Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions


The ability to explicitly represent infectious disease distributions and their risk factors over massive geographical and temporal scales has transformed how we investigate how environment impacts health. While landscape epidemiology studies have shed light on many aspects of disease distribution and risk differentials across geographies, new computational methods combined with new data sources such as citizen sensors, global spatial datasets, sensor networks, and growing availability and variety of satellite imagery offer opportunities for a more integrated approach to understanding these relationships. Additionally, a large number of new modelling and mapping methods have been developed in recent years to support the adoption of these new tools. The complexity of this research context results in study-dependent solutions and prevents landscape approaches from deeper integration into operational models and tools. In this paper we consider three common research contexts for spatial epidemiology; surveillance, modelling to estimate a spatial risk distribution and the need for intervention, and evaluating interventions and improving healthcare. A framework is proposed and a categorization of existing methods is presented. A case study into leptospirosis in Sri Lanka provides a working example of how the different phases of the framework relate to real research problems. The new framework for geocomputational landscape epidemiology encompasses four key phases: characterizing assemblages, characterizing functions, mapping interdependencies, and examining outcomes. Results from Sri Lanka provide evidence that the framework provides a useful way to structure and interpret analyses. The framework reported here is a new way to structure existing methods and tools of geocomputation that are increasingly relevant to researchers working on spatially explicit disease-landscape studies.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. Agampodi, S. B., Peacock, S. J., Thevanesam, V., Nugegoda, D. B., Smythe, L., Thaipadungpanit, J., et al. (2011). Leptospirosis outbreak in Sri Lanka in 2008: Lessons for assessing the global burden of disease. The American Journal of Tropical Medicine and Hygiene, 85(3), 471–478.

  2. Anderson, R. M., & May, R. M. (1979). Population biology of infectious diseases: Part I. Nature, 280(2), 361–367.

  3. Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27(2), 93–115.

  4. Ard, K. (2015). Trends in exposure to industrial air toxins for different racial and socioeconomic groups: A spatial and temporal examination of environmental inequality in the U.S. from 1995 to 2004. Social Science Research, 53, 375–390. doi:10.1016/j.ssresearch.2015.06.019.

  5. Baddeley, A., & Turner, R. (2005). Spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software, 12(6), 1–42.

  6. Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J. J., & Vespignani, A. (2009). Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences, 106(51), 21484–21489.

  7. Barrett, M. A., Humblet, O., Hiatt, R. A., & Adler, N. E. (2013). Big data and disease prevention: From quantified self to quantified communities. Big Data, 1(3), 168–175.

  8. Bernardinelli, L., Clayton, D., Pascutto, C., Montomoli, C., Ghislandi, M., & Songini, M. (1995). Bayesian analysis of space–time variation in disease risk. Statistics in Medicine, 14(21–22), 2433–2443.

  9. Bharti, A. R., Nally, J. E., Ricaldi, J. N., Matthias, M. A., Diaz, M. M., Lovett, M. A., et al. (2003). Leptospirosis: A zoonotic disease of global importance. The Lancet Infectious Diseases, 3(12), 757–771.

  10. Bishop, C. (2006). Pattern recognition and machine learning (information science and statistics). New York, NY: Springer.

  11. Boots, B. (2003). Developing local measure of spatial association for categorical data. Journal of Geographical Systems, 5(2), 139–160.

  12. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Belmont, CA: Wadsworth.

  13. Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008). Surveillance Sans Frontières: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Medicine, 5(7), e151.

  14. Brownstein, J. S., Freifeld, C. C., & Madoff, L. C. (2009). Digital disease detection—Harnessing the web for public health surveillance. New England Journal of Medicine, 360(21), 2153–2157.

  15. Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298.

  16. Claude, B., Perrin, D., & Ruskin, H. J. (2009). Considerations for a social and geographical framework for agent-based epidemics. In International conference on computational aspects of social networks, 2009. CASON’09 (pp. 149–154).

  17. Coker, R., Rushton, J., Mounier-Jack, S., Karimuribo, E., Lutumba, P., Kambarage, D., et al. (2011). Towards a conceptual framework to support one-health research for policy on emerging zoonoses. The Lancet Infectious Diseases, 11(4), 326–331.

  18. Couclelis, H. (1998). Geocomputation in context. In P. A. Longely, S. M. Brooks, R. McDonnell, & B. McMillan (Eds.), Geocomputation: A primer (pp. 17–30). West Sussex, UK: Wiley.

