Literature DB >> 11099077

Modeling contact networks and infection transmission in geographic and social space using GERMS.

J S Koopman1, S E Chick, C S Riolo, A L Adams, M L Wilson, M P Becker.   

Abstract

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.

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Year:  2000        PMID: 11099077     DOI: 10.1097/00007435-200011000-00010

Source DB:  PubMed          Journal:  Sex Transm Dis        ISSN: 0148-5717            Impact factor:   2.830


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