Advertisement

How Behaviour and the Environment Influence Transmission in Mobile Groups

  • Thomas E. Gorochowski
  • Thomas O. Richardson
Part of the Theoretical Biology book series (THBIO)

Abstract

The movement of individuals living in groups leads to the formation of physical interaction networks over which signals such as information or disease can be transmitted. Direct contacts represent the most obvious opportunities for a signal to be transmitted. However, because signals that persist after being deposited into the environment may later be acquired by other group members, indirect environmentally-mediated transmission is also possible. To date, studies of signal transmission within groups have focused on direct physical interactions and ignored the role of indirect pathways. Here, we use an agent-based model to study how the movement of individuals and characteristics of the signal being transmitted modulate transmission. By analysing the dynamic interaction networks generated from these simulations, we show that the addition of indirect pathways speeds up signal transmission, while the addition of physically-realistic collisions between individuals in densely packed environments hampers it. Furthermore, the inclusion of spatial biases that induce the formation of individual territories, reveals the existence of a trade-off such that optimal signal transmission at the group level is only achieved when territories are of intermediate sizes. Our findings provide insight into the selective pressures guiding the evolution of behavioural traits in natural groups, and offer a means by which multi-agent systems can be engineered to achieve desired transmission capabilities.

Notes

Acknowledgements

T.E.G. was supported by an EPSRC Institutional Sponsorship award from the University of Bristol (EP/P511298/1), and BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre (BB/L01386X/1). T.O.R is supported by an EU Marie Curie Actions Intra-European Fellowship, ‘Mapping spatial interaction networks in honeybee colonies’ (project number 30114). Simulations and analyses were carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol, UK.

References

  1. 1.
    Aiello, C., Nussear, K., Walde, A., Esque, T., Emblidge, P., Sah, P., Bansal, S., Hudson, P.: Disease dynamics during wildlife translocations: disruptions to the host population and potential consequences for transmission in desert tortoise contact networks. Anim. Conserv. 17(S1), 27–39 (2014)CrossRefGoogle Scholar
  2. 2.
    Almberg, E., Cross, P., Johnson, C., Heisey, D., Richards, B.: Modeling routes of chronic wasting disease transmission: environmental prion persistence promotes deer population decline and extinction. PloS One 6(5), e19896 (2011)CrossRefGoogle Scholar
  3. 3.
    Axelrod, R.M.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Harvard (1997)Google Scholar
  4. 4.
    Balcan, D., Gonçalves, B., Hu, H., Ramasco, J.J., Colizza, V., Vespignani, A.: Modeling the spatial spread of infectious diseases: the global epidemic and mobility computational model. J. Comput. Sci. 1(3), 132–145 (2010)CrossRefGoogle Scholar
  5. 5.
    Balcan, D., Hu, H., Gonçalves, B., Bajardi, P., Poletto, C., Ramasco, J.J., Paolotti, D., Perra, N., Tizzoni, M., Van den Broeck, W., Colizza, V., Vespignani, A.: Seasonal transmission potential and activity peaks of the new influenza a(h1n1): a monte carlo likelihood analysis based on human mobility. BMC Med. 7(1), 45 (2009)CrossRefGoogle Scholar
  6. 6.
    Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., Zdravkovic, V.: Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim. Behav. 76, 201–215 (2008)CrossRefGoogle Scholar
  7. 7.
    Bansal, S., Grenfell, B.T., Meyers, L.A.: When individual behaviour matters: homogeneous and network models in epidemiology. J. R. Soc. Interface 4(16), 879–891 (2007)CrossRefGoogle Scholar
  8. 8.
    Bar-David, S., Bar-David, I., Cross, P.C., Ryan, S.J., Knechtel, C.U., Getz, W.M.: Methods for assessing movement path recursion with application to African buffalo in South Africa. Ecology 90(9), 2467–2479 (2009)CrossRefGoogle Scholar
  9. 9.
    Barabási, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)CrossRefGoogle Scholar
  10. 10.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Barabási, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–113 (2004)CrossRefGoogle Scholar
  12. 12.
