title: Analysis of a bio-dynamic model via Lyapunov principle and small-world network for tuberculosis.
abstract: The study will apply Lyapunov principle to construct a dynamic model for tuberculosis (TB). The Lyapunov principle is commonly used to examine and determine the stability of a dynamic system. To simulate the transmissions of vector-borne diseases and discuss the related health policies effects on vector-borne diseases, the authors combine the multi-agent-based system, social network and compartmental model to develop an epidemic simulation model. In the identity level, the authors use the multi-agent-based system and the mirror identity concept to describe identities with social network features such as daily visits, long-distance movement, high degree of clustering, low degree of separation and local clustering. The research will analyse the complex dynamic mathematic model of TB epidemic and determine its stability property by using the popular Matlab/Simulink software and relative software packages. Facing the current TB epidemic situation, the development of TB and its developing trend through constructing a dynamic bio-mathematical system model of TB is investigated. After simulating the development of epidemic situation with the solution of the SMIR epidemic model, the authors will come up with a good scheme to control epidemic situation to analyse the parameter values of a model that influence epidemic situation evolved. The authors will try to find the quarantining parameters that are the most important factors to control epidemic situation. The SMIR epidemic model and the results via numerical analysis may offer effective prevention with reference to controlling epidemic situation of TB.
authors.affiliation: Department of Electrical Engineering, National Central University, Jhongli, City Taoyuan County 32001, Taiwan. hychung@ee.ncu.edu.tw
authors.forename: H-Y
authors.initials: HY
authors.lastname: Chung
authors.forename: C-Y
authors.initials: CY
authors.lastname: Chung
authors.forename: S-C
authors.initials: SC
authors.lastname: Ou
mesh_terms: D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D011157:Population Dynamics; D015995:Prevalence; D014376:Tuberculosis / Q000453:epidemiology* / Q000635:transmission*