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11 - Infectious diseases in the historical archives: a modeling approach

Published online by Cambridge University Press:  12 August 2009

By
Lisa Sattenspiel
Edited by
D. Ann Herring  and
Alan C. Swedlund
Lisa Sattenspiel
Affiliation:
Department of Anthropology, 107 Swallow Hall, University of Missouri, Columbia, MO 65211, USA
D. Ann Herring
Affiliation:
McMaster University, Ontario
Alan C. Swedlund
Affiliation:
University of Massachusetts, Amherst
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Summary

Introduction

Human biologists, especially those studying demography and disease in past populations, have long used historical archives to learn more about the populations they are studying. A variety of historical and statistical techniques have been used to analyze and interpret data derived from the archives. Mathematical and computer modeling are approaches that have only rarely been used, but they can provide interesting and valuable insights not generally possible with other methods. In the following I first discuss the nature of models in general and mathematical models in particular. This is followed by an introduction to several of the most commonly used approaches to mathematical and computer modeling in the social sciences and a selective overview of the use of these approaches to study infectious diseases in human populations. Finally, I illustrate in some detail the process of model development and analysis, drawing upon work I have done in conjunction with Ann Herring on the spread of the 1918–19 influenza epidemic in central Canada.

What is a model?

People often use the word ‘model’ very loosely and in different ways. To some degree, the lax usage of the word reflects both real ambiguities in the concept and the use of one word to designate several related ideas. At the most general level, any model can be thought of as an object or concept that is used to represent something else. Models simplify reality and aid in determining the role and importance of factors that influence the real world.

Type
Chapter
Information
Human Biologists in the Archives
Demography, Health, Nutrition and Genetics in Historical Populations
 , pp. 234 - 265
Publisher: Cambridge University Press
Print publication year: 2002

