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Next Steps in Simulating High-risk Infectious Disease Propagation Networks
Author(s) -
Alfredo TiradoRamos,
Chris Kelley
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.05.309
Subject(s) - computer science , data science , context (archaeology) , human immunodeficiency virus (hiv) , risk analysis (engineering) , artificial intelligence , machine learning , medicine , immunology , paleontology , biology
imulating HIV transmission networks using agent-based systems has consistently shown promise during the last ten years. In this position paper we briefly discuss the state of the art, as elaborated on our previous work, and propose next steps that incorporate not only current HIV infection propagation approaches but also promising prevention strategies that include sociological markers. We stress that throughout translational validation will be key for approaching real impact in clinical outcomes. Finally, we place our proposed approach in the context of a real-life cohort that provides significant and relevant data on high-risk populations, HIV+ drug abusing patients that circulate through the U.S. jail system, and propose that the use of more complex sociological prevention markers may greatly enrich currently used biomarker-based approaches and provide with more sophisticated and nuanced results

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