コード例 #1
0
def _distributeContactsOverAgesStochastic(
        ageToSusceptibles: Dict[Age, float], totalSusceptibles: float,
        newContacts: float,
        random_state: np.random.Generator) -> Dict[Age, float]:
    """
    Distribute the number of new contacts across a region using the stochastic algorithm.

    :param ageToSusceptibles: The number of susceptibles in each age group
    :param totalSusceptibles: Total number of susceptible people in all age groups
    :param newContacts: The number of new contacts to be distributed across age ranges.
    :param random_state: Random number generator used for the model
    :return: The number of new infections in each age group.
    """
    assert isinstance(newContacts, int) or newContacts.is_integer()

    ageProbabilities = np.array(list(
        ageToSusceptibles.values())) / totalSusceptibles
    allocationsByAge = random_state.multinomial(newContacts, ageProbabilities)

    return dict(zip(ageToSusceptibles.keys(), allocationsByAge))
コード例 #2
0
def _internalStateDiseaseUpdate(age: Age, state: Compartment, people: float,
                                outTransitions: Dict[Compartment, float],
                                newStates: Dict[Tuple[Age, Compartment],
                                                float], stochastic: bool,
                                random_state: np.random.Generator):
    """
    Returns the status of exposed individuals, moving them into the next disease state with a
    probability defined in the given progression matrix.

    :param age: Current age
    :param age: Current state
    :param people: Number of people in current age, state
    :param outTransitions: Transition probabilities out of the current age, state,
                           to next potential ones
    :param newStates: Distribution of people in next states
    :param stochastic: Use the model in stochastic mode?
    :param random_state: Random number generator used for the model
    :return: the numbers in each exposed state stratified by age
    """
    if stochastic:
        if state == SUSCEPTIBLE_STATE:
            return

        assert isinstance(people, int) or people.is_integer()

        outRepartitions = random_state.multinomial(
            people, list(outTransitions.values()))
        outRepartitions = dict(zip(outTransitions.keys(), outRepartitions))

        for nextState, nextStateCases in outRepartitions.items():
            newStates.setdefault((age, nextState), 0.0)
            newStates[(age, nextState)] += nextStateCases
    else:
        for nextState, transition in outTransitions.items():
            newStates.setdefault((age, nextState), 0.0)
            newStates[(age, nextState)] += transition * people