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 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 run_ode(beta1, rhs_func, bm_coeff_mat, bc_coeff_mat, mos_coeff, tstart,
tend, flag):
p = len(bm_coeff_mat[:, 0]) + 1
beta2 = 1
gammab = .1 * numpy.ones(p)
v = .14 # Biting Rate of Vectors on Hosts
b = 0 # Bird "Recruitment" Rate
d = 0
# Bird "Death" Rate
dv = .10 # Mosquito Mortality Rate
dEEE = 0
# Run for ~ 6 Months
T = scipy.linspace(tstart, tend, 1001)
if flag == 0:
Sv = 1 * bloodmeal.vector_pop(mos_coeff, tstart)
Iv = 0 * bloodmeal.vector_pop(mos_coeff, tstart)
S0 = 1 * BirdCount.birdcounts_function(bc_coeff_mat, tstart)
I0 = 0 * BirdCount.birdcounts_function(bc_coeff_mat, tstart)
R0 = 0 * BirdCount.birdcounts_function(bc_coeff_mat, tstart)
Y0 = numpy.hstack((S0, I0, R0, Sv, Iv))
elif flag == 1:
Sv = .99
Iv = .01
S0 = .99 * numpy.ones(p)
I0 = .01 * numpy.ones(p)
R0 = 0 * numpy.ones(p)
Y0 = numpy.hstack((S0, I0, R0, Sv, Iv))
elif flag == 2:
print("hi")
Sv = numpy.log(.99)
Iv = numpy.log(.01)
S0 = numpy.log(.99) * numpy.ones(p)
I0 = numpy.log(.01) * numpy.ones(p)
Y0 = numpy.hstack((S0, I0, Sv, Iv))
Y = scipy.integrate.odeint(
rhs_func,
Y0,
T,
args=(beta1, beta2, gammab, v, b, d, dv, dEEE, bc_coeff_mat,
bm_coeff_mat, bloodmeal.vector_pop, bloodmeal.vector_derivative,
bloodmeal.vector_in, bloodmeal.fun, mos_coeff, p))
return (Y)
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 run_ode_solver(beta1, rhs_func, bm_coeff_mat, bc_coeff_mat, mos_coeff,
tstart, tend, flag):
# Set up to run the ODE using the ode_solver function, which manually integrates the ode for each iteration
p = len(bm_coeff_mat[:, 0]) + 1
beta2 = 1
gammab = .1 * numpy.ones(p)
v = .14 # Biting Rate of Vectors on Hosts
b = 0 # Bird "Recruitment" Rate
d = 0
# Bird "Death" Rate
dv = .10 # Mosquito Mortality Rate
dEEE = 0
# Run for ~ 6 Months
T = scipy.linspace(tstart, tend, 1001)
if flag == 0:
Sv = 1 * bloodmeal.vector_pop(mos_coeff, tstart)
Iv = 0 * bloodmeal.vector_pop(mos_coeff, tstart)
S0 = 1 * BirdCount.birdcounts_function(bc_coeff_mat, tstart)
I0 = 0 * BirdCount.birdcounts_function(bc_coeff_mat, tstart)
R0 = 0 * BirdCount.birdcounts_function(bc_coeff_mat, tstart)
elif flag == 1:
Sv = .99
Iv = .01
S0 = .99 * numpy.ones(p)
I0 = .01 * numpy.ones(p)
R0 = 0 * numpy.ones(p)
Y0 = numpy.hstack((S0, I0, R0, Sv, Iv))
#Y = scipy.integrate.odeint(rhs_func,Y0,T,args = (beta1, beta2, gammab, v, b, d, dv, dEEE, bc_coeff_mat,bm_amean,bm_bmean,bloodmeal.vector_pop,bloodmeal.vector_derivative,bloodmeal.vector_in,bloodmeal.fun,mos_coeff,p))
Y = ode_solver(Y0,
T,
args=(beta1, beta2, gammab, v, b, d, dv, dEEE, bc_coeff_mat,
bm_coeff_mat, bloodmeal.vector_pop,
bloodmeal.vector_derivative, bloodmeal.vector_in,
bloodmeal.fun, mos_coeff, p))
return (Y)
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)