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Spatio-temporal games of voluntary vaccination in the absence of the infection: the interplay of local versus non-local information about vaccine adverse events

  • Received: 13 June 2019 Accepted: 05 November 2019 Published: 14 November 2019
  • Under voluntary vaccination, a critical role in shaping the level and trends of vaccine uptake is played by the type and structure of information that is received and used by parents of children eligible for vaccination. In this article we investigate the feedbacks of spatial mobility and the spatial structure of information on vaccination dynamics, by extending to a continuous spatially structured setting existing behavioral epidemiology models for the impact of vaccine adverse events (VAEs) on vaccination choices. We considered the simplest spatial setting, namely classical 'Fickian' diffusion, and focused on the noteworthy case where the infection is absent. This scenario mimics the important case of a population where a previously endemic vaccine preventable infection was successfully eliminated, but the re-emergence of the disease must be prevented. This is, for example, the case of poliomyelitis in most countries worldwide. In such a situation, the dynamics of VAEs and of the related information arguably become the key determinant of vaccination decision and of collective coverage. In relation to this 'information issue', we compared the effects of three main cases: (ⅰ) purely local information, where agents react only to locally occurred events; (ⅱ) a mix of purely local and global, country-wide, information due e.g., to country-wide media and the internet; (ⅲ) a mix of local and non-local information. By representing these different information options through a range of different spatial information kernels, we investigated: the presence and stability of space-homogeneous, nontrivial, behavior-induced equilibria; the existence of bifurcations; the existence of classical and generalized traveling waves; and the effects of awareness campaigns enacted by the Public Health System to sustain vaccine uptake. Finally, we analyzed some analogies and differences between our models and those of the Theory of Innovation Diffusion.

    Citation: Antonella Lupica, Piero Manfredi, Vitaly Volpert, Annunziata Palumbo, Alberto d'Onofrio. Spatio-temporal games of voluntary vaccination in the absence of the infection: the interplay of local versus non-local information about vaccine adverse events[J]. Mathematical Biosciences and Engineering, 2020, 17(2): 1090-1131. doi: 10.3934/mbe.2020058

    Related Papers:

  • Under voluntary vaccination, a critical role in shaping the level and trends of vaccine uptake is played by the type and structure of information that is received and used by parents of children eligible for vaccination. In this article we investigate the feedbacks of spatial mobility and the spatial structure of information on vaccination dynamics, by extending to a continuous spatially structured setting existing behavioral epidemiology models for the impact of vaccine adverse events (VAEs) on vaccination choices. We considered the simplest spatial setting, namely classical 'Fickian' diffusion, and focused on the noteworthy case where the infection is absent. This scenario mimics the important case of a population where a previously endemic vaccine preventable infection was successfully eliminated, but the re-emergence of the disease must be prevented. This is, for example, the case of poliomyelitis in most countries worldwide. In such a situation, the dynamics of VAEs and of the related information arguably become the key determinant of vaccination decision and of collective coverage. In relation to this 'information issue', we compared the effects of three main cases: (ⅰ) purely local information, where agents react only to locally occurred events; (ⅱ) a mix of purely local and global, country-wide, information due e.g., to country-wide media and the internet; (ⅲ) a mix of local and non-local information. By representing these different information options through a range of different spatial information kernels, we investigated: the presence and stability of space-homogeneous, nontrivial, behavior-induced equilibria; the existence of bifurcations; the existence of classical and generalized traveling waves; and the effects of awareness campaigns enacted by the Public Health System to sustain vaccine uptake. Finally, we analyzed some analogies and differences between our models and those of the Theory of Innovation Diffusion.