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References

Ackerman, E., Elveback, L. R. and Fox, J. P. (1984). Simulation of Infectious Disease Epidemics. Springfield, IL: C. C. Thomas
Anderson, R. M. and May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press
Bailey, N. T. J. (1975). The Mathematical Theory of Infectious Diseases and its Applications. New York: Hafner Press
Bartlett, M. S. (1957). Measles periodicity and community size. Journal of the Royal Statistical Society A120(1), 48–70CrossRefGoogle Scholar
Bartlett, M. S. (1960). The critical community size for measles in the United States. Journal of the Royal Statistical Society A123(1), 133–41Google Scholar
Bartlett, M. S. (1961). Monte Carlo studies in ecology and epidemiology. Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics, and Probability 4, 39–55Google Scholar
Bernstein, R. S., Sokal, D. C., Seitz, S. T., Auvert, B., Stover, J. and Naamara, W. (1998). Simulating the control of a heterosexual HIV epidemic in a severely affected East African city. Interfaces 28(3 May–June), 101–26CrossRefGoogle Scholar
Black, F. L. (1975). Measles endemicity in insular populations: critical community size and its evolutionary implication. Journal of Theoretical Biology 11 207–11CrossRefGoogle Scholar
Castillo-Chavez, C. (ed.) (1989). Mathematical and Statistical Approaches to AIDS Epidemiology. Berlin: Springer-Verlag
Chin, J. (ed.) (2000). Control of Communicable Diseases Manual, 17th edn. Washington, D. C.: The American Public Health Association
Cliff, A. D. and Haggett, P. (1985). The Spread of Measles in Fiji and the Pacific: Spatial Components in the Transmission of Epidemic Waves through Island Communities. Publication HG18. Canberra: Australian National University, Research School of Pacific Studies, Department of Human Geography
Cliff, A. D. and Haggett, P. (1988). Atlas of the Distribution of Diseases: Analytic Approaches to Epidemiological Data. Oxford: Blackwell
Cliff, A. D., Haggett, P., Ord, J. K. and Versey, G. R. (1981). Spatial Diffusion: An Historical Geography of Epidemics in an Island Community. Cambridge: Cambridge University Press
Cliff, A. D., Haggett, P. and Ord, J. K. (1983). Forecasting epidemic pathways for measles in Iceland: the use of simultaneous equation and logit models. Ecology of Disease 2 377–96Google ScholarPubMed
Cliff, A. D., Haggett, P. and Ord, J. K. (1986). Spatial Aspects of Influenza Epidemics. London: Pion
Cliff, A. D., Haggett, P. and Smallman-Raynor, M. (1993). Measles: An Historical Geography of a Major Human Viral Disease, From Global Expansion to Local Retreat, 1849–1990. Oxford: Blackwell
Cliff, A. D., Haggett, P. and Smallman-Raynor, M. (1998). Deciphering Global Epidemics: Analytical Approaches to the Disease Records of World Cities, 1888–1912. Cambridge: Cambridge University Press
Cross, M. and Moscardini, A. O. (1985). Learning the Art of Mathematical Modeling. New York: Halsted Press
Dean, J. S., Gumerman, G. J., Epstein, J. M., Axtell, R. L., Swedlund, A. C., Parker, M. T. and McCarroll, S. (2000). Understanding Anasazi culture change through agent-based modeling. In Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes, ed. T. Kohler and G. Gumerman, pp. 179–205. Oxford: Oxford University Press
Doran, J. E. (2000). Trajectories to complexity in artificial societies: rationality, belief, and emotions. In Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes. ed. T. Kohler and G. Gumerman, pp. 89–105. Oxford: Oxford University Press
Doran, J. E. and Palmer, M. (1995). The EOS project: integrating two models of Paleolithic social change. In Artificial Societies: The Computer Simulation of Social Life, ed. N. Gilbert and R. Conte, pp. 103–25. London: UCL Press
Doran, J. E., Palmer, M., Gilbert, N. and Mellars, P. (1994). The EOS project: modeling Upper Paleolithic social change. In Simulating Societies: The Computer Simulation of Social Phenomena, ed. N. Gilbert and J. Doran, pp. 195–221. London: UCL Press
Epstein, J. M. and Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Washington, D. C.: Brookings Institution Press
Finkenstädt, B. and Grenfell, B. (1998). Empirical determinants of measles metapopulation dynamics in England and Wales. Proceedings of the Royal Society of London B, 211–20CrossRef
Fix, A. G. (1984). Kin groups and trait groups: population structure and epidemic disease selection. American Journal of Physical Anthropology 65, 201–12CrossRefGoogle ScholarPubMed
