filtering sectionThe 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 settingsWith 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).
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::statisticsThe 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.
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_geoThe 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.
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::priorsIt is now possible to specify prior values for inferred parameters. This will have the effect of speeding up model convergence.
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