Detecting Influenza Outbreaks Based on Spatiotemporal Information from Urban Systems

  • Lars Ole GrottenbergEmail author
  • Ove Njå
  • Erlend Tøssebro
  • Geir Sverre Braut
  • Karoline Bragstad
  • Gry Marysol Grøneng
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal and pandemic influenza outbreaks from a spatiotemporal perspective. We believe that combining travel data with epidemiological data is the first step to develop a tool to predict future epidemics and 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. Historical datasets from public transport and road traffic serves as an initial indication of whether changes in daily transport patterns corresponds to seasonal influenza data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly influenza 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 influenza outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks.


  1. Aleman DM, Wibisono TG, Schwartz B (2009) Accounting for individual behaviors in a pandemic disease spread model. In: Proceedings of the 2009 winter simulation conference (WSC), 1977–1985Google Scholar
  2. Batty M (2009) Cities as complex systems: scaling, interaction, networks, dynamics and urban morphologies. SpringerGoogle Scholar
  3. Batty M (2012) Build Sci Cities Cities 29:S9-S16CrossRefGoogle Scholar
  4. Bragstad K, Hungnes O, Waalen K, Aune T, Tønnessen R, Rydland KM, Klüwer B, Hauge S (2018) Influensasesongen i Norge 2017–18 [Influenza season in Norway 2017–18]. Norwegian Institute of Public HealthGoogle Scholar
  5. Carneiro HA, Mylonakis E (2009) Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infectious Dis 49:1557–1564CrossRefGoogle Scholar
  6. Cook S, Conrad C, Fowlkes AL, Mohebbi MH (2011) Assessing google flu trends performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic PLOS ONE 6:e23610CrossRefGoogle Scholar
  7. Dailey L, Watkins RE, Plant AJ (2007) Timeliness of data sources used for influenza surveillance. J Am Med Inform Assoc 14:626–631CrossRefGoogle Scholar
  8. Dawood FS, Iuliano AD, Reed C, Meltzer MI, Shay DK, Cheng P-Y, Bandaranayake D, Breiman RF, Brooks WA, Buchy P, Feikin DR, Fowler KB, Gordon A, Hien NT, Horby P, Huang QS, Katz MA, Krishnan A, Lal R, Montgomery JM, Mølbak K, Pebody R, Presanis AM, Razuri H, Steens A, Tinoco YO, Wallinga J, Yu H, Vong S, Bresee J, Widdowson M-A (2012) Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study The Lancet Infectious Diseases 12:687–695CrossRefGoogle Scholar
  9. Durham DP, Casman EA, Albert SM (2012) Deriving behavior model parameters from survey data: self-protective behavior adoption during the 2009–2010 influenza A(H1N1) pandemic Risk Analysis: an official publication of the society for risk analysis 32:2020–2031CrossRefGoogle Scholar
  10. ECDC (2014) The European Surveillance System (TESSy). European Centre for Disease Prevention and Control.
  11. Fierro A, Liccardo A (2013) Lattice model for influenza spreading with spontaneous behavioral changes. PLoS ONE 8:1–12CrossRefGoogle Scholar
  12. Gao Y, Wang S, Padmanabhan A, Yin J, Cao G (2018) Mapping spatiotemporal patterns of events using social media: a case study of influenza trends. Int J Geographical Inf Sci 32:425–449CrossRefGoogle Scholar
  13. German RR, Lee L, Horan J, Milstein R, Pertowski C, Waller M (2001) Updated guidelines for evaluating public health surveillance systems MMWR Recomm Rep 50Google Scholar
  14. Grottenberg LO, Njå O (2017) Applying a systems safety approach to the development of GIS in the Norwegian emergency management domain. In: Safety and reliability—theory and applications. CRC Press, pp 484–484Google Scholar
  15. Guan Y, Vijaykrishna D, Bahl J, Zhu H, Wang J, Smith GJD (2010) The emergence of pandemic influenza viruses Protein & Cell 1:9–13CrossRefGoogle Scholar