Gilbert, N. and Conte, R. (eds) (1995). Artificial Societies: The Computer Simulation of Social Life. London: UCL Press
Gilbert, N. and Troitzsch, K. G. (1999). Simulation for the Social Scientist. Buckingham, UK: Open University Press
Government of Canada (1917). Sessional Paper No. 27, George V (9–10): 18–9
Graves, C. (1969). Invasion by Virus. London: Icon Books
Habbema, J. D. F., Vlas, S. J., Plaisier, A. P. and Oortmarssen, G. J. (1996). The microsimulation approach to epidemiologic modeling of helminthic infections, with special reference to schistosomiasis. American Journal of Tropical Medicine and Hygiene 55(5), 165–9CrossRefGoogle ScholarPubMed
HBCA (Provincial Archives of Manitoba, Hudson's Bay Company Archives, Winnipeg, Manitoba) (1918–1923). HBCA B.154/a/87, Norway House Post Journal
HBCA (Provincial Archives of Manitoba, Hudson's Bay Company Archives, Winnipeg, Manitoba) (1918–1922). HBCA B.156/2/44, Oxford House Post Journal
HBCA (Provincial Archives of Manitoba, Hudson's Bay Company Archives, Winnipeg, Manitoba) (1917–1922). HBCA B.283/a/8, God's Lake Post Journal
HBCA (Provincial Archives of Manitoba, Hudson's Bay Company Archives, Winnipeg, Manitoba) (2000). http://www.gov.mb.ca/chc/archives/hbca/
Herring, D. A. (1994). ‘There were young people and old people and babies dying every week’: The 1918–19 influenza pandemic at Norway House. Ethnohistory 41(1), 73–105CrossRefGoogle Scholar
Herring, D. A. and Sattenspiel, L. (2002). Death in winter: the Spanish flu in the Canadian Subarctic. In The Spanish Flu Pandemic of 1918, ed. H. Phillips and D. Killingray. London: Routledge. (In press.)
Jacquez, J. A., Simon, C. P., Koopman, J., Sattenspiel, L. and Perry, T. (1988). Modeling and the analysis of HIV transmission: the effect of contact patterns. Mathematical Biosciences 92, 119–99CrossRefGoogle Scholar
Kaplan, E. H. and Brandeau, M. L. (eds) (1994). Modeling the AIDS Epidemic: Planning, Policy, and Prediction. New York: Raven Press
Kohler, T. A. (2000). Putting social sciences together again: an introduction to the volume. In Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes, ed. T. Kohler and G. Gumerman, pp. 1–18. Oxford: Oxford University Press
Kohler, T. A. and Gumerman, G. J. (eds) (2000). Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes. Oxford: Oxford University Press
Kohler, T. A., Kresl, J., Van West, C., Carr, E. and Wilshusen, R. H. (2000). Be there then: a modeling approach to settlement determinants and spatial efficiency among Late Ancestral Pueblo populations of the Mesa Verde region, U. S. Southwest. In Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes, ed. T. Kohler and G. Gumerman, pp. 145–78. Oxford University Press
Kretzschmar, M. and Morris, M. (1996). Measures of concurrency in networks and the spread of infectious disease. Mathematical Biosciences 133, 165–95CrossRefGoogle ScholarPubMed
Lake, M. W. (2000). MAGICAL computer simulation of Mesolithic foraging. In Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes, ed. T. Kohler and G. Gumerman, pp. 107–43. Oxford University Press
Longini, I. M. Jr. (1988). A mathematical model for predicting the geographic spread of new infectious agents. Mathematical Biosciences 90, 367–83CrossRefGoogle Scholar
McGrath, J. W. (1988). Social networks of disease spread in the Lower Illinois Valley: a simulation approach. American Journal of Physical Anthropology 77, 483–96CrossRefGoogle ScholarPubMed
Mielke, J. H., Jorde, L. B., Trapp, P. G., Anderton, D. L., Pitkänen, K. and Eriksson, A. W. (1984).Historical epidemiology of smallpox in ˚land, Finland, 1751–1890. Demography 21, 271–95CrossRefGoogle Scholar
Milgram, S. (1967). The small world problem. Psychology Today 2, 60–7Google Scholar
Milner, G. R. (1980). Epidemic disease in the postcontact southeast: a reappraisal. Mid-continental Journal of Archaeology 5, 39–56Google ScholarPubMed
Moore, C. and Newman, M. E. J. (2000). Epidemics and percolation in small-world networks. Physical Reviews E 61, 5678–82Google Scholar
Morris, M. (1995). Data driven network models for the spread of disease. In Epidemic Models: Their Structure and Relation to Data, ed. D. Mollison, pp. 302–22. Cambridge: Cambridge University Press
Morris, M. and Kretzschmar, M. (1997). Concurrent partnerships and the spread of HIV. AIDS 11, 641–8CrossRefGoogle ScholarPubMed