    Benhamou, S., Riotte-Lambert, L.: Beyond the utilization distribution: identifying home range areas that are intensively exploited or repeatedly visited. Ecol. Model. 227, 112–116 (2012)CrossRefGoogle Scholar
  13. 13.
    Berger-Tal, O., Bar-David, S.: Recursive movement patterns: review and synthesis across species. Ecol. Soc. Am. 6(9), 1–12 (2015)Google Scholar
  14. 14.
    Blonder, B., Dornhaus, A.: Time-ordered networks reveal limitations to information flow in ant colonies. PLoS One 6(5), e20298 (2011)CrossRefGoogle Scholar
  15. 15.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424, 175–308 (2006)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Bode, N.W., Codling, E.A.: Human exit route choice in virtual crowd evacuations. Anim. Behav. 86(2), 347–358 (2013)CrossRefGoogle Scholar
  17. 17.
    Bohm, M., Hutchings, M., Whiteplain, P.: Contact networks in a wildlife-livestock host community: identifying high-risk individuals in the transmission of bovine TB among badgers and cattle. PLoS One 4(4), e5016 (2009)CrossRefGoogle Scholar
  18. 18.
    Boyer, D., Crofoot, M.C., Walsh, P.D.: Non-random walks in monkeys and humans. J. R. Soc. Interface 9(70), 842–847 (2012)CrossRefGoogle Scholar
  19. 19.
    Breban, R., Drake, J.M., Stallknecht, D.E., Rohani, P.: The role of environmental transmission in recurrent avian influenza epidemics. PLoS Comput. Biol. 5(4), e1000346 (2009)CrossRefGoogle Scholar
  20. 20.
    Brummitt, C.D., D’Souza, R.M., Leicht, E.: Suppressing cascades of load in interdependent networks. Proc. Natl. Acad. Sci. 109(12), E680–E689 (2012)CrossRefGoogle Scholar
  21. 21.
    Chen, S., White, B.J., Sanderson, M.W., Amrine, D.E., Ilany, A., Lanzas, C.: Highly dynamic animal contact network and implications on disease transmission. Sci. Rep. 4, 4472 (2014)CrossRefGoogle Scholar
  22. 22.
    Couzin, I.D., Krause, J., Franks, N.R., Levin, S.A.: Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005)CrossRefGoogle Scholar
  23. 23.
    Crandall, D., Backstromb, L., Cosley, D., Surib, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. Natl. Acad. Sci. 107(52), 22436–22441 (2010)CrossRefGoogle Scholar
  24. 24.
    Croft, D.P., Edenbrow, M., Darden, S.K., Ramnarine, I.W., van Oosterhout, C., Cable, J.: Effect of gyrodactylid ectoparasites on host behaviour and social network structure in guppies Poecilia reticulata. Behav. Ecol. Sociobiol. 65, 2219–2227 (2011)Google Scholar
  25. 25.
    Darden, S.K., Steffensen, L.K., Dabelsteen, T.: Information transfer among widely spaced individuals: latrines as a basis for communication networks in the swift fox? Anim. Behav. 75(2), 425–432 (2008)CrossRefGoogle Scholar
  26. 26.
    Dejean, A., Turillazzi, S.: Territoriality during trophobiosis between wasps and homopterans. Trop. Zool. 5(2), 647–656 (1992)CrossRefGoogle Scholar
  27. 27.
    DeLellis, P., di Bernardo, M., Gorochowski, T., Russo, G.: Synchronization and control of complex networks via contraction, adaptation and evolution. IEEE Circuits Syst. Mag. 10, 64–82 (2010)CrossRefGoogle Scholar
  28. 28.
    Devane, M.L., Nicol, C., Ball, A., Klena, J.D., Scholes, P., Hudson, J.A., Baker, M.G., Gilpin, B.J., Garrett, N., Savill, M.G.: The occurrence of campylobacter subtypes in environmental reservoirs and potential transmission routes. J. Appl. Microbiol. 98, 980–990 (2005)CrossRefGoogle Scholar
  29. 29.
    Dornhaus, A., Chittka, L.: Bumble bees (bombus terrestris) store both food and information in honeypots. Behav. Ecol. 16(3), 661–666 (2005)CrossRefGoogle Scholar
  30. 30.
