synthpops.contacts module

Generate contacts between people in the population, with many options possible.

synthpops.contacts.make_popdict(n=None, uids=None, ages=None, sexes=None, location=None, state_location=None, country_location=None, use_demography=False, id_len=6)

Create a dictionary of n people with age, sex and loc keys

Parameters

  • n (int) – number of people

  • uids (list) – supplied uids of individuals

  • ages (list) – supplied ages of individuals

  • sexes (list) – supplied sexes of individuals

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • use_demography (bool) – If True, use demographic data

  • id_len (int) – length of the uid

Returns

A dictionary where keys are the uid of each person and the values are another dictionary containing values for other attributes of the person

synthpops.contacts.make_contacts_generic(popdict, network_distr_args)

Create contact network regardless of age, according to network distribution properties. Can be used by webapp.

Parameters

  • popdict (dict) – dictionary of all individuals

  • network_distr_args (dict) – network distribution parameters dictionary for average_degree, network_type, and directionality

Returns

A dictionary of individuals with contacts drawn from given network distribution parameters.

synthpops.contacts.make_contacts_without_social_layers_152(popdict, n_contacts_dic, location, state_location, country_location, sheet_name, network_distr_args)

Create contact network according to overall age-mixing contact matrices. Does not capture clustering or microstructure, therefore exact households, schools, or workplaces are not created. However, this does separate agents according to their age and gives them contacts likely for their age. For all ages, the average number of contacts is constant with this method, although location specific data may prove this to not be true.

Parameters

  • popdict (dict) – dictionary of all individuals

  • n_contacts_dic (dict) – number of contacts to draw on average by setting

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • sheet_name (string) – name of the sheet in the excel file with contact patterns

  • network_distr_args (dict) – network distribution parameters dictionary for average_degree, network_type, and directionality, can also include powerlaw exponents, block sizes (re: SBMs), clustering distribution, or other properties needed to generate network structures. Checkout https://networkx.github.io/documentation/stable/reference/generators.html#module-networkx.generators for what’s possible Default ‘network_type’ is ‘poisson_degree’ for Erdos-Renyi random graphs in large n limit.

Returns

A dictionary of individuals with attributes, including their age and the ids of their contacts drawn from given network distribution parameters and the ages of contacts drawn according to overall age mixing data. A single social setting or layer of contacts.

synthpops.contacts.make_contacts_with_social_layers_152(popdict, n_contacts_dic, location, state_location, country_location, sheet_name, activity_args, network_distr_args)

Create contact network according to overall age-mixing contact matrices. Does not capture clustering or microstructure, therefore exact households, schools, or workplaces are not created. However, this does separate agents according to their age and gives them contacts likely for their age specified by the social settings they are likely to participate in. For all ages, the average number of contacts is constant with this method, although location specific data very wel may prove this to not be true. In general, college students may also be workers, however here they are only students and we assume that any of their contacts in the work environment are likely to look like their contacts at school.

Essentially recreates an age-specific compartmental model’s concept of contacts but for an agent based modeling framework.

Parameters

  • popdict (dict) – dictionary of all individuals

  • n_contacts_dic (dict) – number of contacts to draw on average by setting

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • sheet_name (string) – name of the sheet in the excel file with contact patterns

  • activity_args (dict) – dictionary of age bounds for participating in different activities like going to school or working, also student-teacher ratio

  • network_distr_args (dict) – network distribution parameters dictionary for average_degree, network_type, and directionality, can also include powerlaw exponents, block sizes (re: SBMs), clustering distribution, or other properties needed to generate network structures. Checkout https://networkx.github.io/documentation/stable/reference/generators.html#module-networkx.generators for what’s possible Default ‘network_type’ is ‘poisson_degree’ for Erdos-Renyi random graphs in large n limit.

Returns

A dictionary of individuals with contacts with attributes, including their age and the ids of their contacts drawn from given network distribution parameters and the ages of contacts drawn according to age mixing data. Multiple social settings or layers so contacts are listed for different layers.

synthpops.contacts.make_contacts_without_social_layers_and_sex(popdict, n_contacts_dic, location, state_location, country_location, sheet_name, network_distr_args)

Create contact network according to overall age-mixing contact matrices for the US. Does not capture clustering or microstructure, therefore exact households, schools, or workplaces are not created. However, this does separate agents according to their age and gives them contacts likely for their age. For all ages, the average number of contacts is constant, although location specific data may prove this to not be true. Individuals also have a sex, though this in general does not have an impact on their contact patterns.

