(OLD) Configuration setup

Need to add MultiPeriodModifier and hospitalization interventions

Overview

This documentation describes the new YAML configuration file options that may be used when performing inference on model runs. As compared to previous model releases, there are additions to the seeding and interventions sections, the outcomes section replaces the hospitalization section, and the filtering section added to the file.

Importantly, we now name our pipeline modules: seeding, seir, hospitalization and this becomes relevant to some of the new filtering specifications.

Models may be calibrated to any available time series data that is also an outcome of the model (COVID-19 confirmed cases, deaths, hospitalization or ICU admissions, hospital or ICU occupancy, and ventilator use). Our typical usage has calibrated the model to deaths, confirmed cases, or both. We can also perform inference on intervention effectiveness, county-specific baseline R0, and the risk of specific health outcomes.

We describe these options below and present default values in the example configuration sections.

Modifications to seeding

The model can perform inference on the seeding date and initial number of seeding infections in each subpop. An example of this new config section is:

seeding:
  method: FolderDraw
  seeding_file_type: seed
  folder_path: importation/minimal/
  lambda_file: data/minimal/seeding.csv
  perturbation_sd: 3

The method for determining the proposal distribution for the seeding amount is hard-coded in the inference package (R/pkgs/inference/R/functions/perturb_seeding.R). It is pertubed with a normal distribution where the mean of the distribution 10 times the number of confirmed cases on a given date and the standard deviation is 1.

Modifications to interventions

The model can perform inference on the effectiveness of interventions as long as there is at least some calibration health outcome data that overlaps with the intervention period. For example, if calibrating to deaths, there should be data from time points where it would be possible to observe deaths from infections that occurred during the intervention period (e.g., assuming 10-18 day delay between infection and death, on average).

An example configuration file where inference is performed on scenario planning interventions is as follows:

interventions:
  scenarios:
    - Scenario1
  settings:
    local_variance:
      template: SinglePeriodModifierR0
      value:
        distribution: truncnorm
        mean: 0
        sd: .1
        a: -1
        b: 1
      perturbation:
        distribution: truncnorm
        mean: 0
        sd: .1
        a: -1
        b: 1
    stayhome:
      template: SinglePeriodModifierR0
      period_start_date: 2020-04-04
      period_end_date: 2020-04-30
      value:
        distribution: truncnorm
        mean: 0.6
        sd: 0.3
        a: 0
        b: 0.9
      perturbation:
        distribution: truncnorm
        mean: 0
        sd: .1
        a: -1
        b: 1
    Scenario1:
      template: StackedModifier
      scenarios: 
        - local_variance
        - stayhome

interventions::settings::[setting_name]

Interventions may be specified in the same way as before, or with an added perturbation section that indicates that inference should be performed on a given intervention's effectiveness. As previously, interventions with perturbations may be specified for all modeled locations or for explicit subpop. In this setup, both the prior distribution and the range of the support of the final inferred value are specified by the value section. In the configuration above, the inference algorithm will search 0 to 0.9 for all subpops to estimate the effectiveness of the stayhome intervention period. The prior distribution on intervention effectiveness follows a truncated normal distribution with a mean of 0.6 and a standard deviation of 0.3. The perturbation section specifies the perturbation/step size between the previously-accepted values and the next proposal value.

New outcomes section

This section is now structured more like the interventions section of the config, in that it has scenarios and settings. We envision that separate scenarios will be specified for each IFR assumption.

outcomes:
  method: delayframe
  param_from_file: TRUE
  param_subpop_file: "usa-subpop-params-output.parquet" ## ../../Outcomes/data/usa-subpop-params-output.parquet
  scenarios:
    - med
  settings:
    med:
      incidH:
        source: incidI
        probability:
          value:
            distribution: fixed
            value: .035
        delay:
          value:
            distribution: fixed
            value: 7
        duration:
          value:
            distribution: fixed
            value: 7
          name: hosp_curr
      incidD:
        source: incidI
        probability:
          value:
            distribution: fixed
            value: .01
        delay:
          value:
            distribution: fixed
            value: 20
      incidICU:
        source: incidH
        probability: 
          value:
            distribution: fixed
            value: 0.167
        delay:
          value:
            distribution: fixed
            value: 3
        duration:
          value:
            distribution: fixed
            value: 8
      incidVent:
        source: incidICU
        probability: 
          value:
            distribution: fixed
            value: 0.463
        delay:
          value:
            distribution: fixed
            value: 1
        duration:
          value:
            distribution: fixed
            value: 7
      incidC:
        source: incidI
        probability:
          value:
            distribution: truncnorm
            mean: .1
            sd: .1
            a: 0
            b: 10
          perturbation:
            distribution: truncnorm
            mean: 0
            sd: .1
            a: -1
            b: 1
        delay:
          value:
            distribution: fixed
            value: 7

outcomes::settings::[setting_name]

The settings for each scenario correspond to a set of different health outcome risks, most often just differences in the probability of death given infection (Pr(incidD|incidI)) and the probability of hospitalization given infection (Pr(incidH|incidI)). Each health outcome risk is referenced in relation to the outcome indicated in source. For example, the probability and delay in becoming a confirmed case (incidC) is most likely to be indexed off of the number and timing of infection (incidI).

Importantly, we note that incidI is automatically defined from the SEIR transmission model outputs, while the other compartment sources must be defined in the config before they are used.

Users must specific two metrics for each health outcome, probability and delay, while a duration is optional (e.g., duration of time spent in the hospital). It is also optional to specify a perturbation section (similar to perturbations specified in the NPI section) for a given health outcome and metric. If you want to perform inference (i.e., if perturbation is specified) on a given metric, that metric must be specified as a distribution (i.e., not fixed) and the range of support for the distribution represents the range of parameter space explored in the inference.

New filtering section

This 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.

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