emodpy.campaign.individual_intervention module#

class emodpy.campaign.individual_intervention.BroadcastEvent(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, broadcast_event: str, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The BroadcastEvent intervention class is an individual-level class that immediately broadcasts the event trigger you specify. This campaign event is typically used with other classes that monitor for a broadcast event.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
broadcast_event (str, required) – The name of the event to be broadcasted. See Event list for events already used in EMOD or use your own custom event.
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 4 common parameters: disqualifying_properties, new_property_value, intervention_name, dont_allow_duplicates. The following parameters are not valid for this intervention: cost Default value: None

class emodpy.campaign.individual_intervention.BroadcastEventToOtherNodes(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, broadcast_event: str, node_selection_type: ~emodpy.utils.emod_enum.NodeSelectionType = NodeSelectionType.DISTANCE_ONLY, max_distance_to_other_nodes_km: float = 3.40282e+38, include_my_node: bool = False, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The BroadcastEventToOtherNodes intervention class allows events to be sent from one node to another. For example, if an individual in one node has been diagnosed, drugs may be distributed to individuals in surrounding nodes.

When this intervention is updated, the event to be broadcast is cached to be distributed to the nodes. After the people have migrated, the event information is distributed to the nodes - NOTE: it does support multi-core. During the next time step, the nodes will update their node-level interventions and then broadcast the events from other nodes to ALL the people in the node. This is different from interventions that only broadcast the event in the current node for the person who had the intervention. Distances between nodes use the Longitude and Latitude defined in the demographics file, and use the Haversine Formula for calculating the great-circle distance.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
broadcast_event (str, required) – The name of the event to broadcast to the people of ‘nearby’ nodes. For example, if a house is found to have malarai, broadcast an event to the people in the nearby houses so that they can get treatment. For HIV, see Event list, and for malaria, Event list for events already used in EMOD or use your own custom event.
node_selection_type ('NodeSelectionType', optional) – The method by which to select nodes to receive the event. Possible values are: * DISTANCE_ONLY - Nodes located within the distance specified by Max_Distance_To_Other_Nodes_Km are selected. * MIGRATION_NODES_ONLY - Nodes that are local, regional, or connected in the migration file are selected. * DISTANCE_AND_MIGRATION - Nodes are selected using DISTANCE_ONLY and MIGRATION_NODES_ONLY criteria. Default value: DISTANCE_ONLY
max_distance_to_other_nodes_km (float, optional) – The maximum distance, in kilometers, to the destination node for the node to be selected. The location values used are those entered in the demographics file. Used only if Node_Selection_Type is either DISTANCE_ONLY or DISTANCE_AND_MIGRATION. Minimum value: 0 Maximum value: 3.40282e+38 Default value: 3.40282e+38
include_my_node (bool, optional) – Set to True to broadcast the event to the current node. Default value: True
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 4 common parameters: intervention_name, new_property_value, dont_allow_duplicates, disqualifying_properties. The following parameters are not valid for this intervention: cost Default value: None

class emodpy.campaign.individual_intervention.ControlledVaccine(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, waning_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, vaccine_type: ~emodpy.utils.emod_enum.VaccineType = VaccineType.Generic, vaccine_take: float = 1, expired_event_trigger: str | None = None, efficacy_is_multiplicative: bool = True, duration_to_wait_before_revaccination: float = 3.40282e+38, distributed_event_trigger: str | None = None, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The ControlledVaccine intervention class is a subclass of SimpleVaccine so it contains all functionality of SimpleVaccine, but provides more control over additional events and event triggers. This intervention can be configured so that specific events are broadcast when individuals receive an intervention or when the intervention expires. Further, individuals can be re-vaccinated, using a configurable wait time between vaccinations.