Parameters

  • popdict (dict) – dictionary of all individuals

  • n_contacts_dic (dict) – number of contacts to draw on average by setting

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • sheet_name (string) – name of the sheet in the excel file with contact patterns

  • network_distr_args (dict) – network distribution parameters dictionary for average_degree, network_type, and directionality, can also include powerlaw exponents, block sizes (re: SBMs), clustering distribution, or other properties needed to generate network structures. Checkout https://networkx.github.io/documentation/stable/reference/generators.html#module-networkx.generators for what’s possible Default ‘network_type’ is ‘poisson_degree’ for Erdos-Renyi random graphs in large n limit.

Returns

A dictionary of individuals with attributes, including their age and the ids of their contacts drawn from given network distribution parameters and the ages of contacts drawn according to overall age mixing data. A single social setting or layer of contacts.

synthpops.contacts.make_contacts_with_social_layers_and_sex(popdict, n_contacts_dic, location, state_location, country_location, sheet_name, activity_args, network_distr_args)

Create contact network according to overall age-mixing contact matrices for the US. Does not capture clustering or microstructure, therefore exact households, schools, or workplaces are not created. However, this does separate agents according to their age and gives them contacts likely for their age specified by the social settings they are likely to participate in. College students may also be workers, however here they are only students and we assume that any of their contacts in the work environment are likely to look like their contacts at school.

Parameters

  • popdict (dict) – dictionary of all individuals

  • n_contacts_dic (dict) – number of contacts to draw on average by setting

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • sheet_name (string) – name of the sheet in the excel file with contact patterns

  • activity_args (dict) – dictionary of age bounds for participating in different activities like going to school or working, also student-teacher ratio

  • network_distr_args (dict) – network distribution parameters dictionary for average_degree, network_type, and directionality, can also include powerlaw exponents, block sizes (re: SBMs), clustering distribution, or other properties needed to generate network structures. Checkout https://networkx.github.io/documentation/stable/reference/generators.html#module-networkx.generators for what’s possible Default ‘network_type’ is ‘poisson_degree’ for Erdos-Renyi random graphs in large n limit.

Returns

A dictionary of individuals with attributes, including their age and the ids of their contacts drawn from given network distribution parameters and the ages of contacts drawn according to age mixing data. Multiple social settings or layers so contacts are listed for different layers.

synthpops.contacts.rehydrate(data)

Populate popdict with uids, ages and contacts from generated microstructure data that was saved to data object

Parameters

data (pop object) –

Returns

Popdict (sc.objdict)

synthpops.contacts.save_synthpop(datadir, contacts, location)

Save pop data object to file.

Parameters

  • datadir (string) – file path to the data directory

  • contacts (dict) – dictionary of people with contacts

  • location (string) – name of the location

Returns

None

synthpops.contacts.create_reduced_contacts_with_group_types(popdict, group_1, group_2, setting, average_degree=20, p_matrix=None, force_cross_edges=True)

Create contacts between members of group 1 and group 2, fixing the average degree, and the probability of an edge between any two groups controlled by p_matrix if provided. Forces inter group edge for each individual in group 1 with force_cross_groups equal to True. This means not everyone in group 2 will have a contact with group 1.

Parameters

  • group_1 (list) – list of ids for group 1

  • group_2 (list) – list of ids for group 2

  • average_degree (int) – average degree across group 1 and 2

  • p_matrix (np.ndarray) – probability matrix for edges between any two groups

  • force_cross_groups (bool) – If True, force each individual to have at least one contact with a member from the other group

Returns

Popdict with edges added for nodes in the two groups.

Notes

This method uses the Stochastic Block Model algorithm to generate contacts both between nodes in different groups

and for nodes within the same group. In the current version, fixing the average degree and p_matrix, the matrix of probabilities for edges between any two groups is not supported. Future versions may add support for this.

synthpops.contacts.make_contacts_from_microstructure(datadir, location, state_location, country_location, n, with_non_teaching_staff=True, with_school_types=False, school_mixing_type='random', average_class_size=20, inter_grade_mixing=0.1, average_student_teacher_ratio=20, average_teacher_teacher_degree=3, average_student_all_staff_ratio=15, average_additional_staff_degree=20, school_type_by_age=None, with_industry_code=False, verbose=False)

Make a popdict from synthetic household, school, and workplace files with uids. If with_industry_code is True, then individuals will have a workplace industry code as well (default value is -1 to represent that this data is unavailable). Currently, industry codes are only available to assign to populations within the US.

