Source code for the paper:
"Social interaction layers in complex networks for the dynamical epidemic modeling of COVID-19 in Brazil." Physica A: Statistical Mechanics and its Applications 564 (2021): 125498. Leonardo F. S. Scabini, Lucas C. Ribas, Mariane B. Neiva, Altamir G. B. Junior, Alex J. F. Farfán, Odemir M. Bruno
contact: scabini@ifsc.usp.br
The main script to perform an experiment is "run.py"
- Runs the dynamic network in parallel, where each thread is a different iteration (with a different random seed). The final results are the average between iterations. On this code we set 10 iterations; on the paper, 100 iterations where performed for better statistical results.
- The script has several parameters that should be manually adjusted according to the society one wants to model. The comments (in portuguese) should guide you.
Libraries used: networkx 2.4, matplotlib, pickle
If you use this method, please cite our paper:
Scabini, L. F., Ribas, L. C., Neiva, M. B., Junior, A. G., Farfán, A. J., & Bruno, O. M. (2021). Social interaction layers in complex networks for the dynamical epidemic modeling of COVID-19 in Brazil. Physica A: Statistical Mechanics and its Applications, 564, 125498.
@article{scabini2021social,
title={Social interaction layers in complex networks for the dynamical epidemic modeling of COVID-19 in Brazil},
author={Scabini, Leonardo FS and Ribas, Lucas C and Neiva, Mariane B and Junior, Altamir GB and Farf{\'a}n, Alex JF and Bruno, Odemir M},
journal={Physica A: Statistical Mechanics and its Applications},
volume={564},
pages={125498},
year={2021},
publisher={Elsevier}
}