  16. Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, Cohen C, Gran JM, Schanzer D, Cowling BJ, Wu P, Kyncl J, Ang LW, Park M, Redlberger-Fritz M, Yu H, Espenhain L, Krishnan A, Emukule G, van Asten L, Pereira da Silva S, Aungkulanon S, Buchholz U, Widdowson M-A, Bresee JS, Azziz-Baumgartner E, Cheng P-Y, Dawood F, Foppa I, Olsen S, Haber M, Jeffers C, MacIntyre CR, Newall AT, Wood JG, Kundi M, Popow-Kraupp T, Ahmed M, Rahman M, Marinho F, Sotomayor Proschle CV, Vergara Mallegas N, Luzhao F, Sa L, Barbosa-Ramírez J, Sanchez DM, Gomez LA, Vargas XB, Acosta Herrera a, Llanés MJ, Fischer TK, Krause TG, Mølbak K, Nielsen J, Trebbien R, Bruno A, Ojeda J, Ramos H, an der Heiden M, del Carmen Castillo Signor L, Serrano CE, Bhardwaj R, Chadha M, Narayan V, Kosen S, Bromberg M, Glatman-Freedman A, Kaufman Z, Arima Y, Oishi K, Chaves S, Nyawanda B, Al-Jarallah RA, Kuri-Morales PA, Matus CR, Corona MEJ, Burmaa A, Darmaa O, Obtel M, Cherkaoui I, van den Wijngaard CC, van der Hoek W, Baker M, Bandaranayake D, Bissielo A, Huang S, Lopez L, Newbern C, Flem E, Grøneng GM, Hauge S, de Cosío FG, de Moltó Y, Castillo LM, Cabello MA, von Horoch M, Medina Osis J, Machado A, Nunes B, Rodrigues AP, Rodrigues E, Calomfirescu C, Lupulescu E, Popescu R, Popovici O, Bogdanovic D, Kostic M, Lazarevic K, Milosevic Z, Tiodorovic B, Chen M, Cutter J, Lee V, Lin R, Ma S, Cohen AL, Treurnicht F, Kim WJ, Delgado-Sanz C, de mateo Ontañón S, Larrauri A, León IL, Vallejo F, Born R, Junker C, Koch D, Chuang J-H, Huang W-T, Kuo H-W, Tsai Y-C, Bundhamcharoen K, Chittaganpitch M, Green HK, Pebody R, Goñi N, Chiparelli H, Brammer L, Mustaquim D (2017) Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. The Lancet 391(10127):1285–1300Google Scholar
  17. Kitchin R (2014) The real-time city? Big data and smart urbanism GeoJournal 79:1–14Google Scholar
  18. Kitchin R, Lauriault TP, McArdle G (2015) Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards Regional Studies. Reg Sci 2:6–28Google Scholar
  19. Kleczkowski A, Maharaj S, Rasmussen S, Williams L, Cairns N (2015) Spontaneous social distancing in response to a simulated epidemic: a virtual experiment. BMC Public Health 15:1–13CrossRefGoogle Scholar
  20. Lal A, Marshall J, Benschop J, Brock A, Hales S, Baker MG, French NP (2018) A Bayesian spatio-temporal framework to identify outbreaks and examine environmental and social risk factors for infectious diseases monitored by routine surveillance Spatial and Spatio-temporal Epidemiology 25:39–48CrossRefGoogle Scholar
  21. Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of google flu: traps in big data analysis. Science 343:1203–1205CrossRefGoogle Scholar
  22. Leveson N (2011) Engineering a safer world : systems thinking applied to safety. Engineering systems. Cambridge, Mass. MIT Press, copGoogle Scholar
  23. Lewis J, White PJ (2017) Estimating local chlamydia incidence and prevalence using surveillance data epidemiology (Cambridge, Mass) 28:492–502CrossRefGoogle Scholar
  24. Ministry of Health and Care Services (2014) National management plan for pandemic flu. The Norwegian GovernmentGoogle Scholar
  25. MSIS (2003) Forskrift om Meldingssystem for smittsomme sykdommer (MSIS-forskriften). Ministry of Health and Care ServicesGoogle Scholar
  26. National Influenza Centre (2018) Influenza epidemiological information prepared for WHO informal meeting on strain composition for inactivated influenza vaccines for use in season 2018–19 Geneva, Feb 2018. Norwegian Institute for Public HealthGoogle Scholar
  27. Norwegian Directorate for Civil Protection (2015) National Risk Analysis 2014. Norwegian Directorate for Civil ProtectionGoogle Scholar
  28. Norwegian Directorate of Health (2017) Overall risk and vulnerability routines in the healthcare sectorGoogle Scholar
  29. Norwegian Institute of Public Health (2017a) About the Norwegian Syndromic Surveillance System. Norwegian Institute of Public Health. Accessed 03 Oct 2018
  30. Norwegian Institute of Public Health (2017b) Early risk assessment: What to expect of the 2017/18 influenza season in Norway. Accessed 03 October 2018
  31. Norwegian Institute of Public Health (2017c) Influensasesongen i Norge 2016–17 [Influenza season in Norway 2016-17]. Norwegian Institute of Public HealthGoogle Scholar
  32. Norwegian Institute of Public Health (2017d) Influenza surveillance. Norwegian Institute of Public Health. Accessed 03 October 2018
