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Simulation of the spread of infectious diseases in a geographical environment

Abstract

The study of mathematical models for the spread of infectious diseases is an important issue in epidemiology. Given the fact that most existing models cannot comprehensively depict heterogeneities (e.g., the population heterogeneity and the distribution heterogeneity) and complex contagion patterns (which are mostly caused by the human interaction induced by modern transportation) in the real world, a theoretical model of the spread of infectious diseases is proposed. It employs geo-entity based cellular automata to simulate the spread of infectious diseases in a geographical environment. In the model, physical geographical regions are defined as cells. The population within each cell is divided into three classes: Susceptible, Infective, and Recovered, which are further divided into some subclasses by states of individuals. The transition rules, which determine the changes of proportions of those subclasses and reciprocal transformation formulas among them, are provided. Through defining suitable spatial weighting functions, the model is applied to simulate the spread of the infectious diseases with not only local contagion but also global contagion. With some cases of simulation, it has been shown that the results are reasonably consistent with the spread of infectious diseases in the real world. The model is supposed to model dynamics of infectious diseases on complex networks, which is nearly impossible to be achieved with differential equations because of the complexity of the problem. The cases of simulation also demonstrate that efforts of all kinds of interventions can be visualized and explored, and then the model is able to provide decision-making support for prevention and control of infectious diseases.

References

  1. White S H, del Rey A M, Sanchez G R. Modeling epidemics using cellular automata. Appl Math Comput, 2007, 186: 193–202

    Article  Google Scholar 

  2. Fuentes M A, Kuperman M N. Cellular automata and epidemiological models with spatial dependence. Physica A, 1999, 267: 471–486

    Article  Google Scholar 

  3. Anderson R M, May R M. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press, 1991

    Google Scholar 

  4. Ahmed E, Agiza H N. On modeling epidemics including latency, incubation and variable susceptibility. Physica A, 1998, 253: 347–352

    Article  Google Scholar 

  5. Sirakoulis G C, Karafyllidis I, Thanailakis A. A cellular automaton model for the effects of population movement and vaccination on epidemic propagation. Ecol Model, 2000, 133: 209–223

    Article  Google Scholar 

  6. Wang J F. Spatial Analysis. Beijing: Science Press, 2006

    Google Scholar 

  7. Von Neumann J. Theory of Self-Reproducing Automata. Urbana: University of Illinois Press, 1966

    Google Scholar 

  8. Liu Q X, Jin Z. Cellular automata modelling of SEIRS. Chin Phys, 2005, 14: 1370–1377

    Article  Google Scholar 

  9. Mikler A R, Venkatachalam S, Abbas K. Modeling infectious diseases using global stochastic cellular automata. J Biol Syst, 2005, 13: 421–439

    Article  Google Scholar 

  10. Huang C Y, Sun C T, Hsieh J L, et al. Simulating SARS: Small-world epidemiological modeling and public health policy assessments. J Artif Soc Soc Simul, 2004, 7(4), http://jasss.soc.surrey.ac.cuk/7/4/2.html

  11. Zhou C H, Sun Z L, Xie Y C. Geographical Cellular Automata. Beijing: Science Press, 1999

    Google Scholar 

  12. Liu X P, Li X, Anthony G O E, et al. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Sci China Ser-D Earth Sci, 2007, 50(10): 1578–1588

    Article  Google Scholar 

  13. Flache A, Hegselmann R. Do irregular grids make a difference? Relaxing the spatial regularity assumption in cellular models of social dynamics. JASSS, 2001, 4(4), http://jasss.soc.surrey.ac.uk/4/4/6.html

  14. Moreno N, Ménard A, Marceau D J. VecGCA: An vector-based geographic cellular automata model allowing geometric transformations of objects. Environ Plann B, 2008, 35(4): 647–665

    Article  Google Scholar 

  15. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Phys Rev Lett, 2001, 86: 3200

    Article  Google Scholar 

  16. Wang X F. Complex networks: Topology, dynamics and synchronization. Int J Bifurcat Chaos, 2002, 12: 885–916

    Article  Google Scholar 

  17. Barthélemy M, Barrat A, Pastor-Satorras R, et al. Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. Phys Rev Lett, 2004, 92(1): 178701

    Article  Google Scholar 

  18. Shirley M D F, Rushton S P. The impacts of network topology on disease spread. Ecol Complex, 2005, 2: 287–299

    Article  Google Scholar 

  19. Draief M. Epidemic processes on complex networks: The effect of topology on the spread of epidemics. Physica A, 2006, 363: 120–131

    Article  Google Scholar 

  20. Silva S L, Ferreira J A, Martins M L. Epidemic spreading in a scale-free network of regular lattices. Physica A, 2007, 377: 689–697

    Article  Google Scholar 

  21. Allman E S, Rhodes J A. Mathematical Models in Biology: An Introduction. Cambridge: Cambridge University Press, 2004

    Google Scholar 

  22. Tobler W R. A computer movie simulating urban growth in the Detroit region. Econ Geol, 1970, 46: 234–240

    Google Scholar 

  23. Odland J. Spatial Autocorrelation. California: Sage Publications, 1988

    Google Scholar 

  24. Cliff A D, Ord J K. Spatial Processes: Models and Applications. London: Pion, 1981

    Google Scholar 

  25. Tobler W R. Linear Operators Applied to Areal Data. London: John Wiley, 1975

    Google Scholar 

  26. Yue T X, Wang Y A, Zhang Q, et al. YUE-SMPD scenarios of Beijing population distribution (in Chinese). Geo-information Sci, 2008, 10(4): 479–488

    Google Scholar 

  27. Meng B, Wang J F. Understanding the spatial diffusion process of SARS in Beijing. Public Health, 2005, 119: 1080–1087

    Article  Google Scholar 

  28. Wang J F, McMichael A J, Meng B, et al. Spatial dynamics of an epidemic of severe acute respiratory syndrome in an urban area. Bull World Health Organ, 2006, 84: 965–968

    Article  Google Scholar 

  29. Bombardt J N. Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak. Math Biosci, 2006, 203: 171–203

    Article  Google Scholar 

  30. BowenJr J T, Laroe C. Airline networks and the international diffusion of severe acute respiratory syndrome (SARS). Geogr J, 2006, 72: 130–144

    Article  Google Scholar 

  31. Ruan S, Wang W, Levin S A. The effect of global travel on the spread of SARS. Math Biosci Eng, 2006, 3: 205–218

    Google Scholar 

  32. Wang J F, Christakos G, Han W G, et al. Data-driven exploration of “spatial pattern-time process-driving forces” associations of SARS epidemic in Beijing, China. J Public Health, 2008, 30(3): 234–244

    Article  Google Scholar 

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Correspondence to ShaoBo Zhong.

Additional information

Supported by Postdoctoral Foundation of China (Grant No. 20070410552) and Youth Fund of Institute of Policy and Management (IPM), the Chinese Academy of Sciences (Grant No. O700481Q01)

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Zhong, S., Huang, Q. & Song, D. Simulation of the spread of infectious diseases in a geographical environment. Sci. China Ser. D-Earth Sci. 52, 550–561 (2009). https://doi.org/10.1007/s11430-009-0044-9

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  • DOI: https://doi.org/10.1007/s11430-009-0044-9

Keywords

  • cellular automata
  • infectious disease
  • modeling
  • geographic information systems
  • spatial weighting function