Skip to content

MalariaDemographics

This module contains the classes and functions for creating demographics files for malaria simulations. For more information on EMOD demographics files, see :doc:emod/software-demographics.

MalariaDemographics

Bases: Demographics

This class is derived from 🇵🇾class:emod_api:emod_api.demographics.Demographics.Demographics and sets certain defaults for malaria in construction.

Parameters:

Name Type Description Default
nodes int

The number of nodes to create.

 required
idref str

Method describing how the latitude and longitude values are created for each of the nodes in a simulation. "Gridded world" values use a grid overlaid across the globe at some arcsec resolution. You may also generate the grid using another tool or coordinate system. For more information, see :ref:demo-metadata.

 'Gridded world grump2.5arcmin'
base_file str

A basic demographics file used as a starting point for creating more complicated demographics files. For example, using a single node file to create a multi-node file for spatial simulations.

 None
init_prev float

The initial malaria prevalence of the population. Defaults to 0%.

 0.0
include_biting_heterogeneity bool

variable biting rates. Defaults to on.

 True
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
class MalariaDemographics(Demog.Demographics):
    """
    This class is derived from :py:class:`emod_api:emod_api.demographics.Demographics.Demographics`
    and sets certain defaults for malaria in construction.

    Args:
        nodes (int): The number of nodes to create.
        idref (str): Method describing how the latitude and longitude values are created
            for each of the nodes in a simulation. "Gridded world" values use a grid
            overlaid across the globe at some arcsec resolution. You may also generate
            the grid using another tool or coordinate system. For more information,
            see :ref:`demo-metadata`.
        base_file (str): A basic demographics file used as a starting point for
            creating more complicated demographics files. For example,
            using a single node file to create a multi-node file for spatial
            simulations.
        init_prev (float): The initial malaria prevalence of the population. Defaults to 0%.
        include_biting_heterogeneity (bool): variable biting rates. Defaults to on.
     """

    def __init__(self, nodes, idref="Gridded world grump2.5arcmin", base_file=None, init_prev=0.0,
                 include_biting_heterogeneity=True):
        super().__init__(nodes, idref, base_file)
        super().SetDefaultNodeAttributes(birth=True)
        if init_prev > 0:
            # Do constant intial prevalence as uniform with same min and max.
            super().SetInitPrevFromUniformDraw(init_prev, init_prev, f"Constant Initial Prevalence ({init_prev})")
        if include_biting_heterogeneity:
            self.set_risk_lowmedium()  # lognormal, default=1.6

    def set_risk_lowmedium(self):
        """
            Set initial risk for low-medium transmission settings per:
            https://wiki.idmod.org/display/MAL/Heterogeneous+biting+risk+in+simulations+vs+data.
        """
        super().SetHeteroRiskLognormalDist(mean=0.0, sigma=1.6)

    def set_risk_high(self):
        """
            Set initial risk for high transmission settings per:
            https://wiki.idmod.org/display/MAL/Heterogeneous+biting+risk+in+simulations+vs+data.
        """
        super().SetHeteroRiskExponDist(mean=1.0)  # 1.0 is placeholder

    def add_larval_habitat_multiplier(self, schema, hab_type, multiplier, species="ALL_SPECIES", node_id=0):
        """
            Add LarvalHabitatMultiplier to node(s).

            Args:
                schema (str): Path to schema.json.
                hab_type (str): Habitat type.
                multiplier (float): Multiplier or Factor.
                species (str): Specific species (defaults to ALL).
                node_id (int): Nodes for this LHM. Defaults to all.
        """

        lhm = dfs.schema_to_config_subnode(schema, ["idmTypes", "idmType:LarvalHabitatMultiplierSpec"])
        lhm.parameters.Factor = multiplier
        lhm.parameters.Habitat = hab_type
        lhm.parameters.Species = species
        lhm.parameters.finalize()

