def test_readParametersAgeStructured_less_than_100_percent():
with pytest.raises(AssertionError):
df = pd.DataFrame([
{
"age": "70+",
"src": "A",
"dst": "A",
"rate": 0.5
},
{
"age": "70+",
"src": "A",
"dst": "I",
"rate": 0.2
},
])
loaders.readCompartmentRatesByAge(df)
def test_readParametersAgeStructured_invalid_float():
with pytest.raises(AssertionError):
df = pd.DataFrame([
{
"age": "70+",
"src": "A",
"dst": "A",
"rate": 1.5
},
{
"age": "70+",
"src": "A",
"dst": "I",
"rate": -0.5
},
])
loaders.readCompartmentRatesByAge(df)
def test_readCompartmentRatesByAge_approximately_one():
result = loaders.readCompartmentRatesByAge(
pd.DataFrame([{
"age": "70+",
"src": "A",
"dst": "A",
"rate": 0.999999999
}]))
assert result == {"70+": {"A": {"A": 0.999999999}}}
def test_readCompartmentRatesByAge(data_api):
result = loaders.readCompartmentRatesByAge(
data_api.read_table("human/compartment-transition"))
assert result == {
"70+": {
"E": {
"E": pytest.approx(0.573),
"A": pytest.approx(0.427)
},
"A": {
"A": pytest.approx(0.803),
"I": pytest.approx(0.0197),
"R": pytest.approx(0.1773)
},
"I": {
"I": pytest.approx(0.67),
"D": pytest.approx(0.0165),
"H": pytest.approx(0.0495),
"R": pytest.approx(0.264)
},
"H": {
"H": pytest.approx(0.9),
"D": pytest.approx(0.042),
"R": pytest.approx(0.058)
},
"R": {
"R": pytest.approx(1.0)
},
"D": {
"D": pytest.approx(1.0)
},
},
"[17,70)": {
"E": {
"E": pytest.approx(0.573),
"A": pytest.approx(0.427)
},
"A": {
"A": pytest.approx(0.803),
"I": pytest.approx(0.0197),
"R": pytest.approx(0.1773)
},
"I": {
"I": pytest.approx(0.67),
"D": pytest.approx(0.0165),
"H": pytest.approx(0.0495),
"R": pytest.approx(0.264)
},
"H": {
"H": pytest.approx(0.9),
"D": pytest.approx(0.042),
"R": pytest.approx(0.058)
},
"R": {
"R": pytest.approx(1.0)
},
"D": {
"D": pytest.approx(1.0)
},
},
"[0,17)": {
"E": {
"E": pytest.approx(0.573),
"A": pytest.approx(0.427)
},
"A": {
"A": pytest.approx(0.803),
"I": pytest.approx(0.0197),
"R": pytest.approx(0.1773)
},
"I": {
"I": pytest.approx(0.67),
"D": pytest.approx(0.0165),
"H": pytest.approx(0.0495),
"R": pytest.approx(0.264)
},
"H": {
"H": pytest.approx(0.9),
"D": pytest.approx(0.042),
"R": pytest.approx(0.058)
},
"R": {
"R": pytest.approx(1.0)
},
"D": {
"D": pytest.approx(1.0)
},
},
}
def createNetworkOfPopulation(
compartment_transition_table: pd.DataFrame,
population_table: pd.DataFrame,
commutes_table: pd.DataFrame,
mixing_matrix_table: pd.DataFrame,
infectious_states: pd.DataFrame,
infection_prob: pd.DataFrame,
initial_infections: pd.DataFrame,
trials: pd.DataFrame,
movement_multipliers_table: pd.DataFrame = None,
stochastic_mode: pd.DataFrame = None,
random_seed: pd.DataFrame = None) -> NetworkOfPopulation:
"""Create the network of the population, loading data from files.
:param compartment_transition_table: pd.Dataframe specifying the transition rates between infected compartments.
:param population_table: pd.Dataframe with the population size in each region by gender and age.
:param commutes_table: pd.Dataframe with the movements between regions.
:param mixing_matrix_table: pd.Dataframe with the age infection matrix.
