Preliminary analysis of an agent-based model for a tick-borne disease

Holly Gaff

doi:10.3934/mbe.2011.8.463

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2011, 8(2): 463-473. doi: 10.3934/mbe.2011.8.463

Preliminary analysis of an agent-based model for a tick-borne disease

Holly Gaff ^1,

1.	Department of Biological Sciences, Old Dominion University, Norfolk, VA 23529, United States

Received March 2010 Revised September 2010 Published April 2011

Abstract
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Ticks have a unique life history including a distinct set of life stages and a single blood meal per life stage. This makes tick-host interactions more complex from a mathematical perspective. In addition, any model of these interactions must involve a significant degree of stochasticity on the individual tick level. In an attempt to quantify these relationships, I have developed an individual-based model of the interactions between ticks and their hosts as well as the transmission of tick-borne disease between the two populations. The results from this model are compared with those from previously published differential equation based population models. The findings show that the agent-based model produces significantly lower prevalence of disease in both the ticks and their hosts than what is predicted by a similar differential equation model.

Keywords: Agent-based model, tick-borne disease..

Mathematics Subject Classification: Primary: 92B08; Secondary: 90B1.

Citation: Holly Gaff. Preliminary analysis of an agent-based model for a tick-borne disease. Mathematical Biosciences & Engineering, 2011, 8 (2) : 463-473. doi: 10.3934/mbe.2011.8.463

References:

