Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 231-237, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-231-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 231-237, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-231-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

DETECTING FLU OUTBREAKS BASED ON SPATIOTEMPORAL INFORMATION FROM URBAN SYSTEMS – DESIGNING A NOVEL STUDY

L. O. Grottenberg1, O. Njå1, E. Tøssebro2, G. S. Braut3, R. Tønnessen4, and G. M. Grøneng5 L. O. Grottenberg et al.
  • 1Department of Safety, Economics and Planning, Faculty of Science and Technology, University of Stavanger, Norway
  • 2Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger, Norway
  • 3Stavanger University Hospital, Norway
  • 4Department of Influenza, Norwegian Institute of Public Health, Norway
  • 55Department of Infectious Diseases Epidemiology and Modelling, Norwegian Institute of Public Health, Norway

Keywords: GIS, epidemiology, spatial epidemiology, urban information systems, seasonal influenza, infectious disease surveillance and control

Abstract. This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal flu epidemics from a spatial perspective. We believe that combining travel data with epidemiological data will be the first step to develop a tool to predict future epidemics and to better understand the effects that these outbreaks have on societal functions over time. Real-time data-streams provide a powerful, yet underutilised tool when it comes to monitoring and detecting changes to the daily behaviour of inhabitants.
In this paper, we describe and discuss the design of the geospatial project, in which we will draw upon data sources available from the Norwegian cities of Oslo and Bergen. Historical datasets from public transport and road traffic will serve as an initial indication of whether changes in daily transport patterns corresponds to seasonal flu data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly flu activity and that these differences can be measured through geostatistical analysis. Conceptually one could be able to monitor changes in human behaviour and activity in nearly true time by using indicators derived from outside the clinical health services. This type of more up-to-date and geographically precise information could contribute to earlier detection of flu outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks.