beta = 1 #Transmission coefficient
tau = .2 #infectious period. FIXED
tf = 36
y0 = [.999, 0.001, 0.0]
def model(theta):
beta = theta[0]
def sir(y, t):
'''ODE model'''
S, I, R = y
return [-beta * I * S, #dS/dt
beta * I * S - tau * I, #dI/dt
tau * I] #dR/dt
y = odeint(sir, inits, np.arange(0, tf, 1)) #np.arange(t0,tf,step))
return y
F = FitModel(300, model, y0, tf, ['beta'], ['S', 'I', 'R'],
wl=36, nw=1, verbose=1, burnin=100)
F.set_priors(tdists=[st.norm], tpars=[(1.1, .2)], tlims=[(0.5, 1.5)],
pdists=[st.uniform] * 3, ppars=[(0, .1), (0, .1), (.8, .2)], plims=[(0, 1)] * 3)
d = model([1.0]) #simulate some data
noise = st.norm(0, 0.01).rvs(36)
dt = {'I': d[:, 1] + noise} # add noise
F.run(dt, 'MCMC', likvar=1e-5, pool=True, monitor=[])
#==Uncomment the line below to see plots of the results
F.plot_results()
ser = st.nanmean(series, axis=0)
# print series.shape, ser.shape
return ser
d = runModel([beta, alpha, sigma])
# ~ import pylab as P
# ~ P.plot(d)
# ~ P.show()
dt = {'S': d[:, 0], 'E': d[:, 1], 'I': d[:, 2], 'A': d[:, 3], 'R': d[:, 4]}
F = FitModel(900,
runModel,
inits,
tf, ['beta', 'alpha', 'sigma'], ['S', 'E', 'I', 'A', 'R'],
wl=140,
nw=1,
verbose=0,
burnin=100)
F.set_priors(tdists=[st.uniform] * 3,
tpars=[(0.00001, .0006), (.1, .5), (0.0006, 1)],
tlims=[(0, .001), (.001, 1), (0, 1)],
pdists=[st.uniform] * 5,
ppars=[(0, 500)] * 5,
plims=[(0, 500)] * 5)
F.run(dt, 'MCMC', likvar=1e1, pool=0, monitor=[])
# ~ print F.optimize(data=dt,p0=[0.1,.5,.1], optimizer='oo',tol=1e-55, verbose=1, plot=1)
# ==Uncomment the line below to see plots of the results
F.plot_results()
F = FitModel(1000,
model,
inits,
tf,
tnames,
pnames,
wl,
nw,
verbose=1,
burnin=200,
constraints=[])
F.set_priors(tdists=nt * [st.beta],
tpars=tpars,
tlims=tlims,
pdists=[st.beta] * nph,
ppars=[(1, 1)] * nph,
plims=[(0, 1)] * nph)
F.run(dt,
'DREAM',
likvar=1e-4,
pool=False,
ew=0,
adjinits=False,
dbname=modname,
monitor=['I', 'S'])
#~ print F.AIC, F.BIC, F.DIC
#print F.optimize(data=dt,p0=[s0,s1,s2], optimizer='scipy',tol=1e-55, verbose=1, plot=1)
F.plot_results(['S', 'I'], dbname=modname, savefigs=1)
