def test_rhs(Y, t, beta1_rho, beta2, gammab, v, b, d, dv, dEEE, bc_coeff_mat,
bm_coeff_mat, mospop, mosprime, mosin, fun, mos_coeff, p):
# Here beta1 = beta1/rho_v, where Nv = theta_v/rho_v
eps = .001
s = Y[0:p]
i = Y[p:2 * p]
r = Y[2 * p:3 * p]
sv = Y[-2]
iv = Y[-1]
alpha_val = alpha_calc(bloodmeal.bloodmeal_function(bm_coeff_mat, t),
BirdCount.birdcounts_function(bc_coeff_mat, t))
theta = BirdCount.birdcounts_function(
bc_coeff_mat,
t) # Theta is the vector of the MCMC sample coeffieicents
theta_v = bloodmeal.vector_pop(mos_coeff, t)
denom = numpy.dot(theta, alpha_val)
lambdab = beta1_rho * v * iv * numpy.array(alpha_val) * theta_v / denom
lambdav = v * (numpy.dot(beta2 * i * theta, alpha_val)) / denom
# Bird Mortality
ds = BirdCount.Xi(bc_coeff_mat, t) * ((1 - eps) - s) - lambdab * s
di = BirdCount.Xi(bc_coeff_mat, t) * (eps - i) + lambdab * s - gammab * i
dr = gammab * i - r * BirdCount.Xi(bc_coeff_mat, t)
dsv = bloodmeal.Xi(mos_coeff, t) * iv - lambdav * sv
div = lambdav * sv - bloodmeal.Xi(mos_coeff, t) * iv
dY = 365. / 2 * numpy.hstack(
(ds, di, dr, dsv,
div)) # the 365/2 is the rate of change of the time transform
return dY
def rhs(Y, t, beta1, beta2, gammab, v, b, d, dv, dEEE, bc_coeff_mat,
bm_coeff_mat, mospop, mosprime, mosin, fun, mos_coeff, p):
# Consider adding epsilon term , for proportion infected entering population eps = .001
eps = .001
s = Y[0:p]
i = Y[p:2 * p]
r = Y[2 * p:3 * p]
sv = Y[-2]
iv = Y[-1]
alpha_val = alpha_calc(bloodmeal.bloodmeal_function(bm_coeff_mat, t),
BirdCount.birdcounts_function(bc_coeff_mat, t))
N = BirdCount.birdcounts_function(bc_coeff_mat, t)
denom = numpy.dot(N, alpha_val)
lambdab = beta1 * v * iv * numpy.array(alpha_val) / denom
lambdav = v * (numpy.dot(beta2 * i, alpha_val)) / denom
# Bird Mortality
ds = BirdCount.xi(bc_coeff_mat, t) * (1 - eps) - lambdab * s - BirdCount.F(
bc_coeff_mat, t) * s / N
di = BirdCount.xi(bc_coeff_mat,
t) * eps + lambdab * s - gammab * i - BirdCount.F(
bc_coeff_mat, t) * i / N
dr = gammab * i - BirdCount.F(bc_coeff_mat, t) * r / N
dsv = bloodmeal.vector_in(mos_coeff, t) - lambdav * sv - bloodmeal.fun(
mos_coeff, t) * sv #Need to be updated
div = lambdav * sv - bloodmeal.fun(mos_coeff, t) * iv # Need to be updated
#dSv = mospop(t) - dIv
dY = numpy.hstack((ds, di, dr, dsv, div))
return dY
def log_test_rhs(Y, t, beta1_rho, beta2, gammab, v, b, d, dv, dEEE,
bc_coeff_mat, bm_coeff_mat, mospop, mosprime, mosin, fun,
mos_coeff, p):
eps = .001
Y = Y.clip(-20, numpy.inf)
s = Y[0:p]
i = Y[p:2 * p]
sv = Y[-2]
iv = Y[-1]
alpha_val = alpha_calc(bloodmeal.bloodmeal_function(bm_coeff_mat, t),
BirdCount.birdcounts_function(bc_coeff_mat, t))
theta = BirdCount.birdcounts_function(
bc_coeff_mat,
t) # Theta is the vector of the MCMC sample coeffieicents
theta_v = bloodmeal.vector_pop(mos_coeff, t)
denom = numpy.dot(theta, alpha_val)
lambdab = beta1_rho * v * numpy.exp(iv) * numpy.array(
alpha_val) * theta_v / denom
lambdav = v * (numpy.dot(beta2 * numpy.exp(i) * theta, alpha_val)) / denom
# Bird Mortality
#ds = BirdCount.Xi(bc_coeff_mat,t)*((1-eps)*numpy.exp(-s)-1)-lambdab