    Dussutour, A., Nicolis, S.C., Shephard, G., Beekman, M., Sumpter, D.J.T.: The role of multiple pheromones in food recruitment by ants. J. Exp. Biol. 212, 2337–2348 (2009)CrossRefGoogle Scholar
  31. 31.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106(36), 15,274–15,278 (2009)CrossRefGoogle Scholar
  32. 32.
    Edelstein-Keshet, L., Watmough, J., Ermentrout, G.B.: Trail following in ants: individual properties determine population behaviour. Behav. Ecol. Sociobiol. 36(2), 119–133 (1995)CrossRefGoogle Scholar
  33. 33.
    Fagan, W.F., Lewis, M.A., Auger-Méthé, M., Avgar, T., Benhamou, S., Breed, G., LaDage, L., Schlügel, U.E., Tang, W.W., Papastamatiou, Y.P., Forester, J., Mueller, T.: Spatial memory and animal movement. Ecol. Lett. 16(10), 1316–1329 (2013)CrossRefGoogle Scholar
  34. 34.
    Fenichel, E.P., Castillo-Chavez, C., Ceddia, M.G., Chowell, G., Parra, P.A.G., Hickling, G.J., Holloway, G., Horan, R., Morin, B., Perrings, C., Springborn, M., Velazquez, L., Villalobos, C.: Adaptive human behavior in epidemiological models. Proc. Natl. Acad. Sci. 108, 6306–6311 (2011)CrossRefGoogle Scholar
  35. 35.
    Funk, S., Salathé, M., Jansen, V.A.: Modelling the influence of human behaviour on the spread of infectious diseases: a review. J. R. Soc. Interface 7, 1247–1256 (2010)CrossRefGoogle Scholar
  36. 36.
    Giuggioli, L., Bartumeus, F.: Linking site fidelity to animal movement. J. Math. Biol. 64(4), 647–656 (2012)MathSciNetCrossRefMATHGoogle Scholar
  37. 37.
    Giuggioli, L., Potts, J.R., Harris, S.: Animal interactions and the emergence of territoriality. PLoS Comput. Biol. 7(3), e1002008 (2011)CrossRefGoogle Scholar
  38. 38.
    Godfrey, S.S., Bull, C.M., James, R., Murray, K.: Network structure and parasite transmission in a group living lizard, the gidgee skink, Egernia stokesii. Behav. Ecol. Sociobiol. 63(7), 1045–1056 (2009)Google Scholar
  39. 39.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  40. 40.
    Gorochowski, T.E.: Agent-based modelling in synthetic biology. Essays Biochem. 60, 325–336 (2016)CrossRefGoogle Scholar
  41. 41.
    Gorochowski, T.E., di Bernardo, M., Grierson, C.: A dynamical approach to the evolution of complex networks. In: Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems, pp. 1083–1087 (2010)Google Scholar
  42. 42.
    Gorochowski, T.E., di Bernardo, M., Grierson, C.: Evolving enhanced topologies for the synchronization of dynamical complex networks. Phys. Rev. E 81, 23,690 (2010)CrossRefGoogle Scholar
  43. 43.
    Gorochowski, T.E., di Bernardo, M., Grierson, C.S.: Evolving dynamical networks: a formalism for describing complex systems. Complexity 17, 18–25 (2012)CrossRefGoogle Scholar
  44. 44.
    Gorochowski, T.E., di Bernardo, M., Grierson, C.S.: Using aging to visually uncover evolutionary processes on networks. IEEE Trans. Vis. Comput. Graph. 18(8), 1343–1352 (2012)CrossRefGoogle Scholar
  45. 45.
    Gorochowski, T.E., Matyjaszkiewicz, A., Todd, T., Oak, N., Kowalska, K., Reid, S., Tsaneva-Atanasova, K.T., Savery, N.J., Grierson, C.S., di Bernardo, M.: BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PLoS One 7(8), e42790 (2012)CrossRefGoogle Scholar
  46. 46.
    Grassé, P.P.: La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insect. Soc. 6(1), 41–80 (1959)Google Scholar
  47. 47.
