To help the unfamiliar to understand the application and relevance of this algorithm, consider the following dynamical system derived from an epidemiological problem:

Suppose an infected individual arrives in his home town after his/her vacation carrying an infectious disease entirely new to his fellow citizens. This means no one in town has antibodies for that disease. Let this set of people be called the "susceptibles" and let S be the number of people in this group. Our Infected individual will initially be the only member of a group called the "infectious" which size is denoted by I. The rate of encounters between susceptibles and infectious is proportional to their numbers (S*I). Everytime an infectious meet a susceptible there is a chance b that the disease is transmitted, so the probability of a transmission event is b*S*I. This is called an individual state transition, since the individual went from a susceptible state to an infectious one.

The dynamical system used as an example here contains this and other state transitions described in the code below:

As can be seen there are five different states in this model with the last one R, being an absorbing state, since all individuals converge to that state with time."""Example of an SEIR model with two Infectious classes: subclinical(Is) and clinical(Ic)Is/ \S -> E R\ /IcStates:S: SusceptibleE: ExposedIs: Infectious subclinicalIc: Infectious clinicalR: RecoveredTransition rates:b,ks,kc,rs,rc = (0.001, 0.1, 0.1, 0.01, .01)Transitions:S -> E : b*S*(Is+Ic)E -> Is : ks*EE -> Ic : kc*EIs -> R : rs*IsIc -> R : rc*Ic"""

from gillespie import Model

import time

from numpy import array

vars = ['S','E','Is','Ic','R']#rates: b,ks,kc,rs,rc

r = (0.001, 0.1, 0.1, 0.01, .01)

ini = (490,0,10,0,0)

prop = (lambdar,ini:r[0]*ini[0]*(ini[2]+ini[3]),

lambdar,ini:r[1]*ini[1],

lambdar,ini:r[2]*ini[1],

lambdar,ini:r[3]*ini[2],

lambdar,ini:r[4]*ini[3]

)

tmat = array([[-1,0,0,0,0],

[1,-1,-1,0,0],

[0,1,0,-1,0],

[0,0,1,0,-1],

[0,0,0,1,1]

])#for e in prop:# print e()

M=Model(vnames=vars,rates = r,inits=ini,tmat=tmat,propensity=prop)

t0 = time.time()

M.run(tmax=80,reps=100)

t,series,steps = M.getStats()

from pylab import plot , show, legend, errorbar

plot(t,series.mean(axis=2),'-o')

legend(vars,loc=0)

show()

the code that simulates this stochastic dynamic model is in the gillespie module:

from numpy.random import uniform, multinomial, exponential,random

from numpy import arange, array, empty,zeros,log#from math import log

import time

import multiprocessing

import psyco

psyco.full()#global ini#ini=[500,1,0]classModel:

def__init__(self,vnames,rates,inits, tmat,propensity):'''* vnames: list of strings* rates: list of fixed rate parameters* inits: list of initial values of variables* propensity: list of lambda functions of the form:lambda r,ini: some function of rates ans inits.'''

self.vn = vnames

self.rates = rates

self.inits = inits

self.tm = tmat

self.pv = propensity#[compile(eq,'errmsg','eval') for eq in propensity]

self.pvl = len(self.pv)#length of propensity vector

self.nvars = len(self.inits)#number of variables

self.time=None

self.series=None

self.steps=0

defgetStats(self):

returnself.time,self.series,self.steps

defrun(self,method='SSA', tmax=10, reps=1):

self.res = zeros((tmax,self.nvars,reps),dtype=float)

tvec = arange(tmax, dtype=int)

ifmethod =='SSA':

fori in xrange(reps):

steps = self.GSSA(tmax,i)

elifmethod == 'SSAct':

pass

self.time=tvec

self.series=self.res

self.steps=steps

defGSSA(self, tmax=50,round=0):'''Gillespie Direct algorithm'''

ini = self.inits

r = self.rates

pvi = self.pv

l=self.pvl

pv = zeros(l,dtype=float)

tm = self.tm

tc = 0

steps = 0

self.res[0,:,round]= ini

a0=1

fortim in xrange(1,tmax):

whiletc < tim:

fori in xrange(l):

pv[i] = pvi[i](r,ini)

#pv = abs(array([eq() for eq in pvi]))# #propensity vector

a0 = pv.sum()#sum of all transition probabilities# print tim, pv, a0

tau = (-1/a0)*log(random())

event = multinomial(1,pv/a0)# event which will happen on this iteration

ini += tm[:,event.nonzero()[0][0]]