        # set params
        if node_id == 0:
            if "LarvalHabitatMultiplier" in self.raw['Defaults']['NodeAttributes']:
                lhm_dict = self.raw['Defaults']['NodeAttributes']["LarvalHabitatMultiplier"]
            else:
                lhm_dict = []
            lhm_dict.append(lhm.parameters)
            self.SetNodeDefaultFromTemplate({"LarvalHabitatMultiplier": lhm_dict}, setter_fn=None)
        else:
            if self.get_node_by_id(node_id).node_attributes.larval_habitat_multiplier:
                lhm_dict = self.get_node_by_id(node_id).node_attributes.larval_habitat_multiplier
            else:
                lhm_dict = []
            lhm_dict.append(lhm.parameters)
            self.get_node_by_id(node_id).node_attributes.larval_habitat_multiplier = lhm_dict

    def add_initial_vectors_per_species(self, init_vector_species, node_ids=None):
        """
        Add an InitialVectorsForSpecies configuration for all nodes or just a set of nodes.

        Args:
            init_vector_species (dict): Dictionary of vector species (strings) to initial populations. There is no
                checking for coherence of species named in other input settings.
            node_ids (Union[None, list[int]]): Array of node ids. Defaults to None for all nodes.
        """
        if node_ids is None:
            ivs_dict = dict()
            ivs_dict["InitialVectorsPerSpecies"] = init_vector_species
            self.SetNodeDefaultFromTemplate(ivs_dict, setter_fn=None)
        else:
            for node_id in node_ids:
                self.get_node_by_id(node_id).node_attributes.add_parameter("InitialVectorsPerSpecies", init_vector_species)

        # no implicits

    def add_initial_vectors_per_species_from_csv(self, csv_path):
        """
            Add initial vector species population to 'demographics' nodes from a csv file.

        Args:
            csv_path (str): Path to CSV file with the initial vector species populations for each node.
        """
        import csv
        if not os.path.exists(csv_path):
            raise ValueError(f"File not found at {csv_path}.")

        with open(csv_path) as csv_file:
            reader = csv.DictReader(csv_file)
            for line in reader:
                # collect all the initial vector species values for a given node.
                node = int(line["node_id"])
                ivps = dict()
                for species in line:
                    if species != "node_id":
                        ivps[species] = int(line[species])
                self.add_initial_vectors_per_species(ivps, [node])

    def set_innate_immune_distribution(self, distribution_flag: int = 0, param1: float = 0.0, param2: float = 0.0):
        """
        Set the innate immune distribution for individuals in all nodes.

        Args:
            distribution_flag (int): Integer flag to select the distribution type.
                0 (Constant, everyone in the population has the same innate immune coefficient.)
                1 (Uniform, innate immune coefficient is randomly drawn between a minimum and maximum value.)
                2 (Gaussian)
                3 (Exponential)
                4 (Poisson)
                5 (Log normal)
                6 (Bimodal, non-continuous with some individuals having a innate immune coefficient of 1 and others a user-defined innate immune coefficient.)
                7 (Weibull)
            param1 (int): First parameter for the distribution (meaning depends on distribution type).
                0, Innate immune coefficient value to assign.
                1, Minimum innate immune coefficient for a uniform distribution.
                2, Mean innate immune coefficient for a Gaussian distribution.
                3, Exponential decay rate.
                4, Mean innate immune coefficient for a Poisson distribution.
                5, Mu (the mean of the natural log) for a log normal distribution.
                6, ""Proportion of individuals in the second, user-defined innate immune coefficient bin vs. the first innate immune coefficient bin (value of 1) for a bimodal distribution. Must be between 0 and 1.""
                7, Scale parameter for a Weibull distribution.
            param2 (int): Second parameter for the distribution (meaning depends on distribution type).
                0, NA, set to 0.
                1, Maximum innate immune coefficient for a uniform distribution.
                2, Standard deviation in innate immune coefficient for a Gaussian distribution.
                3, NA, set to 0.
                4, NA, set to 0.
                5, Sigma (the standard deviation of the natural log) for a log normal distribution.
                6, The innate immune coefficient for individuals in the second innate immune coefficient bin for a bimodal distribution.
                7, Shape parameter for a Weibull distribution.
        """
        innate_immune_distribution = {"InnateImmuneDistributionFlag": distribution_flag,
                                      "InnateImmuneDistribution1": param1,
                                      "InnateImmuneDistribution2": param2}
        self.SetDefaultFromTemplate(template=innate_immune_distribution, setter_fn=None)

add_initial_vectors_per_species(init_vector_species, node_ids=None)

Add an InitialVectorsForSpecies configuration for all nodes or just a set of nodes.