[1]	Centers for Disease Control and Prevention, Summary of Notifiable Diseases - United States, 2006,, MMWR, 55 (2008), 1. Google Scholar
[2]	D. E. Sonenshine and T. N. Mather, "Ecological Dynamics of Tick-Borne Zoonoses,", Oxford University Press, (1994). Google Scholar
[3]	H. Gaff and L. J. Gross, Analysis of a tick-borne disease model with varying population sizes in various habitats,, Bulletin of Mathematical Biology, 69 (2007), 265. doi: 10.1007/s11538-006-9125-5. Google Scholar
[4]	H. Gaff and E. Schaefer, Metapopulation models in tick-borne disease transmission modelling,, In, (2008). Google Scholar
[5]	H. Gaff, L. Gross and E. Schaefer, Results from a mathematical model for human monocytic ehrlichiosis,, Proceedings of the 5th Conference on Rickettsiae and Rickettsial diseases, 15 (2008), 1. Google Scholar
[6]	D. G. Haile and G. A. Mount, Computer simulation of population dynamics of the lone star tick, Amblyomma americanum (Acari: Ixodidae),, Journal of Medical Entomology, 24 (1987), 356. Google Scholar
[7]	G. A. Mount and D. G. Haile, Computer simulation of population dynamics of the American dog tick (Acari: Ixodidae),, Journal of Medical Entomology, 26 (1989), 60. Google Scholar
[8]	G. A. Mount, D. G. Haile, R. B. Davey and L. M. Cooksey, Computer simulation of boophilus cattle tick (Acari: Ixodidae) population dynamics,, Journal of Medical Entomology, 28 (1991), 223. Google Scholar
[9]	G. A. Mount, D. G. Haile, D. R. Barnard and E. Daniels, New version of LSTSIM for computer simulation of Amblyomma americanum (Acari: Ixodidae) population dynamics,, Journal of Medical Entomology, 30 (1993), 843. Google Scholar
[10]	G. A. Mount, D. G. Haile and E. Daniels, Simulation of blacklegged tick (Acari: Ixodidae) population dynamics and transmission of Borrelia burgdorferi,, Journal of Medical Entomology, 34 (1997), 461. Google Scholar
[11]	G. A. Mount, D. G. Haile and E. Daniels, Simulation of management strategies for the blacklegged tick (Acari: Ixodidae) and the Lyme disease spirochete, Borrelia burgdorferi,, Journal of Medical Entomology, 90 (1997), 672. Google Scholar
[12]	S. Sandberg, T. E. Awerbuch and A. Spielman, A comprehensive multiple matrix model representing the life cycle of the tick that transmits the age of Lyme disease,, Journal of Theoretical Biology, 157 (1992), 203. doi: 10.1016/S0022-5193(05)80621-6. Google Scholar
[13]	T. E. Awerbuch and S. Sandberg, Trends and oscillations in tick population dynamics,, Journal of Theoretical Biology, 175 (1995), 511. doi: 10.1006/jtbi.1995.0158. Google Scholar
[14]	S. Randolph, Epidemiological uses of a population model for the tick Rhipicephalus appendiculatus,, Tropical Medicine and International Health, 4 (1999). doi: 10.1046/j.1365-3156.1999.00449.x. Google Scholar
[15]	J. E. Bunnell, S. D. Price, A. Das, T. M. Shields and G. E. Glass, Geographic Information Systems and Spatial Analysis of Adult Ixodes scapularis (Acari: Ixodidae) in the Middle Atlantic Region of the U.S.A.,, Journal of Medical Entomology, 40 (2003), 570. doi: 10.1603/0022-2585-40.4.570. Google Scholar
[16]	A. Das, S. R. Lele, G. E. Glass, T. Shields and J. Petz, Modelling a discrete spatial response using generalized linear mixed models: Application to Lyme disease vectors,, International Journal of Geographical Information Science, 16 (2002), 151. doi: 10.1080/13658810110099134. Google Scholar
[17]	G. E. Glass, B. S. Schwartz, J. M. Morgan, D. T. Johnson, P. M. Noy and E. Israel, Environmental risk factors for Lyme disease identified with geographic information systems,, American Journal of Public Health, 85 (1995), 944. doi: 10.2105/AJPH.85.7.944. Google Scholar
[18]	W. E. Fitzgibbon, M. E. Parrott and G. F. Webb, A diffusive epidemic model for a host-vector system,, In, (1996). Google Scholar
[19]	J. Radcliffe and L. Rass, The spatial spread and final size of models for the deterministic host-vector epidemic,, Mathematical Biosciences, 70 (1984), 123. doi: 10.1016/0025-5564(84)90094-4. Google Scholar
