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Immunity setter

ImmunitySetter

Source code in june/epidemiology/infection/immunity_setter.py 
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class ImmunitySetter:
    """ """
    def __init__(
        self,
        susceptibility_dict: dict = default_susceptibility_dict,
        multiplier_dict: dict = default_multiplier_dict,
        vaccination_dict: dict = None,
        previous_infections_dict=None,
        multiplier_by_comorbidity: Optional[dict] = None,
        comorbidity_prevalence_reference_population: Optional[dict] = None,
        susceptibility_mode="average",
        previous_infections_distribution="uniform",
        record: "Record" = None,
    ):
        """
        Sets immnuity parameters to different viruses.

        Parameters
        ----------
        susceptibility_dict:
           A dictionary mapping infection_id -> susceptibility by age.
           Example:
            susceptibility_dict = {"123" : {"0-50" : 0.5, "50-100" : 0.2}}
        multiplier_dict:
           A dictionary mapping infection_id -> symptoms reduction by age.
           Example:
            multiplier_dict = {"123" : {"0-50" : 0.5, "50-100" : 0.2}}
        vaccination_dict:
            A dictionary specifying the starting vaccination status of the population.
            Example:
                vaccination_dict = {
                    "Pfizer": {
                        "percentage_vaccinated": {"0-50": 0.7, "50-100": 1.0},
                        "infections": {
                            Covid19.infection_id(): {
                                "sterilisation_efficacy": {"0-100": 0.5},
                                "symptomatic_efficacy": {"0-100": 0.5},
                            },
                        },
                    },
                    "sputnik": {
                        "percentage_vaccinated": {"0-30": 0.3, "30-100": 0.0},
                        "infections": {
                            B117.infection_id(): {
                                "sterilisation_efficacy": {"0-100": 0.8},
                                "symptomatic_efficacy": {"0-100": 0.8},
                            },
                        },
                    },
                }
            previous_infections_dict:
                A dictionary specifying the current seroprevalence per region and age.
                Example:
                    previous_infections_dict = {
                        "infections": {
                            Covid19.infection_id(): {
                                "sterilisation_efficacy": 0.5,
                                "symptomatic_efficacy": 0.6,
                            },
                            B117.infection_id(): {
                                "sterilisation_efficacy": 0.2,
                                "symptomatic_efficacy": 0.3,
                            },
                        },
                        "ratios": {
                            "London": {"0-50": 0.5, "50-100": 0.2},
                            "North East": {"0-70": 0.3, "70-100": 0.8},
                        },
                    }
        """
        self.susceptibility_dict = self._read_susceptibility_dict(susceptibility_dict)
        if multiplier_dict is None:
            self.multiplier_dict = {}
        else:
            self.multiplier_dict = multiplier_dict
        self.vaccination_dict = self._read_vaccination_dict(vaccination_dict)
        self.previous_infections_dict = self._read_previous_infections_dict(
            previous_infections_dict
        )
        self.multiplier_by_comorbidity = multiplier_by_comorbidity
        if comorbidity_prevalence_reference_population is not None:
            self.comorbidity_prevalence_reference_population = (
                parse_prevalence_comorbidities_in_reference_population(
                    comorbidity_prevalence_reference_population
                )
            )
        else:
            self.comorbidity_prevalence_reference_population = None
        self.susceptibility_mode = susceptibility_mode
        self.previous_infections_distribution = previous_infections_distribution
        self.record = record

    @classmethod
    def from_file_with_comorbidities(
        cls,
        susceptibility_dict: dict = default_susceptibility_dict,
        multiplier_dict: dict = default_multiplier_dict,
        vaccination_dict: dict = None,
        previous_infections_dict: dict = None,
        comorbidity_multipliers_path: Optional[str] = None,
        male_comorbidity_reference_prevalence_path: Optional[str] = None,
        female_comorbidity_reference_prevalence_path: Optional[str] = None,
        susceptibility_mode="average",
        record: "Record" = None,
    ) -> "ImmunitySetter":
        """

