def test_hard(self):
# the SLIARD model is considered to be hard because a state can
# go to multiple state. This is not as hard as the SEIHFR model
# below.
stateList = ['S', 'L', 'I', 'A', 'R', 'D']
paramList = [
'beta', 'p', 'kappa', 'alpha', 'f', 'delta', 'epsilon', 'N'
]
odeList = [
Transition('S', '- beta * S/N * ( I + delta * A)', 'ODE'),
Transition('L', 'beta * S/N * (I + delta * A) - kappa * L', 'ODE'),
Transition('I', 'p * kappa * L - alpha * I', 'ODE'),
Transition('A', '(1-p) * kappa * L - epsilon * A', 'ODE'),
Transition('R', 'f * alpha * I + epsilon * A', 'ODE'),
Transition('D', '(1-f) * alpha * I', 'ODE')
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("Hard: SLIARD Decomposition failed")
def test_simple(self):
ode1 = Transition('S','-beta*S*I', 'ode')
ode2 = Transition('I','beta*S*I - gamma * I', 'ode')
ode3 = Transition('R','gamma*I', 'ode')
stateList = ['S','I','R']
paramList = ['beta','gamma']
ode = SimulateOdeModel(stateList, paramList, odeList=[ode1,ode2,ode3])
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("Simple: SIR Decomposition failed")
def test_simple(self):
ode1 = Transition('S', '-beta*S*I', 'ode')
ode2 = Transition('I', 'beta*S*I - gamma * I', 'ode')
ode3 = Transition('R', 'gamma*I', 'ode')
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma']
ode = SimulateOdeModel(stateList,
paramList,
odeList=[ode1, ode2, ode3])
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("Simple: SIR Decomposition failed")
def test_derived_param(self):
# the derived parameters are treated separately when compared to the
# normal parameters and the odes
ode = common_models.Legrand_Ebola_SEIHFR()
odeList = [
Transition(
'S',
'-(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)'
),
Transition(
'E',
'(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)-alpha * E'
),
Transition(
'I',
'-gamma_I * (1 - theta_1) * (1 - delta_1) * I - gamma_D * (1 - theta_1) * delta_1 * I - gamma_H * theta_1 * I + alpha * E'
),
Transition(
'H',
'gamma_H * theta_1 * I - gamma_DH * delta_2 * H - gamma_IH * (1 - delta_2) * H'
),
Transition(
'F',
'- gamma_F * F + gamma_DH * delta_2 * H + gamma_D * (1 - theta_1) * delta_1 * I'
),
Transition(
'R',
'gamma_I * (1 - theta_1) * (1 - delta_1) * I + gamma_F * F + gamma_IH * (1 - delta_2) * H'
),
Transition('tau', '1')
]
ode1 = SimulateOdeModel(ode._stateList,
ode._paramList,
ode._derivedParamEqn,
odeList=odeList)
ode2 = ode1.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("FAILED!")
def test_derived_param(self):
# the derived parameters are treated separately when compared to the
# normal parametes and the odes
ode = common_models.Legrand_Ebola_SEIHFR()
odeList = [
Transition('S', '-(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)'),
Transition('E', '(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)-alpha * E'),
Transition('I','-gamma_I * (1 - theta_1) * (1 - delta_1) * I - gamma_D * (1 - theta_1) * delta_1 * I - gamma_H * theta_1 * I + alpha * E'),
Transition('H', 'gamma_H * theta_1 * I - gamma_DH * delta_2 * H - gamma_IH * (1 - delta_2) * H'),
Transition('F','- gamma_F * F + gamma_DH * delta_2 * H + gamma_D * (1 - theta_1) * delta_1 * I'),
Transition('R', 'gamma_I * (1 - theta_1) * (1 - delta_1) * I + gamma_F * F + gamma_IH * (1 - delta_2) * H'),
Transition('tau', '1')
]
ode1 = SimulateOdeModel(ode._stateList, ode._paramList, ode._derivedParamEqn, odeList=odeList)
ode2 = ode1.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("FAILED!")
def test_hard(self):
# the SLIARD model is considered to be hard because a state can
# go to multiple state. This is not as hard as the SEIHFR model
# below.
stateList = ['S', 'L','I','A','R','D']
paramList = ['beta','p','kappa','alpha','f','delta','epsilon', 'N']
odeList = [
Transition('S', '- beta * S/N * ( I + delta * A)', 'ODE'),
Transition('L', 'beta * S/N * (I + delta * A) - kappa * L', 'ODE'),
Transition('I', 'p * kappa * L - alpha * I', 'ODE'),
Transition('A', '(1-p) * kappa * L - epsilon * A', 'ODE'),
Transition('R', 'f * alpha * I + epsilon * A', 'ODE'),
Transition('D', '(1-f) * alpha * I', 'ODE')
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("Hard: SLIARD Decomposition failed")
def test_bd(self):
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma', 'B', 'mu']
odeList = [
Transition(origState='S',
equation='-beta * S * I + B - mu * S',
transitionType=TransitionType.ODE),
Transition(origState='I',
equation='beta * S * I - gamma * I - mu * I',
transitionType=TransitionType.ODE),
Transition(origState='R',
destState='R',
equation='gamma * I',
transitionType=TransitionType.ODE)
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("Birth Death: SIR+BD Decomposition failed")
def test_bd(self):
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma', 'B', 'mu']
odeList = [
Transition(origState='S',
equation='-beta * S * I + B - mu * S',
transitionType=TransitionType.ODE),
Transition(origState='I',
equation='beta * S * I - gamma * I - mu * I',
transitionType=TransitionType.ODE),
Transition(origState='R',
destState='R',
equation='gamma * I',
transitionType=TransitionType.ODE)
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("Birth Death: SIR+BD Decomposition failed")