Parameters:

Name Type Description Default
init_vector_species dict

Dictionary of vector species (strings) to initial populations. There is no checking for coherence of species named in other input settings.

 required
node_ids Union[None, list[int]]

Array of node ids. Defaults to None for all nodes.

 None
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
def add_initial_vectors_per_species(self, init_vector_species, node_ids=None):
    """
    Add an InitialVectorsForSpecies configuration for all nodes or just a set of nodes.

    Args:
        init_vector_species (dict): Dictionary of vector species (strings) to initial populations. There is no
            checking for coherence of species named in other input settings.
        node_ids (Union[None, list[int]]): Array of node ids. Defaults to None for all nodes.
    """
    if node_ids is None:
        ivs_dict = dict()
        ivs_dict["InitialVectorsPerSpecies"] = init_vector_species
        self.SetNodeDefaultFromTemplate(ivs_dict, setter_fn=None)
    else:
        for node_id in node_ids:
            self.get_node_by_id(node_id).node_attributes.add_parameter("InitialVectorsPerSpecies", init_vector_species)

add_initial_vectors_per_species_from_csv(csv_path) 

Add initial vector species population to 'demographics' nodes from a csv file.

Parameters:

Name Type Description Default
csv_path str

Path to CSV file with the initial vector species populations for each node.

 required
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
def add_initial_vectors_per_species_from_csv(self, csv_path):
    """
        Add initial vector species population to 'demographics' nodes from a csv file.

    Args:
        csv_path (str): Path to CSV file with the initial vector species populations for each node.
    """
    import csv
    if not os.path.exists(csv_path):
        raise ValueError(f"File not found at {csv_path}.")

    with open(csv_path) as csv_file:
        reader = csv.DictReader(csv_file)
        for line in reader:
            # collect all the initial vector species values for a given node.
            node = int(line["node_id"])
            ivps = dict()
            for species in line:
                if species != "node_id":
                    ivps[species] = int(line[species])
            self.add_initial_vectors_per_species(ivps, [node])

add_larval_habitat_multiplier(schema, hab_type, multiplier, species='ALL_SPECIES', node_id=0)

Add LarvalHabitatMultiplier to node(s).

Parameters:

Name Type Description Default
schema str

Path to schema.json.

 required
hab_type str

Habitat type.

 required
multiplier float

Multiplier or Factor.

 required
species str

Specific species (defaults to ALL).

 'ALL_SPECIES'
node_id int

Nodes for this LHM. Defaults to all.

 0
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
def add_larval_habitat_multiplier(self, schema, hab_type, multiplier, species="ALL_SPECIES", node_id=0):
    """
        Add LarvalHabitatMultiplier to node(s).

        Args:
            schema (str): Path to schema.json.
            hab_type (str): Habitat type.
            multiplier (float): Multiplier or Factor.
            species (str): Specific species (defaults to ALL).
            node_id (int): Nodes for this LHM. Defaults to all.
    """

    lhm = dfs.schema_to_config_subnode(schema, ["idmTypes", "idmType:LarvalHabitatMultiplierSpec"])
    lhm.parameters.Factor = multiplier
    lhm.parameters.Habitat = hab_type
    lhm.parameters.Species = species
    lhm.parameters.finalize()

    # set params
    if node_id == 0:
        if "LarvalHabitatMultiplier" in self.raw['Defaults']['NodeAttributes']:
            lhm_dict = self.raw['Defaults']['NodeAttributes']["LarvalHabitatMultiplier"]
        else:
            lhm_dict = []
        lhm_dict.append(lhm.parameters)
        self.SetNodeDefaultFromTemplate({"LarvalHabitatMultiplier": lhm_dict}, setter_fn=None)
    else:
        if self.get_node_by_id(node_id).node_attributes.larval_habitat_multiplier:
            lhm_dict = self.get_node_by_id(node_id).node_attributes.larval_habitat_multiplier
        else:
            lhm_dict = []
        lhm_dict.append(lhm.parameters)
        self.get_node_by_id(node_id).node_attributes.larval_habitat_multiplier = lhm_dict

set_innate_immune_distribution(distribution_flag=0, param1=0.0, param2=0.0)

Set the innate immune distribution for individuals in all nodes.

