(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).

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.

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.

filtering::priors

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

Ground truth data

Likelihood function

Fitting parameters

Ground truth data

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