ds = BirdCount.Xi(bc_coeff_mat, t) * (
(1 - eps) * numpy.exp(-s) - 1) - lambdab * numpy.where(s <= -20, 0, 1)
di = BirdCount.Xi(bc_coeff_mat, t) * (
eps * numpy.exp(-i) - 1) + lambdab * numpy.exp(s - i) - gammab
#dr = gammab*numpy.exp(i-r) - BirdCount.Xi(bc_coeff_mat,t)
dsv = bloodmeal.Xi(mos_coeff, t) * numpy.exp(iv - sv) - lambdav
div = lambdav * numpy.exp(sv - iv) - bloodmeal.Xi(mos_coeff, t)
dY = numpy.hstack((ds, di, dsv, div))
return dY
def eval_ode_results(Y,
bm_coeff_mat,
bc_coeff_mat,
mos_coeff,
tstart,
tend,
bird_data_file,
flag,
alpha=1):
import pylab
birdnames = pd.read_csv(bird_data_file, index_col=0).index
name_list = list(birdnames)
name_list.append('Vector')
T = scipy.linspace(tstart, tend, 1001)
p = len(bm_coeff_mat[:, 0]) + 1
s, i, r, sv, iv = get_SIR_vals(Y, p)
bc = numpy.zeros((p, len(T)))
bm = numpy.zeros((p, len(T)))
alpha_val = numpy.zeros((p, len(T)))
mos_pop = numpy.zeros(len(T))
for j in range(len(T)):
bc[:, j] = BirdCount.birdcounts_function(bc_coeff_mat, T[j])
bm[:, j] = bloodmeal.bloodmeal_function(bm_coeff_mat, T[j])
alpha_val[:, j] = alpha_calc(bm[:, j], bc[:, j])
mos_pop[j] = bloodmeal.vector_pop(mos_coeff, T[j])
sym = ['b', 'g', 'r', 'c', 'm', 'y', 'k', '--', 'g--']
pylab.figure(1)
for k in range(p):
#if k==p:
# pylab.plot(T,mos_pop,sym[k])
#else:
pylab.plot(T, bc[k], sym[k], alpha=alpha)
pylab.title("Populations")
pylab.legend(name_list)
N = s + i + r
N = numpy.clip(N, 0, numpy.inf)
pylab.figure(2)
for k in range(p):
if flag == 0:
temp = numpy.divide(i[:, k], N[:, k])
elif flag == 1:
temp = i[:, k]
elif flag == 2:
temp = numpy.exp(i[:, k])
pylab.plot(T, temp, sym[k], alpha=alpha)
pylab.legend(birdnames)
pylab.title("Infected Birds")
pylab.figure(3)
for k in range(p):
pylab.plot(T, alpha_val[k], alpha=alpha)
pylab.legend(birdnames)
pylab.title("Feeding Index Values")
return ()
def get_ODE_vals(Y, T, beta1_rho, beta2, gammab, v, b, d, dv, dEEE,
bc_coeff_mat, bm_coeff_mat, mospop, mosprime, mosin, fun,
mos_coeff, p):
ds = []
di = []
dsv = []
div = []
lbdv = []
alpha = []
eps = .001
s = Y[0:p]
i = Y[p:2 * p]
sv = Y[-2]
iv = Y[-1]
dt = T[1] - T[0]
for t in T:
alpha_val = alpha_calc(bloodmeal.bloodmeal_function(bm_coeff_mat, t),
BirdCount.birdcounts_function(bc_coeff_mat, t))
theta = BirdCount.birdcounts_function(
bc_coeff_mat,
t) # Theta is the vector of the MCMC sample coeffieicents
theta_v = bloodmeal.vector_pop(mos_coeff, t)
denom = numpy.dot(theta, alpha_val)
lambdab = beta1_rho * v * iv * numpy.array(alpha_val) * theta_v / denom
lambdav = v * (numpy.dot(beta2 * i * theta, alpha_val)) / denom
# Bird Mortality
ds.append(
BirdCount.Xi(bc_coeff_mat, t) * ((1 - eps) - s) - lambdab * s)
di.append(
BirdCount.Xi(bc_coeff_mat, t) * (eps - i) + lambdab * s -
gammab * i)
#dr = gammab*numpy.exp(i-r) - BirdCount.Xi(bc_coeff_mat,t)
dsv.append(bloodmeal.Xi(mos_coeff, t) * iv - lambdav * sv)
div.append(lambdav * sv - bloodmeal.Xi(mos_coeff, t) * iv)
alpha.append(alpha_val)
s += ds[-1] * dt
i += di[-1] * dt
sv += dsv[-1] * dt
iv += div[-1] * dt
lbdv.append(lambdav)
return (ds, di, dsv, div, lbdv, alpha)