Note that one of the controls of this intervention is to not allow a person to receive an additional dose if they received a dose within a certain amount of time. This applies only to ControlledVaccine interventions with the same Intervention_Name, so people can be given multiple vaccines as long as each vaccine has a different value for Intervention_Name.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
waning_config (AbstractWaningConfig, required) – The configuration of the vaccine’s efficacy and waning over time. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
vaccine_type (VaccineType, optional) – The type of vaccine to distribute in a vaccine intervention. Possible values are: * Generic - The vaccine can reduce transmission, acquisition, and mortality. * TransmissionBlocking - The vaccine will reduce pathogen transmission. * AcquisitionBlocking - The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. * MortalityBlocking - The vaccine reduces the disease-mortality rate of a vaccinated individual. Default value: Generic
vaccine_take (float, optional) – The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all. Minimum value: 0 Maximum value: 1 Default value: 1
expired_event_trigger (str, optional) – The name of the event to be broadcast when the intervention expires. See Event list for events already used in EMOD or use your own custom event. Default value: None
efficacy_is_multiplicative (bool, optional) – The overall vaccine efficacy when individuals receive more than one vaccine. When set to True, the vaccine efficacies are multiplied together; when set to False, the efficacies are additive. Default value: True
duration_to_wait_before_revaccination (float, optional) – The length of time, in days, to wait before revaccinating an individual. After this time has passed, the individual can be revaccinated. If the first vaccine has not expired, the individual can receive the effect from both doses of the vaccine. Minimum value: 0 Maximum value: 3.40282e+38 Default value: 3.40282e+38
distributed_event_trigger (str, optional) –
The name of the event to be broadcast when the intervention is distributed to an individual. See Event list for events already used in EMOD or use your own custom event.

Default value: None
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 5 common parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates. Default value: None

class emodpy.campaign.individual_intervention.DelayedIntervention(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, delay_period_distribution: ~emodpy.utils.distributions.BaseDistribution, intervention_to_distribute_at_delay_completion: ~emodpy.campaign.base_intervention.IndividualIntervention | list[emodpy.campaign.base_intervention.IndividualIntervention], common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The DelayedIntervention intervention class introduces a delay between when the intervention is distributed to the individual and when they receive the actual intervention. This is due to the frequent occurrences of time delays as individuals seek care and receive treatment. This intervention allows configuration of the distribution type for the delay.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
delay_period_distribution (BaseDistribution, required) – The distribution type to use for assigning the delay period for distributing interventions. Each assigned value is a random draw from the distribution. Please use the following distribution classes from emodpy.utils.distributions to define the distribution: * ConstantDistribution * UniformDistribution * GaussianDistribution * ExponentialDistribution * PoissonDistribution * LogNormalDistribution * DualConstantDistribution * WeibullDistribution * DualExponentialDistribution
intervention_to_distribute_at_delay_completion (Union[IndividualIntervention, list[IndividualIntervention]], required) – An Individual Intervention or an array of nested Individual Interventions to be distributed at the end of the delay period. Either intervention_to_broadcast_at_delay_completion or event_to_broadcast_at_delay_completion must be set.
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 4 common parameters: intervention_name, new_property_value, dont_allow_duplicates, disqualifying_properties. The following parameters are not valid for this intervention: cost Default value: None

class emodpy.campaign.individual_intervention.IVCalendar(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, intervention_list: list[emodpy.campaign.base_intervention.IndividualIntervention], dropout: bool = False, calendar: list['AgeAndProbability'] | None = None, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The IVCalendar intervention class contains a list of ages when an individual will receive the actual intervention. In IVCalendar, there is a list of actual interventions where the distribution is dependent on whether the individual’s age matches the next date in the calendar. This implies that at a certain age, the list of actual interventions will be distributed according to a given probability. While a typical use case might involve the distribution of calendars due to a Births event in the context of a routine vaccination schedule, calendars may also be distributed directly to individuals at at times other than birth.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
intervention_list (list[IndividualIntervention], required) – An array of interventions that will be distributed as specified by the calendar. Each time the calendar says it is time for the intervention, this list of interventions will be distributed to the person with this intervention.
dropout (bool, optional) – If set to true, when an intervention distribution is missed, all subsequent interventions are also missed. If set to false, all calendar dates/doses are applied independently of each other. Default value: True
calendar (list['AgeAndProbability'], optional) – An array of JSON objects where each object specifies the age and probability of receiving the interventions. The parameters of the Calendar objects are Age and Probability. Default value: None
common_intervention_parameters (CommonInterventionParameters, optional) – The CommonInterventionParameters object that contains the 4 common parameters: intervention_name, new_property_value, disqualifying_properties, dont_allow_duplicates. The following parameters are not valid for this intervention: cost Default value: None

class AgeAndProbability(age_days: float = 0.0, probability: float = 1.0)[source]#

Bases: object

This class defines a single entry into the calendar of when an individual is to get a collection of interventions. When IVCalendar is distributed to the individual, the ‘probability’ is used to determine if the person will get the interventions as this age. If the person gets IVCalendar after ‘age’, they will not get the interventions.

