Models for Assessing Population Effects of Risk Factors Affecting Transmission. J. Koopman, S. E. Chick, and A. Adams, University of Michigan, Departments of Epidemiology and Industrial and Operations Engineering, Ann Arbor, MI
Risk factors for infectious agent transmission often have population impacts beyond the individuals exposed to the risk factor. Risk factors that have small effects using individual risk assessment methods can have large effects at the population level. Population transmission effects are commonly assessed using differential equation models of continuous population segments. This type of model often generates useful insights regarding fundamental determinants of population infection patterns. But models of continuous population segments have three deficiencies that will be addressed. We present a discrete individual transmission system model approach that is particularly flexible with regard to generating realistic contact patterns in social and geographic space. Its structure allows for solutions in limited settings using either probabilistic analyses or differential equation analogues. We demonstrate that the computer implementation of this simulation approach generates observations that are narrowly confined around the theoretical expectations we derive. This new approach thus has the potential to integrate discrete individual simulation models of transmission in populations into the huge and exponentially growing literature on differential equation model analysis of transmission systems.
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