{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T22:42:29Z","timestamp":1791067349830,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"14","license":[{"start":{"date-parts":[[2011,4,11]],"date-time":"2011-04-11T00:00:00Z","timestamp":1302480000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2011,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Background: The need to deliver interventions targeting multiple diseases in a cost\u2010effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co\u2010infection is particularly high. Co\u2010infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data.<\/jats:p><jats:p>Methods: Bayesian geostatistical shared component models (allowing for covariates, disease\u2010specific and shared spatial and non\u2010spatial random effects) are proposed to model the geographical distribution and burden of co\u2010infection risk from single\u2010disease surveys. The ability of the models to capture co\u2010infection risk is assessed on simulated data sets based on multinomial distributions assuming light\u2010 and heavy\u2010dependent diseases, and a real data set of <jats:bold><jats:italic>Schistosoma mansoni<\/jats:italic><\/jats:bold>\u2013hookworm co\u2010infection in the region of Man, C\u00f4te d'Ivoire. The data were restructured as if obtained from single\u2010disease surveys. The estimated results of co\u2010infection risk, together with independent and multinomial model results, were compared via different validation techniques.<\/jats:p><jats:p>Results: The results showed that shared component models result in more accurate estimates of co\u2010infection risk than models assuming independence in settings of heavy\u2010dependent diseases. The shared spatial random effects are similar to the spatial co\u2010infection random effects of the multinomial model for heavy\u2010dependent data.<\/jats:p><jats:p>Conclusions: In the absence of true co\u2010infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co\u2010infection risk from single\u2010disease survey data, especially in settings of heavy\u2010dependent diseases. 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