Parameters

  • datadir (string) – The file path to the data directory

  • location (string) – The name of the location

  • state_location (string) – The name of the state the location is in

  • country_location (string) – The name of the country the location is in

  • n (int) – The number of people in the population

  • with_non_teaching_staff (bool) – If True, includes non teaching staff.

  • with_school_types (bool) – If True, creates explicit school types.

  • school_mixing_type (str or dict) – The mixing type for schools, ‘random’, ‘age_clustered’, or ‘age_and_class_clustered’ if string, and a dictionary of these by school type otherwise. ‘random’ means random graphs for each school, ‘age_clustered’ means random graphs but with students mostly mixing within the age/grade (inter_grade_mixing controls mixing between grades), ‘age_and_grade_clustered’ means students cohorted into classes with their own teachers.

  • average_class_size (float) – The average classroom size.

  • inter_grade_mixing (float) – The average fraction of mixing between grades in the same school for clustered school mixing types.

  • average_student_teacher_ratio (float) – The average number of students per teacher.

  • average_teacher_teacher_degree (float) – The average number of contacts per teacher with other teachers.

  • average_student_all_staff_ratio (float) – The average number of students per staff members at school (including both teachers and non teachers).

  • average_additional_staff_degree (float) – The average number of contacts per additional non teaching staff in schools.

  • with_industry_code (bool) – If True, assign workplace industry code read in from cached file

  • verbose (bool) – If True, print debugging statements.

Returns

A popdict of people with attributes. Dictionary keys are the IDs of individuals in the population and the values are a dictionary for each individual with their attributes, such as age, household ID (hhid), school ID (scid), workplace ID (wpid), workplace industry code (wpindcode) if available, and the IDs of their contacts in different layers. Different layers available are households (‘H’), schools (‘S’), and workplaces (‘W’). Contacts in these layers are clustered and thus form a network composed of groups of people interacting with each other. For example, all household members are contacts of each other, and everyone in the same school is considered a contact of each other.

Notes

Methods to trim large groups of contacts down to better approximate a sense of close contacts (such as classroom sizes or smaller work groups are available via sp.trim_contacts() - see below).

synthpops.contacts.make_contacts_from_microstructure_objects(age_by_uid_dic, homes_by_uids, schools_by_uids, teachers_by_uids, workplaces_by_uids, non_teaching_staff_uids=None, with_school_types=False, school_mixing_type='random', average_class_size=20, inter_grade_mixing=0.1, average_student_teacher_ratio=20, average_teacher_teacher_degree=3, average_student_all_staff_ratio=15, average_additional_staff_degree=20, school_type_by_age=None, workplaces_by_industry_codes=None, verbose=False)

From microstructure objects (dictionary mapping ID to age, lists of lists in different settings, etc.), create a dictionary of individuals. Each key is the ID of an individual which maps to a dictionary for that individual with attributes such as their age, household ID (hhid), school ID (scid), workplace ID (wpid), workplace industry code (wpindcode) if available, and contacts in different layers.

Parameters

  • age_by_uid_dic (dict) – A dictionary mapping id to age for all individuals in the population

  • homes_by_uids (list) – A list of lists, where each sublist is a household and the IDs of the household members.

  • schools_by_uids (list) – A list of lists, where each sublist represents a school and the ids of the students within it

  • teachers_by_uids (list) – A list of lists, where each sublist represents a school and the ids of the teachers within it

  • workplaces_by_uids (list) – A list of lists, where each sublist represents a workplace and the ids of the workers within it

  • non_teaching_staff_uids (list) – None or a list of lists, where each sublist represents a school and the ids of the non teaching staff within it

  • with_school_types (bool) – If True, creates explicit school types.

  • school_mixing_type (str or dict) – The mixing type for schools, ‘random’, ‘age_clustered’, or ‘age_and_class_clustered’ if string, and a dictionary of these by school type otherwise. ‘random’ means random graphs for each school, ‘age_clustered’ means random graphs but with students mostly mixing within the age/grade (inter_grade_mixing controls mixing between grades), ‘age_and_grade_clustered’ means students cohorted into classes with their own teachers.