Parameters:

age_days (float,optional) – As a parameter of a Calendar object, this parameter determines the age (in days) that the individual must be in order to receive the list of actual interventions. Minimum value: 0 Maximum value: 125 * 365 = 45,625 Default value: 0
probability (float,optional) – As a parameter of a Calendar object, this parameter determines the probability of an individual receiving the list of actual interventions at the corresponding age. Minimum value: 0 Maximum value: 1 Default value: 1

to_schema_dict(campaign) → ReadOnlyDict[source]#: A function that converts the Sigmoid object to a schema dictionary.

class emodpy.campaign.individual_intervention.IndividualImmunityChanger(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, prime_transmit: float = 0, prime_mortality: float = 0, prime_acquire: float = 0, boost_transmit: float = 0, boost_threshold_transmit: float = 0, boost_threshold_mortality: float = 0, boost_threshold_acquire: float = 0, boost_mortality: float = 0, boost_acquire: float = 0, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The IndividualImmunityChanger intervention class acts essentially as a MultiEffectVaccine, with the exception of how the behavior is implemented. Rather than attaching a persistent vaccine intervention object to an individual’s intervention list (as a campaign-individual-multieffectboostervaccine does), the IndividualImmunityChanger directly alters the immune modifiers of the individual’s susceptibility object and is then immediately disposed of. Any immune waning is not governed by Waning effect classes, as MultiEffectVaccine is, but rather by the immunity waning parameters in the configuration file.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
prime_transmit (float, optional) – Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. Minimum value: 0 Maximum value: 1 Default value: 0
prime_mortality (float, optional) – Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. Minimum value: 0 Maximum value: 1 Default value: 0
prime_acquire (float, optional) – Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. Minimum value: 0 Maximum value: 1 Default value: 0
boost_transmit (float, optional) – Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 0
boost_threshold_transmit (float, optional) – Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost. Minimum value: 0 Maximum value: 1 Default value: 0
boost_threshold_mortality (float, optional) – Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost. Minimum value: 0 Maximum value: 1 Default value: 0
boost_threshold_acquire (float, optional) – Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost. Minimum value: 0 Maximum value: 1 Default value: 0
boost_mortality (float, optional) – Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 0
boost_acquire (float, optional) – Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 0
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 1 common parameters: cost. The following parameters are not valid for this intervention: intervention_name dont_allow_duplicates new_property_value disqualifying_properties Default value: None

class emodpy.campaign.individual_intervention.IndividualNonDiseaseDeathRateModifier(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, duration_to_modifier: ~emodpy.campaign.common.ValueMap, expiration_event: str | None = None, expiration_duration_distribution: ~emodpy.utils.distributions.BaseDistribution | None = None, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The IndividualNonDiseaseDeathRateModifier intervention class provides a method of modifying an individual’s non-disease mortality rate over time, until an expiration event is reached. For example, this intervention could be given to people who have access to health care to model that they have a different life expectancy than those who do not. Different distribution patterns can be designated, and linear interpolation will be used to calculate values between time points.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
duration_to_modifier (ValueMap, required) – An array of Times and Values used to specify different modifiers over the duration of the intervention. Linear interpolation is used to find the values between time points. If the duration exceeds the max time, then the last modifier value will be used.
expiration_event (str, optional) – When the person stops using the intervention (intervetion expires), this event will be broadcasted. See Event list for events already used in EMOD or use your own custom event. Default value: None
expiration_duration_distribution (BaseDistribution, optional) – For the distribution of each intervention, a randomly selected duration from this distribution will determine when the person stops using the intervention. This is independent of how long the intervention is effective. Please use the following distribution classes from emodpy.utils.distributions to define the distribution: * ConstantDistribution * UniformDistribution * GaussianDistribution * ExponentialDistribution * PoissonDistribution * LogNormalDistribution * DualConstantDistribution * WeibullDistribution * DualExponentialDistribution Default value: None
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 5 common parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates. Default value: None

class emodpy.campaign.individual_intervention.MigrateIndividuals(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, nodeid_to_migrate_to: int = 0, is_moving: bool = False, duration_before_leaving_distribution: ~emodpy.utils.distributions.BaseDistribution | None = None, duration_at_node_distribution: ~emodpy.utils.distributions.BaseDistribution | None = None, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The MigrateIndividuals intervention class is an individual-level intervention used to force migration and is separate from the normal migration system. However, it does require that human migration is enabled by setting the configuration parameters Migration_Model to FIXED_RATE_MIGRATION and Migration_Pattern to SINGLE_ROUND_TRIP.