  19. Cressie, N. (1991). Statistics for spatial data. Toronto: Wiley.

  20. Cressie, N., & Wikle, C. K. (2011). Statistics for spatio-temporal data. Toronto: Wiley.

  21. Cromley, E. K. (2003). GIS and disease. Annual Review of Public Health, 24(1), 7–24.

  22. Dale, M. R. T., & Fortin, M.-J. (2010). From graphs to spatial graphs. Annual Review of Ecology Evolution and Systematics, 41(1), 21–38.

  23. Diggle, P. (2003). Statistical analysis of spatial point patterns. London: Academic Press.

  24. Estabrooks, C. A., Thompson, D. S., Lovely, J. J. E., & Hofmeyer, A. (2006). A guide to knowledge translation theory. Journal of Continuing Education in the Health Professions, 26(1), 25–36.

  25. Field, H., Young, P., Yob, J. M., Mills, J., Hall, L., & Mackenzie, J. (2001). The natural history of Hendra and Nipah viruses. Microbes and Infection, 3(4), 307–314.

  26. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. London: Wiley.

  27. Freier, J. E., Miller, R. S., & Geter, K. D. (2007). Geospatial analysis and modelling in the prevention and control of animal diseases in the United States. Special Issue. Geographic Information Systems, 43(3), 549–557.

  28. Freifeld, C. C., Chunara, R., Mekaru, S. R., Chan, E. H., Kass-Hout, T., Iacucci, A. A., et al. (2010). Participatory epidemiology: Use of mobile phones for community-based health reporting. PLoS Medicine, 7(12), e1000376.

  29. Fritz, C. E., Schuurman, N., Robertson, C., & Lear, S. (2013). A scoping review of spatial cluster analysis techniques for point-event data. Geospatial Health, 7(2), 183.

  30. Gahegan, M. (2000). On the application of inductive machine learning tools to geographical analysis. Geographical Analysis, 32(1), 113–139.

  31. Gamage, C. D., Yasuda, S. P., & Nishio, S. (2011). Serological evidence of Thailand virus-related hantavirus infection among suspected leptospirosis patients in Kandy, Sri Lanka. Japanese Journal of Infectious Diseases, 64(1), 72–75.

  32. Gerber, P., Chilonda, P., Franceschini, G., & Menzi, H. (2005). Geographical determinants and environmental implications of livestock production intensification in Asia. Bioresource Technology, 96(2), 263–276.

  33. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.

  34. Graham, J. P., Leibler, J. H., Price, L. B., Otte, J. M., Pfeiffer, D. U., Tiensin, T., et al. (2008). The animal–human interface and infectious disease in industrial food animal production: Rethinking biosecurity and biocontainment. Public Health Reports, 123(3), 282–299.

  35. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., et al. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1–2), 115–126.

  36. Grogan, L. F., Berger, L., Rose, K., Grillo, V., Cashins, S. D., & Skerratt, L. F. (2014). Surveillance for emerging biodiversity diseases of wildlife. PLoS Pathogens, 10(5), e1004015.

  37. Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. USA: CRC Press.

  38. Haynes, K. E., & Fotheringham, S. (1984). Gravity and spatial interaction models. Beverly Hills, CA: Sage.

  39. Hulth, A., Rydevik, G., & Linde, A. (2009). Web queries as a source for syndromic surveillance. PLoS ONE, 4(2), e4378.

  40. Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L., et al. (2008). Global trends in emerging infectious diseases. Nature, 451(7181), 990–993.

  41. Karesh, W. B., Cook, R. A., Bennett, E. L., & Newcomb, J. (2005). Wildlife trade and global disease emergence. Emerging Infectious Diseases, 11(7), 1000–1002.

  42. Kearns, R. A. (1993). Place and health: Towards a reformed medical geography*. The Professional Geographer, 45(2), 139–147.

  43. Kearns, R., & Moon, G. (2002). From medical to health geography: Novelty, place and theory after a decade of change. Progress in Human Geography, 26(5), 605–625.

  44. Kelegama, S. (2010). Managing food price inflation in Sri Lanka. In S. Ahmed & H. G. P. Jansen (Eds.), Managing food price inflation in South Asia (p. 290). Washington, DC: World Bank.

  45. Khan, K., Arino, J., Hu, W., Raposo, P., Sears, J., Calderon, F., et al. (2009). Spread of a novel influenza A (H1N1) virus via global airline transportation. New England Journal of Medicine, 361(2), 212–214.