        Args:
            susceptibility_dict (dict, optional): (Default value = default_susceptibility_dict)
            multiplier_dict (dict, optional): (Default value = default_multiplier_dict)
            vaccination_dict (dict, optional): (Default value = None)
            previous_infections_dict (dict, optional): (Default value = None)
            comorbidity_multipliers_path (Optional[str], optional): (Default value = None)
            male_comorbidity_reference_prevalence_path (Optional[str], optional): (Default value = None)
            female_comorbidity_reference_prevalence_path (Optional[str], optional): (Default value = None)
            susceptibility_mode: (Default value = "average")
            record ("Record", optional): (Default value = None)

        """
        if comorbidity_multipliers_path is not None:
            with open(comorbidity_multipliers_path) as f:
                comorbidity_multipliers = yaml.load(f, Loader=yaml.FullLoader)
            female_prevalence = read_comorbidity_csv(
                female_comorbidity_reference_prevalence_path
            )
            male_prevalence = read_comorbidity_csv(
                male_comorbidity_reference_prevalence_path
            )
            comorbidity_prevalence_reference_population = (
                convert_comorbidities_prevalence_to_dict(
                    female_prevalence, male_prevalence
                )
            )
        else:
            comorbidity_multipliers = None
            comorbidity_prevalence_reference_population = None
        return ImmunitySetter(
            susceptibility_dict=susceptibility_dict,
            multiplier_dict=multiplier_dict,
            vaccination_dict=vaccination_dict,
            previous_infections_dict=previous_infections_dict,
            multiplier_by_comorbidity=comorbidity_multipliers,
            comorbidity_prevalence_reference_population=comorbidity_prevalence_reference_population,
            susceptibility_mode=susceptibility_mode,
            record=record,
        )

    def set_immunity(self, world):
        """

        Args:
            world: 

        """
        if self.multiplier_dict:
            self.set_multipliers(world.people)
        if self.susceptibility_dict:
            self.set_susceptibilities(world.people)
        if self.previous_infections_dict:
            self.set_previous_infections(world)
        if self.vaccination_dict:
            self.set_vaccinations(world.people)

    def get_multiplier_from_reference_prevalence(self, age, sex):
        """Compute mean comorbidity multiplier given the prevalence of the different comorbidities
        in the reference population (for example the UK). It will be used to remove effect of
        comorbidities in the reference population

        Args:
            age: age group to compute average multiplier
            sex: 

        """
        weighted_multiplier = 0.0
        for comorbidity in self.comorbidity_prevalence_reference_population.keys():
            weighted_multiplier += (
                self.multiplier_by_comorbidity[comorbidity]
                * self.comorbidity_prevalence_reference_population[comorbidity][sex][
                    age
                ]
            )
        return weighted_multiplier

    def get_weighted_multipliers_by_age_sex(
        self,
    ):
        """ """
        reference_multipliers = {"m": [], "f": []}
        for sex in ("m", "f"):
            for age in range(100):
                reference_multipliers[sex].append(
                    self.get_multiplier_from_reference_prevalence(age=age, sex=sex)
                )
        return reference_multipliers

    def set_multipliers(self, population):
        """

        Args:
            population: 

        """
        if (
            self.multiplier_by_comorbidity is not None
            and self.comorbidity_prevalence_reference_population is not None
        ):
            set_comorbidity_multipliers = True
            reference_weighted_multipliers = self.get_weighted_multipliers_by_age_sex()
        else:
            set_comorbidity_multipliers = False
        for person in population:
            for inf_id in self.multiplier_dict:
                person.immunity.effective_multiplier_dict[
                    inf_id
                ] = self.multiplier_dict[inf_id]
                if set_comorbidity_multipliers:
                    multiplier = self.multiplier_by_comorbidity.get(
                        person.comorbidity, 1.0
                    )
                    reference_multiplier = reference_weighted_multipliers[person.sex][
                        person.age
                    ]
                    person.immunity.effective_multiplier_dict[inf_id] += (
                        multiplier / reference_multiplier
                    ) - 1.0

    def _read_susceptibility_dict(self, susceptibility_dict):
        """

        Args:
            susceptibility_dict: 