Parameters:

Name Type Description Default
distribution_flag int

Integer flag to select the distribution type. 0 (Constant, everyone in the population has the same innate immune coefficient.) 1 (Uniform, innate immune coefficient is randomly drawn between a minimum and maximum value.) 2 (Gaussian) 3 (Exponential) 4 (Poisson) 5 (Log normal) 6 (Bimodal, non-continuous with some individuals having a innate immune coefficient of 1 and others a user-defined innate immune coefficient.) 7 (Weibull)

 0
param1 int

First parameter for the distribution (meaning depends on distribution type). 0, Innate immune coefficient value to assign. 1, Minimum innate immune coefficient for a uniform distribution. 2, Mean innate immune coefficient for a Gaussian distribution. 3, Exponential decay rate. 4, Mean innate immune coefficient for a Poisson distribution. 5, Mu (the mean of the natural log) for a log normal distribution. 6, ""Proportion of individuals in the second, user-defined innate immune coefficient bin vs. the first innate immune coefficient bin (value of 1) for a bimodal distribution. Must be between 0 and 1."" 7, Scale parameter for a Weibull distribution.

 0.0
param2 int

Second parameter for the distribution (meaning depends on distribution type). 0, NA, set to 0. 1, Maximum innate immune coefficient for a uniform distribution. 2, Standard deviation in innate immune coefficient for a Gaussian distribution. 3, NA, set to 0. 4, NA, set to 0. 5, Sigma (the standard deviation of the natural log) for a log normal distribution. 6, The innate immune coefficient for individuals in the second innate immune coefficient bin for a bimodal distribution. 7, Shape parameter for a Weibull distribution.

 0.0
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
def set_innate_immune_distribution(self, distribution_flag: int = 0, param1: float = 0.0, param2: float = 0.0):
    """
    Set the innate immune distribution for individuals in all nodes.

    Args:
        distribution_flag (int): Integer flag to select the distribution type.
            0 (Constant, everyone in the population has the same innate immune coefficient.)
            1 (Uniform, innate immune coefficient is randomly drawn between a minimum and maximum value.)
            2 (Gaussian)
            3 (Exponential)
            4 (Poisson)
            5 (Log normal)
            6 (Bimodal, non-continuous with some individuals having a innate immune coefficient of 1 and others a user-defined innate immune coefficient.)
            7 (Weibull)
        param1 (int): First parameter for the distribution (meaning depends on distribution type).
            0, Innate immune coefficient value to assign.
            1, Minimum innate immune coefficient for a uniform distribution.
            2, Mean innate immune coefficient for a Gaussian distribution.
            3, Exponential decay rate.
            4, Mean innate immune coefficient for a Poisson distribution.
            5, Mu (the mean of the natural log) for a log normal distribution.
            6, ""Proportion of individuals in the second, user-defined innate immune coefficient bin vs. the first innate immune coefficient bin (value of 1) for a bimodal distribution. Must be between 0 and 1.""
            7, Scale parameter for a Weibull distribution.
        param2 (int): Second parameter for the distribution (meaning depends on distribution type).
            0, NA, set to 0.
            1, Maximum innate immune coefficient for a uniform distribution.
            2, Standard deviation in innate immune coefficient for a Gaussian distribution.
            3, NA, set to 0.
            4, NA, set to 0.
            5, Sigma (the standard deviation of the natural log) for a log normal distribution.
            6, The innate immune coefficient for individuals in the second innate immune coefficient bin for a bimodal distribution.
            7, Shape parameter for a Weibull distribution.
    """
    innate_immune_distribution = {"InnateImmuneDistributionFlag": distribution_flag,
                                  "InnateImmuneDistribution1": param1,
                                  "InnateImmuneDistribution2": param2}
    self.SetDefaultFromTemplate(template=innate_immune_distribution, setter_fn=None)

set_risk_high()

Set initial risk for high transmission settings per: https://wiki.idmod.org/display/MAL/Heterogeneous+biting+risk+in+simulations+vs+data.

