Other configuration options

Command line inputs

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.

Command-line only inputs

Argument	Env. Variable	Value type	Description	Required?	Default
-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	integar $\geq$1	The index of the first simulation	No	1
-j or --jobs	FLEPI_NJOBS	integar $\geq$1	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	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 parquet files.	No	write_parquet

Command-line versions of configuration file inputs

Argument	Config item	Env. Variable	Value type	Description	Required?	Default
-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	integar $\geq$1	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

Example

As an example, consider running the following configuration file

name: sir
setup_name: minimal
start_date: 2020-01-31
end_date: 2020-05-31
data_path: data
nslots: 1

spatial_setup:
  geodata: geodata_sample_1pop.csv
  mobility: mobility_sample_1pop.csv
  popnodes: population
  nodenames: name

seeding:
  method: FromFile
  seeding_file: data/seeding_1pop.csv

compartments:
  infection_stage: ["S", "I", "R"]

seir:
  integration:
    method: stochastic
    dt: 1 / 10
  parameters:
    gamma:
      value:
        distribution: fixed
        value: 1 / 5
    Ro:
      value:
        distribution: uniform
        low: 2
        high: 3
  transitions:
    - source: ["S"]
      destination: ["I"]
      rate: ["Ro * gamma"]
      proportional_to: [["S"],["I"]]
      proportion_exponent: ["1","1"]
    - source: ["I"]
      destination: ["R"]
      rate: ["gamma"]
      proportional_to: ["I"]
      proportion_exponent: ["1"]

interventions:
  scenarios:
    - None
    - Lockdown
  modifiers:
    None:
      method: SinglePeriodModifier
      parameter: r0
      period_start_date: 2020-04-01
      period_end_date: 2020-05-15
      value:
        distribution: fixed
        value: 0
        settings:
    Lockdown:
      method: SinglePeriodModifier
      parameter: r0
      period_start_date: 2020-04-01
      period_end_date: 2020-05-15
      value:
        distribution: fixed
        value: 0.7

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

> gempyor-seir -c sir_control.yml

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

/> gempyor-seir -c sir_control.yml -n 100 -j 4 -npi_scenario None --non_stochastic --write_csv

Environmental variables

TBA

US-specific configuration file options

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 US-specific population structures

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

Config Item	Required?	Type/Format	Description
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

Example 2

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.

subpop_setup:
  census_year: 2010
  state_level: TRUE
  geodata: geodata_2019_statelevel.csv
  mobility: mobility_2011-2015_statelevel.csv
  modeled_states:
    - CT
    - MA
    - ME
    - NH
    - RI
    - VT
  

geodata.csv contains

USPS	subpop	population
AL	01000	4876250
AK	02000	737068
AZ	04000	7050299
AR	05000	2999370
CA	06000	39283497
.....

mobility_2011-2015_statelevel.csv contains

ori	dest	amount
01000	02000	198
01000	04000	292
01000	05000	570
01000	06000	1030
01000	08000	328
.....

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.

Config Item	Required?	Type/Format	Description
census_api_key	required	string	get an API key
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_list

Config Item	Required?	Type/Format	Description
incub_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

importation:
  census_api_key: "fakeapikey00000"
  travel_dispersion: 3
  maximum_destinations: Inf
  dest_type: state
  dest_county: USA
  aggregate_to: airport
  cache_work: TRUE
  update_case_data: TRUE
  draw_travel_from_distribution: FALSE
  print_progress: FALSE
  travelers_threshold: 10000
  airport_cluster_distance: 80
  param_list:
    incub_mean_log: log(5.89)
    incub_sd_log: log(1.74)
    inf_period_nohosp_mean: 15
    inf_period_nohosp_sd: 5
    inf_period_hosp_mean_log: 1.23
    inf_period_hosp_sd_log: 0.79
    p_report_source: [0.05, 0.25]
    shift_incid_days: -10
    delta: 1

report section

The 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.

Config Item	Required?	Type/Format	Description
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

report:
  data_settings:
    pop_year: 2018
  plot_settings:
    plot_intervention: TRUE
  formatting:
    scenario_labels_short: ["UC", "S1"]
    scenario_labels:
      - Uncontrolled
      - Scenario 1
    scenario_colors: ["#D95F02", "#1B9E77"]
    pdeath_labels: ["0.25% IFR", "0.5% IFR", "1% IFR"]
    display_dates: ["2020-04-15", "2020-05-01", "2020-05-15", "2020-06-01", "2020-06-15"]
    display_dates2: ["2020-04-15", "2020-05-15", "2020-06-15"]