Detecting spatial clusters of disease infection risk using sparsely sampled social media mobility patterns

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Standard spatial cluster detection methods used in public health surveillance assign each disease case to a single location (typically, the patient's home address), aggregate locations to small areas, and monitor the number of cases in each area over time. However, such methods cannot detect clusters of disease resulting from visits to non-residential locations, such as a park or a university campus. Thus we develop two new spatial scan methods, the unconditional and conditional spatial logistic models, to search for spatial clusters of increased infection risk. We use mobility data from two sets of individuals, disease cases and healthy individuals, where each individual is represented by a sparse sample of geographical locations (e.g., from geo-tagged social media data). The methods account for the multiple, varying number of spatial locations observed per individual, either by non-parametric estimation of the odds of being a case, or by matching case and control individuals with similar numbers of observed locations. Applying our methods to synthetic and real-world scenarios, we demonstrate robust performance on detecting spatial clusters of infection risk from mobility data, outperforming competing baselines.

Original languageEnglish (US)
Title of host publication27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
EditorsFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
PublisherAssociation for Computing Machinery
Pages359-368
Number of pages10
ISBN (Electronic)9781450369091
DOIs
StatePublished - Nov 5 2019
Event27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 - Chicago, United States
Duration: Nov 5 2019Nov 8 2019

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
CountryUnited States
CityChicago
Period11/5/1911/8/19

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Keywords

  • Social media data
  • Spatial cluster detection
  • Spatial scan statistics

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Souza, R. C. S. N. P., Assunção, R. M., Neill, D. B., & Meira, W. (2019). Detecting spatial clusters of disease infection risk using sparsely sampled social media mobility patterns. In F. Banaei-Kashani, G. Trajcevski, R. H. Guting, L. Kulik, & S. Newsam (Eds.), 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 (pp. 359-368). (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery. https://doi.org/10.1145/3347146.3359369