        """
        if susceptibility_dict is None:
            return {}
        ret = {}
        for inf_id in susceptibility_dict:
            ret[inf_id] = parse_age_probabilities(
                susceptibility_dict[inf_id], fill_value=1.0
            )
        return ret

    def _read_vaccination_dict(self, vaccination_dict):
        """

        Args:
            vaccination_dict: 

        """
        if vaccination_dict is None:
            return {}
        ret = {}
        for vaccine, vdata in vaccination_dict.items():
            ret[vaccine] = {}
            ret[vaccine]["percentage_vaccinated"] = parse_age_probabilities(
                vdata["percentage_vaccinated"]
            )
            ret[vaccine]["infections"] = {}
            for inf_id in vdata["infections"]:
                ret[vaccine]["infections"][inf_id] = {}
                for key in vdata["infections"][inf_id]:
                    ret[vaccine]["infections"][inf_id][key] = parse_age_probabilities(
                        vdata["infections"][inf_id][key], fill_value=0.0
                    )
        return ret

    def _read_previous_infections_dict(self, previous_infections_dict):
        """

        Args:
            previous_infections_dict: 

        """
        if previous_infections_dict is None:
            return {}
        ret = {}
        ret["infections"] = previous_infections_dict["infections"]
        ret["ratios"] = {}
        for region, region_ratios in previous_infections_dict["ratios"].items():
            ret["ratios"][region] = parse_age_probabilities(region_ratios)
        return ret

    def set_susceptibilities(self, population):
        """

        Args:
            population: 

        """
        if self.susceptibility_mode == "average":
            self._set_susceptibilities_avg(population)
        elif self.susceptibility_mode == "individual":
            self._set_susceptibilities_individual(population)
        else:
            raise NotImplementedError()

    def _set_susceptibilities_avg(self, population):
        """

        Args:
            population: 

        """
        for person in population:
            for inf_id in self.susceptibility_dict:
                if person.age >= len(self.susceptibility_dict[inf_id]):
                    continue
                person.immunity.susceptibility_dict[inf_id] = self.susceptibility_dict[
                    inf_id
                ][person.age]

    def _set_susceptibilities_individual(self, population):
        """

        Args:
            population: 

        """
        for person in population:
            for inf_id in self.susceptibility_dict:
                if person.age >= len(self.susceptibility_dict[inf_id]):
                    continue
                fraction = self.susceptibility_dict[inf_id][person.age]
                if random() > fraction:
                    person.immunity.susceptibility_dict[inf_id] = 0.0

    def set_vaccinations(self, population):
        """Sets previous vaccination on the starting population.

        Args:
            population: 

        """
        vaccine_type = []
        susccesfully_vaccinated = np.zeros(len(population), dtype=int)
        if not self.vaccination_dict:
            return
        vaccines = list(self.vaccination_dict.keys())
        for i, person in enumerate(population):
            if person.age > 99:
                age = 99
            else:
                age = person.age
            vaccination_rates = np.array(
                [
                    self.vaccination_dict[vaccine]["percentage_vaccinated"][age]
                    for vaccine in vaccines
                ]
            )
            total_vacc_rate = np.sum(vaccination_rates)
            if random() < total_vacc_rate:
                vaccination_rates /= total_vacc_rate
                vaccine = np.random.choice(vaccines, p=vaccination_rates)
                vdata = self.vaccination_dict[vaccine]
                for inf_id, inf_data in vdata["infections"].items():
                    person.immunity.add_multiplier(
                        inf_id, 1.0 - inf_data["symptomatic_efficacy"][age]
                    )
                    person.immunity.susceptibility_dict[inf_id] = (
                        1.0 - inf_data["sterilisation_efficacy"][age]
                    )
                    susccesfully_vaccinated[i] = 1
                person.vaccinated = True
                vaccine_type.append(vaccine)
            else:
                vaccine_type.append("none")
        if self.record is not None:
            self.record.statics["people"].extra_str_data["vaccine_type"] = vaccine_type
            self.record.statics["people"].extra_int_data[
                "susccesfully_vaccinated"
            ] = susccesfully_vaccinated 

    def set_previous_infections(self, world):
        """

        Args:
            world: 

        """
        if self.previous_infections_distribution == "uniform":
            self.set_previous_infections_uniform(world.people)
        elif self.previous_infections_distribution == "clustered":
            self.set_previous_infections_clustered(world)
        else:
            raise ValueError(
                f"Previous infection distr. {self.previous_infections_distribution} not recognised"
            )

    def set_previous_infections_uniform(self, population):
        """Sets previous infections on the starting population in a uniform way.

