(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

Config Item	Required?	Type/Format	Description
method	required	"FolderDraw"
seeding_file_type	required for FolderDraw	"seed" or "impa"	indicates which seeding file type the SEIR model will look for, "seed", which is generated from create_seeding.R, or "impa", which refers to importation
folder_path	required	path to folder where importation inference files will be saved
lambda_file	required	path to seeding file
perturbation_sd	required	standard deviation for the proposal value of the seeding date, in number of days

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]

This configuration allows us to infer subpop-level baseline R0 estimates by adding a local_variance intervention. The baseline subpop-specific R0 estimate may be calculated as $R0*(1-local_variance),$ where R0 is the baseline simulation R0 value, and local_variance is an estimated subpop-specific value.

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.

Item	Required?	Type/Format
template	Required	"SinglePeriodModifierR0" or "StackedModifier"
period_start_date	optional for SinglePeriodModifierR0	date between global start_date and end_date; default is global start_date
period_end_date	optional for SinglePeriodModifierR0	date between global start_date and end_date; default is global end_date
value	required for SinglePeriodModifierR0	specifies both the prior distribution and range of support for the final inferred values
perturbation	optional for SinglePeriodModifierR0	this option indicates whether inference will be performed on this setting and how the proposal value will be identified from the last accepted value
subpop	optional for SinglePeriodModifierR0	list of subpops, which must be in geodata

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

Item	Required?	Type/Format
method	required	"delayframe"
param_from_file	required	if TRUE, will look for param_subpop_file
param_subpop_file	optional	path to subpop-params parquet file, which indicates location specific risk values. Values in this file will override values in the config if there is overlap.
scenarios	required	user-defined scenario name
settings	required	See details below

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.

Item	Required?	Type/Format
(health outcome metric)	required	"incidH", "incidD", "incidICU", "incidVent", "incidC", corresponding to variable names
source	required	name of health outcome metric that is used as the reference point
probability	required	health outcome risk
probability::value	required	specifies whether the value is fixed or distributional and the parameters specific to that metric and distribution
probability::perturbation	optional	inference settings for the probability metric
delay	required	time delay between source and the specified health outcome
delay::value	required	specifies whether the value is fixed or distributional and the parameters specific to that metric and distribution
delay::perturbation	optional	inference settings for the time delay metric (coming soon)
duration	optional	duration that health outcome status endures
duration::value	required	specifies whether the value is fixed or distributional and the parameters specific to that metric and distribution
duration::perturbation	optional	inference settings for the duration metric (coming soon)

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).

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