Python code for simulating the dynamics of infectious diseases on complex networks and studying the effects of different vaccination strategies on the spread of a disease.
disease.py: This script simulates disease dynamics on complex networks
using the parameters specified in <params file>, and prints the final
fractions (s, i, and r) of the susceptible, intected, and recovered
individuals, along with the standard deviation of r.
> python disease.py <params file>disease_verbose.py: This script behaves similarly to disease.py, but for
output, prints the time-evolution of the s, i, r values.
> python disease_verbose.py <params file>params.json.sample: Sample parameter file. The allowed vaccination
strategies are: random_vaccination, random_walk_vaccination,
page_rank_vaccination, referral_vaccination, betweenness_vaccination,
closeness_vaccination, degree_vaccination, and eigenvector_vaccination.
For the allowed network parameters, consult [https://github.com/swamiiyer/network].
sir_curves.py: This script plots the s-i-r curves from the results produced
by disease_verbose.py (fed via STDIN) and saves the plot in a file called
sir.pdf.
prevalence_curve.py: This script plots the prevalence curve (prevalence
versus fraction vaccinated) from the results produced by disease.py (names
of the result files are fed via STDIN) and saves the plot in a file called
prevalence.pdf. The script also calculates and prints the P-index value and
the critical vaccination threshold value, vstar.
gr_network.py: This script generates an exponential (growing random)
network with n vertices and mean degree k, and saves it in graphml format.
> python gr_network.py <n> <k>If you have any questions about the software, please email swami.iyer@gmail.com.