An Extension of Discrete Tribocharging Models to Continuous Size Distributions

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Continuous and discrete SIR-models with spatial distributions

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Abstract

The SIR-model is a basic epidemic model that classifies a population into three subgroups: susceptible S, infected I and removed R. This model does not take into consideration the spatial distribution of each subgroup, but considers the total number of individuals belonging to each subgroup. There are many variants of the SIR-model. For studying the spatial distribution, stochastic processes have often been introduced to describe the dispersion of individuals. Such assumptions do not seem to be applicable to humans, because almost everyone moves within a small fixed radius in practice. Even if individuals do not disperse, the transmission of disease occurs. In this paper, we do not assume the dispersion of individuals, and instead use the infectious radius. Then, we propose simple continuous and discrete SIR-models that show spatial distributions. The results of our simulations show that the propagation speed and size of an epidemic depend on the population density and the infectious radius.

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References

  • Arino J, Van Den Driessche P (2003) A multi-city epidemic model. Math Popul Stud 10:175–193

    Article  MathSciNet  MATH  Google Scholar

  • Bauer F, Jost F, Liu S (2012) Ollivier–Ricci curvature and the spectrum of the normalized graph laplace operator. Math Res Lett 19:1185–1205

    Article  MathSciNet  MATH  Google Scholar

  • Berres S, Ruiz-Baier R (2011) A fully adaptive numerical approximation for a two-dimensional epidemic model with nonlinear cross-diffusion. Nonlinear Anal Real World Appl 12:2888–2903. doi:10.1016/j.nonrwa.2011.04.014

    Article  MathSciNet  MATH  Google Scholar

  • Burger R, Chowell G, Mulet PEP, Villada LM (2009) Modelling the spatial-temporal evolution of the 2009 A/H1N1 influenza pandemic in Chile. Math Biosci Eng 13:1–17

  • Capasso V, Di Liddo A (1994) Asymptotic behaviour of reaction-diffusion systems in population and epidemic models. J Math Biol 32:453–463. doi:10.1007/BF00160168

    Article  MathSciNet  MATH  Google Scholar

  • Chavel I (1984) Eigenvalues in Riemannian geometry. Academic press, New York

    MATH  Google Scholar

  • Chinviriyasit S, Chinviriyasit W (2010) Numerical modelling of an SIR epidemic model with diffusion. Appl Math Comput 216:395–409. doi:10.1016/j.amc.2010.01.028

    MathSciNet  MATH  Google Scholar

  • Hilker FM, Langlais M, Petrovskii SV, Malchow H (2007) A diffusive SI model with Allee effect and application to FIV. Math Biosci 206:61–80. doi:10.1016/j.mbs.2005.10.003

    Article  MathSciNet  MATH  Google Scholar

  • Hyman JM, Laforce T (2003) Modeling the spread of influenza among cities. Bioterrorism Math Model Appl Homel Secur 28:211

    Article  MathSciNet  Google Scholar

  • Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. In: Proceedings of the royal society a: mathematical, physical and engineering sciences. The royal society, pp 700–721

  • Lee JM, Choi D, Cho G, Kim Y (2012) The effect of public health interventions on the spread of influenza among cities. J Theor Biol 293:131–142. doi:10.1016/j.jtbi.2011.10.008

    Article  MathSciNet  MATH  Google Scholar

  • Lee S, Castillo-Chavez C (2015) The role of residence times in two-patch dengue transmission dynamics and optimal strategies. J Theor Biol 374:152–164. doi:10.1016/j.jtbi.2015.03.005

    Article  MathSciNet  MATH  Google Scholar

  • Lee J, Jung E (2015) A spatial-temporal transmission model and early intervention policies of 2009 A/H1N1 influenza in South Korea. J Theor Biol 380:60–73. doi:10.1016/j.jtbi.2015.05.008

    Article  MathSciNet  MATH  Google Scholar

  • Milner F, Zhao R (2008) S-I-R Model with Directed Spatial Diffusion. Math Popul Stud 15:160–181

    Article  MathSciNet  MATH  Google Scholar

  • Reluga T (2004) A two-phase epidemic driven by diffusion. J Theor Biol 229:249–261. doi:10.1016/j.jtbi.2004.03.018

    Article  MathSciNet  Google Scholar

  • Robinson M, Stilianakis NI, Drossinos Y (2012) Spatial dynamics of airborne infectious diseases. J Theor Biol 297:116–126. doi:10.1016/j.jtbi.2011.12.015

    Article  MathSciNet  Google Scholar

  • Sattenspiel L, Dietz K (1995) A structured epidemic model incorporating geographic mobility among regions. Math Biosci 128:71–91. doi:10.1016/0025-5564(94)00068-B

    Article  MATH  Google Scholar

  • Sattenspiel L, Herring DA (2003) Simulating the effect of quarantine on the spread of the 1918–19 flu in central Canada. Bull Math Biol 65:1–26

    Article  MATH  Google Scholar

  • Wang Y, Wang J, Zhang L (2010) Cross diffusion-induced pattern in an SI model. Appl Math Comput 217:1965–1970. doi:10.1016/j.amc.2010.06.052

    MathSciNet  MATH  Google Scholar

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Acknowledgments

The first author was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03931459).

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Correspondence to Seong-Hun Paeng.

Appendix: Map of the Seoul metropolitan area and table for populations

Appendix: Map of the Seoul metropolitan area and table for populations

Figure 10 displays the map and the induced graph of the Seoul metropolitan area consisting of 33 districts, respectively. Table 1 concerns the populations of 33 districts in the Seoul metropolitan area. Figure 11 concerns the numbers of the infected between our discrete model and the spatial-temporal model of Lee and Jung (2015) for whole 33 districts.

Fig. 10
figure 10

Map of Seoul metropolitan area and the induced graph

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Table 1 Populations of 33 districts in Seoul metropolitan area

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Fig. 11
figure 11

Comparison of the cumulative numbers of the infected between our discrete model and the spatial-temporal model of Lee and Jung (2015) for 33 districts

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Paeng, SH., Lee, J. Continuous and discrete SIR-models with spatial distributions. J. Math. Biol. 74, 1709–1727 (2017). https://doi.org/10.1007/s00285-016-1071-8

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  • DOI : https://doi.org/10.1007/s00285-016-1071-8

Keywords

  • SIR-model
  • Spatial distribution
  • Infectious radius

Mathematics Subject Classification

  • 92D30

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