  • average_class_size (float) – The average classroom size.

  • inter_grade_mixing (float) – The average fraction of mixing between grades in the same school for clustered school mixing types.

  • average_student_teacher_ratio (float) – The average number of students per teacher.

  • average_teacher_teacher_degree (float) – The average number of contacts per teacher with other teachers.

  • average_student_all_staff_ratio (float) – The average number of students per staff members at school (including both teachers and non teachers).

  • average_additional_staff_degree (float) – The average number of contacts per additional non teaching staff in schools.

  • school_type_by_age (dict) – A dictionary of probabilities for the school type likely for each age.

  • workplaces_by_industry_codes (np.ndarray or None) – array with workplace industry code for each workplace

  • verbose (bool) – If True, print debugging statements.

Returns

A popdict of people with attributes. Dictionary keys are the IDs of individuals in the population and the values are a dictionary for each individual with their attributes, such as age, household ID (hhid), school ID (scid), workplace ID (wpid), workplace industry code (wpindcode) if available, and the IDs of their contacts in different layers. Different layers available are households (‘H’), schools (‘S’), and workplaces (‘W’). Contacts in these layers are clustered and thus form a network composed of groups of people interacting with each other. For example, all household members are contacts of each other, and everyone in the same school is considered a contact of each other.

Notes

Methods to trim large groups of contacts down to better approximate a sense of close contacts (such as classroom sizes or smaller work groups are available via sp.trim_contacts() - see below).

synthpops.contacts.make_contacts_with_facilities_from_microstructure(datadir, location, state_location, country_location, n, use_two_group_reduction=False, average_LTCF_degree=20, with_non_teaching_staff=True, with_school_types=False, school_mixing_type='random', average_class_size=20, inter_grade_mixing=0.1, average_student_teacher_ratio=20, average_teacher_teacher_degree=3, average_student_all_staff_ratio=15, average_additional_staff_degree=20, school_type_by_age=None, verbose=False)

Make a popdict from synthetic household, school, and workplace files with uids. If with_industry_code is True, then individuals will have a workplace industry code as well (default value is -1 to represent that this data is unavailable). Currently, industry codes are only available to assign to populations within the US.

Parameters

  • datadir (string) – file path to the data directory

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • n (int) – number of people in the population

  • use_two_group_reduction (bool) – If True, create long term care facilities with reduced contacts across both groups

  • average_LTCF_degree (int) – default average degree in long term care facilities

  • with_non_teaching_staff (bool) – If True, includes non teaching staff.

  • with_school_types (bool) – If True, creates explicit school types.

  • school_mixing_type (str or dict) – The mixing type for schools, ‘random’, ‘age_clustered’, or ‘age_and_class_clustered’ if string, and a dictionary of these by school type otherwise. ‘random’ means random graphs for each school, ‘age_clustered’ means random graphs but with students mostly mixing within the age/grade (inter_grade_mixing controls mixing between grades), ‘age_and_grade_clustered’ means students cohorted into classes with their own teachers.

  • average_class_size (float) – The average classroom size.

  • inter_grade_mixing (float) – The average fraction of mixing between grades in the same school for clustered school mixing types.

  • average_student_teacher_ratio (float) – The average number of students per teacher.

  • average_teacher_teacher_degree (float) – The average number of contacts per teacher with other teachers.

  • average_student_all_staff_ratio (float) – The average number of students per staff members at school (including both teachers and non teachers).

  • average_additional_staff_degree (float) – The average number of contacts per additional non teaching staff in schools.

  • school_type_by_age (dict) – A dictionary of probabilities for the school type likely for each age.

  • verbose (bool) – If True, print debugging statements.

Returns

A popdict of people with attributes. Dictionary keys are the IDs of individuals in the population and the values are a dictionary for each individual with their attributes, such as age, household ID (hhid), school ID (scid), workplace ID (wpid), workplace industry code (wpindcode) if available, and the IDs of their contacts in different layers. Different layers available are households (‘H’), schools (‘S’), and workplaces (‘W’), and long term care facilities (‘LTCF’). Contacts in these layers are clustered and thus form a network composed of groups of people interacting with each other. For example, all household members are contacts of each other, and everyone in the same school is considered a contact of each other. If use_two_group_reduction is True, then contracts within ‘LTCF’ are reduced from fully connected.