#print tc, ini

tc += tau

steps +=1

ifa0 == 0:break

self.res[tim,:,round] = ini

ifa0 == 0:break# tvec = tvec[:tim]# self.res = res[:tim,:,round]

returnsteps

defCR(self,pv):"""Composition reaction algorithm"""

pass

defmain():

vars = ['s','i','r']

ini= [500,1,0]

rates = [.001,.1]

tm = array([[-1,0],[1,-1],[0,1]])

prop=[lambdar, ini:r[0]*ini[0]*ini[1],lambdar,ini:r[0]*ini[1]]

M = Model(vnames = vars,rates = rates,inits=ini, tmat=tm,propensity=prop)

t0=time.time()

M.run(tmax=80,reps=1000)

#print res# from pylab import plot , show, legend# plot(t,res,'-.')# legend(M.vn,loc=0)# show()

if__name__=="__main__":

import cProfile

cProfile.run('main()',sort=1)

main()

The Gillespie SSA is a very simple algorithm, so I thought it would be nice to blog about it and have other Pythonistas take a look at my code and possibly find a way to optimize it further. Having the core code run as fast as possible is very important for this application, because you normally have to run thousands of replicates of a every run due to its stochastic nature. This "need for speed", made me try Cython, but due to my inexperience with it, I couldn't squeeze much performance out of it:

That's it. I hope this code will serve to inspire other scientists which need to use this type of simulations. In the next few days I will organize it in a package and make it available somewhere.

from numpy.random import uniform, multinomial, exponential#from numpy import arange, array, empty,zeros

import numpy as np

cimport numpy as np

import time

DTYPE = np.double

ctypedef np.double_t DTYPE_t

ctypedef np.int_t INT_t

cdefclassModel:

cdef object vn,rates,inits,pv

cdef np.ndarray tm,res,time,series

cdef int pvl,nvars,steps

cdef object ts

def__init__(self,vnames,rates,inits, tmat,propensity):'''* vnames: list of strings* rates: list of fixed rate parameters* inits: list of initial values of variables* propensity: list of lambda functions of the form:lambda r,ini: some function of rates ans inits.'''

self.vn = vnames

self.rates = rates

self.inits = inits

self.tm = tmat

self.pv = propensity#[compile(eq,'errmsg','eval') for eq in propensity]

self.pvl = len(self.pv)#length of propensity vector

self.nvars = len(self.inits)#number of variables

self.time = np.zeros(1)

self.series = np.zeros(1)

self.steps = 0

defrun(self, method='SSA', int tmax=10, int reps=1):

cdef np.ndarray[DTYPE_t,ndim=3] res = np.zeros((tmax,self.nvars,reps),dtype=float)

tvec = np.arange(tmax)

self.res = res

cdef int i, steps

ifmethod =='SSA':

fori from 0 <= i<reps:

steps = self.GSSA(tmax,i)

elifmethod == 'SSAct':

pass

self.time=tvec

self.series=self.res

self.steps=steps

defgetStats(self):

returnself.time,self.series,self.steps

cpdef int GSSA(self, int tmax=50,int round=0):'''Gillespie Direct algorithm'''

ini = self.inits

r = self.rates

pvi = self.pv

cdef int l,steps,i,tim

cdef double a0,tc, tau

#cdef np.ndarray[INT_t] tvec

cdef np.ndarray[DTYPE_t] pv

l=self.pvl

pv = np.zeros(l, dtype=float)

tm = self.tm

#tvec = np.arange(tmax,dtype=int)

tc = 0

steps = 0

self.res[0,:,round]= ini

a0=1.

fortim from 1<= tim <tmax:

whiletc < tim:

fori from 0 <= i <l:

pv[i] = pvi[i](r,ini)

#pv = abs(array([eq() for eq in pvi]))# #propensity vector

a0 = a_sum(pv,l)#sum of all transition probabilities

#print ini#,tim, pv, a0

tau = (-1/a0)*np.log(np.random.random())

event = multinomial(1,(pv/a0))# event which will happen on this iteration

ini += tm[:,event.nonzero()[0][0]]

#print tc, ini

tc += tau

steps +=1

ifa0 == 0:break

self.res[tim,:,round] = ini

ifa0 == 0:break# tvec = tvec[:tim]# self.res = res[:tim,:,round]

returnsteps

defCR(self,pv):"""Composition reaction algorithm"""

pass

defmain():

vars = ['s','i','r']

cdef np.ndarray ini= np.array([500,1,0],dtype = int)

cdef np.ndarray rates = np.array([.001,.1],dtype=float)

cdef np.ndarray tm = np.array([[-1,0],[1,-1],[0,1]])

prop = [l1,l2]