As individuals migrate, there are three ways to categorize nodes:

Home: the node where the individuals reside; each individual has a single home node.

Origin: the “starting point” node for each leg of the migration. The origin updates as individuals move between nodes.

Destination: the node the individual is traveling to. The destination updates as individuals move between nodes.

For example, Individual 1 has a home node of Node A. They migrate from Node A to Node B. Node A is both the home node and the origin node, and Node B is the destination node. If Individual 1 migrates from Node B to Node C, Node A remains the home node, but now Node B is the origin node, and Node C is the destination node. If Individual 1 migrates from Node C back to Node A, Node C is the origin node, and Node A becomes the destination node and still remains the home node.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
nodeid_to_migrate_to (int, optional) – The destination node ID for intervention-based migration. Minimum value: 0 Maximum value: 4294970000.0 Default value: 0
is_moving (bool, optional) – Set to True to indicate the individual is permanently moving to a new home node for intervention-based migration. Once at the new home node, trips will be made with this node as the root (i.e. round trips come back to this node). Default value: True
duration_before_leaving_distribution (BaseDistribution, optional) – The distribution type to use for assigning the duration of time an individual waits before migrating to the destination node after intervention-based migration. Each assigned value is a random draw from the distribution. Please use the following distribution classes from emodpy.utils.distributions to define the distribution: * ConstantDistribution * UniformDistribution * GaussianDistribution * ExponentialDistribution * PoissonDistribution * LogNormalDistribution * DualConstantDistribution * WeibullDistribution * DualExponentialDistribution Default value: None
duration_at_node_distribution (BaseDistribution, optional) – The distribution type to use for assigning the duration of time an individual or family spends at a destination node after intervention-based migration. Each assigned value is a random draw from the distribution. Please use the following distribution classes from emodpy.utils.distributions to define the distribution: * ConstantDistribution * UniformDistribution * GaussianDistribution * ExponentialDistribution * PoissonDistribution * LogNormalDistribution * DualConstantDistribution * WeibullDistribution * DualExponentialDistribution Default value: None
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 4 common parameters: intervention_name, new_property_value, dont_allow_duplicates, disqualifying_properties. The following parameters are not valid for this intervention: cost Default value: None

class emodpy.campaign.individual_intervention.MultiEffectBoosterVaccine(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, transmit_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, mortality_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, acquire_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, vaccine_take: float = 1, prime_transmit: float = 0, prime_mortality: float = 0, prime_acquire: float = 0, boost_transmit: float = 0, boost_threshold_transmit: float = 0, boost_threshold_mortality: float = 0, boost_threshold_acquire: float = 0, boost_mortality: float = 0, boost_acquire: float = 0, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The MultiEffectBoosterVaccine intervention class is derived from MultiEffectVaccine and preserves many of the same parameters. Upon distribution and successful take, the vaccine’s effect in each immunity compartment (acquisition, transmission, and mortality) is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding compartment is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boost parameter. After distribution, the effect wanes, just like a MultiEffectVaccine. The behavior is intended to mimic biological priming and boosting.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
transmit_config (AbstractWaningConfig, required) – The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
mortality_config (AbstractWaningConfig, required) – The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
acquire_config (AbstractWaningConfig, required) – The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
vaccine_take (float, optional) – The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all. Minimum value: 0 Maximum value: 1 Default value: 1
prime_transmit (float, optional) – Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. Minimum value: 0 Maximum value: 1 Default value: 0
prime_mortality (float, optional) – Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. Minimum value: 0 Maximum value: 1 Default value: 0
prime_acquire (float, optional) – Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. Minimum value: 0 Maximum value: 1 Default value: 0
boost_transmit (float, optional) – Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 0
boost_threshold_transmit (float, optional) – Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost. Minimum value: 0 Maximum value: 1 Default value: 0
boost_threshold_mortality (float, optional) – Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost. Minimum value: 0 Maximum value: 1 Default value: 0
boost_threshold_acquire (float, optional) – Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost. Minimum value: 0 Maximum value: 1 Default value: 0
boost_mortality (float, optional) – Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 0
boost_acquire (float, optional) – Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 0
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 5 common parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates. Default value: None

class emodpy.campaign.individual_intervention.MultiEffectVaccine(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, transmit_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, mortality_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, acquire_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, vaccine_take: float = 1, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The MultiEffectVaccine intervention class implements vaccine campaigns in the simulation. Vaccines can effect all of the following:

Reduce the likelihood of acquiring an infection

Reduce the likelihood of transmitting an infection

Reduce the likelihood of death

After distribution, the effect wanes over time.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
transmit_config (AbstractWaningConfig, required) – The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
mortality_config (AbstractWaningConfig, required) – The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
acquire_config (AbstractWaningConfig, required) – The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
vaccine_take (float, optional) – The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all. Minimum value: 0 Maximum value: 1 Default value: 1
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 5 common parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates. Default value: None

class emodpy.campaign.individual_intervention.MultiInterventionDistributor(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, intervention_list: list[emodpy.campaign.base_intervention.IndividualIntervention], common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The MultiInterventionDistributor intervention class allows you to input a list of interventions, rather than just a single intervention, to be distributed simultaneously to the same individuals.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
intervention_list (list[IndividualIntervention], required) – The list of individual interventions that is distributed by MultiInterventionDistributor.
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 4 common parameters: intervention_name, new_property_value, dont_allow_duplicates, disqualifying_properties. The following parameters are not valid for this intervention: cost Default value: None

class emodpy.campaign.individual_intervention.OutbreakIndividual(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, incubation_period_override: int = -1, ignore_immunity: bool = True, genome: int = 0, antigen: int = 0)[source]#

Bases: IndividualIntervention

The OutbreakIndividual intervention class introduces contagious diseases that are compatible with the simulation type to existing individuals using the individual targeted features configured in the appropriate event coordinator. To instead add new infection individuals, use Outbreak.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
incubation_period_override (int, optional) – The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Note for HIV simulations only: If set to 0, infection is assumed to be part of an outbreak event and a random duration until death is determined. For all other simulation types: Set to -1 to honor the configuration parameter settings. Minimum value: -1 Maximum value: 2147480000.0 Default value: -1
ignore_immunity (bool, optional) – Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to True. Default value: True
genome (int, optional) – The genetic substrain ID of the outbreak infection. Together with Antigen, they are a unitary object representing a strain of infection, which allows for differentiation among infections. Minimum value: -1 Maximum value: 16777200.0 Default value: 0
antigen (int, optional) – The antigenic base strain ID of the outbreak infection. Minimum value: 0 Maximum value: 10 Default value: 0

class emodpy.campaign.individual_intervention.PropertyValueChanger(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, target_property_value: str, target_property_key: str, revert: float = 0, maximum_duration: float = 3.40282e+38, daily_probability: float = 1, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The PropertyValueChanger intervention class assigns new individual property values to individuals You must update one property value and have the option to update another using New_Property_Value. This parameter is generally used to move patients from one intervention state in the health care cascade (InterventionStatus/CascadeState) to another, though it can be used for any individual property. Individual property values are user-defined in the demographics file (see NodeProperties and IndividualProperties for more information). Note that the HINT feature does not need to be enabled to use this intervention. To instead change node properties, use NodePropertyValueChanger.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
target_property_value (str, required) – The user-defined value of the individual property that will be assigned to the individual.
target_property_key (str, required) – The name of the individual property type whose value will be updated by the intervention.
revert (float, optional) – The number of days to keep the value (Target_Property_Value) of the property (Target_Property_Key) set by the intervenion for the individual. When the time has expired, the intervention will reset the property back to the value it had when the intervention was first applied. Minimum value: 0 Maximum value: 3.40282e+38 Default value: 0
maximum_duration (float, optional) – The maximum amount of time individuals have to move to a new group. This timing works in conjunction with Daily_Probability. Minimum value: -1 Maximum value: 3.40282e+38 Default value: 3.40282e+38
daily_probability (float, optional) – The daily probability that an individual’s property value changes to the Target_Property_Value. Minimum value: 0 Maximum value: 1 Default value: 1
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 4 common parameters: intervention_name, new_property_value, dont_allow_duplicates, disqualifying_properties. The following parameters are not valid for this intervention: cost Default value: None