Notes

Methods to trim large groups of contacts down to better approximate a sense of close contacts (such as classroom sizes or smaller work groups are available via sp.trim_contacts() or sp.create_reduced_contacts_with_group_types(): see these methods for more details).

synthpops.contacts.make_contacts_with_facilities_from_microstructure_objects(age_by_uid_dic, homes_by_uids, schools_by_uids, teachers_by_uids, workplaces_by_uids, facilities_by_uids, facilities_staff_uids, non_teaching_staff_uids=None, use_two_group_reduction=False, average_LTCF_degree=20, with_school_types=False, school_mixing_type='random', average_class_size=20, inter_grade_mixing=0.1, average_student_teacher_ratio=20, average_teacher_teacher_degree=3, average_student_all_staff_ratio=15, average_additional_staff_degree=20, school_type_by_age=None, workplaces_by_industry_codes=None, verbose=False)

From microstructure objects (dictionary mapping ID to age, lists of lists in different settings, etc.), create a dictionary of individuals. Each key is the ID of an individual which maps to a dictionary for that individual with attributes such as their age, household ID (hhid), school ID (scid), workplace ID (wpid), workplace industry code (wpindcode) if available, and contacts in different layers.

Parameters

  • age_by_uid_dic (dict) – dictionary mapping id to age for all individuals in the population

  • homes_by_uids (list) – A list of lists where each sublist is a household and the IDs of the household members.

  • schools_by_uids (list) – A list of lists, where each sublist represents a school and the ids of the students and teachers within it

  • teachers_by_uids (list) – A list of lists, where each sublist represents a school and the ids of the teachers within it

  • workplaces_by_uids (list) – A list of lists, where each sublist represents a workplace and the ids of the workers within it

  • facilities_by_uids (list) – A list of lists, where each sublist represents a skilled nursing or long term care facility and the ids of the residents living within it

  • facilities_staff_uids (list) – A list of lists, where each sublist represents a skilled nursing or long term care facility and the ids of the staff working within it

  • non_teaching_staff_uids (list) – None or a list of lists, where each sublist represents a school and the ids of the non teaching staff within it

  • use_two_group_reduction (bool) – If True, create long term care facilities with reduced contacts across both groups

  • average_LTCF_degree (int) – default average degree in long term care facilities

  • with_school_types (bool) – If True, creates explicit school types.

  • school_mixing_type (str or dict) – The mixing type for schools, ‘random’, ‘age_clustered’, or ‘age_and_class_clustered’ if string, and a dictionary of these by school type otherwise. ‘random’ means random graphs for each school, ‘age_clustered’ means random graphs but with students mostly mixing within the age/grade (inter_grade_mixing controls mixing between grades), ‘age_and_grade_clustered’ means students cohorted into classes with their own teachers.

  • average_class_size (float) – The average classroom size.

  • inter_grade_mixing (float) – The average fraction of mixing between grades in the same school for clustered school mixing types.

  • average_student_teacher_ratio (float) – The average number of students per teacher.

  • average_teacher_teacher_degree (float) – The average number of contacts per teacher with other teachers.

  • average_student_all_staff_ratio (float) – The average number of students per staff members at school (including both teachers and non teachers).

  • average_additional_staff_degree (float) – The average number of contacts per additional non teaching staff in schools.

  • school_type_by_age (dict) – A dictionary of probabilities for the school type likely for each age.

  • workplaces_by_industry_codes (np.ndarray or None) – array with workplace industry code for each workplace

  • verbose (bool) – If True, print debugging statements.

Returns

A popdict of people with attributes. Dictionary keys are the IDs of individuals in the population and the values are a dictionary for each individual with their attributes, such as age, household ID (hhid), school ID (scid), workplace ID (wpid), workplace industry code (wpindcode) if available, and the IDs of their contacts in different layers. Different layers available are households (‘H’), schools (‘S’), and workplaces (‘W’), and long term care facilities (‘LTCF’). Contacts in these layers are clustered and thus form a network composed of groups of people interacting with each other. For example, all household members are contacts of each other, and everyone in the same school is considered a contact of each other. If use_two_group_reduction is True, then contracts within ‘LTCF’ are reduced from fully connected.