        Args:
            population: 

        """
        for i, person in enumerate(population):
            if person.region.name not in self.previous_infections_dict["ratios"]:
                continue
            ratio = self.previous_infections_dict["ratios"][person.region.name][
                person.age
            ]
            if random() < ratio:
                for inf_id, inf_data in self.previous_infections_dict[
                    "infections"
                ].items():
                    person.immunity.add_multiplier(
                        inf_id, 1.0 - inf_data["symptomatic_efficacy"]
                    )
                    person.immunity.susceptibility_dict[inf_id] = (
                        1.0 - inf_data["sterilisation_efficacy"]
                    )

    def _get_people_to_infect_by_age(self, people, seroprev_by_age):
        """Returns total people to infect according to the serorev age profile

        Args:
            people: 
            seroprev_by_age: 

        """
        people_by_age = Counter([person.age for person in people])
        people_to_infect = {
            age: people_by_age[age] * seroprev_by_age[age] for age in people_by_age
        }
        return people_to_infect

    def _get_household_score(self, household, age_distribution):
        """

        Args:
            household: 
            age_distribution: 

        """
        if len(household.residents) == 0:
            return 0
        ret = 0
        for resident in household.residents:
            ret += age_distribution[resident.age]
        return ret / np.sqrt(len(household.residents))

    def set_previous_infections_clustered(self, world):
        """Sets previous infections on the starting population by clustering households.

        Args:
            world: 

        """
        infection_ids = list(self.previous_infections_dict["infections"].keys())
        infection_data = list(self.previous_infections_dict["infections"].values())
        for region in world.regions:
            seroprev_by_age = self.previous_infections_dict["ratios"][region.name]
            people = region.people
            to_infect_by_age = self._get_people_to_infect_by_age(
                people=people, seroprev_by_age=seroprev_by_age
            )
            total_to_infect = sum(to_infect_by_age.values())
            age_distribution = {
                age: to_infect_by_age[age] / total_to_infect for age in to_infect_by_age
            }
            households = np.array(region.households)
            scores = [
                self._get_household_score(h, age_distribution) for h in households
            ]
            cum_scores = np.cumsum(scores)
            prev_inf_households = set()
            while total_to_infect > 0:
                num = random() * cum_scores[-1]
                idx = np.searchsorted(cum_scores, num)
                household = households[idx]
                if household.id in prev_inf_households:
                    continue
                for person in household.residents:
                    for inf_id, inf_data in zip(infection_ids, infection_data):
                        target_symptom_mult = 1.0 - inf_data["symptomatic_efficacy"]
                        target_susceptibility = 1.0 - inf_data["sterilisation_efficacy"]
                        current_multiplier = person.immunity.get_effective_multiplier(
                            inf_id
                        )
                        current_susc = person.immunity.get_susceptibility(inf_id)
                        person.immunity.effective_multiplier_dict[inf_id] = min(
                            current_multiplier, target_symptom_mult
                        )
                        person.immunity.susceptibility_dict[inf_id] = min(
                            current_susc, target_susceptibility
                        )
                    total_to_infect -= 1
                    if total_to_infect < 1:
                        return
                    prev_inf_households.add(household.id)

__init__(susceptibility_dict=default_susceptibility_dict, multiplier_dict=default_multiplier_dict, vaccination_dict=None, previous_infections_dict=None, multiplier_by_comorbidity=None, comorbidity_prevalence_reference_population=None, susceptibility_mode='average', previous_infections_distribution='uniform', record=None)

Sets immnuity parameters to different viruses.

