| Full text | |
| Author(s): |
Pinto, Eduardo R.
;
Nepomuceno, Erivelton G.
;
Campanharo, Andriana S. L. O.
;
Cota, VR
;
Barone, DAC
;
Dias, DRC
;
Damazio, LCM
Total Authors: 7
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL NEUROSCIENCE; v. 1068, p. 8-pg., 2019-01-01. |
| Abstract | |
This paper presents the influence of the complex networks topology on the spread of Tuberculosis with the use of the Individual-Based Model (IBM). Five complex network models were used with the IBM, namely, random, small world, scale-free, modular and hierarchical models. For every model, we applied the usual topological properties available in literature for the characterization of complex networks. Afterwards, we verified the topological effect of the contact networks in the evolution of tuberculosis and it was observed that different contact networks result in different epidemic thresholds (beta*) for the spread of tuberculosis. More specifically, we noted that networks that have greater heterogeneity of connections need a lower beta*, however when the value of the infection rate (beta) is large, the number of individuals infected are similar. It is believed that this observation may contribute to actions to reduce and eradicate the disease. (AU) | |
| FAPESP's process: | 18/25358-9 - Use of complex networks for the automatic detection, diagnosis and classification of the Alzheimer's Disease |
| Grantee: | Andriana Susana Lopes de Oliveira Campanharo |
| Support Opportunities: | Regular Research Grants |