[20]	J. Radcliffe and L. Rass, The rate of spread of infection in models for the deterministic host-vector epidemic,, Mathematical Biosciences, 74 (1985), 257. doi: 10.1016/0025-5564(85)90059-8. Google Scholar
[21]	A. R. Giardina, K. A. Schmidt, E. M. Schauber and R. S. Ostfeld, Modeling the role of songbirds and rodents in the ecology of Lyme disease,, Canadian Journal of Zoology, 78 (2000), 2184. doi: 10.1139/cjz-78-12-2184. Google Scholar
[22]	K. LoGiudice, R. S. Ostfeld, K. A. Schmidt and F. Keesing, The ecology of infectious disease: Effects of host diversity and community composition on Lyme disease risk,, Proceedings of the National Academies of Science, 100 (2003), 567. Google Scholar
[23]	M. Ghosh and A. Pugliese, Seasonal population dynamics of ticks, and its influence on infection transmission: A semi- discrete approach,, Bulletin of Mathematical Biology, 66 (2004), 1659. doi: 10.1016/j.bulm.2004.03.007. Google Scholar
[24]	W. Ding, Optimal Control on Hybrid ODE Systems with Application to a Tick Disease Model,, Mathematical Biosciences and Engineering, 4 (2007), 633. Google Scholar
[25]	D. L. DeAngelis and L. J. Gross, "Individual-based Models and Approaches in Ecology: Populations, Communities and Ecosystems,", Taylor and Francis, (1992). Google Scholar
[26]	D. L. DeAngelis, L. J. Gross, W. F. Wolff, D. M. Fleming, M. P. Nott and E. J. Comiskey, Individual-based models on the landscape: Applications to the Everglades,, in, (2000). Google Scholar
[27]	S. Eubank, H. Guclu, V. S. A. Kumar, M. V. Marathe, A. Srinivasan, Z. Toroczkai and N. Wang, Modelling disease outbreaks in realistic urban social networks,, Nature, 429 (2004), 180. doi: 10.1038/nature02541. Google Scholar
[28]	A. G. Barbour, "Lyme Disease: The Cause, the Cure, the Controversy,", John Hopkins University Press, (1996). Google Scholar
[29]	F. Des Vignes, M. L. Levin and D. Fish, Comparative vector competence of Dermacentor variabilis and Ixodes scapularis (Acari: Ixodidae) for the agent of human granulocytic ehrlichiosis,, Journal of Medical Entomology, 36 (1999), 182. Google Scholar
[30]	Dania Richter, Andrew Spielman, Nicholas Komar and Franz-Rainer Matuschka, Competence of American robins as reservoir hosts for Lyme disease spirochetes,, Emerging Infectious Diseases, 6 (2000), 133. doi: 10.3201/eid0602.000205. Google Scholar
[31]	V. Grimm, U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss-Custard, T. Grand, S. K. Heinz, G. Huse, A. Huth, J. U. Jepsen, C. Jørgensen, W. M. Mooij, B. Müller, G. Peer, C. Piou, S. F. Railsback, A. M. Robbins, M. M. Robbins, E. Rossmanith, N. Rüger, E. Strand, S. Souissi, R. A. Stillman, R. Vabø, U. Visser and D. L. DeAngelis, A standard protocol for describing individual-based and agent-based models,, Ecological Modelling \textbf{198} (2006), 198 (2006), 115. doi: 10.1016/j.ecolmodel.2006.04.023. Google Scholar
[32]	A. L. Bauer, C. A. A. Beauchemin and A. S. Perelson, Agent-based modeling of host-pathogen systems: The successes and the challenges,, Information Sciences, 179 (2009), 1379. doi: 10.1016/j.ins.2008.11.012. Google Scholar
[33]	V. Grimm and S. F. Railsbeck, "Individual-based Modeling and Ecology,", Princeton University Press, (2005). Google Scholar
[34]	J. M. Lockhart, W. R. Davidson, J. E. Dawson and D. E. Stallknecht, Temporal association of Amblyomma americanum with the presence of Ehrlichia chaffeensis reactive antibodies in white-tailed deer,, Journal of Wildlife Diseases, 31 (1995), 119. Google Scholar
[35]	J. E. Dawson, J. E. Childs, K. L. Biggie, C. Moore, D. Stallknecht, J. Shaddock, J. Bouseman, E. Hofmeister and J. G. Olson, White-tailed deer as a potential reservoir of Ehrlichia spp.,, Journal of Wildlife Diseases, 30 (1994), 162. Google Scholar
[36]	B. E. Anderson, K. G. Sims, J. G. Olson, J. E. Childs, J. F. Piesman, C. M. Happ, G. O. Maupin and B. J. B. Johnson, Amblyomma americanum: A potential vector of human ehrlichiosis,, American Journal of Tropical Medicine and Hygiene, 49 (1993), 239. Google Scholar