Parameters

susceptibility_dict: A dictionary mapping infection_id -> susceptibility by age. Example: susceptibility_dict = {"123" : {"0-50" : 0.5, "50-100" : 0.2}} multiplier_dict: A dictionary mapping infection_id -> symptoms reduction by age. Example: multiplier_dict = {"123" : {"0-50" : 0.5, "50-100" : 0.2}} vaccination_dict: A dictionary specifying the starting vaccination status of the population. Example: vaccination_dict = { "Pfizer": { "percentage_vaccinated": {"0-50": 0.7, "50-100": 1.0}, "infections": { Covid19.infection_id(): { "sterilisation_efficacy": {"0-100": 0.5}, "symptomatic_efficacy": {"0-100": 0.5}, }, }, }, "sputnik": { "percentage_vaccinated": {"0-30": 0.3, "30-100": 0.0}, "infections": { B117.infection_id(): { "sterilisation_efficacy": {"0-100": 0.8}, "symptomatic_efficacy": {"0-100": 0.8}, }, }, }, } previous_infections_dict: A dictionary specifying the current seroprevalence per region and age. Example: previous_infections_dict = { "infections": { Covid19.infection_id(): { "sterilisation_efficacy": 0.5, "symptomatic_efficacy": 0.6, }, B117.infection_id(): { "sterilisation_efficacy": 0.2, "symptomatic_efficacy": 0.3, }, }, "ratios": { "London": {"0-50": 0.5, "50-100": 0.2}, "North East": {"0-70": 0.3, "70-100": 0.8}, }, }

Source code in june/epidemiology/infection/immunity_setter.py 
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def __init__(
    self,
    susceptibility_dict: dict = default_susceptibility_dict,
    multiplier_dict: dict = default_multiplier_dict,
    vaccination_dict: dict = None,
    previous_infections_dict=None,
    multiplier_by_comorbidity: Optional[dict] = None,
    comorbidity_prevalence_reference_population: Optional[dict] = None,
    susceptibility_mode="average",
    previous_infections_distribution="uniform",
    record: "Record" = None,
):
    """
    Sets immnuity parameters to different viruses.

    Parameters
    ----------
    susceptibility_dict:
       A dictionary mapping infection_id -> susceptibility by age.
       Example:
        susceptibility_dict = {"123" : {"0-50" : 0.5, "50-100" : 0.2}}
    multiplier_dict:
       A dictionary mapping infection_id -> symptoms reduction by age.
       Example:
        multiplier_dict = {"123" : {"0-50" : 0.5, "50-100" : 0.2}}
    vaccination_dict:
        A dictionary specifying the starting vaccination status of the population.
        Example:
            vaccination_dict = {
                "Pfizer": {
                    "percentage_vaccinated": {"0-50": 0.7, "50-100": 1.0},
                    "infections": {
                        Covid19.infection_id(): {
                            "sterilisation_efficacy": {"0-100": 0.5},
                            "symptomatic_efficacy": {"0-100": 0.5},
                        },
                    },
                },
                "sputnik": {
                    "percentage_vaccinated": {"0-30": 0.3, "30-100": 0.0},
                    "infections": {
                        B117.infection_id(): {
                            "sterilisation_efficacy": {"0-100": 0.8},
                            "symptomatic_efficacy": {"0-100": 0.8},
                        },
                    },
                },
            }
        previous_infections_dict:
            A dictionary specifying the current seroprevalence per region and age.
            Example:
                previous_infections_dict = {
                    "infections": {
                        Covid19.infection_id(): {
                            "sterilisation_efficacy": 0.5,
                            "symptomatic_efficacy": 0.6,
                        },
                        B117.infection_id(): {
                            "sterilisation_efficacy": 0.2,
                            "symptomatic_efficacy": 0.3,
                        },
                    },
                    "ratios": {
                        "London": {"0-50": 0.5, "50-100": 0.2},
                        "North East": {"0-70": 0.3, "70-100": 0.8},
                    },
                }
    """
    self.susceptibility_dict = self._read_susceptibility_dict(susceptibility_dict)
    if multiplier_dict is None:
        self.multiplier_dict = {}
    else:
        self.multiplier_dict = multiplier_dict
    self.vaccination_dict = self._read_vaccination_dict(vaccination_dict)
    self.previous_infections_dict = self._read_previous_infections_dict(
        previous_infections_dict
    )
    self.multiplier_by_comorbidity = multiplier_by_comorbidity
    if comorbidity_prevalence_reference_population is not None:
        self.comorbidity_prevalence_reference_population = (
            parse_prevalence_comorbidities_in_reference_population(
                comorbidity_prevalence_reference_population
            )
        )
    else:
        self.comorbidity_prevalence_reference_population = None
    self.susceptibility_mode = susceptibility_mode
    self.previous_infections_distribution = previous_infections_distribution
    self.record = record