    Gravish, N., Gold, G., Zangwill, A., Goodisman, M.A., Goldman, D.I.: Glass-like dynamics in confined and congested ant traffic. Soft Matter 11(33), 6552–6561 (2015)CrossRefGoogle Scholar
  48. 48.
    Grimm, V., Berger, U., Jeltsch, F., Mooij, W.M., Railsback, S.F., Thulke, H.H., Weiner, J., Wiegand, T., DeAngelis, D.L.: Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310, 987–991 (2005)CrossRefGoogle Scholar
  49. 49.
    Gross, T., Blasius, B.: Adaptive coevolutionary networks: a review. J. R. Soc. Interface 5, 259–271 (2008)CrossRefGoogle Scholar
  50. 50.
    Hahn, M., Maschwitz, U.: Foraging strategies and recruitment behaviour in the European harvester ant Messor rufitarsis (f.). Oecologia 68(1), 45–51 (1985)Google Scholar
  51. 51.
    Hellweger, F.L., Clegg, R.J., Clark, J.R., Plugge, C.M., Kreft, J.U.: Advancing microbial sciences by individual-based modelling. Nat. Rev. Microbiol. 14, 461–471 (2016)CrossRefGoogle Scholar
  52. 52.
    Hethcote, H.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)MathSciNetCrossRefMATHGoogle Scholar
  53. 53.
    Holme, P.: Modern temporal network theory: a colloquium. Eur. Phys. J. B 88(9), 1–30 (2015)CrossRefGoogle Scholar
  54. 54.
    Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)CrossRefGoogle Scholar
  55. 55.
    Hoppensteadt, F., Waltman, P.: A problem in the theory of epidemics. Math. Biosci. 9, 71–91 (1970)MathSciNetCrossRefMATHGoogle Scholar
  56. 56.
    Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J., Van den Broeck, W.: What’s in a crowd? Analysis of face-to-face behavioral networks. J. Theor. Biol. 271(1), 166–180 (2011)Google Scholar
  57. 57.
    Jackson, D., Martin, S., Holcombe, M., Ratnieks, F.: Longevity and detection of persistent foraging trails in pharaoh’s ants, Monomorium pharaonis (l.). Anim. Behav. 71(2), 351–359 (2006)Google Scholar
  58. 58.
    Joh, R.I., Wang, H., Weiss, H., Weitz, J.S.: Dynamics of indirectly transmitted infectious diseases with immunological threshold. Bull. Math. Biol. 71(4), 845–862 (2009)MathSciNetCrossRefMATHGoogle Scholar
  59. 59.
    Karsai, M., Kivela, M., Pan, R.K., Kaski, K., Kertész, J., Barabási, A.L., Saramaki, J.: Small but slow world: How network topology and burstiness slow down spreading. Phys. Rev. E 83, 025102 (2011)CrossRefGoogle Scholar
  60. 60.
    Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics. III. Further studies of the problem of endemicity. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 141, 94–122 (1933)CrossRefMATHGoogle Scholar
  61. 61.
    King, A.J., Sueur, C., Huchard, E., Cowlishaw, G.: A rule-of-thumb based on social affiliation explains collective movements in desert baboons. Anim. Behav. 82, 1337–1345 (2011)CrossRefGoogle Scholar
  62. 62.
    Kivelä, M., Pan, R.K., Kaski, K., Kertész, J., Saramäki, J., Karsai, M.: Multiscale analysis of spreading in a large communication network. J. Stat. Mech. Theory Exp. 2012(03), P03005 (2012)CrossRefGoogle Scholar
  63. 63.
    Kramar, M., Goullet, A., Kondic, L., Mischaikow, K.: Persistence of force networks in compressed granular media. Phys. Rev. E 87, 042207 (2013)CrossRefGoogle Scholar
  64. 64.
    à l’Allemand, S., Witte, V.: A sophisticated, modular communication contributes to ecological dominance in the invasive ant Anoplolepis gracilipes. Biol. Invasions 12(10), 3551–3561 (2010)Google Scholar
  65. 65.
    Lamb, A., Ollason, J.: Site fidelity in foraging wood-ants Formica aquilonia yarrow and its influence on the distribution of foragers in a regenerating environment. Behav. Process. 31(2), 309–321 (1994)CrossRefGoogle Scholar
  66. 66.