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    [1] N. T. J. Bailey, The mathematical theory of infectious diseases and its applications (2nd edition), 2nd edition, Charles Griffin & Company Ltd, 1975.
    [2] V. Capasso and V. Capasso, Mathematical structures of epidemic systems, Springer, 1993.
    [3] C. Metcalf, E. Jessica, W. J. Edmunds, et al., Six challenges in modelling for public health policy, Epidemics, 10 (2015), 93-96.
    [4] P. Manfredi and A. d'Onofrio, Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases, Springer, 2013.
    [5] Z. Wang, C. T. Bauch, S. Bhattacharyya, et al., Statistical physics of vaccination, Phys. Rep., 664 (2016), 1-113.
    [6] V. Capasso and G. Serio, A generalization of the Kermack-McKendrick deterministic epidemic model, Math. Biosci., 42 (1978), 43-61.
    [7] R. Casiday, T. Cresswell, D. Wilson, et al., A survey of UK parental attitudes to the MMR vaccine and trust in medical authority, Vaccine, 24 (2006), 177-184.
    [8] E. Dubé, C. Laberge, M. Guay, et al., Vaccine hesitancy: an overview, Human Vaccin. Immunother., 9 (2013), 1763-1773.
    [9] V. A. A. Jansen, N. Stollenwerk, H. J. Jensen, et al., Measles outbreaks in a population with declining vaccine uptake, Science, 301 (2003), 804-804. doi: 10.1126/science.1086726
    [10] S. B. Omer, D. A. Salmon, W. A. Orenstein, et al., Vaccine refusal, mandatory immunization, and the risks of vaccine-preventable diseases, N. Engl. J. Med., 360 (2009), 1981-1988.
    [11] R. Löfstedt, Risk management in post-trust societies, Palgrave-McMillan, 2005.
    [12] C. T. Bauch, Imitation dynamics predict vaccinating behaviour, Proc. R. Soc. Lond. B Biol. Sci., 272 (2005), 1669-1675.
    [13] A. d'Onofrio, P. Manfredi and P. Poletti, The impact of vaccine side effects on the natural history of immunization programmes: an imitation-game approach, J. Theor. Biol., 273 (2011), 63-71.
    [14] A. d'Onofrio, P. Manfredi and P. Poletti, The interplay of public intervention and private choices in determining the outcome of vaccination programmes, PLoS ONE, 7 (2012), e45653.
    [15] B. Buonomo, G. Carbone and A. d'Onofrio, Effect of seasonality on the dynamics of an imitation- based vaccination model with public health intervention, Math. Biosci. Eng., 15 (2018), 299-321.
    [16] D. G. Kendall, Mathematical models of the spread of infection, Math. Comput. Sci. Biol. Med. (1965), 213-225.
    [17] N. T. J. Bailey, The simulation of stochastic epidemics in two dimensions, Proc. Fifth Berkeley Symp. Math. Statist. Prob., 4 (1967), 237-257.
    [18] D. G. Aronson, The asymptotic speed of propagation of a simple epidemic, Nonlinear Diffusion, 14 (1977), 1-23.
    [19] V. Capasso, Global solution for a diffusive nonlinear deterministic epidemic model, SIAM J. Appl. Math., 35 (1978), 274-284.
    [20] F. Hoppensteadt, Mathematical Theories of Populations: Deomgraphics, Genetics, and Epidemics, Siam, 1975.
    [21] H. Malchow, S. V. Petrovskii and E. Venturino, Spatiotemporal patterns in ecology and epidemiology: theory, models, and simulation, Chapman and Hall/CRC, 2007.
    [22] J. D. Murray, Mathematical biology II. Spatial models and biomedical applications, SpringerVerlag New York Incorporated New York, 2001.
    [23] S. Ruan and J. Wu, Modeling spatial spread of communicable diseases involving animal hosts, Spatial Ecol., (2009), 293-316.
    [24] J. V. Noble, Geographic and temporal development of plagues, Nature, 250 (1974), 726.
    [25] A. Ducrot and T. Giletti, Convergence to a pulsating travelling wave for an epidemic reactiondiffusion system with non-diffusive susceptible population, J. Math. Biol., 69 (2014), 533-552.
    [26] P. Magal, G. F. Webb and Y. Wu, On the Basic Reproduction Number of Reaction-Diffusion Epidemic Models, SIAM J. Appl. Math., 79 (2019), 284-304.
    [27] P. Magal, G. F. Webb and Y. Wu, Spatial spread of epidemic diseases in geographical settings: seasonal influenza epidemics in Puerto Rico, arXiv preprint arXiv:1801.01856 (2018).
    [28] W. E. Fitzgibbon, J. J. Morgan, G. F. Webb, et al., A vector-host epidemic model with spatial structure and age of infection, Nonlinear Anal-Real., 41 (2018), 692-705.
    [29] L. Zhao, Z. C. Wang and S. Ruan, Traveling wave solutions in a two-group SIR epidemic model with constant recruitment, J. Math. Biol., 77 (2018), 1871-1915.
    [30] S. Ruan, Spatial-temporal dynamics in nonlocal epidemiological models, Math. Life Sci. Med., (2007), 97-122.
    [31] B. Buonomo, P. Manfredi and A. d'Onofrio, Optimal time-profiles of public health intervention to shape voluntary vaccination for childhood diseases, J. Math. Biol., 78 (2019), 1089-1113.
    [32] R. Peres, E. Muller and V. Mahajan, Innovation diffusion and new product growth models: A critical review and research directions, Int. J. Res. Market., 27 (2010), 91-106.
