Disease Mapping with Spatially Uncertain Data

Justin Manjourides, Ted Cohen, Caroline Jeffery, Marcello Pagano


We present a disease mapping method that accounts for spatially uncertain data by informatively weighting the locations of interest. This method is applied to programmatic tuberculosis data collected over three years in Lima, Peru, with the goal of identifying potential hotspots of drug-resistance transmission. The flexibility of this method, which accommodates any general weighting scheme, allows us to examine the affects of different assumptions regarding the uncertainty present in the data.

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DOI: https://doi.org/10.5210/ojphi.v5i1.4380

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org