class emodpy.campaign.individual_intervention.SimpleBoosterVaccine(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, waning_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, vaccine_type: ~emodpy.utils.emod_enum.VaccineType = VaccineType.Generic, vaccine_take: float = 1, prime_effect: float = 1, efficacy_is_multiplicative: bool = True, boost_threshold: float = 0, boost_effect: float = 1, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The SimpleBoosterVaccine intervention class is derived from SimpleVaccine and preserves many of the same parameters. The behavior is much like SimpleVaccine, except that upon distribution and successful take, the vaccine’s effect is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding channel (acquisition, transmission, or mortality) is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boosting parameter. After distribution, the effect wanes, just like SimpleVaccine. In essence, this intervention provides a SimpleVaccine intervention with one effect to all naive (below- threshold) individuals, and another effect to all primed (above-threshold) individuals; this behavior is intended to mimic biological priming and boosting.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
waning_config (AbstractWaningConfig, required) – The configuration of the vaccine’s efficacy and waning over time. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waninng_config.
vaccine_type (VaccineType, optional) – The type of vaccine to distribute in a vaccine intervention. Possible values are: * Generic - The vaccine can reduce transmission, acquisition, and mortality. * TransmissionBlocking - The vaccine will reduce pathogen transmission. * AcquisitionBlocking - The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. * MortalityBlocking - The vaccine reduces the disease-mortality rate of a vaccinated individual. Default value: Generic
vaccine_take (float, optional) – The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all. Minimum value: 0 Maximum value: 1 Default value: 1
prime_effect (float, optional) – Specifies the priming effect on [acquisition/transmission/mortality] immunity for naive individuals (without natural or vaccine-derived immunity). Minimum value: 0 Maximum value: 1 Default value: 1
efficacy_is_multiplicative (bool, optional) – The overall vaccine efficacy when individuals receive more than one vaccine. When set to True, the vaccine efficacies are multiplied together; when set to False, the efficacies are additive. Default value: True
boost_threshold (float, optional) – Specifies how much immunity is required before the vaccine changes from a priming effect to a boosting effect. Minimum value: 0 Maximum value: 1 Default value: 0
boost_effect (float, optional) – Specifies the boosting effect on [acquisition/transmission/mortality] immunity for previously exposed individuals (either natural or vaccine-derived). This does not replace current immunity, it builds multiplicatively on top of it. Minimum value: 0 Maximum value: 1 Default value: 1
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 5 common parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates. Default value: None

class emodpy.campaign.individual_intervention.SimpleVaccine(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, waning_config: ~emodpy.campaign.waning_config.AbstractWaningConfig, vaccine_type: ~emodpy.utils.emod_enum.VaccineType = VaccineType.Generic, vaccine_take: float = 1, efficacy_is_multiplicative: bool = True, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The SimpleVaccine intervention class implements vaccine campaigns in the simulation. Vaccines can have an effect on one of the following:

Reduce the likelihood of acquiring an infection

Reduce the likelihood of transmitting an infection

Reduce the likelihood of death

To configure vaccines that have an effect on more than one of these, use MultiEffectVaccine instead.

Parameters:

campaign (api_campaign, required) – An instance of the emod_api.campaign module.
waning_config (AbstractWaningConfig, required) – The configuration of the vaccine’s efficacy and waning over time. Specify how this effect decays over time using one of the Waning Config classes in emodpy.campaign.waning_config.
vaccine_type (VaccineType, optional) – The type of vaccine to distribute in a vaccine intervention. Possible values are: * Generic - The vaccine can reduce transmission, acquisition, and mortality. * TransmissionBlocking - The vaccine will reduce pathogen transmission. * AcquisitionBlocking - The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. * MortalityBlocking - The vaccine reduces the disease-mortality rate of a vaccinated individual. Default value: Generic
vaccine_take (float, optional) – The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all. Minimum value: 0 Maximum value: 1 Default value: 1
efficacy_is_multiplicative (bool, optional) – The overall vaccine efficacy when individuals receive more than one vaccine. When set to True, the vaccine efficacies are multiplied together; when set to False, the efficacies are additive. Default value: True
common_intervention_parameters (CommomInterventionParameters, optional) – The CommonInterventionParameters object that Additional parameters that contains the 5 common parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates. Default value: None

