Research article

Modeling epidemic flow with fluid dynamics


  • Received: 07 April 2022 Revised: 27 May 2022 Accepted: 31 May 2022 Published: 09 June 2022
  • In this paper, a new mathematical model based on partial differential equations is proposed to study the spatial spread of infectious diseases. The model incorporates fluid dynamics theory and represents the epidemic spread as a fluid motion generated through the interaction between the susceptible and infected hosts. At the macroscopic level, the spread of the infection is modeled as an inviscid flow described by the Euler equation. Nontrivial numerical methods from computational fluid dynamics (CFD) are applied to investigate the model. In particular, a fifth-order weighted essentially non-oscillatory (WENO) scheme is employed for the spatial discretization. As an application, this mathematical and computational framework is used in a simulation study for the COVID-19 outbreak in Wuhan, China. The simulation results match the reported data for the cumulative cases with high accuracy and generate new insight into the complex spatial dynamics of COVID-19.

    Citation: Ziqiang Cheng, Jin Wang. Modeling epidemic flow with fluid dynamics[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 8334-8360. doi: 10.3934/mbe.2022388

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  • In this paper, a new mathematical model based on partial differential equations is proposed to study the spatial spread of infectious diseases. The model incorporates fluid dynamics theory and represents the epidemic spread as a fluid motion generated through the interaction between the susceptible and infected hosts. At the macroscopic level, the spread of the infection is modeled as an inviscid flow described by the Euler equation. Nontrivial numerical methods from computational fluid dynamics (CFD) are applied to investigate the model. In particular, a fifth-order weighted essentially non-oscillatory (WENO) scheme is employed for the spatial discretization. As an application, this mathematical and computational framework is used in a simulation study for the COVID-19 outbreak in Wuhan, China. The simulation results match the reported data for the cumulative cases with high accuracy and generate new insight into the complex spatial dynamics of COVID-19.



