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

Published online by Cambridge University Press:  12 August 2009

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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