from_file_with_comorbidities(susceptibility_dict=default_susceptibility_dict, multiplier_dict=default_multiplier_dict, vaccination_dict=None, previous_infections_dict=None, comorbidity_multipliers_path=None, male_comorbidity_reference_prevalence_path=None, female_comorbidity_reference_prevalence_path=None, susceptibility_mode='average', record=None) classmethod

Parameters:

Name Type Description Default
susceptibility_dict dict

(Default value = default_susceptibility_dict)

 default_susceptibility_dict
multiplier_dict dict

(Default value = default_multiplier_dict)

 default_multiplier_dict
vaccination_dict dict

(Default value = None)

 None
previous_infections_dict dict

(Default value = None)

 None
comorbidity_multipliers_path Optional[str]

(Default value = None)

 None
male_comorbidity_reference_prevalence_path Optional[str]

(Default value = None)

 None
female_comorbidity_reference_prevalence_path Optional[str]

(Default value = None)

 None
susceptibility_mode

(Default value = "average")

 'average'
record Record

(Default value = None)

 None
Source code in june/epidemiology/infection/immunity_setter.py 
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@classmethod
def from_file_with_comorbidities(
    cls,
    susceptibility_dict: dict = default_susceptibility_dict,
    multiplier_dict: dict = default_multiplier_dict,
    vaccination_dict: dict = None,
    previous_infections_dict: dict = None,
    comorbidity_multipliers_path: Optional[str] = None,
    male_comorbidity_reference_prevalence_path: Optional[str] = None,
    female_comorbidity_reference_prevalence_path: Optional[str] = None,
    susceptibility_mode="average",
    record: "Record" = None,
) -> "ImmunitySetter":
    """

    Args:
        susceptibility_dict (dict, optional): (Default value = default_susceptibility_dict)
        multiplier_dict (dict, optional): (Default value = default_multiplier_dict)
        vaccination_dict (dict, optional): (Default value = None)
        previous_infections_dict (dict, optional): (Default value = None)
        comorbidity_multipliers_path (Optional[str], optional): (Default value = None)
        male_comorbidity_reference_prevalence_path (Optional[str], optional): (Default value = None)
        female_comorbidity_reference_prevalence_path (Optional[str], optional): (Default value = None)
        susceptibility_mode: (Default value = "average")
        record ("Record", optional): (Default value = None)

    """
    if comorbidity_multipliers_path is not None:
        with open(comorbidity_multipliers_path) as f:
            comorbidity_multipliers = yaml.load(f, Loader=yaml.FullLoader)
        female_prevalence = read_comorbidity_csv(
            female_comorbidity_reference_prevalence_path
        )
        male_prevalence = read_comorbidity_csv(
            male_comorbidity_reference_prevalence_path
        )
        comorbidity_prevalence_reference_population = (
            convert_comorbidities_prevalence_to_dict(
                female_prevalence, male_prevalence
            )
        )
    else:
        comorbidity_multipliers = None
        comorbidity_prevalence_reference_population = None
    return ImmunitySetter(
        susceptibility_dict=susceptibility_dict,
        multiplier_dict=multiplier_dict,
        vaccination_dict=vaccination_dict,
        previous_infections_dict=previous_infections_dict,
        multiplier_by_comorbidity=comorbidity_multipliers,
        comorbidity_prevalence_reference_population=comorbidity_prevalence_reference_population,
        susceptibility_mode=susceptibility_mode,
        record=record,
    )

get_multiplier_from_reference_prevalence(age, sex)

