Disease Mapping with Spatially Uncertain Data

Authors

  • Justin Manjourides Dept of Health Sciences, Northeastern University
  • Ted Cohen Div of Global Health Equity, Brigham & Women's Hospital; Dept of Epidemiology, Harvard School of Public Health
  • Caroline Jeffery Intl Health Group, Liverpool School of Tropical Medicine
  • Marcello Pagano Dept of Biostatistics, Harvard School of Public Health

DOI:

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

Abstract

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.

Author Biography

Justin Manjourides, Dept of Health Sciences, Northeastern University

Justin Manjourides is an Assistant Professor of Biostatistics in the Department of Health Sciences at Northeastern University. His research interests are in the areas of disease surveillance, spatial statistics, cluster detection methods, and methods for understanding and accounting for spatial uncertainty in health data.

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Published

2013-03-21

How to Cite

Manjourides, J., Cohen, T., Jeffery, C., & Pagano, M. (2013). Disease Mapping with Spatially Uncertain Data. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4380

Issue

Section

Oral Presentations: Data Quality and Underlying Patterns in Data Streams