Source code in emodpy_malaria/demographics/MalariaDemographics.py 
48
49
50
51
52
53
def set_risk_high(self):
    """
        Set initial risk for high transmission settings per:
        https://wiki.idmod.org/display/MAL/Heterogeneous+biting+risk+in+simulations+vs+data.
    """
    super().SetHeteroRiskExponDist(mean=1.0)  # 1.0 is placeholder

set_risk_lowmedium()

Set initial risk for low-medium transmission settings per: https://wiki.idmod.org/display/MAL/Heterogeneous+biting+risk+in+simulations+vs+data.

Source code in emodpy_malaria/demographics/MalariaDemographics.py 
41
42
43
44
45
46
def set_risk_lowmedium(self):
    """
        Set initial risk for low-medium transmission settings per:
        https://wiki.idmod.org/display/MAL/Heterogeneous+biting+risk+in+simulations+vs+data.
    """
    super().SetHeteroRiskLognormalDist(mean=0.0, sigma=1.6)

from_csv(input_file, res=30 / 3600, id_ref='from_csv', init_prev=0.0, include_biting_heterogeneity=True)

Create a multi-node 🇵🇾class:~emodpy_malaria.demographics.MalariaDemographics instance from a CSV file describing a population.

Parameters:

Name Type Description Default
input_file str

The path to the csv file to ingest.

 required
res float

Resolution.

 30 / 3600
id_ref str

A string to identify the file, needs to match other input files.

 'from_csv'
init_prev float

The initial malaria prevalence of the population. Defaults to 0%.

 0.0
include_biting_heterogeneity bool

variable biting rates. Defaults to on.

 True

Returns:

Type Description
MalariaDemographics

Demographics object
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
def from_csv(input_file, res=30 / 3600, id_ref="from_csv", init_prev=0.0, include_biting_heterogeneity=True):
    """
    Create a multi-node :py:class:`~emodpy_malaria.demographics.MalariaDemographics`
    instance from a CSV file describing a population.

    Args:
        input_file (str): The path to the csv file to ingest.
        res (float): Resolution.
        id_ref (str): A string to identify the file, needs to match other input files.
        init_prev (float): The initial malaria prevalence of the population. Defaults to 0%.
        include_biting_heterogeneity (bool): variable biting rates. Defaults to on.

    Returns:
        (MalariaDemographics): Demographics object
    """
    if not os.path.exists(input_file):
        raise ValueError(f"Can't find input data file {input_file}")

    generic_demog = Demog.from_csv(input_file, res, id_ref)
    nodes = generic_demog.nodes
    return MalariaDemographics(nodes=nodes, idref=id_ref, init_prev=init_prev,
                               include_biting_heterogeneity=include_biting_heterogeneity)

from_params(tot_pop=1000000.0, num_nodes=100, frac_rural=0.3, id_ref='from_params')

Creates nodes with following logic: First node is the urban node, which contains tot_pop * (1-frac_rural) of the population, the rest of the nodes splip the left-over population with less and less people in each node.

Create a multi-node 🇵🇾class:~emodpy_malaria.demographics.MalariaDemographics instance as a synthetic population based on a few parameters.

Parameters:

Name Type Description Default
tot_pop int

The total human population in the node.

 1000000.0
num_nodes int

The number of nodes to create.

 100
frac_rural float

The fraction of the population that will be distributed between nodes 2 and higher

 0.3
id_ref str

Method describing how the latitude and longitude values are created for each of the nodes in a simulation. "Gridded world" values use a grid overlaid across the globe at some arcsec resolution. You may also generate the grid using another tool or coordinate system. For more information, see :ref:demo-metadata.

 'from_params'

Returns:

Type Description
MalariaDemographics

Demographics object
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
def from_params(tot_pop=1e6, num_nodes=100, frac_rural=0.3, id_ref="from_params"):
    """
    Creates nodes with following logic: First node is the urban node, which contains
    tot_pop * (1-frac_rural) of the population, the rest of the nodes splip the left-over
    population with less and less people in each node.