Compute mean comorbidity multiplier given the prevalence of the different comorbidities in the reference population (for example the UK). It will be used to remove effect of comorbidities in the reference population

Parameters:

Name Type Description Default
age

age group to compute average multiplier

 required
sex
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def get_multiplier_from_reference_prevalence(self, age, sex):
    """Compute mean comorbidity multiplier given the prevalence of the different comorbidities
    in the reference population (for example the UK). It will be used to remove effect of
    comorbidities in the reference population

    Args:
        age: age group to compute average multiplier
        sex: 

    """
    weighted_multiplier = 0.0
    for comorbidity in self.comorbidity_prevalence_reference_population.keys():
        weighted_multiplier += (
            self.multiplier_by_comorbidity[comorbidity]
            * self.comorbidity_prevalence_reference_population[comorbidity][sex][
                age
            ]
        )
    return weighted_multiplier

get_weighted_multipliers_by_age_sex()

Source code in june/epidemiology/infection/immunity_setter.py 
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def get_weighted_multipliers_by_age_sex(
    self,
):
    """ """
    reference_multipliers = {"m": [], "f": []}
    for sex in ("m", "f"):
        for age in range(100):
            reference_multipliers[sex].append(
                self.get_multiplier_from_reference_prevalence(age=age, sex=sex)
            )
    return reference_multipliers

set_immunity(world)

Parameters:

Name Type Description Default
world
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_immunity(self, world):
    """

    Args:
        world: 

    """
    if self.multiplier_dict:
        self.set_multipliers(world.people)
    if self.susceptibility_dict:
        self.set_susceptibilities(world.people)
    if self.previous_infections_dict:
        self.set_previous_infections(world)
    if self.vaccination_dict:
        self.set_vaccinations(world.people)

set_multipliers(population)

Parameters:

Name Type Description Default
population
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_multipliers(self, population):
    """

    Args:
        population: 

    """
    if (
        self.multiplier_by_comorbidity is not None
        and self.comorbidity_prevalence_reference_population is not None
    ):
        set_comorbidity_multipliers = True
        reference_weighted_multipliers = self.get_weighted_multipliers_by_age_sex()
    else:
        set_comorbidity_multipliers = False
    for person in population:
        for inf_id in self.multiplier_dict:
            person.immunity.effective_multiplier_dict[
                inf_id
            ] = self.multiplier_dict[inf_id]
            if set_comorbidity_multipliers:
                multiplier = self.multiplier_by_comorbidity.get(
                    person.comorbidity, 1.0
                )
                reference_multiplier = reference_weighted_multipliers[person.sex][
                    person.age
                ]
                person.immunity.effective_multiplier_dict[inf_id] += (
                    multiplier / reference_multiplier
                ) - 1.0

set_previous_infections(world)

Parameters:

Name Type Description Default
world
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_previous_infections(self, world):
    """

    Args:
        world: 

    """
    if self.previous_infections_distribution == "uniform":
        self.set_previous_infections_uniform(world.people)
    elif self.previous_infections_distribution == "clustered":
        self.set_previous_infections_clustered(world)
    else:
        raise ValueError(
            f"Previous infection distr. {self.previous_infections_distribution} not recognised"
        )

set_previous_infections_clustered(world)

Sets previous infections on the starting population by clustering households.

Parameters:

Name Type Description Default
world
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_previous_infections_clustered(self, world):
    """Sets previous infections on the starting population by clustering households.

    Args:
        world: 