    Landau, H., Rapoport, A.: Contribution to the mathematical theory of contagion and spread of information: I. Spread through a thoroughly mixed population. Bull. Math. Biophys. 15, 173 (1953)Google Scholar
  67. 67.
    Liljeros, F., Edling, C.R., Amaral, L.A.N., Stanley, H.E., Åberg, Y.: The web of human sexual contacts. Nature 411(6840), 907–908 (2001)CrossRefGoogle Scholar
  68. 68.
    Lind, P.G., da Silva, L.R., Andrade Jr, J.S., Herrmann, H.J.: Spreading gossip in social networks. Phys. Rev. E 76(3), 036117 (2007)CrossRefGoogle Scholar
  69. 69.
    Lloyd-Smith, J.O., Schreiber, S.J., Kopp, P.E., Getz, W.M.: Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005)CrossRefGoogle Scholar
  70. 70.
    Mardia, K., Jupp, P.: Directional statistics. John Wiley & Sons, New York (1999)Google Scholar
  71. 71.
    Mimee, M., Tucker, A., Voigt, C., Lu, T.: Programming a human commensal bacterium, Bacteroides thetaiotaomicron, to sense and respond to stimuli in the murine gut microbiota. Cell Syst. 1, 62–71 (2015)CrossRefGoogle Scholar
  72. 72.
    Moore, C., Newman, M.E.J.: Epidemics and percolation in small-world networks. Phys. Rev. E 61, 5678–5682 (2000)CrossRefGoogle Scholar
  73. 73.
    Moser, J.C., Blum, M.S.: Trail marking substance of the texas leaf-cutting ant: source and potency. Science 140(3572), 1228–1228 (1963)CrossRefGoogle Scholar
  74. 74.
    Nagy, M., Akos, Z., Biro, D., Vicsek, T.: Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010)CrossRefGoogle Scholar
  75. 75.
    Naug, D.: Structure of the social network and its influence on transmission dynamics in a honeybee colony. Behav. Ecol. Sociobiol. 62, 1719–1725 (2008)CrossRefGoogle Scholar
  76. 76.
    Olsen, S.J., Chang, H.L., Cheung, T.Y.Y., Tang, A.F.Y., Fisk, T.L., Ooi, S.P.L., Kuo, H.W., Jiang, D.D.S., Chen, K.T., Lando, J., et al.: Transmission of the severe acute respiratory syndrome on aircraft. N. Engl. J. Med. 349(25), 2416–2422 (2003)CrossRefGoogle Scholar
  77. 77.
    Otterstatter, M., Thomson, J.: Contact networks and transmission of an intestinal pathogen in bumble bee (bombus impatiens) colonies. Oecologia 154(2), 411–421 (2007)CrossRefGoogle Scholar
  78. 78.
    Ozbudak, E.M., Thattai, M., Lim, H.N., Shraiman, B.I., van Oudenaarden, A.: Multistability in the lactose utilization network of Escherichia coli. Nature 427, 737–740 (2004)Google Scholar
  79. 79.
    Paquet, P.C.: Scent-marking behavior of sympatric wolves (Canis lupus) and coyotes (C. latrans) in riding mountain national park. Can. J. Zool. 69(7), 1721–1727 (1991)Google Scholar
  80. 80.
    Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015)MathSciNetCrossRefGoogle Scholar
  81. 81.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)CrossRefGoogle Scholar
  82. 82.
    Perna, A., Granovskiy, B., Garnier, S., Garnier, S.C., Labédan, M., Theraulaz, G., Fourcassié, V., Sumpter, D.J.T.: Individual rules for trail pattern formation in argentine ants (Linepithema humile). PLoS Comput. Biol. 8, e1002592 (2012)MathSciNetCrossRefGoogle Scholar
  83. 83.
    Richardson, T.O., Giuggioli, L., Franks, N.R., Sendova-Franks, A.B.: Measuring site fidelity and spatial segregation within animal societies. Methods Ecol. Evol. (2017). DOI 10.1111/2041-210X.12751Google Scholar
  84. 84.
