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Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely, that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle upon Tyne, UK. Our results suggest that, when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. The proposed methodology is implemented in the open\u2010source R package<jats:styled-content>SDALGCP<\/jats:styled-content>.<\/jats:p>","DOI":"10.1002\/sim.8339","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T04:15:41Z","timestamp":1566879341000},"page":"4871-4887","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A spatially discrete approximation to log\u2010Gaussian Cox processes for modelling aggregated disease count data"],"prefix":"10.1002","volume":"38","author":[{"given":"Olatunji","family":"Johnson","sequence":"first","affiliation":[{"name":"CHICAS, Lancaster Medical School Lancaster University Lancaster UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Diggle","sequence":"additional","affiliation":[{"name":"CHICAS, Lancaster Medical School Lancaster University Lancaster UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0640-181X","authenticated-orcid":false,"given":"Emanuele","family":"Giorgi","sequence":"additional","affiliation":[{"name":"CHICAS, Lancaster Medical School Lancaster University Lancaster UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"issue":"2","key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1111\/j.2517-6161.1974.tb00999.x","article-title":"Spatial interaction and the statistical analysis of lattice systems","volume":"36","author":"Besag J","year":"1974","journal-title":"J Royal Stat Soc Ser B Methodol"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780203492024"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-3758(03)00111-3"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-STS441"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj017"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280212446326"},{"key":"e_1_2_9_8_1","unstructured":"JohnsonO GiorgiE DiggleP.SDALGCP: Spatially Discrete Approximation to Log\u2010Gaussian Cox Processes for Aggregated Disease Count Data.https:\/\/CRAN.R-project.org\/package=SDALGCP. 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