Effect of the epidemiological heterogeneity on the outbreak outcomes

  • Received: 08 May 2016 Accepted: 15 October 2016 Published: 01 June 2017
  • MSC : Primary: 92D30; Secondary: 92D25, 37N25

  • Multi-host pathogens infect and are transmitted by different kinds of hosts and, therefore, the host heterogeneity may have a great impact on the outbreak outcome of the system. This paper deals with the following problem: consider the system of interacting and mixed populations of hosts epidemiologically different, what would be the outbreak outcome for each host population composing the system as a result of mixing in comparison to the situation with zero mixing? To address this issue we have characterized the epidemic response function for a single-host population and defined a heterogeneity index measuring how host systems are epidemiologically different in terms of generation time, basic reproduction number $R_0$ and, therefore, epidemic response function. Based on the individual epidemiological characteristics of populations, with heterogeneities and mixing affinities, the response of subpopulations in a multi-host system is compared to that of a single-host system. The case of a two-host system, in which the infection transmission depends solely on the infection susceptibility of the receiver, is analyzed in detail. Three types of responses are observed: dilution, amplification or no effect, corresponding to lower, higher or equal attack rates, respectively, for a host population in an interacting multi-host system compared to the zero-mixing situation. We find that no effect is generally observed for zero heterogeneity. A dilution effect is always observed for all the host populations when their individual $R_{0,i} \lt 1$. Whereas, when at least one of the individual $R_{0,i} \gt 1$, then the hosts "$i$" with $R_{0,i} \gt R_{0,j}$ undergo a dilution effect while the hosts "$j$" undergo an amplification effect.

    Citation: Alina Macacu, Dominique J. Bicout. Effect of the epidemiological heterogeneity on the outbreak outcomes[J]. Mathematical Biosciences and Engineering, 2017, 14(3): 735-754. doi: 10.3934/mbe.2017041

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  • Multi-host pathogens infect and are transmitted by different kinds of hosts and, therefore, the host heterogeneity may have a great impact on the outbreak outcome of the system. This paper deals with the following problem: consider the system of interacting and mixed populations of hosts epidemiologically different, what would be the outbreak outcome for each host population composing the system as a result of mixing in comparison to the situation with zero mixing? To address this issue we have characterized the epidemic response function for a single-host population and defined a heterogeneity index measuring how host systems are epidemiologically different in terms of generation time, basic reproduction number $R_0$ and, therefore, epidemic response function. Based on the individual epidemiological characteristics of populations, with heterogeneities and mixing affinities, the response of subpopulations in a multi-host system is compared to that of a single-host system. The case of a two-host system, in which the infection transmission depends solely on the infection susceptibility of the receiver, is analyzed in detail. Three types of responses are observed: dilution, amplification or no effect, corresponding to lower, higher or equal attack rates, respectively, for a host population in an interacting multi-host system compared to the zero-mixing situation. We find that no effect is generally observed for zero heterogeneity. A dilution effect is always observed for all the host populations when their individual $R_{0,i} \lt 1$. Whereas, when at least one of the individual $R_{0,i} \gt 1$, then the hosts "$i$" with $R_{0,i} \gt R_{0,j}$ undergo a dilution effect while the hosts "$j$" undergo an amplification effect.