    Richardson, T.O., Gorochowski, T.E.: Beyond close-proximity interactions: the role of spatial coincidence in transmission networks. J. R. Soc. Interface 12, 111 (2015)CrossRefGoogle Scholar
  85. 85.
    Richardson, T.O., Liechti, J.I., Stroeymeyt, N., Bonhoeffer, S., Keller, L.: Short-term activity cycles impede information transmission in ant colonies. PLoS Comput. Biol. 13(5), e1005527 (2017)CrossRefGoogle Scholar
  86. 86.
    Rocha, L.E., Liljeros, F., Holme, P.: Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput. Biol. 7(3), e1001,109 (2011)CrossRefGoogle Scholar
  87. 87.
    Roche, B., Drake, J.M., Rohani, P.: The curse of the pharaoh revisited: evolutionary bi-stability in environmentally transmitted pathogens. Ecol. Lett. 14(6), 569–575 (2011)CrossRefGoogle Scholar
  88. 88.
    Rohani, P., Breban, R., Stallknecht, D., Drake, J.: Environmental transmission of low pathogenicity avian influenza viruses and its implications for pathogen invasion. Proc. Natl. Acad. Sci. 106(25), 10365–10369 (2009)CrossRefGoogle Scholar
  89. 89.
    Rohani, P., Zhong, X., King, A.A.: Contact network structure explains the changing epidemiology of pertussis. Science 330, 982–985 (2010)CrossRefGoogle Scholar
  90. 90.
    Rosengren, R.: Route fidelity, visual memory and recruitment behaviour in foraging wood ants of the genus Formica (Hymenoptera, Formicidae), vol. 133. Societas pro Fauna et Flora Fennica (1971)Google Scholar
  91. 91.
    Rosengren, R., Fortelius, W.: Ortstreue in foraging ants of the Formica rufa group – hierarchy of orienting cues and long-term memory. Insect. Soc. 33(3), 306–337 (1986)CrossRefGoogle Scholar
  92. 92.
    Rosenthal, S.B., Twomey, C.R., Hartnett, A.T., Wu, H.S., Couzin, I.D.: Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion. Proc. Natl. Acad. Sci. 112, 4690–4695 (2015)CrossRefGoogle Scholar
  93. 93.
    Roshani, F., Naimi, Y.: Effects of degree-biased transmission rate and nonlinear infectivity on rumor spreading in complex social networks. Phys. Rev. E 85(3), 036109 (2012)CrossRefGoogle Scholar
  94. 94.
    Rushmore, J., Caillaud, D., Matamba, L., Stumpf, R.M., Borgatti, S.P., Altizer, S.: Social network analysis of wild chimpanzees provides insights for predicting infectious disease risk. J. Anim. Ecol. 82(5), 976–986 (2013)CrossRefGoogle Scholar
  95. 95.
    Salathé, M., Jones, J.H.: Dynamics and control of diseases in networks with community structure. PLoS Comput. Biol. 6, e1000736 (2010)MathSciNetCrossRefGoogle Scholar
  96. 96.
    Salathé, M., Kazandjieva, M., Lee, J., Levis, P., Feldman, M., Jones, J.: A high-resolution human contact network for infectious disease transmission. Proc. Natl. Acad. Sci. 107(51), 22020–22025 (2010)CrossRefGoogle Scholar
  97. 97.
    Salo, O., Rosengren, R.: Memory of location and site recognition in the ant Formica uralensis (hymenoptera: Formicidae). Ethology 107(8), 737–752 (2001)CrossRefGoogle Scholar
  98. 98.
    Schneirla, T.: Raiding and other outstanding phenomena in the behavior of army ants. Proc. Natl. Acad. Sci. 20(5), 316 (1934)CrossRefGoogle Scholar
  99. 99.
    Schwarzkopf, L., Alford, R.A.: Nomadic movement in tropical toads. Oikos 96(3), 492–506 (2002)CrossRefGoogle Scholar
  100. 100.
    Sendova-Franks, A., Franks, N.: Spatial relationships within nests of the ant Leptothorax unifasciatus (Latr.) and their implications for the division of labour. Anim. Behav. 50(1), 121–136 (1995)Google Scholar
  101. 101.
