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https://github.com/benmaier/gillepi
Provides classes to simulate epidemics on (potentially time-varying) networks using a Gillespie stochastic simulation algorithm or the classic agent based method.
https://github.com/benmaier/gillepi
agent-based-modeling complex-networks dynamic-networks epidemics si simulate-epidemics sir sirs sis
Last synced: 2 months ago
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Provides classes to simulate epidemics on (potentially time-varying) networks using a Gillespie stochastic simulation algorithm or the classic agent based method.
- Host: GitHub
- URL: https://github.com/benmaier/gillepi
- Owner: benmaier
- License: mit
- Created: 2016-04-26T19:04:25.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-10-22T08:11:52.000Z (about 5 years ago)
- Last Synced: 2024-10-11T11:07:47.665Z (3 months ago)
- Topics: agent-based-modeling, complex-networks, dynamic-networks, epidemics, si, simulate-epidemics, sir, sirs, sis
- Language: Python
- Homepage:
- Size: 36.1 KB
- Stars: 6
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
BEWARE: THIS PACKAGE IS DEPRECATED
Please use [tacoma](github.com/benmaier/tacoma) instead, which is maintained and works better in every way.
# GillEpi
Provides pure Python classes to simulate epidemics on (potentially time varying) networks using a Gillespie stochastic simulation algorithm or the standard ABM SIS, SIR models.
## Install
### Development version
$ make
### Standard
$ make install
## Examples
### List of classes
```python
from GillEpi import SI, SIS, SIR, SIRS
from GillEpi.agent_based_epidemics import SIR as AB_SIR
from GillEpi.agent_based_epidemics import SIS as AB_SIS
```
Find out the functionality using Python's help function and the examples below.
### Standard
```python
import GillEpi
import matplotlib.pyplot as pl
import networkx as nx
N = 100
k = 8
p = k / (N-1.0)
G = nx.fast_gnp_random_graph(N, p)
R0 = 1.5
recovery_rate = 1.0
infection_rate = R0 * recovery_rate / k
tmax = 1000
sis = GillEpi.SIS(
G,
infection_rate = infection_rate,
recovery_rate = recovery_rate,
)
# simulate
sis.simulate(tmax)
# plot infected cluster
i, t = sis.get_i_of_t()
pl.step(t,i)
pl.show()
```
### Agent-based model
I'm not a big fan of the node-centric ABM since the reaction `S+I - > I+I` is not being reflected with the right rates.
```python
from GillEpi.agent_based_epidemics import SIS as ABM_SIS
import matplotlib.pyplot as pl
import numpy as np
import networkx as nx
N = 100
k = 8
p = k / (N-1.0)
G = nx.fast_gnp_random_graph(N, p)
R0 = 1.5
recovery_probability = 0.01
infection_probability = R0 * recovery_probability / k
tmax = 1000
sis = ABM_SIS(
G,
infection_probability = infection_probability,
recovery_probability = recovery_probability,
patients_zero = [0,1,2,45,34],
)
# simulate
sis.step(tmax)
# plot infected cluster
i = sis.I
t = sis.time
pl.step(t, i)
pl.show()
```
### Dynamic network
As an example for time-varying networks, I use the flockwork model (https://github.com/benmaier/flockworks).
```python
from flockworks import flockwork
import GillEpi
import pylab as pl
# initialize time-varying network
F = flockwork(Q=0.7,N=100,k0=2)
F.equilibrate()
#initialize SIR simulation
sir = GillEpi.SIR(
F.G,
infection_rate = 1.,
recovery_rate = 1.,
rewiring_rate = 1.,
rewire_function = F.rewire,
mean_degree_function = F.mean_degree
)
# simulate
sir.simulate()
# initialize analysis
fig, ax = pl.subplots(2,1)
# plot susceptible cluster
s, t = sir.get_s_of_t()
ax[0].step(t,s)
# plot resistant cluster
r, t = sir.get_r_of_t()
ax[0].step(t,r)
# plot infected cluster
i, t = sir.get_i_of_t()
ax[0].step(t,i)
# plot basic reproduction number
R0,t = sir.get_R0_of_t()
ax[1].step(t,R0)
pl.show()
```