:param movement_multipliers_table: pd.Dataframe with the movement multipliers. This may be None, in
which case no multipliers are applied to the movements.
:param infectious_states: States that are considered infectious
:param infection_prob: Probability that a given contact will result in an infection
:param initial_infections: Initial infections of the population at time 0
:param trials: Number of trials for the model
:param stochastic_mode: Use stochastic mode for the model
:param random_seed: Random number generator seed used for stochastic mode
:return: The constructed network
"""
infection_prob = loaders.readInfectionProbability(infection_prob)
infectious_states = loaders.readInfectiousStates(infectious_states)
initial_infections = loaders.readInitialInfections(initial_infections)
trials = loaders.readTrials(trials)
stochastic_mode = loaders.readStochasticMode(stochastic_mode)
random_seed = loaders.readRandomSeed(random_seed)
# diseases progression matrix
progression = loaders.readCompartmentRatesByAge(
compartment_transition_table)
# population census data
population = loaders.readPopulationAgeStructured(population_table)
# Check some requirements for this particular model to work with the progression matrix
all_states = set()
for states in progression.values():
assert SUSCEPTIBLE_STATE not in states, "progression from susceptible state is not allowed"
for state, nextStates in states.items():
for nextState in nextStates:
all_states.add(state)
all_states.add(nextState)
assert state == nextState or nextState != EXPOSED_STATE, \
"progression into exposed state is not allowed other than in self reference"
assert (set(infectious_states) - all_states) == set(), \
f"mismatched infectious states and states {infectious_states} {set(progression.keys())}"
# people movement's graph
graph = loaders.genGraphFromContactFile(commutes_table)
# movement multipliers (dampening or heightening)
if movement_multipliers_table is not None:
movementMultipliers = loaders.readMovementMultipliers(
movement_multipliers_table)
else:
movementMultipliers: Dict[int, loaders.Multiplier] = {}
# age-based infection matrix
mixingMatrix = loaders.MixingMatrix(mixing_matrix_table)
agesInInfectionMatrix = set(mixingMatrix)
for age in mixingMatrix:
assert agesInInfectionMatrix == set(
mixingMatrix[age]), "infection matrix columns/rows mismatch"
# Checks across datasets
assert agesInInfectionMatrix == set(
progression.keys()), "infection matrix and progression ages mismatch"
assert agesInInfectionMatrix == {age for region in population.values() for age in region}, \
"infection matrix and population ages mismatch"
assert set(graph.nodes()) == set(
population.keys()), "regions mismatch between graph and population"
state0: Dict[str, Dict[Tuple[str, str], float]] = {}
for node in list(graph.nodes()):
region = state0.setdefault(node, {})
for age, compartments in progression.items():
region[(age, SUSCEPTIBLE_STATE)] = population[node][age]
for compartment in compartments:
region[(age, compartment)] = 0
logger.info("Nodes: %s, Ages: %s, States: %s", len(state0),
agesInInfectionMatrix, all_states)
return NetworkOfPopulation(progression=progression,
graph=graph,
initialState=state0,
mixingMatrix=mixingMatrix,
movementMultipliers=movementMultipliers,
infectiousStates=infectious_states,
infectionProb=infection_prob,
initialInfections=initial_infections,
trials=trials,
stochastic=stochastic_mode,
randomState=np.random.default_rng(random_seed))
def createNetworkOfPopulation(
compartment_transition_table: pd.DataFrame,
population_table: pd.DataFrame,
commutes_table: pd.DataFrame,
mixing_matrix_table: pd.DataFrame,
infectious_states: pd.DataFrame,
infection_prob: pd.DataFrame,
initial_infections: pd.DataFrame,
trials: pd.DataFrame,
start_end_date: pd.DataFrame,
movement_multipliers_table: pd.DataFrame = None,
stochastic_mode: pd.DataFrame = None,
) -> Tuple[NetworkOfPopulation, List[standard_api.Issue]]:
"""Create the network of the population, loading data from files.
:param compartment_transition_table: pd.Dataframe specifying the transition rates between infected compartments.
:param population_table: pd.Dataframe with the population size in each region by gender and age.
