# (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`

`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:

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`

`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::settings::[setting_name]`

`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

`outcomes`

sectionThis 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::settings::[setting_name]`

`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

`filtering`

sectionThis 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`

settings

`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).

`filtering::statistics`

`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`

`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`

`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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