Methods Inf Med 2005; 44(05): 603-608
DOI: 10.1055/s-0038-1634015
Original Article
Schattauer GmbH

Containing Acute Disease Outbreak

A. Prince
1   School of Biological Sciences, Nanyang Technological University, Singapore
,
Xin Chen
1   School of Biological Sciences, Nanyang Technological University, Singapore
,
K. C. Lun
1   School of Biological Sciences, Nanyang Technological University, Singapore
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

Summary

Objective: The objectives of epidemiological surveillance and research of infectious diseases are to address disease prevention, identify outbreaks and monitor and evaluate control strategies. In this paper, we report on the development of a Geographical Information System (GIS) based on a novel Digital Ring Fence (DRiF) strategy for the containment of acute infectious diseases.

Method: Data from probable cases are captured in a secure database. Postal codes of addresses facilitate precise mapping of the location of each probable case on a multi-layered GIS system. A digital ring fence is constructed around each location (hot-spot) using Non-Homogeneous Poisson Process (NHPP) modeling based on data of individuals coming into contact with each probable case. The radius of the DRiF gives the overall risk of infection from its epicenter, the probable case. By annotating the DRiF to a GIS, areas of population concentrations could be readily identified to direct outbreak containment efforts.

Results: Simulation studies have demonstrated that the DRiF strategy could provide a novel approach to containment of acute disease outbreaks.

Conclusion: SARS has provided convincing evidence that the key to tackling acute infectious disease outbreaks lies in containment and making disease containment one step ahead of its spread. The DRiF strategy achieves this by providing a zone to corral the spread of infection through person-to-person transmission. Other useful applications of the DRiF technique include demarcating culling zone for the containment of bird flu infection and containment of person-to-person transmission should it occur.

 
  • References

  • 1 Ancel LW, Newman MEJ, Martin M, Schrag S. Applying network theory to epidemics: Modeling the spread and control of Mycoplasma pneumoniae. Working paper 01–12–078, Santa Fe Institute 2001
  • 2 Dixon P. The Truth about SARS Infection. Global Change Ltd http://www.globalchange.com/sars.htm May 13, 2003
  • 3 Harrison P. SARS-Toronto: Fail-Proof Public Health System Key to Containing SARS http://www.pslgroup.com/dg/23189A.htm
  • 4 Newman MEJ. Spread of epidemic disease on networks, Physical Review. 2002; E 66: 016128
  • 5 Montoya JM, Sole RV. Small World Patterns in Food Webs. J Theor Biol 2002; 214: 405
  • 6 Moore C, Newman MEJ. Epidemics and percolation in small-world networks. Phys Rev 2000; E 61: 5678
  • 7 Tsimring LS, Huerta R. Modeling of contact tracing in social networks. Physica A 2003; 325: 33-9.
  • 8 Meyers LA, Pourbohloul B, Newman MEJ, Skowronski DM, Brunham RC. Network theory and SARS: Predicting outbreak diversity. J Theor Biol 2005; 232: 71-81.
  • 9 Girvan M, Newman MEJ. Community structure in social and biological networks. Proc Natl Acad Sci USA (in press)
  • 10 Sattenspiel L, Simon CP. The spread and persistence of infectious diseases in structured populations. Math Biosci 1988; 90: 341-66.
  • 11 Johnson CP, Johnson J. GIS: A Tool for monitoring and management of epidemics. Map India 2003
  • 12 Barnes S, Peck A. Mapping the future of health care: GIS applications in Health care analysis. Geographic Information systems 1994; 4: 31-3.
  • 13 Newman MEJ. The structure and function of networks, Computer Physics Communications. 2002; 147: 40-5.
  • 14 Huerta R, Tsimring LS. Contact tracing and epidemics control in social networks. Phys Rev 2002; 66: 056115
  • 15 Andersson H, Britton T. Epidemics: Stochastic Models and Their Statistical Analysis, Springer Lecture Notes in Statistics, Vol. 151. New York: Springer-Verlag; 2000
  • 16 Moore C, Newman MEJ. Epidemics and percolation in small-world networks. Phys Rev 2000; E 61: 5679
  • 17 Chouinard A, Mcdonald D. A Characterization of Non-Homogeneous Poisson Processes. Stochastics 1985; 15: 113-9.
  • 18 Lipsitch M, Cohen T, Cooper B, Robins JM, Ma S, James L, Gopalakrishna G, Chew S, Tan C-C, Samore MH, Fisman D, Murray M. Transmission Dynamics and Control of Severe Acute Respiratory Syndrome. 20. Science 2003; 300 (www.sciencemag.org)
  • 19 Lee S, Wilson JR, Crawford MM. Modeling and Simulation of a Nonhomogeneous Poisson Process with Cyclic Features. Communications in Statistics – Simulation and Computation (1991)
  • 20 Franceschetti M, Meester R. Navigation in small world networks, a scale-free continuum model. Preprint
  • 21 Lewis PAW, Orav EJ. Simulation Methodology for Statisticians, Operations Analysts and Engineers. Volume I. Wadsworth & Brooks/Cole. 1989
  • 22 Reka A, Barabasi A-L. Statistical mechanics of complex networks. Reviews of Modern Physics. 2002. Vol 74
  • 23 Leemis LM. Nonparametric Estimation of the Intensity Function for a Nonhomogeneous Poisson Process. Management Science 1991; 37: 886-900.
  • 24 Arkin B, Leemis L. Nonparametric Estimation of the Cumulative Intensity Function for a Nonhomogeneous Poisson Process from Overlapping Intervals, Management Science. 2000; 46 (07) 989-98.
  • 25 Hollander M, Wolfe DA. Nonparametric statistical methods. New York: Wiley; 1999
  • 26 Small M, Tse CK. Small World and Scale Free Model of Transmission of SARS. Preprint. 2004