show all references

References:

[1]	Centers for Disease Control and Prevention, Summary of Notifiable Diseases - United States, 2006,, MMWR, 55 (2008), 1. Google Scholar
[2]	D. E. Sonenshine and T. N. Mather, "Ecological Dynamics of Tick-Borne Zoonoses,", Oxford University Press, (1994). Google Scholar
[3]	H. Gaff and L. J. Gross, Analysis of a tick-borne disease model with varying population sizes in various habitats,, Bulletin of Mathematical Biology, 69 (2007), 265. doi: 10.1007/s11538-006-9125-5. Google Scholar
[4]	H. Gaff and E. Schaefer, Metapopulation models in tick-borne disease transmission modelling,, In, (2008). Google Scholar
[5]	H. Gaff, L. Gross and E. Schaefer, Results from a mathematical model for human monocytic ehrlichiosis,, Proceedings of the 5th Conference on Rickettsiae and Rickettsial diseases, 15 (2008), 1. Google Scholar
[6]	D. G. Haile and G. A. Mount, Computer simulation of population dynamics of the lone star tick, Amblyomma americanum (Acari: Ixodidae),, Journal of Medical Entomology, 24 (1987), 356. Google Scholar
[7]	G. A. Mount and D. G. Haile, Computer simulation of population dynamics of the American dog tick (Acari: Ixodidae),, Journal of Medical Entomology, 26 (1989), 60. Google Scholar
[8]	G. A. Mount, D. G. Haile, R. B. Davey and L. M. Cooksey, Computer simulation of boophilus cattle tick (Acari: Ixodidae) population dynamics,, Journal of Medical Entomology, 28 (1991), 223. Google Scholar
[9]	G. A. Mount, D. G. Haile, D. R. Barnard and E. Daniels, New version of LSTSIM for computer simulation of Amblyomma americanum (Acari: Ixodidae) population dynamics,, Journal of Medical Entomology, 30 (1993), 843. Google Scholar
[10]	G. A. Mount, D. G. Haile and E. Daniels, Simulation of blacklegged tick (Acari: Ixodidae) population dynamics and transmission of Borrelia burgdorferi,, Journal of Medical Entomology, 34 (1997), 461. Google Scholar
[11]	G. A. Mount, D. G. Haile and E. Daniels, Simulation of management strategies for the blacklegged tick (Acari: Ixodidae) and the Lyme disease spirochete, Borrelia burgdorferi,, Journal of Medical Entomology, 90 (1997), 672. Google Scholar
[12]	S. Sandberg, T. E. Awerbuch and A. Spielman, A comprehensive multiple matrix model representing the life cycle of the tick that transmits the age of Lyme disease,, Journal of Theoretical Biology, 157 (1992), 203. doi: 10.1016/S0022-5193(05)80621-6. Google Scholar
[13]	T. E. Awerbuch and S. Sandberg, Trends and oscillations in tick population dynamics,, Journal of Theoretical Biology, 175 (1995), 511. doi: 10.1006/jtbi.1995.0158. Google Scholar
[14]	S. Randolph, Epidemiological uses of a population model for the tick Rhipicephalus appendiculatus,, Tropical Medicine and International Health, 4 (1999). doi: 10.1046/j.1365-3156.1999.00449.x. Google Scholar
[15]	J. E. Bunnell, S. D. Price, A. Das, T. M. Shields and G. E. Glass, Geographic Information Systems and Spatial Analysis of Adult Ixodes scapularis (Acari: Ixodidae) in the Middle Atlantic Region of the U.S.A.,, Journal of Medical Entomology, 40 (2003), 570. doi: 10.1603/0022-2585-40.4.570. Google Scholar
[16]	A. Das, S. R. Lele, G. E. Glass, T. Shields and J. Petz, Modelling a discrete spatial response using generalized linear mixed models: Application to Lyme disease vectors,, International Journal of Geographical Information Science, 16 (2002), 151. doi: 10.1080/13658810110099134. Google Scholar
[17]	G. E. Glass, B. S. Schwartz, J. M. Morgan, D. T. Johnson, P. M. Noy and E. Israel, Environmental risk factors for Lyme disease identified with geographic information systems,, American Journal of Public Health, 85 (1995), 944. doi: 10.2105/AJPH.85.7.944. Google Scholar
[18]	W. E. Fitzgibbon, M. E. Parrott and G. F. Webb, A diffusive epidemic model for a host-vector system,, In, (1996). Google Scholar
[19]	J. Radcliffe and L. Rass, The spatial spread and final size of models for the deterministic host-vector epidemic,, Mathematical Biosciences, 70 (1984), 123. doi: 10.1016/0025-5564(84)90094-4. Google Scholar
[20]	J. Radcliffe and L. Rass, The rate of spread of infection in models for the deterministic host-vector epidemic,, Mathematical Biosciences, 74 (1985), 257. doi: 10.1016/0025-5564(85)90059-8. Google Scholar
