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dc.creatorHincapie, D.
dc.creatorOspina, J.
dc.date.available2021-03-26T21:35:21Z
dc.date.issued2011-01-01
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6791
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79959814734&doi=10.1117%2f12.883702&partnerID=40&md5=24b454dd2ef20117b6561ecff63d410f
dc.identifier.urihttp://hdl.handle.net/10784/27430
dc.description.abstractMathematical modeling in Epidemiology is an important tool to understand the ways under which the diseases are transmitted and controlled. The mathematical modeling can be implemented via deterministic or stochastic models. Deterministic models are based on short systems of non-linear ordinary differential equations and the stochastic models are based on very large systems of linear differential equations. Deterministic models admit complete, rigorous and automatic analysis of stability both local and global from which is possible to derive the algebraic expressions for the basic reproductive number and the corresponding epidemic thresholds using computer algebra software. Stochastic models are more difficult to treat and the analysis of their properties requires complicated considerations in statistical mathematics. In this work we propose to use computer algebra software with the aim to solve epidemic stochastic models such as the SIR model and the carrier-borne model. Specifically we use Maple to solve these stochastic models in the case of small groups and we obtain results that do not appear in standard textbooks or in the books updated on stochastic models in epidemiology. From our results we derive expressions which coincide with those obtained in the classical texts using advanced procedures in mathematical statistics. Our algorithms can be extended for other stochastic models in epidemiology and this shows the power of computer algebra software not only for analysis of deterministic models but also for the analysis of stochastic models. We also perform numerical simulations with our algebraic results and we made estimations for the basic parameters as the basic reproductive rate and the stochastic threshold theorem. We claim that our algorithms and results are important tools to control the diseases in a globalized world. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
dc.languageeng
dc.publisherSPIE-INT SOC OPTICAL ENGINEERING
dc.relationDOI;10.1117/12.883702
dc.relationWOS;000295931800007
dc.relationSCOPUS;2-s2.0-79959814734
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/0277-786X
dc.sourceProceedings of SPIE
dc.sourceISSN: 0277786X
dc.sourceISSN: 1996756X
dc.subjectBasic reproductive number; Carrier-borne model; contact network; Maple; Mathematical epidemiology; SAGE; SIR model; Tutte polynomial; Algebra; Algorithms; Biometrics; Computer simulation; Computer software; Disease control; Diseases; Environmental engineering; Graph theory; Identification (control systems); Ordinary differential equations; Statistics; Stochastic systems; Technology; Stochastic models
dc.titleSolving stochastic epidemiological models using computer algebra
dc.typeConference Paper
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.date.accessioned2021-03-26T21:35:21Z


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