Skip to main content
SpringerLink
Log in
Book cover

Infectious Disease Informatics and Biosurveillance pp 191–206Cite as

Modeling in Space and Time

A Framework for Visualization and Collaboration

  • Chapter
  • First Online:

Part of the book series: Integrated Series in Information Systems ((ISIS,volume 27))

Chapter Overview

This chapter describes the Spatiotemporal Epidemiological Modeler (STEM), now being developed as an open source computer software system for defining and visualizing simulations of the spread of infectious disease in space and time. Part of the Eclipse Technology Project, http://www.eclipse.org/ stem, STEM is designed to offer the research community the power and extensibility to develop, validate, and share models on a common collaborative platform. Its innovations include a common representational framework that supports users in creating and configuring the components that constitute a model. This chapter defines modeling terms (canonical graph, decorators, etc.) and key concepts (e.g., labels, disease model computations) are discussed. Figures illustrate the types of visualizations STEM provides, including geographical views via GIS and Google Earth™ and report generated graphics.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    1 These are set forth in ISO 3166–1 Geographic Coding Standard: Codes for the Representation of Names of Countries and Their Subdivisions — Part 1.

References

  • Ash, C., and Roberts, L. (2006). “Influenza: The state of our ignorance,” Science, 312 (April 21, 2006), 379. http://www.sciencemag.org.

  • Cummings, DA., Irizarry, R.A., Endy, T.P., Nisalak, A., and Burke, D. (2004). “Travelling waves in dengue hemorrhagic fever incidence in Thailand,” Nature, 427:344–347.

    Article  PubMed  CAS  Google Scholar 

  • Epstein, J.M., and Cummings, D. (2002). Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational Approach, CSED Working Paper 31 (December 2002). Washington, DC: Brookings Institution.

    Google Scholar 

  • Ford, DA., Kaufman, J.H., and Eiron, I. (2006). “An extensible spatial and temporal epidemiological modeling system,” International Journal of Health Geographics, 5(4) (January 17, 2006). http://www.ij-healthgeographic.

  • Gross, J.L., and Yellen, J. (2003). Handbook of Graph Theory. Boca Baton, FL: CRC Press.

    Book  Google Scholar 

  • Haberman, R. (1998). Mathematical Models: Mechanical Vibrations, Population Dynamics, & Traffic Flow (Classics in Applied Mathematics). Philadelphia, PA: Society for Industrial & Applied Mathematics.

    Book  Google Scholar 

  • Liu, W-M., Hethcote, H.W., and Levin, SA. (1987). “Dynamical behavior of epidemiological models with nonlinear incidence rates,” Journal of Mathematical Biology, 25:359–380.

    Article  PubMed  CAS  Google Scholar 

  • Myers, LA., Newman, ME.J., Martin, M., and Schrag, S. (2003). “Applying network theory to epidemics: Control measures for mycoplasma pneumonia outbreaks.” Emerging Infectious Diseases, 9(2):204–210. (February 2003). http://www.cdc.gov/ncidod/EID/vol9no2/02-0188.

  • Schaffer, W.M., and Bronnikova, T.V. (2001). Ecology/Mathematics 380: Modeling Micro-parasitic Infections. See for example and references therein. Available at: http://www.bill.srnr.arizona.edu/classes/195b/195b.epmodel

  • Widgren, S. (2004). Graph Theory in Veterinary Epidemiology - Modelling an Outbreak of Classical Swine Fever. Thesis. Institution for Ruminant Medicine and Veterinary Epidemiology, Swedish University of Agricultural Science.

    Google Scholar 

Suggested Reading

  • Anderson, R.M. (1982). Population Dynamics of Infectious Diseases: Theory and Applications. New York: Chapman and Hall.

    Book  Google Scholar 

  • Barrett, C.L., Eubank, S.G., and Smith, J.P. (2005). “If smallpox strikes Portland,” Scientific American 292:42–49, How modeling might be used to plan for and develop responses to epidemic events.

    Article  PubMed  Google Scholar 

  • Enserink, M. (2003a). Science 301:294–296.

    Google Scholar 

  • Enserink, M. (2003b). Science 301:299.

    Google Scholar 

  • Normile D., Enserink, M. (2003c). Science 301:297–299.

    Google Scholar 

Online Resources

Download references

Author information

Authors and Affiliations

  1. Healthcare Informatics Research, Department of Computer Science, IBM Almaden Research Center, 650 Harry Road, San Jose, CA, 95120, USA

    Daniel A. Ford & James H. Kaufman

  2. Healthcare and Life Sciences, IBM Haifa Research Lab, Haifa University Campus, Mount Carmel, Haifa, 31905, Israel

    Yossi Mesika

Authors
  1. Daniel A. Ford
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James H. Kaufman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yossi Mesika
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel A. Ford .

Editor information

Editors and Affiliations

  1. Dept. Mathematics & Statistics, Arizona State University, Tempe, Arizona, USA

    Carlos Castillo-Chavez

  2. Eller College of Management, University of Arizona, E. Helen St. 1130, Tucson, 85721, Arizona, USA

    Hsinchun Chen

  3. Health Sciences Center, University of Washington, NE. Pacific St. 1959, Seattle, 98195, Washington, USA

    William B. Lober

  4. School of Veterinary Medicine, Dept. Medicine & Epidemiology, University of California, Davis, Shields Avenue 1, Davis, 95616, California, USA

    Mark Thurmond

  5. Eller College of Management, Dept. Management Information Systems, University of Arizona, Tucson, 85721, Arizona, USA

    Daniel Zeng

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Ford, D.A., Kaufman, J.H., Mesika, Y. (2011). Modeling in Space and Time. In: Castillo-Chavez, C., Chen, H., Lober, W., Thurmond, M., Zeng, D. (eds) Infectious Disease Informatics and Biosurveillance. Integrated Series in Information Systems, vol 27. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6892-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-6892-0_9

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-6891-3

  • Online ISBN: 978-1-4419-6892-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation