2021-03-262013-01-0101240064http://hdl.handle.net/10784/27339Objective: 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.https://v2.sherpa.ac.uk/id/publication/issn/0124-0064Basic reproduction numberNon-linear dynamicsTheoretical modelarticle2021-03-26Hincapie-Palacio, D.Ospina-Giraldo, J.F.