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Modeling contact networks and infection transmission in geographic and social space using GERMS
J S Koopman1, S E Chick, C S Riolo
1Department of Epidemiology, University of Michigan, Ann Arbor 48109, USA.
Background:
Stochastic models of discrete individuals and deterministic models of continuous populations may give different answers to questions about infectious diseases.
Goal:
Discrete individual model formulations are sought that extend deterministic models of infection transmission systems so that both model forms contribute cooperatively to model-based decision making.
Study Design:
GERMS models are defined as stochastic processes in continuous time with parameters analogous to those in deterministic models. A GERMS model simulator was developed that insured that the rate of events depended only on the current state of model.
Results:
The confidence intervals of long-term averages of infection level in simulated GERMS models were shown to contain the deterministic model means.
Conclusion:
GERMS models provide a convenient framework for testing the sensitivity of model-based decisions to a variety of unrealistic assumptions that are characteristic of differential equation models. GERMS especially facilitates making more realistic assumptions about contact patterns in geographic and social space.