class emodpy.campaign.individual_intervention.StandardDiagnostic(campaign: <module 'emod_api.campaign' from '/home/docs/checkouts/readthedocs.org/user_builds/institute-for-disease-modeling-emodpy/envs/latest/lib/python3.9/site-packages/emod_api/campaign.py'>, positive_diagnosis_config: ~emodpy.campaign.base_intervention.IndividualIntervention | None = None, negative_diagnosis_config: ~emodpy.campaign.base_intervention.IndividualIntervention | None = None, positive_diagnosis_event: str | None = None, negative_diagnosis_event: str | None = None, treatment_fraction: float = 1, enable_is_symptomatic: bool = False, days_to_diagnosis: float = 0, base_specificity: float = 1, base_sensitivity: float = 1, common_intervention_parameters: ~emodpy.campaign.common.CommonInterventionParameters | None = None)[source]#

Bases: IndividualIntervention

The StandardDiagnostic intervention class identifies infected individuals, regardless of disease state, based on specified diagnostic sensitivity and specificity. Diagnostics are a key component of modern disease control efforts, whether used to identify high-risk individuals, infected individuals, or drug resistance. The individual receives the diagnostic test immediately when the intervention is distributed, but there may be a delay in receiving a positive result. This intervention class distributes a specified intervention to a fraction of individuals who test positive.

You can use either the XXX_diagnosis_config or XXX_diagnosis_event parameters, but not both.

You must specifiy a response for a postive diagnosis, but not for a negative diagnosis.

Parameters:

campaign (api_campaign, required) –
- An instance of the emod_api.campaign module.
positive_diagnosis_config (IndividualIntervention, optional) –
- The intervention distributed to individuals if they test positive.
- Must be defined if not using postive_diagnosis_event.
- Cannot have both positive_diagnosis_config and positive_diagnosis_event.
- Default value: None
negative_diagnosis_config (IndividualIntervention, optional) –
- The intervention distributed to individuals if they test negative.
- If using postive_diagnosis_config, you can use negative_diagnosis_config, but not negative_diagnosis_event.
- Can use this or negative_diagnosis_event, but not both.
- Default value: None
positive_diagnosis_event (str, optional) –
- If the test is positive, this specifies an event that can trigger another intervention when the event occurs.
- Must be defined if not using positive_diagnosis_config.
- Cannot have both positive_diagnosis_config and positive_diagnosis_event.
- For HIV, see Event list, and for malaria, Event list for events already used in EMOD or use your own custom event.
- Default value: None
negative_diagnosis_event (str, optional) –
- This parameter defines the event to be broadcasted on a negative test result.
- Cannot have both negative_diagnosis_config and negative_diagnosis_event.
- if using positive_diagnosis_config, you can use negative_diagnosis_config, but not negative_diagnosis_event.
- For HIV, see Event list, and for malaria, Event list for events already used in EMOD or use your own custom event.
- Default value: None
treatment_fraction (float, optional) –
- The fraction of positive diagnoses that are given the positive_diagnosis_config or positive_diagnosis_event, whichever is defined. This does not affect the distribution of the negative diagnosis.
- Minimum value: 0
- Maximum value: 1
- Default value: 1
enable_is_symptomatic (bool, optional) –
- If True, requires an infection to be symptomatic to return a positive test.
- Default value: True
days_to_diagnosis (float, optional) –
- The number of days from the test, which is done when the intervention is distributed, until the positive_diagnosis_config or positive_diagnosis_event (whichever is defined) are distributed if the test had a positive diagnosis. The negative_diagnosis_config or negative_diagnosis_event is distributed immediately if the test is negative.
- Minimum value: 0
- Maximum value: 3.40282e+38
- Default value: 0
base_specificity (float, optional) –
- The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.
- Minimum value: 0
- Maximum value: 1
- Default value: 1
base_sensitivity (float, optional) –
- The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to 1, the diagnostic always accurately reflects the condition. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.
- Minimum value: 0
- Maximum value: 1
- Default value: 1
common_intervention_parameters (CommomInterventionParameters, optional) –
- The CommonInterventionParameters object that contains the 5 common additional parameters: cost, new_property_value, intervention_name, disqualifying_properties, dont_allow_duplicates.
- Default value: None