Rule-based modeling of chronic disease epidemiology: elderly depression as an illustration.

Citation data:

PloS one, ISSN: 1932-6203, Vol: 7, Issue: 8, Page: e41452

Publication Year:
2012
Usage 5401
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Citations 2
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PMID:
22952581
DOI:
10.1371/journal.pone.0041452; 10.1371/journal.pone.0041452.g008; 10.1371/journal.pone.0041452.g002; 10.1371/journal.pone.0041452.t001; 10.1371/journal.pone.0041452.g007; 10.1371/journal.pone.0041452.g009; 10.1371/journal.pone.0041452.g001; 10.1371/journal.pone.0041452.g006; 10.1371/journal.pone.0041452.g005; 10.1371/journal.pone.0041452.g003; 10.1371/journal.pone.0041452.g004
PMCID:
PMC3429481; 3429481
Author(s):
Jean-Christophe Chiêm; Jean Macq; Niko Speybroeck; Tricia A. Thornton-Wells
Publisher(s):
Public Library of Science (PLoS); Figshare
Tags:
Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Medicine; Biotechnology; Information and Computing Sciences; Mathematics; 111714 Mental Health; prevalences; typology; algorithm; public health and epidemiology; Mental health; computer science; mathematics; Non-clinical medicine; rule-based; modeling; illustration; numerical
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article description
Rule-based Modeling (RBM) is a computer simulation modeling methodology already used to model infectious diseases. Extending this technique to the assessment of chronic diseases, mixing quantitative and qualitative data appear to be a promising alternative to classical methods. Elderly depression reveals an important source of comorbidities. Yet, the intertwined relationship between late-life events and the social support of the elderly person remains difficult to capture. We illustrate the usefulness of RBM in modeling chronic diseases using the example of elderly depression in Belgium.