Hong, Andrew EBarbu, Corentin MSmall, Dylan SLevy, Michael Z2023-05-232023-05-232014-09-112018-07-13https://repository.upenn.edu/handle/20.500.14332/48044Maps of the distribution of epidemiological data often ignore surveillance error or possible correlations between missing information and outcomes. We analyse presence–absence data at the household level (12050 points) of a disease‐carrying insect in Mariano Melgar, Peru, collected as part of the Arequipan Ministry of Health's efforts to control Chagas disease. We construct a Bayesian hierarchical model to locate regions that are vulnerable to under‐reporting due to surveillance error, accounting for variability in participation due to infestation status. The spatial correlation in the data allows us to identify relative inspector sensitivity and to elucidate the relationship between participation and infestation. We show that naive estimates of prevalence would be biased by surveillance error and missingness at random assumptions. We validate our results through simulations and observe how randomized inspector assignments may improve prevalence estimates. Our results suggests that bias due to imperfect observations and missingness at random can be assessed and corrected in prevalence estimates of spatially auto-correlated binary variables.This is the peer reviewed version of the following article: [Hong, A.E., Barbu, C.M., Small, D.S., & Levy, M.Z. (2014). Mapping the Spatial Distribution of a Disease-Transmitting Insect in the Presence of Surveillance Error and Missing Data. Journal of the Royal Statistical Society: Series A 178, no. 3: pp. 641-658], which has been published in final form at http://dx.doi.org/10.1111/rssa.12077. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Bayesian hierarchical modellingspatial analysisstatistical epidemiologysurveillance errorBusinessBusiness AnalyticsManagement Sciences and Quantitative MethodsStatistics and ProbabilityReport