Spatial Risk Modeling for Infectious Disease Surveillance Using Population Movement Data

Roberto C.S.N.P. Souza, Renato M. Assunção, Daniel B. Neill, Luís G.S. Silva, Wagner Meira Jr.

Abstract:

We compare three recently proposed subset scan methods for spatial disease surveillance that use movement data from case and control individuals, rather than a single location per individual, in order to identify areas with a high relative risk of infection. We illustrate the use of these methods to detect spatial clusters of dengue infection risk using
geo-located data from Twitter classified into infected cases and non-infected controls.

Keywords: event detection, risk modeling, spatial scan statistics, mobility patterns, movement data

TL;DR: We compare three recently proposed subset scan methods for spatial disease surveillance that use movement data from case and control individuals.