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    [1] [ F. R. Adler, The effects of averaging on the basic reproduction ratio, Mathematical Biosciences, 111 (1992): 89-98.
    [2] [ R. M. Anderson and R. M. May, Infectious Diseases of Humans/ Dynamics and Control, Oxford Science Publications, Oxford, 1991.
    [3] [ D. J. Bicout, Modélisation des Maladies Vectorielles, Habilitation á Diriger des Recherches -Université Joseph Fourier -Grenoble I, 2006.
    [4] [ J. D. Brown,D. E. Stallknecht,D. E. Swayne, Experimental infection of swans and geese with highly pathogenic avian influenza virus (H5N1) of asian lineage, Emerging Infectious Diseases, 14 (2008): 136-142.
    [5] [ J. D. Brown,D. E. Stallknecht,J. R. Beck,D. L. Suarez,D. E. Swayne, Susceptibility of North american ducks and gulls to (H5N1) highly pathogenic avian influenza viruses, Emerging Infectious Diseases, 12 (2006): 1663-1670.
    [6] [ H. Chen,Y. Li,Z. Li,J. Shi,K. Shinya,G. Deng,Q. Qi,G. Tian,S. Fan,H. Zhao,Y. Sun,Y. Kawaoka, Properties and Dissemination of H5N1 Viruses Isolated during an Influenza Outbreak in Migratory Waterfowl in Western China, Journal of Virology, 80 (2006): 5976-5983.
    [7] [ H. Chen,G. J. D. Smith,S. Y. Zhang,K. Qin,J. Wang,K. S. Li,R. G. Webster,J. S. M. Peiris,Y. Guan, H5N1 virus outbreak in migratory waterfowl, Nature, 436 (2005): 191-192.
    [8] [ M. de Jong, O. Diekmann and H. Heesterbeek, How does transmission of infection depend on population size, In Epidemic models: their structure and relation to data (eds. D. Mollison) Cambridge: Press Syndicate of the University of Cambridge, (1995), 84–94.
    [9] [ M. C. M. de Jong,O. Diekmann,J. A. P. Heesterbeek, The computation of R0 for discrete-time epidemic models with dynamic heterogeneity, Mathematical Biosciences, 119 (1994): 97-114.
    [10] [ O. Diekmann,J. A. P. Heesterbeek,J. A. J. Metz, On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations, Journal of Mathematical Biology, 28 (1990): 365-382.
    [11] [ O. Diekmann,J. A. P. Heesterbeek,M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, Journal of the Royal Society Interface, 7 (2010): 873-885.
    [12] [ A. P. Dobson, Population dynamics of pathogens with multiple host species, Am. Nat., 164 (2004): S64-S78.
    [13] [ D. Doctrinal,S. Ruette,J. Hars,M. Artois,D. J. Bicout, Spatial and temporal analysis of the highly pathogenic avian influenza (H5N1) outbreak in the Dombes Area, France in 2006, Wildfowl, 2 (2009): 202-214.
    [14] [ J. Dushoff,S. Levin, The effects of population heterogeneity on disease invasion, Mathematical Biosciences, 128 (1995): 25-40.
    [15] [ P. L. Flint, Applying the scientific method when assessing the influence of migratory birds on the dispersal of H5N1, Virology Journal, 4 (2007): 132 (1-3).
    [16] [ L. Gall-Reculé,F. X. Briand,A. Schmitz,O. Guionie,P. Massin,V. Jestin, Double introduction of highly pathogenic H5N1 avian influenza virus into France in early 2006, Avian Pathology, 37 (2008): 15-23.
    [17] [ M. Gauthier-Clerc,C. Lebarbenchon,F. Thomas, Recent expansion of highly pathogenic avian influenza H5N1: a critical review, Ibis, 149 (2007): 202-214.
    [18] [ V. Guberti,S. H. Newman, Guidelines on Wild Bird Surveillance for Highly Pathogenic Avian Influenza H5N1 Virus, Journal of Wildlife Diseases, 43 (2007): S29-S34.
    [19] [ J. Hars,S. Ruette,M. Benmergui,C. Fouque,J. Y. Fournier,A. Legouge,M. Cherbonnel,D. Baroux,C. Dupuy,V. Jestin, The epidemiology of the highly pathogenic H5N1 avian influenza in Mute Swan (Cygnus olor) and other Anatidae in the Dombes region (France), 2006, J Wildlife Dis, 44 (2008): 811-823.
    [20] [ J. Hars, S. Ruette, M. Benmergui, C. Fouque, J. Y. Fournier, A. Legouge, M. Cherbonnel, D. Baroux, C. Dupuy and V. Jestin, Rôle Epidémiologique du Cygne Tuberculé et des Autres Anatidés Dans L'épisode D'influenza Aviaire H5N1 HP Dans la Dombes en 2006, ONCFS Rapport Scientifique, 2006.
    [21] [ J. A. P. Heesterbeek, Abrief history of R0 and a recipe for its calculation, Acta Biotheoretica, 50 (2002): 189-204.
    [22] [ D. Kalthoff,A. Breithaupt,J. P. Teifke,A. Globig,T. Harder,T. C. Mettenleiter,M. Beer, Highly pathogenic avian influenza virus (H5N1) in experimentally infected adult mute swans, Emerging Infectious Diseases, 14 (2008): 1267-1270.
    [23] [ J. Keawcharoen,D. van Riel,G. van Amerongen,T. Bestebroer,W. E. Beyer,R. van Lavieren,A. D. M. E. Osterhaus,R. A. M. Fouchier,T. Kuiken, Wild ducks as long-distance vectors of highly pathogenic avian influenza virus (H5N1), Emerging Infectious Diseases, 14 (2008): 600-607.
    [24] [ F. Keesing,R. D. Holt,R. S. Ostfeld, Effects of species diversity on disease risk, Ecology letters, 9 (2006): 485-498.
    [25] [ W. O. Kermack,A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. Roy. Soc. Lond. A, 115 (1927): 700-721.
    [26] [ H. Kida,R. Yanagawa,Y. Matsuoka, Duck influenza lacking evidence of disease signs and immune response, Infect. Immun, 30 (1980): 547-553.
    [27] [ A. M. Kilpatrick,A. A. Chmura,D.W. Gibbons,R. C. Fleischer,P. P. Marra,P. Daszak, Predicting the global spread of H5N1 avian influenza, Proc Natl Acad Sci USA, 103 (2006): 19368-19373.
    [28] [ J. Liu, H. Xiao, F. Lei, Q. Zhu, K. Qin, X. -w Zhang, X. -l. Zhang, D. Zhao, G. Wang, Y. Feng, J. Ma, W. Liu, J. Wang and G. F. Gao, Highly pathogenic H5N1 influenza virus infection in migratory birds, Science, 309 (2005), 1206.
    [29] [ H. Nishiura,B. Hoye,M. Klaassen,S. Bauer,H. Heesterbeek, How to find natural reservoir hosts from endemic prevalence in a multi-host population: A case study of influenza in waterfowl, Epidemics, 1 (2009): 118-128.
    [30] [ B. Olsen,V. J. Munster,A. Wallensten,J. Waldenström,A. D. M. E. Osterhaus,R. A. M. Fouchier, Global Patterns of Influenza A Virus in Wild Birds, Science, 312 (2006): 384-388.
    [31] [ M. René,D. J. Bicout, Influenza aviaire: Modélisation du risque d'infection des oiseaux á partir d'étangs contaminés, Epidémiologie et santé animale, 51 (2007): 95-109.
    [32] [ A. Satelli,S. Tarantola,K. P.-S. Chan, Quantitative model-independent method for global sensitivity analysis of model output, Technometrics, 41 (1999): 39-56.
    [33] [ M. E. J. Woolhouse,L. H. Taylor,D. T. Haydon, Population biology of multi-host pathogens, Science, 292 (2001): 1109-1112.
    [34] [ G. Zhang,D. Shoham,S. Davydof,J. D. Castello,S. O. Rogers,D. Gilichinsky, Evidence of influenza A virus RNA in Siberian lake ice, Journal of Virology, 80 (2006): 12229-12235.
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