Palkovich, A. M. (1981). Demography and disease patterns in a protohistoric Plains group: a study of the Mobridge site (39WW1). Plains Anthropologist 26(Memoir 17), 71–84CrossRefGoogle Scholar
Patterson, K. D. (1986). Pandemic Influenza 1700–1900. Totowa, NJ: Rowman & Littlefield
Plaisier, A. P., Oortmarssen, G. J., Habbema, J. D. F., Remme, J. and Alley, E. S. (1990). ONCHOSIM: a model and computer simulation program for the transmission and control of onchocerciasis. Computer Methods and Programs in Biomedicine 31, 43–56CrossRefGoogle ScholarPubMed
Pool, I. and Kochen, M. (1978). Contacts and influence. Social Networks 1, 1–48Google Scholar
Ramenofsky, A. F. (1987). Vectors of Death. Albuquerque: University of New Mexico Press
Ray, A. J. (1974). Indians in the Fur Trade: Their Role as Hunters, Trappers and Middlemen in the Lands Southwest of Hudson Bay, 1660–1870. Toronto: University of Toronto Press
Ray, A. J. (1976). Diffusion of diseases in the western interior of Canada, 1830–1850. Geographical Review 66(2), 139–157CrossRefGoogle Scholar
Rhodes, C. J. and Anderson, R. M. (1996). A scaling analysis of measles epidemics in a small population. Philosophical Transactions of the Royal Society of London B 351, 1679–88CrossRefGoogle Scholar
Robinson, T. P. (2000). Spatial statistics and geographical information systems in epidemiology and public health. Advances in Parasitology 47, 81–128CrossRefGoogle ScholarPubMed
Russell, S. J. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall
Rvachev, L. A. and Longini, I. M. Jr. (1985). A mathematical model for the global spread of influenza. Mathematical Biosciences 75, 3–22CrossRefGoogle Scholar
Sattenspiel, L. (1987a). Population structure and the spread of disease. Human Biology 59, 411–38Google Scholar
Sattenspiel, L. (1987b). Spread and maintenance of a disease in a structured population. American Journal of Physical Anthropology 77, 497–504CrossRefGoogle Scholar
Sattenspiel, L. and Dietz, K. (1995). A structured epidemic model incorporating geographic mobility among regions. Mathematical Biosciences 128, 71–91CrossRefGoogle ScholarPubMed
Sattenspiel, L. and Herring, D. A. (1998). Structured epidemic models and the spread of the 1918–1919 influenza epidemic in the central Subarctic. Human Biology 70(1), 91–115Google ScholarPubMed
Sattenspiel, L., Mobarry, A. and Herring, D. A. (2000). Modeling the influence of settlement structure on the spread of influenza. American Journal of Human Biology 12, 736–483.0.CO;2-4>CrossRefGoogle ScholarPubMed
Sattenspiel, L. and Powell, C. (1993). Geographic spread of measles on the island of Dominica, West Indies. Human Biology 65, 107–129Google ScholarPubMed
Sattenspiel, L. and Simon, C. P. (1988). The spread and persistence of infectious diseases in a subdivided population. Mathematical Biosciences 90, 341–66CrossRefGoogle Scholar
Scott, S. and Duncan, C. J. (1998). Human Demography and Disease. Cambridge: Cambridge University Press
Scott, S. and Duncan, C. J. (2001). Biology of Plagues. Cambridge: Cambridge University Press
Smallman-Raynor, M., Cliff, A. D. and Haggett, P. (1992). Atlas of AIDS. Oxford: Blackwell
Thornton, R., Miller, T. and Warren, J. (1991). American Indian population recovery following smallpox epidemics. American Anthropologist 93, 28–45CrossRefGoogle Scholar
Upham, S. (1986). Smallpox and climate in the American Southwest. American Anthropologist 88, 115–28CrossRefGoogle Scholar
Ploeg, C. P. B., Vliet, C., Vlas, S. J., Ndinya-Achola, J. O., Fransen, L., Oortmarssen, G. J. and Habbema, J. D. F. (1998). STDSIM: A microsimulation model for decision support in STD control. Interfaces 28(3 May–June), 84–100CrossRefGoogle Scholar
Vlas, S. J.de, Van Oortmarssen, G. J., Gryseels, B., Polderman, A. M., Plaisier, A. P. and Habbema, J. D. F. (1996). SCHISTOSIM: A microsimulation model for the epidemiology and control of schistosomiasis. American Journal of Tropical Medicine and Hygiene 55(5), 170–75CrossRefGoogle ScholarPubMed
Walters, J. H. (1978). Influenza 1918: the contemporary perspective. Bulletin of the New York Academy of Medicine 54, 855–64Google ScholarPubMed
Watts, D. J. (1999). Networks, dynamics, and the small-world phenomenon. American Journal of Sociology 105, 493–527CrossRefGoogle Scholar
Whitmore, T. M. (1992). Disease and Death in Early Colonial Mexico. Boulder, CO: Westview Press

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