Usage

To run any Opqua model (including the tutorials in the examples/tutorials folder), save the model as a .py file and execute from the console using python my_model.py.

You may also run the models from a notebook environment such as Jupyter or an integrated development environment (IDE) such as Spyder, both available through Anaconda.

Minimal example

The simplest model you can make using Opqua looks like this:

# This simulates a pathogen with genome "AAAAAAAAAA" spreading in a single
# population of 100 hosts, 20 of which are initially infected, under example
# preset conditions for host-host transmission.

from opqua.model import Model

my_model = Model()
my_model.newSetup('my_setup', preset='host-host')
my_model.newPopulation('my_population', 'my_setup', num_hosts=100)
my_model.addPathogensToHosts( 'my_population',{'AAAAAAAAAA':20} )
my_model.run(0,100)
data = my_model.saveToDataFrame('my_model.csv')
graph = my_model.compartmentPlot('my_model.png', data)

For more example usage, have a look at the examples folder. For an overview of how Opqua models work, check out the Materials and Methods section on the manuscript here. A summarized description is shown below in the How Does Opqua Work? section. For more information on the details of each function, head over to the Documentation section.

Example Plots

These are some of the plots Opqua is able to produce, but you can output the raw simulation data yourself to make your own analyses and plots. These are all taken from the examples in the examples/tutorials folder—try them out yourself! See the

Population genetic composition plots for pathogens

An optimal pathogen genome arises and outcompetes all others through intra-host competition. See fitness_function_mutation_example.py in the examples/tutorials/evolution folder. Compartments

Host/vector compartment plots

A population with natural birth and death dynamics shows the effects of a pathogen. “Dead” denotes deaths caused by pathogen infection. See vector-borne_birth-death_example.py in the examples/tutorials/vital_dynamics folder. Compartments

Plots of a host/vector compartment across different populations in a metapopulation

Pathogens spread through a network of interconnected populations of hosts. Lines denote infected pathogens. See metapopulations_migration_example.py in the examples/tutorials/metapopulations folder. Compartments

Host/vector compartment plots

A population undergoes different interventions, including changes in epidemiological parameters and vaccination. “Recovered” denotes immunized, uninfected hosts. See intervention_examples.py in the examples/tutorials/interventions folder. Compartments

Pathogen phylogenies

Phylogenies can be computed for pathogen genomes that emerge throughout the simulation. See fitness_function_mutation_example.py in the examples/tutorials/evolution folder. Compartments

For advanced examples (including multiple parameter sweeps), check out this separate repository (preprint forthcoming).