  46. Kienberger, S., & Hagenlocher, M. (2014). Spatial-explicit modeling of social vulnerability to malaria in East Africa. International Journal of Health Geographics, 13(1), 29.

  47. Kulldorff, M., & Nagarwalla, N. (1995). Spatial disease clusters: Detection and inference. Statistics in Medicine, 14(8), 799–810.

  48. Kwan, M.-P. (2013). Beyond space (as we knew it): Toward temporally integrated geographies of segregation, health, and accessibility. Annals of the Association of American Geographers, 103(5), 1078–1086.

  49. Lai, P.-C., So, F.-M., & Chan, K.-W. (2008). Spatial epidemiological approaches in disease mapping and analysis. Boca Raton, FL: CRC Press.

  50. Lambin, E. F., Tran, A., Vanwambeke, S. O., Linard, C., & Soti, V. (2010). Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts. International Journal of Health Geographics, 9(1), 54.

  51. Lash, R. R., Brunsell, N. A., & Peterson, A. T. (2008). Spatiotemporal environmental triggers of Ebola and Marburg virus transmission. Geocarto International, 23(6), 451–466.

  52. Lawson, A. B. (2008). Bayesian disease mapping: Hierarchical modeling in spatial epidemiology (1st ed.). London: Chapman and Hall/CRC.

  53. Leidner, J. L. (2008). Toponym resolution in text: Annotation, evaluation and applications of spatial grounding of place names. Universal-Publishers.

  54. Lengeler, C., Armstrong‐Schellenberg, J., D'Alessandro, U., Binka, F., & Cattani, J. (1998). Relative versus absolute risk of dying reduction after using insecticide-treated nets for malaria control in Africa. Tropical Medicine and International Health, 3(4), 286–290.

  55. Levin, S. A. (1998). Ecosystems and the biosphere as complex adaptive systems. Ecosystems, 1(5), 431–436.

  56. Long, J. A., Nelson, T. A., & Wulder, M. A. (2010). Local indicators for categorical data: Impacts of scaling decisions. Canadian Geographer/Le Géographe Canadien, 54(1), 15–28.

  57. Merler, S., Ajelli, M., Fumanelli, L., Gomes, M. F., y Piontti, A. P., Rossi, L., et al. (2015). Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis. The Lancet Infectious Diseases, 15(2), 204–211.

  58. Murray, C. J. L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., et al. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2197–2223.

  59. Nychka, D., Furrer, R., & Sain, S. (2015). Tools for spatial data. Accessed 24 March 2015.

  60. O’Neillr, R. V., Krummel, J. R., Gardner, R. H., Sugihara, G., Jackson, B., & DeAngelist, D. L., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.

  61. Openshaw, S. (2014). Geocomputation. In R. J. Abrahart & L. M. See (Eds.), GeoComputation. London: CRC Press.

  62. Pavlovsky, E. N. (1966). In N. D. Levine (Ed.), Natural nidality of transmissible diseases with special reference to the landscape epidemiology of zooanthroponoses. Urbana, IL: University of Illinois Press.

  63. Pfeifer, B., Kugler, K., Tejada, M. M., Baumgartner, C., Seger, M., Osl, M., et al. (2008). A cellular automaton framework for infectious disease spread simulation. The Open Medical Informatics Journal, 2, 70–81.

  64. Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259.

  65. Plouffe, C. C. F., Robertson, C., & Chandrapala, L. (2015). Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka. Environmental Modelling and Software, 67, 57–71.

  66. Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press.

  67. Riley, S. (2007). Large-scale spatial-transmission models of infectious disease. Science, 316(5829), 1298–1301.

  68. Riley, S., Eames, K., Isham, V., Mollison, D., & Trapman, P. (2015). Five challenges for spatial epidemic models. Epidemics, 10(5829), 68–71.

  69. Robertson, C., Nelson, T. A., MacNab, Y. C., & Lawson, A. B. (2010). Review of methods for space–time disease surveillance. Spatial and Spatio-Temporal Epidemiology, 1(2), 105–116.

  70. Robertson, C., Nelson, T. A., & Stephen, C. (2012). Spatial epidemiology of suspected clinical leptospirosis in Sri Lanka. Epidemiology and Infection, 140(4), 731–743.

  71. Robertson, C., Long, J. A., Nathoo, F. S., Nelson, T. A., & Plouffe, C. C. (2014). Assessing quality of spatial models using the structural similarity index and posterior predictive checks. Geographical Analysis, 46(1), 53–74.