    """
    infection_ids = list(self.previous_infections_dict["infections"].keys())
    infection_data = list(self.previous_infections_dict["infections"].values())
    for region in world.regions:
        seroprev_by_age = self.previous_infections_dict["ratios"][region.name]
        people = region.people
        to_infect_by_age = self._get_people_to_infect_by_age(
            people=people, seroprev_by_age=seroprev_by_age
        )
        total_to_infect = sum(to_infect_by_age.values())
        age_distribution = {
            age: to_infect_by_age[age] / total_to_infect for age in to_infect_by_age
        }
        households = np.array(region.households)
        scores = [
            self._get_household_score(h, age_distribution) for h in households
        ]
        cum_scores = np.cumsum(scores)
        prev_inf_households = set()
        while total_to_infect > 0:
            num = random() * cum_scores[-1]
            idx = np.searchsorted(cum_scores, num)
            household = households[idx]
            if household.id in prev_inf_households:
                continue
            for person in household.residents:
                for inf_id, inf_data in zip(infection_ids, infection_data):
                    target_symptom_mult = 1.0 - inf_data["symptomatic_efficacy"]
                    target_susceptibility = 1.0 - inf_data["sterilisation_efficacy"]
                    current_multiplier = person.immunity.get_effective_multiplier(
                        inf_id
                    )
                    current_susc = person.immunity.get_susceptibility(inf_id)
                    person.immunity.effective_multiplier_dict[inf_id] = min(
                        current_multiplier, target_symptom_mult
                    )
                    person.immunity.susceptibility_dict[inf_id] = min(
                        current_susc, target_susceptibility
                    )
                total_to_infect -= 1
                if total_to_infect < 1:
                    return
                prev_inf_households.add(household.id)

set_previous_infections_uniform(population)

Sets previous infections on the starting population in a uniform way.

Parameters:

Name Type Description Default
population
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_previous_infections_uniform(self, population):
    """Sets previous infections on the starting population in a uniform way.

    Args:
        population: 

    """
    for i, person in enumerate(population):
        if person.region.name not in self.previous_infections_dict["ratios"]:
            continue
        ratio = self.previous_infections_dict["ratios"][person.region.name][
            person.age
        ]
        if random() < ratio:
            for inf_id, inf_data in self.previous_infections_dict[
                "infections"
            ].items():
                person.immunity.add_multiplier(
                    inf_id, 1.0 - inf_data["symptomatic_efficacy"]
                )
                person.immunity.susceptibility_dict[inf_id] = (
                    1.0 - inf_data["sterilisation_efficacy"]
                )

set_susceptibilities(population)

Parameters:

Name Type Description Default
population
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_susceptibilities(self, population):
    """

    Args:
        population: 

    """
    if self.susceptibility_mode == "average":
        self._set_susceptibilities_avg(population)
    elif self.susceptibility_mode == "individual":
        self._set_susceptibilities_individual(population)
    else:
        raise NotImplementedError()

set_vaccinations(population)

Sets previous vaccination on the starting population.

Parameters:

Name Type Description Default
population
required
Source code in june/epidemiology/infection/immunity_setter.py 
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def set_vaccinations(self, population):
    """Sets previous vaccination on the starting population.

    Args:
        population: 

    """
    vaccine_type = []
    susccesfully_vaccinated = np.zeros(len(population), dtype=int)
    if not self.vaccination_dict:
        return
    vaccines = list(self.vaccination_dict.keys())
    for i, person in enumerate(population):
        if person.age > 99:
            age = 99
        else:
            age = person.age
        vaccination_rates = np.array(
            [
                self.vaccination_dict[vaccine]["percentage_vaccinated"][age]
                for vaccine in vaccines
            ]
        )
        total_vacc_rate = np.sum(vaccination_rates)
        if random() < total_vacc_rate:
            vaccination_rates /= total_vacc_rate
            vaccine = np.random.choice(vaccines, p=vaccination_rates)
            vdata = self.vaccination_dict[vaccine]
            for inf_id, inf_data in vdata["infections"].items():
                person.immunity.add_multiplier(
                    inf_id, 1.0 - inf_data["symptomatic_efficacy"][age]
                )
                person.immunity.susceptibility_dict[inf_id] = (
                    1.0 - inf_data["sterilisation_efficacy"][age]
                )
                susccesfully_vaccinated[i] = 1
            person.vaccinated = True
            vaccine_type.append(vaccine)
        else:
            vaccine_type.append("none")
    if self.record is not None:
        self.record.statics["people"].extra_str_data["vaccine_type"] = vaccine_type
        self.record.statics["people"].extra_int_data[
            "susccesfully_vaccinated"
        ] = susccesfully_vaccinated