Examinando por Materia "Theoretical model"

Mostrando 1 - 2 de 2
  • No hay miniatura disponible
    Ítem
    Bases para la modelación de epidemias: El caso del síndrome respiratorio agudo severo en Canadá
    (Universidad Nacional de Colombia, 2007-01-01) Hincapié, D.; Ospina, J.; Hincapié, D.; Ospina, J.; Universidad EAFIT. Departamento de Ciencias; Lógica y Computación
    Objective: Severe acute respiratory syndrome propagation in Canada during 2003 is analysed by means of simple models, comparing the influence of isolation measures on two epidemic waves. Methods: Deterministic susceptible-infected and susceptible-infected-removed models were used for both epidemic waves, using official published information. NLREG 6.2 was used for estimating deterministic parameters and analytical solutions were obtained with Maple 9 software. Dynamical indicators were obtained for the epidemic. Results: Suitable adjustment of the data was observed with both models, but smaller adjustment was observed during the second wave with the non- removed model. The highest rate of infectiousness was shown (35 new cases per 10 000 susceptible people) during the second wave (with R0 near to one), in spite of presenting greater incidence (8.8 cases per day), compensated for by a high rate of removal (11,5 cases per day) which lasted less than the epidemic (11,1 days), and a lower rate of attack (1 case per each 100 susceptible people). Conclusions: The susceptible-infected model can be useful during an epidemic's initial phase (prior to removal); however, closer monitoring of an epidemic's development is required for modelling the strength of removal and deriving useful information for decision-making.
  • No hay miniatura disponible
    Ítem
    Disease transmission dynamics according to complexity theory
    (Universidad Nacional de Colombia, 2013-01-01) Hincapie-Palacio, D.; Ospina-Giraldo, J.F.; Hincapie-Palacio, D.; Ospina-Giraldo, J.F.; Universidad EAFIT. Departamento de Ciencias; Lógica y Computación
    Objective: Illustrating disease transmission as a complex system according to complexity theory. Methods: A SIR mathematical model (S=number susceptible, I=number infectious, and R=number recovered or immune) reflecting disease transmission from the connection between states of susceptibility, infection, disease, recovery and non-linearity in the interaction between susceptible and infected was simulated. Infection rate temporal fluctuations were described by logistic mapping. Results: Transmission occurs with the reduction of susceptible states as people become infected and sick, followed by an increase in individuals' recovery following diagnosis and treatment. Small increases in infection rate value led to fluctuations in the number of susceptible and exposed people and randomness in the relationship between being susceptible and infected, until converging towards a regular pattern. Conclusion: The model reflected the connection between states of susceptibility, nonlinearity and chaotic behavior following small increases in infection rate. A historical and trans-disciplinary perspective could help in understanding transmission complexity and coordinating control options.