    [33] V. Capasso and M. Zonno, Mathematical Models for the Diffusion of Innovations, Proc. Fourth Eur. Conference Math. Industry, (1991), 225-233.
    [34] V. Mahajan and R. A. Peterson, Models for innovation diffusion, Sage Publications Inc, 1985.
    [35] V. Capasso, A. Di Liddo and L. Maddalena, A nonlinear model for the geographical spread of innovations, Dyn. Syst Appl., 3 (1994), 207-220.
    [36] F. M. Bass, A new product growth for model consumer durables, Manag. Sci., 15 (1969), 215-227.
    [37] S. Funk, E. Gilad, C. Watkins, et al., The spread of awareness and its impact on epidemic outbreaks, Proc. Natl. Acad. Sci., 106 (2009), 6872-6877.
    [38] E. Frey, Evolutionary game theory: Theoretical concepts and applications to microbial communities, Physica A, 389 (2010), 4265-4298.
    [39] R. A. Fisher, The wave of advance of advantageous genes, Annals Eugenics, 7 (1937), 355-369.
    [40] V. Volpert, Elliptic Partial Differential Equations: Volume 2: Reaction-Diffusion Equations, 104, Springer, 2014.
    [41] A. N. Kolmogorov, I. N. Petrovsky and N. S. Piskunov, Étude de l'équation de la diffusion avec croissance de la quantité de matière et son application à un problème biologique, Bull. Univ. Moskow, Ser. Internat., Sec. A, 1 (1937), 1-25.
    [42] A. D. Polyanin and V. F. Zaitsev, Handbook of ordinary differential equations: exact solutions, methods, and problems, Chapman and Hall/CRC, 2017.
    [43] J. D. Murray, Mathematical biology I: an introduction, Springer New York, 2002.
    [44] A. I. Volpert, V. A. Volpert and V. A. Volpert, Traveling wave solutions of parabolic systems, 140 American Mathematical Soc., 1994.
    [45] S. Vakulenko and V. Volpert, Generalized travelling waves for perturbed monotone reactiondiffusion systems, Nonlinear Analysis TMA, 6 (2001), 757-776.
    [46] A. d'Onofrio, P. Manfredi and E. Salinelli, Vaccinating behaviour and the dynamics of vaccine preventable infections, Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases (2013), 267-287.
    [47] M. Mincheva and M. R. Roussel, Turing-Hopf instability in biochemical reaction networks arising from pairs of subnetworks, Math. Biosci., 240 (2012), 1-11.
    [48] B. Ermentrout and M. Lewis, Pattern formation in systems with one spatially distributed species, Bull. Math. Biol., 59 (1997), 533-549.
    [49] A. M. Turing, The chemical basis of morphogenesis, Phil. Trans. R. Soc. Lond. B, 237 (1952), 37-72.
    [50] N. Stollenwerk and V. Jansen, Population Biology and Criticality: From critical birth-death processes to self-organized criticality in mutation pathogen systems, World Scientific, 2011.
    [51] N. Stollenwerk, S. van Noort, J. Martins, et al., A spatially stochastic epidemic model with partial immunization shows in mean field approximation the reinfection threshold, J. Biol. Dyn., 4 (2010), 634-649.
    [52] A. M. Albano, N. B. Abraham, D. E. Chyba, et al., Bifurcations, propagating solutions, and phase transitions in a nonlinear chemical reaction with diffusion, Am. J. Phys., 52 (1984), 161-167.
    [53] J. P. Eckmann and T. Gallay, Front solutions for the Ginzburg-Landau equation, Commun. Math. Phys., 152 (1993), 221-248.
    [54] B. J. Matkowsky and V. A. Volpert, Stability of plane wave solutions of complex Ginzburg-Landau equations, Quart. Appl. Math., 51 (1993), 265-281.
    [55] L. E. Reichl, A modern course in statistical physics, Wiley-VCH, 2016.
    [56] M. C. Gonzalez, C. A. Hidalgo and A. L. Barabasi, Understanding individual human mobility patterns, Nature, 453 (2008), 779.
    [57] D. Balcan and A. Vespignani, Phase transitions in contagion processes mediated by recurrent mobility patterns, Nature Phys., 7 (2011), 581.
    [58] U. Skwara, J. Martins, P. Ghaffari, et al., Fractional calculus and superdiffusion in epidemiology: shift of critical thresholds, Proceedings of the 12th International Conference on Computational and Mathematical Methods in Science and Engineering, La Manga (2012).
    [59] J. P. Boto and N. Stollenwerk, Fractional calculus and Levy flights: modelling spatial epidemic spreading, Computational and Mathematical Methods in Science and Engineering, (2009).
    [60] U. Skwara, J. Martins, P. Ghaffari, et al., Applications of fractional calculus to epidemiological models, AIP Conf. Proc., 1479 (2012), 1339-1342.
    [61] U. Skwara, L. Mateus, R. Filipe, et al., Superdiffusion and epidemiological spreading, Ecol. Complex., 36 (2018), 168-183.
    [62] D. Brockmann and L. Hufnagel, Front propagation in reaction-superdiffusion dynamics: Taming Lévy flights with fluctuations, Phys. Rev. Lett., 98 (2009), 178301.
    [63] D. R. Sinclair, J. J. Grefenstette, M. G. Krauland, et al., Forecasted Size of Measles Outbreaks Associated With Vaccination Exemptions for Schoolchildren, JAMA Network Open, 2 (2019), e199768-e199768.
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