Welcome to Epydemix’s Documentation!

The source code for Epydemix is available on GitHub: Epydemix Repository.

Epydemix is a Python package for epidemic modeling. It provides tools to create, calibrate, and analyze epidemic models, allowing users to simulate the spread of infectious diseases using different compartmental models, contact layers, and calibration techniques.

Features:

• Define and simulate compartmental models (e.g., SIR, SEIR).

• Integrate real-world population data with contact matrices.

• Calibrate models using Approximate Bayesian Computation (ABC).

• Visualize simulation results with built-in plotting tools.

• Extensible framework for modeling interventions and policy scenarios.

Installation

To install Epydemix, use the following command:

pip install epydemix

Get started

We provide a series of tutorials to help you get started with Epydemix:

• Tutorial 1: Model Definition and Simulation

• Tutorial 2: Using Population Data

• Tutorial 3: Modeling Interventions

• Tutorial 4: Model Calibration with ABC (Part 1)

• Tutorial 5: Model Calibration with ABC (Part 2)

• Tutorial 6: Advanced Modeling Features

• Tutorial 7: COVID-19 Case Study

API Reference

• epydemix.calibration package
  • Submodules
  • epydemix.calibration.abc module
    • ABCSampler
  • epydemix.calibration.calibration_results module
    • CalibrationResults
  • epydemix.calibration.metrics module
    • ae()
    • mae()
    • mape()
    • rmse()
    • validate_data()
    • wmape()
  • Module contents
    • ABCSampler
    • CalibrationResults
    • ae()
    • mae()
    • mape()
    • rmse()
    • wmape()
• epydemix.model package
  • Submodules
  • epydemix.model.epimodel module
    • EpiModel
    • compute_mediated_transition_probability()
    • compute_spontaneous_transition_probability()
    • multinomial()
    • simulate()
    • stochastic_simulation()
    • validate_transition_function()
  • epydemix.model.predefined_models module
    • create_seir()
    • create_sir()
    • create_sis()
    • load_predefined_model()
  • epydemix.model.simulation_output module
    • Trajectory
  • epydemix.model.simulation_results module
    • SimulationResults
  • epydemix.model.transition module
    • Transition
  • Module contents
    • EpiModel
    • SimulationResults
    • Transition
    • load_predefined_model()
    • simulate()
• epydemix.population package
  • Submodules
  • epydemix.population.population module
    • Population
    • aggregate_demographic()
    • aggregate_matrix()
    • get_available_locations()
    • get_primary_contacts_source()
    • load_epydemix_population()
    • map_age_groups_to_idx()
    • validate_age_group_mapping()
    • validate_contacts_source()
    • validate_population_name()
  • Module contents
    • Population
    • get_available_locations()
    • load_epydemix_population()
• epydemix.utils package
  • Submodules
  • epydemix.utils.abc_smc_utils module
    • DefaultPerturbationContinuous
    • DefaultPerturbationDiscrete
    • Perturbation
    • compute_effective_sample_size()
    • fast_normal_pdf()
    • sample_prior()
    • weighted_quantile()
  • epydemix.utils.utils module
    • apply_initial_conditions()
    • apply_overrides()
    • combine_simulation_outputs()
    • compute_days()
    • compute_simulation_dates()
    • convert_to_2Darray()
    • create_definitions()
    • evaluate()
    • format_simulation_output()
    • generate_unique_string()
    • is_iterable()
    • is_scalar()
    • resize_parameter()
    • str_to_date()
    • validate_parameter_shape()
  • Module contents
    • DefaultPerturbationContinuous
    • DefaultPerturbationDiscrete
    • Perturbation
    • combine_simulation_outputs()
    • compute_days()
    • compute_effective_sample_size()
    • compute_simulation_dates()
    • convert_to_2Darray()
    • sample_prior()
    • weighted_quantile()
• epydemix.visualization package
  • Submodules
  • epydemix.visualization.plotting module
    • get_black_to_grey()
    • get_timeseries_data()
    • plot_contact_matrix()
    • plot_distance_distribution()
    • plot_population()
    • plot_posterior_distribution()
    • plot_posterior_distribution_2d()
    • plot_quantiles()
    • plot_selected_trajectories()
    • plot_spectral_radius()
    • plot_trajectories()
  • Module contents
    • plot_contact_matrix()
    • plot_distance_distribution()
    • plot_population()
    • plot_posterior_distribution()
    • plot_posterior_distribution_2d()
    • plot_quantiles()
    • plot_spectral_radius()
    • plot_trajectories()

Indices and tables

• Index

• Module Index

• Search Page