  33. Poletto C, Tizzoni M, Colizza V (2013) Human mobility and time spent at destination: Impact on spatial epidemic spreading. J Theor Biol 7(338):41–58. Scholar
  34. Robertson C (2017) Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions GeoJournal 82:397–414CrossRefGoogle Scholar
  35. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS (2015) Combining search, social media, and traditional data sources to improve influenza surveillance PLOS computational biology 11:e1004513CrossRefGoogle Scholar
  36. Simonsen L, Spreeuwenberg P, Lustig R, Taylor RJ, Fleming DM, Kroneman M, Van Kerkhove MD, Mounts AW, Paget WJ, the GCT (2013) Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study PLOS Medicine 10:e1001558CrossRefGoogle Scholar
  37. Thacker SB, Berkelman RL (1988) Public health surveillance in the United States Epidemiologic reviews 10:164–190CrossRefGoogle Scholar
  38. Timpka T, Eriksson H, Gursky EA, Nyce JM, Morin M, Jenvald J, Strömgren M, Holm E, Ekberg J (2009) Population-based simulations of influenza pandemics: validity and significance for public health policy. Bull World Health Organ 87:305–311CrossRefGoogle Scholar
  39. Van Kerckhove K, Hens N, Edmunds WJ, Eames KTD (2013) The Impact of Illness on Social Networks: Implications for Transmission and Control of Influenza. Am J Epidemiol 178:1655–1662CrossRefGoogle Scholar
  40. Vanja D, Hedibert FL, Nicholas GP (2012) Tracking epidemics with google flu trends data and a state-space SEIR model. J Am Statist Assoc, 1410Google Scholar
  41. Vega T, Lozano JE, Meerhoff T, Snacken R, Mott J, Ortiz de Lejarazu R, Nunes B (2013) Influenza surveillance in Europe: establishing epidemic thresholds by the moving epidemic method influenza and other respiratory viruses 7:546–558CrossRefGoogle Scholar
  42. Wang Z, Ye X (2018) Social media analytics for natural disaster management. Int J Geograph Inf Sci 32:49–72CrossRefGoogle Scholar
  43. WICC (1998) International Classification of Primary Care, ICPC-2 Oxford University Press, OxfordGoogle Scholar
  44. Wolf M (2017) Knowing pandemics: an investigation into the enactment of pandemic influenza preparedness in urban environments science & technology studies 30:8–29Google Scholar
  45. World Health Organization (2013) Pandemic influenza risk management: WHO interim guidanceGoogle Scholar
  46. World Health Organization (2017) Pandemic influenza severity assessment (PISA): a WHO guide to assess the severity of influenza in seasonal epidemics and pandemicsGoogle Scholar
  47. World Health Organization (2018) Essential steps for developing or updating a national pandemic influenza preparedness planGoogle Scholar
  48. Xue Y, Kristiansen IS, de Blasio BF (2010) Modeling the cost of influenza: the impact of missing costs of unreported complications and sick leave. BMC Public Health 10:724CrossRefGoogle Scholar
  49. Yan SJ, Chughtai AA, Macintyre CR (2017) Utility and potential of rapid epidemic intelligence from internet-based sources. Int J Infectious Dis 63:77–87CrossRefGoogle Scholar
  50. Yang B, Pei H, Chen H, Liu J, Xia S (2017) Characterizing and discovering spatiotemporal social contact patterns for healthcare. IEEE Trans Pattern Anal Mach Intell 39:1532–1546CrossRefGoogle Scholar
  51. Yao Y, Liu X, Li X, Zhang J, Liang Z, Mai K, Zhang Y (2017) Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. Int J Geogr Inf Sci 31:1220–1244Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lars Ole Grottenberg
    • 1
    Email author
  • Ove Njå
    • 1
  • Erlend Tøssebro
    • 2
  • Geir Sverre Braut
    • 3
  • Karoline Bragstad
    • 4
  • Gry Marysol Grøneng
    • 5
  1. 1.Department of Safety, Economics and PlanningUniversity of StavangerStavangerNorway
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of StavangerStavangerNorway
  3. 3.Stavanger University HospitalStavangerNorway
  4. 4.Department of InfluenzaNorwegian Institute of Public HealthOsloNorway
  5. 5.Department of Infectious Diseases Epidemiology and ModellingNorwegian Institute of Public HealthOsloNorway

Personalised recommendations