:param commutes_table: pd.Dataframe with the movements between regions.
:param mixing_matrix_table: pd.Dataframe with the age infection matrix.
:param infectious_states: States that are considered infectious
:param infection_prob: Probability that a given contact will result in an infection
:param initial_infections: Initial infections of the population at time 0
:param trials: Number of trials for the model
:param start_end_date: Starting and ending dates of the model
:param movement_multipliers_table: pd.Dataframe with the movement multipliers. This may be None, in
which case no multipliers are applied to the movements.
:param stochastic_mode: Use stochastic mode for the model
:return: A tuple with the constructed network and a list of any issues found. Although it may look convenient,
storing this list inside of NetworkOfPopulation is not a good idea, as that may give the functions that
receive it the false impression they can just append new issues there
"""
issues: List[standard_api.Issue] = []
infection_prob = loaders.readInfectionProbability(infection_prob)
infectious_states = loaders.readInfectiousStates(infectious_states)
initial_infections = loaders.readInitialInfections(initial_infections)
trials = loaders.readTrials(trials)
start_date, end_date = loaders.readStartEndDate(start_end_date)
stochastic_mode = loaders.readStochasticMode(stochastic_mode)
# diseases progression matrix
progression = loaders.readCompartmentRatesByAge(
compartment_transition_table)
# population census data
population = loaders.readPopulationAgeStructured(population_table)
# Check some requirements for this particular model to work with the progression matrix
all_states = set()
for states in progression.values():
assert SUSCEPTIBLE_STATE not in states, "progression from susceptible state is not allowed"
for state, nextStates in states.items():
for nextState in nextStates:
all_states.add(state)
all_states.add(nextState)
assert state == nextState or nextState != EXPOSED_STATE, \
"progression into exposed state is not allowed other than in self reference"
assert (set(infectious_states) - all_states) == set(), \
f"mismatched infectious states and states {infectious_states} {all_states}"
# people movement's graph
graph = loaders.genGraphFromContactFile(commutes_table)
# movement multipliers (dampening or heightening)
if movement_multipliers_table is not None:
movementMultipliers = loaders.readMovementMultipliers(
movement_multipliers_table)
else:
movementMultipliers = {}
# age-based infection matrix
mixingMatrix = loaders.MixingMatrix(mixing_matrix_table)
agesInInfectionMatrix = set(mixingMatrix)
for age in mixingMatrix:
assert agesInInfectionMatrix == set(
mixingMatrix[age]), "infection matrix columns/rows mismatch"
# Checks across datasets
assert agesInInfectionMatrix == set(
progression.keys()), "infection matrix and progression ages mismatch"
assert agesInInfectionMatrix == {age for region in population.values() for age in region}, \
"infection matrix and population ages mismatch"
disconnected_nodes = set(population.keys()) - set(graph.nodes())
if disconnected_nodes:
log_issue(
logger,
f"These nodes have no contacts in the current network: {','.join(disconnected_nodes)}",
IssueSeverity.MEDIUM,
issues,
)
state0: Dict[str, Dict[Tuple[str, str], float]] = {}
for node in list(graph.nodes()):
region = state0.setdefault(node, {})
for age, compartments in progression.items():
if node not in population:
log_issue(
logger,
f"Node {node} is not in the population table, assuming population of 0 for all ages",
IssueSeverity.MEDIUM,
issues,
)
pop = 0.0
else:
pop = population[node][age]
region[(age, SUSCEPTIBLE_STATE)] = pop
for compartment in compartments:
region[(age, compartment)] = 0
logger.info("Nodes: %s, Ages: %s, States: %s", len(state0),
agesInInfectionMatrix, all_states)
nop = NetworkOfPopulation(
progression=progression,
graph=graph,
initialState=state0,
mixingMatrix=mixingMatrix,
movementMultipliers=movementMultipliers,
infectiousStates=infectious_states,
infectionProb=infection_prob,
initialInfections=initial_infections,
trials=trials,
startDate=start_date,
endDate=end_date,
stochastic=stochastic_mode,
)
return (nop, issues)