M = Model(vnames = vars,rates = rates,inits=ini, tmat=tm,propensity=prop)

t0=time.time()

M.run(tmax=80,reps=1000)

cdef double a_sum(np.ndarray a, int len):

cdef double s

cdef int i

s=0

fori from 0 <=i <len:

s+=a[i]

returns

defl1(np.ndarray r,np.ndarray ini):

returnr[0]*ini[0]*ini[1]defl2(np.ndarray r,np.ndarray ini):

returnr[1]*ini[1]

I would appreciate any suggestions to speed up the gillespie module, both the Python and the Cython versions.

## 12 comments:

Could you give the reference for the SEIR model example which you have taken here?

@vumavu: This is a standard textbook epidemiological model. You can read about it on Wikipedia

http://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology

Thanks for your reply. I am specifically asking about the following example .

"Example of an SEIR model with two Infectious classes: subclinical(Is) and clinical(Ic)"

Where can I find about this particular example? I was browsing for this example. I could not find the reference any where.

@vumavu: I have a paper with coleagues submitted to Plos One. And I am writing another one that uses this model. This particular variation is a good match for the epidemiology of Influenza.

can you send me the paper which you send to Plos One?

my email: vumavu@gmail.com

@Coehlo. Hi there. I have written in python examples of the exact ssa and tau-leap ssa, from the book of M. Keeling & P. Rohani "Modeling Infectious Diseases in Humans and Animals". You can find the specific programs in chapter 6 (6.3 to 6.6) either on the online material of the book @ http://modelinginfectiousdiseases.org/ or on the wiki of the website i am keeping @ http://wiki.deductivethinking.com/wiki/Python_Programs_for_Modelling_Infectious_Diseases_book

To my view in order to make an workable programming algorithm out of gillespie ssa needs a meta-programming approach, that will give the user the flexibility to try different scenarios, such as the classes of the individuals and enable more complex models. I didn't have the time to look at R implementation of gillespieSSA to see how it handles it.

Using numpy arrays is fast by default. I managed to optimize for my paper on S. typhimurium at pig farms on risk analysis (DOI:http://dx.doi.org/10.1111/j.1539-6924.2009.01274.x) the tau-leap method using Pyrex and parallel computing, but trying to optimize the ssa algorithm I had to leave some arrays and functions of numpy out leading to the same or worst results with the C translated code than with the Python code.

@ vumavu. Hi there. The theory of different infectious classes is gaining more respect time to time. These different classes may differ in observation or not of symptoms, duration of stage, different effect in transmission. ISVEE conference XII (2009) (http://blog.deductivethinking.com/?p=167) had examples of Salmonella and E. coli on this. There are published papers also on these (salmonella and e.coli in cattle and pig farms). I have also a paper published on S. typhimurium at pig farms on risk analysis (DOI:http://dx.doi.org/10.1111/j.1539-6924.2009.01274.x) using two different infectious classes with two different propagation rates. Other human diseases like hepatitis B are cases of two different rates of infection.

@Ilias: First of all congratulatin on your effort to port the examples in Keeling&Rohani's book to Python and also release the code under GPL. Good Job!.

I took a look at the Tau-leap implementation and I am currently in the precess of trying to optimize it and also modify it so that it can be easily used for any kind of model. Given that the code is GPL licensed, I'll probably add the implementation to BIP (http://code.google.com/p/bayesian-inference/) which is where my Gillespie code is maintained (more specifically in the subpackage SDE). You are naturally welcome to contribute code directly to BIP if you want. If you feel inclined to do it, we should talk more over e-mail since the project's roadmap page is a bit outdated.

Regarding you idea of metaprogramming for implementing more complex models: I fully agree, and I also have a student that did just that to implement a 200+ reactions model, using BIP. She did it using string formatting + eval.

I also liked you paper on Risk Analysis.

thanks for sharing this site. download lots of ebook from here

http://feboook.blogspot.com

hello friends I really liked this information, a few days ago I read something similar on a site called wound infections, I would like to receive updates on this issue, as it is very interesting, thanks!

Hi there,

Would mind explaining how you arrived at the tmat?

I see that it has to do with the reaction equations but cant quite get it easily.. .

u made my work much easier btw, thanks :)

please do reply..

-bhanu

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