Notes

Methods to trim large groups of contacts down to better approximate a sense of close contacts (such as classroom sizes or smaller work groups are available via sp.trim_contacts() or sp.create_reduced_contacts_with_group_types(): see these methods for more details).

synthpops.contacts.make_graphs(popdict, layers)

Make a dictionary of Networkx by layer.

Parameters

  • popdict (dict) – dictionary of individuals with attributes, including their age, household ID, school ID, workplace ID, and the ids of their contacts by layer

  • layers (list) – list of contact layers

Retuns:

Dictionary of Networkx graphs, one for each layer of contacts.

synthpops.contacts.write_edgelists(popdict, layers, G_dic=None, location=None, state_location=None, country_location=None)

Write edgelists for each layer of contacts.

Parameters

  • popdict (dict) – dictionary of individuals with attributes, including their age, household ID, school ID, workplace ID, and the ids of their contacts by layer

  • layers (list) – list of contact layers

  • G_dic (dict) – dictionary of Networkx graphs, one for each layer of contacts

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

Returns

None

synthpops.contacts.make_contacts(popdict=None, n_contacts_dic=None, location=None, state_location=None, country_location=None, sheet_name=None, options_args=None, activity_args=None, network_distr_args=None)

Generates a list of contacts for everyone in the population. popdict is a dictionary with N keys (one for each person), with subkeys for age, sex, location, and potentially other factors. This function adds a new subkey, contacts, which is a list of contact IDs for each individual. If directed=False (default), if person A is a contact of person B, then person B is also a contact of person A.

Example output (input is the same, minus the “contacts” field):

popdict = {
    '8acf08f0': {
        'age': 57,
        'sex': 0,
        'loc': (47.6062, 122.3321),
        'contacts': {'M': 2, 34}
        },
    '43da76b5': {
        'age': 55,
        'sex': 1,
        'loc': (47.2473, 122.6482),
        'contacts': {'M': 20, 8, 49}
        },
}
Parameters

  • popdict (dict) – dictionary, should have ages of individuals if not using cached microstructure data

  • n_contacts_dic (dict) – average number of contacts by setting

  • location (string) – name of the location

  • state_location (string) – name of the state the location is in

  • country_location (string) – name of the country the location is in

  • sheet_name (string) – name of the sheet in the excel file with contact patterns

  • options_args (dict) – dictionary of flags to set different population and contact generating options

  • activity_args (dict) – dictionary of age bounds for participating in different activities like going to school or working, also student-teacher ratio

  • network_distr_args (dict) – network distribution parameters dictionary for average_degree, network_type, and directionality, can also include powerlaw exponents, block sizes (re: SBMs), clustering distribution, or other properties needed to generate network structures. Checkout https://networkx.github.io/documentation/stable/reference/generators.html#module-networkx.generators for what’s possible Default ‘network_type’ is ‘poisson_degree’ for Erdos-Renyi random graphs in large n limit.

Returns

A dictionary of individuals with attributes, including their age and the ids of their contacts.

synthpops.contacts.choose_contacts(a, size)

Numbafy np.random.choice(); about twice as fast

synthpops.contacts.trim_contacts(contacts, trimmed_size_dic=None, use_clusters=False, verbose=False)

Trim down contacts in school or work environments from everyone.

Parameters

  • contacts (dict) – dictionary of individuals with attributes, including their age and the ids of their contacts

  • trimmed_size_dic (dict) – dictionary of threshold values for the number of contacts in school (‘S’) and work (‘W’) so that for individuals with more contacts than this, we select a smaller subset of contacts considerd close contacts

  • use_clusters (bool) – If True, trimmed down contact networks will preserve clustering so that an individual’s close contacts in school or at work are also contacts of each other

  • verbose (bool) – If True, print average number of close contacts in school and at work

Returns

A dictionary of individuals with attributes, including their age and the ids of their close contacts.

synthpops.contacts.show_layers(popdict, show_ages=False, show_n=20)

Print out the contacts for individuals in the different possible social settings or layers.

Parameters

  • popdict (dict) – dictionary of individuals with attributes, including their age and the ids of their close contacts

  • show_ages (bool) – If True, show the ages of contacts, else show their ids

  • show_n (int) – number of individuals to show contacts for

Returns

None