Vital dynamics

A. Vector-borne disease with natality spreading

Simple model of a vector-borne disease with 10% host mortality spreading among hosts and vectors that have natural birth and death rates in a single population. There is no evolution and pathogen genomes don’t affect spread.

from opqua.model import Model

Model initialization and setup

Create a new Model object

my_model = Model() # Make a new model object.

Define a Setup for our system

Create a new set of parameters called my_setup to be used to simulate a population in the model. Use the default parameter set for a vector-borne model.

my_model.newSetup(  # Create a new Setup.
    'my_setup',
        # Name of the setup.
    preset='vector-borne',
        # Use default 'vector-borne' parameters.
    mortality_rate_host=1e-2,
        # change the default host mortality rate to 10% of recovery rate
    protection_upon_recovery_host=[0,10],
        # make hosts immune to the genome that infected them if they recover
        # [0,10] means that pathogen genome positions 0 through 9 will be saved
        # as immune memory
    birth_rate_host=1.5e-2,
        # change the default host birth rate to 0.015 births/time unit
    death_rate_host=1e-2,
        # change the default natural host death rate to 0.01 births/time unit
    birth_rate_vector=1e-2,
        # change the default vector birth rate to 0.01 births/time unit
    death_rate_vector=1e-2
        # change the default natural vector death rate to 0.01 deaths/time unit
    )

Create a population in our model

Create a new population of 100 hosts and 100 vectors called my_population. The population uses parameters stored in my_setup.

my_model.newPopulation( # Create a new Population.
    'my_population',
        # Unique identifier for this population in the model.
    'my_setup',
        # Predefined Setup object with parameters for this population.
    num_hosts=100,
        # Number of hosts in the population with.
    num_vectors=100
        # Number of vectors in the population with.
    )

Manipulate hosts and vectors in the population

Add pathogens with a genome of AAAAAAAAAA to 20 random hosts in population my_population.

my_model.addPathogensToHosts( # Add specified pathogens to random hosts.
    'my_population',
        # ID of population to be modified.
    {'AAAAAAAAAA':20}
        # Dictionary containing pathogen genomes to add as keys and
        # number of hosts each one will be added to as values.
    )

Model simulation

my_model.run(   # Simulate model for a specified time between two time points.
    0,          # Initial time point.
    200         # Final time point.
    )

Simulating time: 66.7483164411631, event: BIRTH_HOST
Simulating time: 175.53517979111868, event: CONTACT_HOST_VECTOR
Simulating time: 200.00318125185066 END

Output data manipulation and visualization

Create a table with the results of the given model history

data = my_model.saveToDataFrame(
        # Creates a pandas Dataframe in long format with the given model history,
        # with one host or vector per simulation time in each row.
    'vector-borne_birth-death_example.csv'
        # Name of the file to save the data to.
    )
data

Saving file...

[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done   2 tasks      | elapsed:    0.3s
[Parallel(n_jobs=8)]: Batch computation too fast (0.1995853034973145s.) Setting batch_size=2.
[Parallel(n_jobs=8)]: Done   9 tasks      | elapsed:    0.3s
[Parallel(n_jobs=8)]: Done  16 tasks      | elapsed:    0.3s
[Parallel(n_jobs=8)]: Done  26 tasks      | elapsed:    0.3s
[Parallel(n_jobs=8)]: Batch computation too fast (0.019458293914794922s.) Setting batch_size=4.
[Parallel(n_jobs=8)]: Done  44 tasks      | elapsed:    0.4s
[Parallel(n_jobs=8)]: Batch computation too fast (0.020659446716308594s.) Setting batch_size=8.
[Parallel(n_jobs=8)]: Done  76 tasks      | elapsed:    0.4s
[Parallel(n_jobs=8)]: Done 120 tasks      | elapsed:    0.4s
[Parallel(n_jobs=8)]: Batch computation too fast (0.0252227783203125s.) Setting batch_size=16.
[Parallel(n_jobs=8)]: Done 224 tasks      | elapsed:    0.5s
[Parallel(n_jobs=8)]: Batch computation too fast (0.04323148727416992s.) Setting batch_size=32.
[Parallel(n_jobs=8)]: Done 408 tasks      | elapsed:    0.7s
[Parallel(n_jobs=8)]: Batch computation too fast (0.08730292320251465s.) Setting batch_size=64.
[Parallel(n_jobs=8)]: Done 792 tasks      | elapsed:    0.9s
[Parallel(n_jobs=8)]: Batch computation too fast (0.18108701705932617s.) Setting batch_size=128.
[Parallel(n_jobs=8)]: Done 1233 tasks      | elapsed:    1.1s
[Parallel(n_jobs=8)]: Done 1613 tasks      | elapsed:    1.1s
[Parallel(n_jobs=8)]: Done 1698 tasks      | elapsed:    1.1s
[Parallel(n_jobs=8)]: Done 1793 tasks      | elapsed:    1.1s
[Parallel(n_jobs=8)]: Done 1888 tasks      | elapsed:    1.1s
[Parallel(n_jobs=8)]: Done 1977 out of 1977 | elapsed:    1.1s finished

...file saved.

	Time	Population	Organism	ID	Pathogens	Protection	Alive
0	0.0	my_population	Host	my_population_0	NaN	NaN	True
1	0.0	my_population	Host	my_population_1	AAAAAAAAAA	NaN	True
2	0.0	my_population	Host	my_population_2	NaN	NaN	True
3	0.0	my_population	Host	my_population_3	NaN	NaN	True
4	0.0	my_population	Host	my_population_4	NaN	NaN	True
...	...	...	...	...	...	...	...
443810	200.0	my_population	Host	my_population_120	AAAAAAAAAA	NaN	False
443811	200.0	my_population	Host	my_population_136	AAAAAAAAAA	NaN	False
443812	200.0	my_population	Host	my_population_117	AAAAAAAAAA	NaN	False
443813	200.0	my_population	Host	my_population_136	AAAAAAAAAA	NaN	False
443814	200.0	my_population	Host	my_population_112	AAAAAAAAAA	NaN	False

443815 rows × 7 columns

Create a compartment plot

Plot the number of susceptible and infected hosts in the model over time.

plot = my_model.compartmentPlot(
        # Create plot with number of naive, infected, recovered, dead hosts/vectors vs. time.
    'vector-borne_birth-death_example.png',
        # File path, name, and extension to save plot under.
    data
        # Dataframe containing model history.
    )