  72. Rytkönen, M. J. P. (2004). Not all maps are equal: GIS and spatial analysis in epidemiology. International Journal of Circumpolar Health, 63(1), 9–24.

  73. Scott, J. (2012). Social network analysis. London: Sage.

  74. Shankardass, K. (2012). Place-based stress and chronic disease: A systems view of environmental determinants. In P. O’Campo & J. R. Dunn (Eds.), Rethinking social epidemiology (pp. 113–136). Netherlands: Springer.

  75. Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS ONE, 6(5), e19467.

  76. Stephen, C. (2014). Toward a modernized definition of wildlife health. Journal of Wildlife Diseases, 50(3), 427–430.

  77. Straus, S. E., Tetroe, J. M., & Graham, I. D. (2011). Knowledge translation is the use of knowledge in health care decision making. Journal of Clinical Epidemiology, 64(1), 6–10.

  78. Sunil-Chandra, N. P., Clement, J., Maes, P., De Silva, H. J., Van Esbroeck, M., & Van Ranst, M. (2015). Concomitant leptospirosis-hantavirus co-infection in acute patients hospitalized in Sri Lanka: Implications for a potentially worldwide underestimated problem. Epidemiology & Infection FirstView, 1–13.

  79. Tatem, A. J. (2014). Mapping population and pathogen movements. International Health, 6(1), 5–11.

  80. Torrens, P. (2010). Geography and computational social science. GeoJournal, 75(2), 133–148.

  81. Vitolo, C., Elkhatib, Y., Reusser, D., Macleod, C. J., & Buytaert, W. (2015). Web technologies for environmental big data. Environmental Modelling and Software, 63, 185–198.

  82. Waltner-Toews, D., Kay, J. J., & Lister, N.-M. E. (2008). The ecosystem approach: Complexity, uncertainty, and managing for sustainability. New York, NY: Columbia University Press.

  83. Wang, L.-F., & Eaton, B. T. (2007). Bats, civets and the emergence of SARS. In S. R. S. J. E. Childs, P. J. S. Mackenzie, & V. M. O. J. A. Richt (Eds.), Wildlife and emerging zoonotic diseases: The biology, circumstances and consequences of cross-species transmission. Current topics in microbiology and immunology (pp. 325–344). Berlin: Springer.

  84. Wang, F. H., & Luo, W. (2005). Assessing spatial and nonspatial factors for healthcare access: Towards an integrated approach to defining health professional shortage areas. Health and Place, 11(2), 131–146.

  85. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P., et al. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

  86. Ward, M. P., Laffan, S. W., & Highfield, L. D. (2009). Modelling spread of foot-and-mouth disease in wild white-tailed deer and feral pig populations using a geographic-automata model and animal distributions. Preventive Veterinary Medicine, 91(1), 55–63.

  87. Weiss, R. A., & McMichael, A. J. (2004). Social and environmental risk factors in the emergence of infectious diseases. Nature Medicine, 10, S70–S76.

  88. Wilkinson, R. G. (1994). The epidemiological transition: From material scarcity to social disadvantage? Daedalus, 123(4), 61–77.

  89. World Health Organization. (2005). International health regulations. Geneva: World Health Organization.

  90. World Health Organization. (2014). Early detection, assessment and response to acute public health events: Implementation of early warning and response with a focus on event-based surveillance. Geneva: World Health Organization.

  91. Wu, J. (2004). Effects of changing scale on landscape pattern analysis: Scaling relations. Landscape Ecology, 19(2), 125–138.

  92. Yang, T.-C., Shoff, C., & Noah, A. J. (2013). Spatializing health research: What we know and where we are heading. Geospatial Health, 7(2), 161–168.

  93. Young, S. G. (2013). Landscape epidemiology and machine learning: A geospatial approach to modeling West Nile virus risk in the United States. Ph.D. Thesis, p. 5.

Download references


The authors gratefully acknowledge the Epidemiological Unit of the Ministry of Health, Government of Sri Lanka, for providing access to the leptospirosis surveillance data used in this paper.

Author information

Correspondence to Colin Robertson.

Ethics declarations

Conflict of interest


Research involving human participants and/or animals

No humans were involved in this research.

Informed consent

No human participants were involved in this study.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Robertson, C. Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions. GeoJournal 82, 397–414 (2017).

Download citation


  • Geocompuation
  • Landscape change
  • Disease risk
  • Framework
  • GIS