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    [1] A. Afzal, C. A. Saleel, S. Bhattacharyya, N. Satish, O. D. Samuel, I. A. Badruddin, Merits and limitations of mathematical modeling and computational simulations in mitigation of COVID-19 pandemic: A comprehensive review, Arch. Comput. Methods Eng., 29 (2022), 1311–1337. https://doi.org/10.1007/s11831-021-09634-2 doi: 10.1007/s11831-021-09634-2
    [2] R. Padmanabhan, H. S. Abed, N. Meskin, T. Khattab, M. Shraim, M. A. Al-Hitmi, A review of mathematical model-based scenario analysis and interventions for COVID-19, Comput. Methods Programs Biomed., 209 (2021), 106301. https://doi.org/10.1016/j.cmpb.2021.106301 doi: 10.1016/j.cmpb.2021.106301
    [3] J. Wang, Mathematical models for COVID-19: applications, limitations, and potentials, J. Public Health Emerg., 4 (2020), 9. https://doi.org/10.21037/jphe-2020-05 doi: 10.21037/jphe-2020-05
    [4] C. Yang, J. Wang, A mathematical model for the novel coronavirus epidemic in Wuhan, China, Math. Biosci. Eng., 17 (2020), 2708–2724. https://doi.org/10.3934/mbe.2020148 doi: 10.3934/mbe.2020148
    [5] S. Ahmad, A. Ullah, Q. M. Al-Mdallal, H. Khan, K. Shah, A. Khan, Fractional order mathematical modeling of COVID-19 transmission, Chaos Solitons Fractals, 139 (2020), 110256. https://doi.org/10.1016/j.chaos.2020.110256 doi: 10.1016/j.chaos.2020.110256
    [6] K. Leung, J. T. Wu, D. Liu, G. M. Leung, First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: A modelling impact assessment, Lancet, 395 (2020), 1382–1393. https://doi.org/10.1016/S0140-6736(20)30746-7 doi: 10.1016/S0140-6736(20)30746-7
    [7] R. Li, S. Pei, B. Chen, Y. Song, T. Zhang, W. Yang, et al., Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2), Science, 368 (2020), 489–493. https://doi.org/10.1126/science.abb3221 doi: 10.1126/science.abb3221
    [8] C. Yang, J. Wang, Modeling the transmission of COVID-19 in the US – A case study, Infect. Dis. Model., 6 (2021), 195–211. https://doi.org/10.1016/j.idm.2020.12.006 doi: 10.1016/j.idm.2020.12.006
    [9] A. Khan, H. M. Alshehri, T. Abdeljawad, Q. M. Al-Mdallal, H. Khan, Stability analysis of fractional nabla difference COVID-19 model, Results Phys., 22 (2021), 103888. https://doi.org/10.1016/j.rinp.2021.103888 doi: 10.1016/j.rinp.2021.103888
    [10] P. G. Kevrekidis, J. Cuevas-Maraver, Y. Drossinos, Z. Rapti, G. A. Kevrekidis, Reaction-diffusion spatial modeling of COVID-19: Greece and Andalusia as case examples, Phys. Rev. E, 104 (2021), 024412. https://doi.org/10.1103/PhysRevE.104.024412 doi: 10.1103/PhysRevE.104.024412
    [11] M. Sher, K. Shah, Z. A. Khan, H. Khan, A. Khan, Computational and theoretical modeling of the transmission dynamics of novel COVID-19 under Mittag-Leffler Power Law, Alexandria Eng. J., 59 (2020), 3133–3147. https://doi.org/10.1016/j.aej.2020.07.014 doi: 10.1016/j.aej.2020.07.014
    [12] C. Yang, J. Wang, COVID-19 and underlying health conditions: A modeling investigation, Math. Biosci. Eng., 18 (2021), 3790–3812. https://doi.org/10.3934/mbe.2021191 doi: 10.3934/mbe.2021191
    [13] A. Viguerie, G. Lorenzo, F. Auricchio, D. Baroli, T. J. R. Hughes, A. Patton, et al., Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion, Appl. Math. Lett., 111 (2021), 106617. https://doi.org/10.1016/j.aml.2020.106617 doi: 10.1016/j.aml.2020.106617
    [14] E. Kuhl, Data-driven modeling of COVID-19 – Lessons learned, Extreme Mech. Lett., 40 (2020), 100921. https://doi.org/10.1016/j.eml.2020.100921 doi: 10.1016/j.eml.2020.100921
    [15] L. J. S. Allen, B. M. Bolker, Y. Lou, A. L. Nevai, Asymptotic profiles of the steady states for an SIS epidemic reaction-diffusion model, Discrete Continuous Dyn. Syst. Ser. B, 21 (2008), 1–20. https://doi.org/10.3934/dcds.2008.21.1 doi: 10.3934/dcds.2008.21.1
    [16] E. Bertuzzo, R. Casagrandi, M. Gatto, I. Rodriguez-Iturbe, A. Rinaldo, On spatially explicit models of cholera epidemics, J. R. Soc. Interface, 7 (2010), 321–333. https://doi.org/10.1098/rsif.2009.0204 doi: 10.1098/rsif.2009.0204
    [17] R. S. Cantrell, C. Cosner, The effects of spatial heterogeneity in population dynamics, J. Math. Biol., 29 (1991), 315–338. https://doi.org/10.1007/BF00167155 doi: 10.1007/BF00167155
    [18] R. S. Cantrell, C. Cosner, Spatial Ecology via Reaction-Diffusion Equations, Wiley, 2003. https://doi.org/10.1002/0470871296
    [19] H. R. Thieme, Spectral bound and reproduction number for infinite-dimensional population structure and time heterogeneity, SIAM J. Appl. Math., 70 (2009), 188–211. https://doi.org/10.1137/080732870 doi: 10.1137/080732870
    [20] W. Wang, X. Q. Zhao, Basic reproduction numbers for reaction-diffusion epidemic models, SIAM J. Appl. Dyn. Syst., 11 (2012), 1652–1673. https://doi.org/10.1137/120872942 doi: 10.1137/120872942
    [21] P. Magal, G. F. Webb, Y. Wu, On the basic reproduction number of reaction-diffusion epidemic models, SIAM J. Appl. Math., 79 (2019), 284–304. https://doi.org/10.1137/18M1182243 doi: 10.1137/18M1182243
