flepiMoP allows some input parameters/options to be specified in the command line at the time of model submission, in addition to or instead of in the configuration file. This can be helpful for users who want to quickly run different versions of the model – typically a different number of simulations or a different intervention scenario from among all those specified in the config – without having to edit or create a new configuration file every time. In addition, some arguments can only be specified via the command line.
In addition to the configuration file and the command line, the inputs described below can also be specified as environmental variables.
In all cases, command line arguments override configuration file entries which override environmental variables. The order of command line arguments does not matter.
Details on how to run the model, including how to add command line arguments or environmental variables, are in the section How to Run.
-c or --config
CONFIG_PATH
file path
Name of configuration file. Must be located in the current working directory, or else relative or absolute file path must be provided.
Yes
NA
-i or --first_sim_index
FIRST_SIM_INDEX
The index of the first simulation
No
1
-j or --jobs
FLEPI_NJOBS
Number of parallel processors used to run the simulation. If there are more slots that jobs, slots will be divided up between processors and run in series on each.
No
Number of processors on the computer used to run the simulation
--interactiveor --batch
NA
Choose either option
Run simulation in interactive or batch mode
No
batch
--write-csv or --no-write-csv
NA
Choose either option
Whether model output will be saved as .csv files
No
no_write_csv
--write-parquet or --no-write-parquet
NA
Choose either option
No
write_parquet
-s or --npi_scenario
interventions: scenarios
FLEPI_NPI_SCENARIOS
list of strings
Names of the intervention scenarios described in the config file that will be run. Must be a subset of scenarios defined.
No
All scenarios described in config
-n or --nslots
nslots
FLEPI_NUM_SLOTS
Number of independent simulations of the model to be run
No
Config value
--stochastic or --non-stochastic
seir: integration: method
FLEPI_STOCHASTIC_RUN
choose either option
Whether the model will be run stochastically or non-stochastically (deterministic numerical integration of equations using the RK4 algorithm)
No
Config value
--in-id
FLEPI_RUN_INDEX
string
Unique ID given to the model runs. If the same config is run multiple times, you can avoid the output being overwritten by using unique model run IDs.
No
Constructed from current date and time as YYYY.MM.DD.HH/MM/SS
--out-id
FLEPI_RUN_INDEX
string
Unique ID given to the model runs. If the same config is run multiple times, you can avoid the output being overwritten by using unique model run IDs.
No
Constructed from current date and time as YYYY.MM.DD.HH/MM/SS
As an example, consider running the following configuration file
To run this model directly in Python (it can alternatively be run from R, for all details see section How to Run), we could use the command line entry
Alternatively, to run 100 simulations using only 4 of the available processors on our computer, but only running the "" scenario with a deterministic model, and to save the files as .csv (since the model is relatively simple), we could call the model using the command line entry
TBA
Things below here are very out of date. Put here as place holder but not updated recently.
global: smh_round, setup_name, disease
spatial_setup: census_year, modeled_states, state_level
For creating US-based population structures using the helper script build_US_setup.R which is run before the main model simulation script, the following extra parameters can be specified
census_year
optional
integer (year)
Determines the year for which census population size data is pulled.
state_level
optional
boolean
Determines whether county-level population-size data is instead grouped into state-level data (TRUE). Default FALSE
modeled_states
optional
list of location codes
A vector of locations that will be modeled; others will be ignored
To simulate an epidemic across all 50 states of the US or a subset of them, users can take advantage of built in machinery to create geodata and mobility files for the US based on the population size and number of daily commuting trips reported in the US Census.
Before running the simulation, the script build_US_setup.R can be run to get the required population data files from online census data and filter out only states/territories of interest for the model. More details are provided in the How to Run section.
This example simulates COVID-19 in the New England states, assuming no transmission from other states, using 2019 census data for the population sizes and a pre-created file for estimated interstate commutes during the 2011-2015 period.
geodata.csv contains
mobility_2011-2015_statelevel.csv contains
importation section (optional)This section is optional. It is used by the covidImportation package to import global air importation data for seeding infections into the United States.
If you wish to include it, here are the options.
census_api_key
required
string
travel_dispersion
required
number
ow dispersed daily travel data is; default = 3.
maximum_destinations
required
integer
number of airports to limit importation to
dest_type
required
categorical
location type
dest_country
required
string (Country)
ISO3 code for country of importation. Currently only USA is supported
aggregate_to
required
categorical
location type to aggregate to
cache_work
required
boolean
whether to save case data
update_case_data
required
boolean
deprecated; whether to update the case data or used saved
draw_travel_from_distribution
required
boolean
whether to add additional stochasticity to travel data; default is FALSE
print_progress
required
boolean
whether to print progress of importation model simulations
travelers_threshold
required
integer
include airports with at least the travelers_threshold mean daily number of travelers
airport_cluster_distance
required
numeric
cluster airports within airport_cluster_distance km
param_list
required
See section below
see below
importation::param_listincub_mean_log
required
numeric
incubation period, log mean
incub_sd_log
required
numeric
incubation period, log standard deviation
inf_period_nohosp_mean
required
numeric
infectious period, non-hospitalized, mean
inf_period_nohosp_sd
required
numeric
infectious period, non-hospitalized, sd
inf_period_hosp_mean_log
required
numeric
infectious period, hospitalized, log-normal mean
inf_period_hosp_sd_log
required
numeric
infectious period, hospitalized, log-normal sd
p_report_source
required
numeric
reporting probability, Hubei and elsewhere
shift_incid_days
required
numeric
mean delay from infection to reporting of cases; default = -10
delta
required
numeric
days per estimations period
report sectionThe report section is completely optional and provides settings for making an R Markdown report. For an example of a report, see the Supplementary Material of our preprint
If you wish to include it, here are the options.
data_settings::pop_year
integer
plot_settings::plot_intervention
boolean
formatting::scenario_labels_short
list of strings; one for each scenario in interventions::scenarios
formatting::scenario_labels
list of strings; one for each scenario in interventions::scenarios
formatting::scenario_colors
list of strings; one for each scenario in interventions::scenarios
formatting::pdeath_labels
list of strings
formatting::display_dates
list of dates
formatting::display_dates2
optional
list of dates
a 2nd string of display dates that can optionally be supplied to specific report functions
integar 1
integar 1
Whether model output will be saved as .parquet files (a compressed representation that can be opened and manipulated with minimal memory. May be required for large simulations). Read more about .
integar 1