(OLD) Configuration options

filtering section

The filtering section configures the settings for the inference algorithm. The below example shows the settings for some typical default settings, where the model is calibrated to the weekly incident deaths and weekly incident confirmed cases for each subpop. Statistics, hierarchical_stats_geo, and priors each have scenario names (e.g., sum_deaths, local_var_hierarchy, and local_var_prior, respectively).

filtering:
  simulations_per_slot: 350
  do_filtering: TRUE
  data_path: data/observed_data.csv
  likelihood_directory: importation/likelihood/
  statistics:
    sum_deaths:
      name: sum_deaths
      aggregator: sum ## function applied over the period
      period: "1 weeks"
      sim_var: incidD
      data_var: death_incid
      remove_na: TRUE
      add_one: FALSE
      likelihood:
        dist: sqrtnorm
        param: [.1]
    sum_confirmed:
      name: sum_confirmed
      aggregator: sum
      period: "1 weeks"
      sim_var: incidC
      data_var: confirmed_incid
      remove_na: TRUE
      add_one: FALSE
      likelihood:
        dist: sqrtnorm
        param: [.2]
  hierarchical_stats_geo:
    local_var_hierarchy:
      name: local_variance
      module: seir
      geo_group_col: USPS
      transform: none
    local_conf:
      name: probability_incidI_incidC
      module: hospitalization
      geo_group_col: USPS
      transform: logit
  priors:
    local_var_prior:
      name: local_variance
      module: seir
      likelihood:
        dist: normal
        param:
        - 0
        - 1

filtering settings

With inference model runs, the number of simulations nsimulations refers to the number of final model simulations that will be produced. The filtering$simulations_per_slot setting refers to the number of iterative simulations that will be run in order to produce a single final simulation (i.e., number of simulations in a single MCMC chain).

Item	Required?	Type/Format
simulations_per_slot	required	number of iterations in a single MCMC inference chain
do_filtering	required	TRUE if inference should be performed
data_path	required	file path where observed data are saved
likelihood_directory	required	folder path where likelihood evaluations will be stored as the inference algorithm runs
statistics	required	specifies which data will be used to calibrate the model. see filtering::statistics for details
hierarchical_stats_geo	optional	specifies whether a hierarchical structure should be applied to any inferred parameters. See filtering::hierarchical_stats_geo for details.
priors	optional	specifies prior distributions on inferred parameters. See filtering::priors for details

filtering::statistics

The statistics specified here are used to calibrate the model to empirical data. If multiple statistics are specified, this inference is performed jointly and they are weighted in the likelihood according to the number of data points and the variance of the proposal distribution.

Item	Required?	Type/Format
name	required	name of statistic, user defined
aggregator	required	function used to aggregate data over the period, usually sum or mean
period	required	duration over which data should be aggregated prior to use in the likelihood, may be specified in any number of days, weeks, months
sim_var	required	column name where model data can be found, from the hospitalization outcomes files
data_var	required	column where data can be found in data_path file
remove_na	required	logical
add_one	required	logical, TRUE if evaluating the log likelihood
likelihood::dist	required	distribution of the likelihood
likelihood::param	required	parameter value(s) for the likelihood distribution. These differ by distribution so check the code in inference/R/functions.R/logLikStat function.

filtering::hierarchical_stats_geo

The hierarchical settings specified here are used to group the inference of certain parameters together (similar to inference in "hierarchical" or "fixed/group effects" models). For example, users may desire to group all counties in a given state because they are geograhically proximate and impacted by the same statewide policies. The effect should be to make these inferred parameters follow a normal distribution and to observe shrinkage among the variance in these grouped estimates.

Item	Required?	Type/Format
scenario name	required	name of hierarchical scenario, user defined
name	required	name of the estimated parameter that will be grouped (e.g., the NPI scenario name or a standardized, combined health outcome name like probability_incidI_incidC)
module	required	name of the module where this parameter is estimated (important for finding the appropriate files)
geo_group_col	required	geodata column name that should be used to group parameter estimation
transform	required	type of transform that should be applied to the likelihood: "none" or "logit"

filtering::priors

It is now possible to specify prior values for inferred parameters. This will have the effect of speeding up model convergence.

Item	Required?	Type/Format
scenario name	required	name of prior scenario, user defined
name	required	name of NPI scenario or parameter that will have the prior
module	required	name of the module where this parameter is estimated
likelihood	required	specifies the distribution of the prior

Ground truth data

Likelihood function

Fitting parameters

Ground truth data