Parameter Categories of MultiMalModPy

MultiMalModPy requires users to specify three key types of parameters:

Malaria
Define and align malaria-related inputs across all models to ensure comparability.
This includes key factors influencing malaria transmission dynamics, such as seasonality, case management, and importation.

Simulation
Specify details for each simulation experiment, including population size, number of seeds, simulation duration, and burn-in period.
Additionally, define scenario-specific parameters that impact transmission, seasonality, case management, and other interventions.

Framework
Configure overarching settings, such as model selection, output specifications, and directory structures for storing inputs and results.
Cluster-specific parameters are also required to optimize simulation execution across computing environments.

NOTE: Not all parameters can be aligned, even though those parameters shared are likely aligned. A detailed checklist of how the models were aligned will be made available soon.

Malaria Parameters

These parameters are primarily defined in the SimulationConfig class within the setup_sim.py script.

Parameter Description
seasonality Seasonal variation in malaria transmission due to climatic factors.
case_management Diagnosis and treatment for uncomplicated and severe malaria cases.
intervention_list Introduction of malaria cases from external sources (e.g., travel, migration).
importation_rate Introduction of malaria cases from external sources (e.g., travel, migration).
entomology_mode Mosquito population dynamics, biting behavior, and vector control strategies.
sweep_list Creates a full factorial design of the selected sweep parameter values

Parameter names with an asteriks (*) denote model-specific parameters

Simulation Parameters

These parameters are primarily defined in the SimulationConfig class within the setup_sim.py script.

Parameter Description
start_year, end_year Start year of the monitoring period
burnin* Initial period for reaching an equilibrium.
seed Random seed(s) for stochastic replications.
pop_size* Simulated number of individuals.
agebins Age bins to simulate.
age_groups_aggregates Age groups for output aggregation.
run_mode Simulation execution mode, supporting testing and production configurations.
emodstep* EMOD: use serialization to separate burn-in and pickup simulation, or run all together.
emod_calib_params* EMOD: run simulations with alternative within-host calibration.
mpv* malariasimulation parameter variation.
target_output_name, target_output_values Output type and target location for CM 0 and 15% simulations using EMOD (pop: 1,000), OpenMalaria (pop: 50,000), and malariaSimulation (pop: 50,000) under seasonal and perennial profiles.
test Reduces the computational demand by lowering pop_size, burn-in and seeds

Parameter names with an asteriks (*) denote model-specific parameters

Framework Parameters

These parameters are primarily defined through the arguments provided when launching simulations via python launch_sim.py <args>. They are specified in the parse_launch_args function within the utility/helper.py script.

Parameter Description
directory Specifies the directory for storing simulation inputs, config, and model outputs.
suite Suite (collection of) of simulation experiments.
expname, exp_name Name of the simulation experiment.
models, models_to_run Specifies which models to run (one or multiple).
user Creates a user-specific directory for storing experiments and simulation outputs.
rownum If running from CSV, defines row of the simulation scenario to run.
memory_per_sim* Memory allocation per simulation, ensuring appropriate resources.
time_per_sim* Max time for each simulation to manage computational time and resources
analyzer_list Type of standardized outputs to generate (i.e. select default or custom ones)
analyzer_script Name of the analyzer script (default, optional for advanced users)
plots_to_run Plots to generate (allows custom plots for advanced users)
sim_params_list Lists the simulation parameters to be used across the models, allowing for consistent configuration of the simulation environment.

Parameter names with an asteriks (*) denote model-specific parameters