EoN.basic_discrete_SIR¶
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EoN.
basic_discrete_SIR
(G, p, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=inf, return_full_data=False, sim_kwargs=None)[source]¶ Performs simple discrete SIR simulation assuming constant transmission probability p.
From figure 6.8 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.
Does a simulation of the simple case of all nodes transmitting with probability p independently to each neighbor and then recovering.
Arguments: - G networkx Graph
- The network the disease will transmit through.
- p number
- transmission probability
- initial_infecteds node or iterable of nodes (default None)
- if a single node, then this node is initially infected if an iterable, then whole set is initially infected if None, then choose randomly based on rho. If rho is also None, a random single node is chosen. If both initial_infecteds and rho are assigned, then there is an error.
- initial_recovereds as for initial_infecteds, but for initially
- recovered nodes.
- rho number (default None)
initial fraction infected. number initially infected is int(round(G.order()*rho))
The default results in a single randomly chosen initial infection.
- tmin float (default 0)
- start time
- tmax float (default infinity)
- stop time (if not extinct first).
- return_full_data boolean (default False)
- Tells whether a Simulation_Investigation object should be returned.
- sim_kwargs keyword arguments
- Any keyword arguments to be sent to the Simulation_Investigation object
Only relevant if
return_full_data=True
Returns: if return_full_data is False returns
t, S, I, R numpy arrays
these numpy arrays give all the times observed and the number in each state at each time.
- Or
if return_full_data is True
returns full_data Simulation_Investigation object
from this we can extract the status history of all nodes We can also plot the network at given times and even create animations using class methods.
SAMPLE USE: import networkx as nx import EoN import matplotlib.pyplot as plt G = nx.fast_gnp_random_graph(1000,0.002) t, S, I, R = EoN.basic_discrete_SIR(G, 0.6) plt.plot(t,S) #This sample may be boring if the randomly chosen initial infection #doesn't trigger an epidemic.