[21]	A. R. Giardina, K. A. Schmidt, E. M. Schauber and R. S. Ostfeld, Modeling the role of songbirds and rodents in the ecology of Lyme disease,, Canadian Journal of Zoology, 78 (2000), 2184. doi: 10.1139/cjz-78-12-2184. Google Scholar
[22]	K. LoGiudice, R. S. Ostfeld, K. A. Schmidt and F. Keesing, The ecology of infectious disease: Effects of host diversity and community composition on Lyme disease risk,, Proceedings of the National Academies of Science, 100 (2003), 567. Google Scholar
[23]	M. Ghosh and A. Pugliese, Seasonal population dynamics of ticks, and its influence on infection transmission: A semi- discrete approach,, Bulletin of Mathematical Biology, 66 (2004), 1659. doi: 10.1016/j.bulm.2004.03.007. Google Scholar
[24]	W. Ding, Optimal Control on Hybrid ODE Systems with Application to a Tick Disease Model,, Mathematical Biosciences and Engineering, 4 (2007), 633. Google Scholar
[25]	D. L. DeAngelis and L. J. Gross, "Individual-based Models and Approaches in Ecology: Populations, Communities and Ecosystems,", Taylor and Francis, (1992). Google Scholar
[26]	D. L. DeAngelis, L. J. Gross, W. F. Wolff, D. M. Fleming, M. P. Nott and E. J. Comiskey, Individual-based models on the landscape: Applications to the Everglades,, in, (2000). Google Scholar
[27]	S. Eubank, H. Guclu, V. S. A. Kumar, M. V. Marathe, A. Srinivasan, Z. Toroczkai and N. Wang, Modelling disease outbreaks in realistic urban social networks,, Nature, 429 (2004), 180. doi: 10.1038/nature02541. Google Scholar
[28]	A. G. Barbour, "Lyme Disease: The Cause, the Cure, the Controversy,", John Hopkins University Press, (1996). Google Scholar
[29]	F. Des Vignes, M. L. Levin and D. Fish, Comparative vector competence of Dermacentor variabilis and Ixodes scapularis (Acari: Ixodidae) for the agent of human granulocytic ehrlichiosis,, Journal of Medical Entomology, 36 (1999), 182. Google Scholar
[30]	Dania Richter, Andrew Spielman, Nicholas Komar and Franz-Rainer Matuschka, Competence of American robins as reservoir hosts for Lyme disease spirochetes,, Emerging Infectious Diseases, 6 (2000), 133. doi: 10.3201/eid0602.000205. Google Scholar
[31]	V. Grimm, U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss-Custard, T. Grand, S. K. Heinz, G. Huse, A. Huth, J. U. Jepsen, C. Jørgensen, W. M. Mooij, B. Müller, G. Peer, C. Piou, S. F. Railsback, A. M. Robbins, M. M. Robbins, E. Rossmanith, N. Rüger, E. Strand, S. Souissi, R. A. Stillman, R. Vabø, U. Visser and D. L. DeAngelis, A standard protocol for describing individual-based and agent-based models,, Ecological Modelling \textbf{198} (2006), 198 (2006), 115. doi: 10.1016/j.ecolmodel.2006.04.023. Google Scholar
[32]	A. L. Bauer, C. A. A. Beauchemin and A. S. Perelson, Agent-based modeling of host-pathogen systems: The successes and the challenges,, Information Sciences, 179 (2009), 1379. doi: 10.1016/j.ins.2008.11.012. Google Scholar
[33]	V. Grimm and S. F. Railsbeck, "Individual-based Modeling and Ecology,", Princeton University Press, (2005). Google Scholar
[34]	J. M. Lockhart, W. R. Davidson, J. E. Dawson and D. E. Stallknecht, Temporal association of Amblyomma americanum with the presence of Ehrlichia chaffeensis reactive antibodies in white-tailed deer,, Journal of Wildlife Diseases, 31 (1995), 119. Google Scholar
[35]	J. E. Dawson, J. E. Childs, K. L. Biggie, C. Moore, D. Stallknecht, J. Shaddock, J. Bouseman, E. Hofmeister and J. G. Olson, White-tailed deer as a potential reservoir of Ehrlichia spp.,, Journal of Wildlife Diseases, 30 (1994), 162. Google Scholar
[36]	B. E. Anderson, K. G. Sims, J. G. Olson, J. E. Childs, J. F. Piesman, C. M. Happ, G. O. Maupin and B. J. B. Johnson, Amblyomma americanum: A potential vector of human ehrlichiosis,, American Journal of Tropical Medicine and Hygiene, 49 (1993), 239. Google Scholar

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Preliminary analysis of an agent-based model for a tick-borne disease

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Preliminary analysis of an agent-based model for a tick-borne disease

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