    Create a multi-node :py:class:`~emodpy_malaria.demographics.MalariaDemographics`
    instance as a synthetic population based on a few parameters.

    Args:
        tot_pop (int): The total human population in the node.
        num_nodes (int): The number of nodes to create.
        frac_rural (float): The fraction of the population that will be distributed between
            nodes 2 and higher
        id_ref (str): Method describing how the latitude and longitude values are created
            for each of the nodes in a simulation. "Gridded world" values use a grid
            overlaid across the globe at some arcsec resolution. You may also generate
            the grid using another tool or coordinate system. For more information,
            see :ref:`demo-metadata`.

    Returns:
        (MalariaDemographics): Demographics object
    """
    generic_demog = Demog.from_params(tot_pop, num_nodes, frac_rural, id_ref)
    nodes = generic_demog.nodes
    return MalariaDemographics(nodes=nodes, idref=id_ref)

from_pop_csv(pop_filename_in, pop_filename_out='spatial_gridded_pop_dir', site='No_Site')

Create a multi-node 🇵🇾class:~emodpy_malaria.demographics.MalariaDemographics instance from a CSV file describing a population.

Parameters:

Name Type Description Default
pop_filename_in str

The path to the demographics file to ingest.

 required
pop_filename_out str

The path to the file to output.

 'spatial_gridded_pop_dir'
site str

A string to identify the country, village, or trial site.

 'No_Site'

Returns:

Type Description
MalariaDemographics

Demographics object
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
def from_pop_csv(pop_filename_in, pop_filename_out="spatial_gridded_pop_dir", site="No_Site"):
    """
    Create a multi-node :py:class:`~emodpy_malaria.demographics.MalariaDemographics`
    instance from a CSV file describing a population.

    Args:
        pop_filename_in (str): The path to the demographics file to ingest.
        pop_filename_out (str): The path to the file to output.
        site (str): A string to identify the country, village, or trial site.

    Returns:
        (MalariaDemographics): Demographics object
    """
    if not os.path.exists(pop_filename_in):
        raise ValueError(f"Can't find input data file {pop_filename_in}")

    generic_demog = Demog.from_pop_csv(pop_filename_in=pop_filename_in, res=1 / 120, id_ref="from_pop_csv",
                                       pop_filename_out=pop_filename_out, site=site)

    nodes = generic_demog.nodes
    return MalariaDemographics(nodes=nodes, idref=site)

from_template_node(lat=0, lon=0, pop=1000000.0, name=1, forced_id=1, init_prev=0.2, include_biting_heterogeneity=True)

Create a single-node 🇵🇾class:~emodpy_malaria.demographics.MalariaDemographics instance from the parameters you supply.

Parameters:

Name Type Description Default
lat float

Latitude of the centroid of the node to create.

 0
lon float

Longitude of the centroid of the node to create.

 0
pop int

Human population of the node.

 1000000.0
name str

The name of the node. This may be a characteristic of the node, such as "rural" or "urban", or an identifying integer.

 1
forced_id int

The node ID for the single node.

 1
init_prev float

The initial malaria prevalence of the node.

 0.2

Returns:

Type Description
MalariaDemographics

Demographics object
Source code in emodpy_malaria/demographics/MalariaDemographics.py 
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
def from_template_node(lat=0, lon=0, pop=1e6, name=1, forced_id=1, init_prev=0.2, include_biting_heterogeneity=True):
    """
    Create a single-node :py:class:`~emodpy_malaria.demographics.MalariaDemographics`
    instance from the parameters you supply.

    Args:
        lat (float): Latitude of the centroid of the node to create.
        lon (float): Longitude of the centroid of the node to create.
        pop (int): Human population of the node.
        name (str): The name of the node. This may be a characteristic of the
            node, such as "rural" or "urban", or an identifying integer.
        forced_id (int): The node ID for the single node.
        init_prev (float): The initial malaria prevalence of the node.

    Returns:
        (MalariaDemographics): Demographics object
    """
    new_nodes = [Demog.Node(lat=lat, lon=lon, pop=pop, name=name, forced_id=forced_id)]
    return MalariaDemographics(nodes=new_nodes, init_prev=init_prev,
                               include_biting_heterogeneity=include_biting_heterogeneity)