2_9_6_1","first-page":"45","article-title":"Hierarchical multivariate CAR models for spatio\u2010temporally correlated survival data","volume":"7","author":"Carlin B. 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