    [22] X. Wang, D. Gao, J. Wang, Influence of human behavior on cholera dynamics, Math. Biosci., 267 (2015), 41–52. https://doi.org/10.1016/j.mbs.2015.06.009 doi: 10.1016/j.mbs.2015.06.009
    [23] J. Wu, Spatial structure: partial differential equations models, in Mathematical Epidemiology, Lecture Notes in Mathematics, Springer, 2008. https://doi.org/10.1007/978-3-540-78911-6_8
    [24] C. Yang, J. Wang, Basic reproduction numbers for a class of reaction-diffusion epidemic models, Bull. Math. Biol., 82 (2020), 111. https://doi.org/10.1007/s11538-020-00788-x doi: 10.1007/s11538-020-00788-x
    [25] J. Arino, P. van den Driessche, A multi-city epidemic model, Math. Popul. Stud., 10 (2003), 175–193. https://doi.org/10.1080/08898480306720
    [26] C. Cosner, J. C. Beier, R. S. Cantrell, D. Impoinvil, L. Kapitanski, M. D. Potts, et al., The effects of human movement on the persistence of vector-borne diseases, J. Theor. Biol., 258 (2009), 550–560. https://doi.org/10.1016/j.jtbi.2009.02.016 doi: 10.1016/j.jtbi.2009.02.016
    [27] I. Hanski, Metapopulation Ecology, Oxford University Press, 1999.
    [28] Y. H. Hsieh, P. van den Driessche, L. Wang, Impact of travel between patches for spatial spread of disease, Bull. Math. Biol., 69 (2007), 1355–1375. https://doi.org/10.1007/s11538-006-9169-6 doi: 10.1007/s11538-006-9169-6
    [29] R. Levins, Some demographic and genetic consequences of environmental heterogeneity for biological control, Bull. Entomol. Soc. Am., 15 (1969), 237–240. https://doi.org/10.1093/besa/15.3.237 doi: 10.1093/besa/15.3.237
    [30] D. J. Rodriguez, L. Torres-Sorando, Models for infectious diseases in spatially heterogeneous environments, Bull. Math. Biol., 63 (2001), 547–571. https://doi.org/10.1006/bulm.2001.0231 doi: 10.1006/bulm.2001.0231
    [31] S. Ruan, W. Wang, S. A. Levin, The effect of global travel on the spread of SARS, Math. Biosci. Eng., 3 (2006), 205–218. https://doi.org/10.3934/mbe.2006.3.205 doi: 10.3934/mbe.2006.3.205
    [32] G. F. Newell, A simplified theory of kinematic waves in highway traffic, part I: general theory, Transp. Res. B, 27 (1993), 281–287. https://doi.org/10.1016/0191-2615(93)90038-C doi: 10.1016/0191-2615(93)90038-C
    [33] P. I. Richards, Shock waves on the highway, Oper. Res., 4 (1956), 42–51. https://doi.org/10.1287/opre.4.1.42
    [34] D. Sun, J. Lv, S. Waller, In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method, J. Mod. Transp., 19 (2011), 58–67. https://doi.org/10.1007/BF03325741 doi: 10.1007/BF03325741
    [35] H. M. Zhang, Analyses of the stability and wave properties of a new continuum traffic theory, Transp. Res. B, 36 (1999), 399–415. https://doi.org/10.1016/S0191-2615(98)00044-7 doi: 10.1016/S0191-2615(98)00044-7
    [36] G. K. Batchelor, An Introduction to Fluid Dynamics, Cambridge University Press, 1967. https://doi.org/10.1017/CBO9780511800955
    [37] H. Lamb, Hydrodynamics, Cambridge University Press, 2006. https://doi.org/10.5962/bhl.title.18729
    [38] L. D. Landau, E.M. Lifshitz, Fluid Mechanics, Pergamon Press, 1987.
    [39] J. C. Tannehill, D. A. Anderson, R. H. Pletcher, Computational Fluid Mechanics and Heat Transfer, Second Edition, Taylor and Francis, 1997. https://doi.org/10.1017/S0022112000003049
    [40] X. Liu, S. Osher, T. Chan, Weighted essentially non-oscillatory schemes, J. Comput. Phys., 115 (1994), 200–212. https://doi.org/10.1006/jcph.1994.1187 doi: 10.1006/jcph.1994.1187
    [41] P. Attard, Non-Equilibrium Thermodynamics and Statistical Mechanics: Foundations and Applications, Oxford University Press, 2012. https://doi.org/10.1093/acprof:oso/9780199662760.001.0001
    [42] J. Pedlosky, Geophysical Fluid Dynamics, Springer, 1987. https://doi.org/10.1007/978-1-4612-4650-3
    [43] N. W. Tschoegl, Fundamentals of Equilibrium and Steady-State Thermodynamics, Elsevier Science, 2000. https://doi.org/10.1016/B978-0-444-50426-5.X5000-9
    [44] P. Perrot, A to Z of Thermodynamics, Oxford University Press, 1998.
    [45] C. W. Shu, Essentially non-oscillatory and weighted essentially non-oscillatory schemes for hyperbolic conservation laws, in Advanced Numerical Approximation of Nonlinear Hyperbolic Equations, Springer, Berlin, 1998. https://doi.org/10.1007/BFb0096355
    [46] C. W. Shu, S. Osher, Efficient implementation of essentially non-oscillatory shock-capturing schemes, J. Comput. Phys., 77 (1988), 439–471. https://doi.org/10.1016/0021-9991(88)90177-5 doi: 10.1016/0021-9991(88)90177-5
    [47] Wikipedia: Wuhan. Available from: http://en.wikipedia.org/wiki/Wuhan.
    [48] S. Benzoni-Gavage, J. F. Coulombel, S. Aubert, Boundary conditions for Euler equations, AIAA J., 41 (2003), 56–63. https://doi.org/10.2514/2.1913 doi: 10.2514/2.1913
    [49] Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, N. Engl. J. Med., 382 (2020), 1199e1207. https://doi.org/10.1056/NEJMoa2001316 doi: 10.1056/NEJMoa2001316
    [50] Q. Zhuang, J. Wang, A spatial epidemic model with a moving boundary, Infect. Dis. Model., 6 (2021), 1046–1060. https://doi.org/10.1016/j.idm.2021.08.005 doi: 10.1016/j.idm.2021.08.005
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