    Sendova-Franks, A., Hayward, R., Wulf, B., Klimek, T., James, R., Planqué, R., Britton, N., Franks, N.: Emergency networking: famine relief in ant colonies. Anim. Behav. 79(2), 473–485 (2010)CrossRefGoogle Scholar
  102. 102.
    Serfling, R.: Historical review of epidemic theory. Hum. Biol. 24, 145–166 (1952)Google Scholar
  103. 103.
    Smieszek, T., Salathé, M.: A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks. BMC Med. 11(1), 1 (2013)CrossRefGoogle Scholar
  104. 104.
    Song, C., Koren, T., Wang, P., Barabási, A.L.: Modelling the scaling properties of human mobility. Nat. Phys. 6(10), 818–823 (2010)CrossRefGoogle Scholar
  105. 105.
    Starnini, M., Baronchelli, A., Pastor-Satorras, R.: Modeling human dynamics of face-to-face interaction networks. Phys. Rev. Lett. 110(16), 16871 (2013)CrossRefGoogle Scholar
  106. 106.
    Starninia, M., Baronchelli, A., Pastor-Satorrasc, R.: Model reproduces individual, group and collective dynamics of human contact networks. Soc. Networks 47, 130–137 (2016)CrossRefGoogle Scholar
  107. 107.
    Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J., Quaggiotto, M., Van den Broeck, W., Regis, C., Lina, B., Vanhems, P.: High-resolution measurements of face-to-face contact patterns in a primary school. PLoS One 6(8), e23176 (2011)CrossRefGoogle Scholar
  108. 108.
    Sun, L., Axhausen, K.W., Lee, D.H., Huang, X.: Understanding metropolitan patterns of daily encounters. Proc. Natl. Acad. Sci. 110(34), 13774–13779 (2013)CrossRefGoogle Scholar
  109. 109.
    Tabor, J., Levskaya, A., Voigt, C.: Multichromatic control of gene expression in Escherichia coli. J. Mol. Biol. 405, 315–324 (2011)Google Scholar
  110. 110.
    Tang, W., D.A., B.: Agent-based modeling of animal movement: a review. Geogr. Compass 4(7), 682–700 (2010)Google Scholar
  111. 111.
    Tesfatsion, L., Judd, K.L. (eds.): Handbook of Computational Economics: Agent-Based Computational Economics, vol. 2. Elsevier, Harvard (2006)Google Scholar
  112. 112.
    Tizzoni, M., Bajardi, P., Poletto, C., Ramasco, J.J., Balcan, D., Gonçalves, B., Perra, N., Colizza, V., Vespignani, A.: Real-time numerical forecast of global epidemic spreading: case study of 2009 a/h1n1pdm. BMC Med. 10(1), 165 (2012)CrossRefGoogle Scholar
  113. 113.
    Vespignani, A.: Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8(1), 32–39 (2012)MathSciNetCrossRefGoogle Scholar
  114. 114.
    Weber, D.J., Rutala, W.A., Miller, M.B., Huslage, K., Sickbert-Bennett, E.: Role of hospital surfaces in the transmission of emerging health care-associated pathogens: norovirus, Clostridium difficile, and Acinetobacter species. Am. J. Infect. Control 38, S25–S33 (2010)CrossRefGoogle Scholar
  115. 115.
    Wilson, E.O.: Chemical communication among workers of the fire ant Solenopsis saevissima (fr. smith) 1. the organization of mass-foraging. Anim. Behav. 10(1), 134–147 (1962)Google Scholar
  116. 116.
    Xiao, Y., French, N., Bowers, R., Clancy, D.: Pair approximations and the inclusion of indirect transmission: Theory and application to between farm transmission of Salmonella. J. Theor. Biol. 244(3), 532–540 (2007)Google Scholar
  117. 117.
    Zitterbart, D.P., Wienecke, B., Butler, J.P., Fabry, B.: Coordinated movements prevent jamming in an emperor penguin huddle. PLoS One 6(6), e20260 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.BrisSynBioUniversity of BristolBristolUK
  2. 2.School of Biological SciencesUniversity of BristolBristolUK
  3. 3.Department of Ecology and EvolutionUniversity of LausanneLausanneSwitzerland

Personalised recommendations