This repo contains statistical tools to predict the uptake of immunizations (primarily vaccines and boosters). The three primary steps are:
- Import data sets on past uptake and cast them into a standardize format
- Fit a variety of models that both capture past uptake as well as project future uptake, and
- Evaluate model projections against realized uptake.
All three steps are currently under development.
This approach is applicable to seasonal adult immunizations. Each year, the uptake process starts afresh on the immunization rollout date, and individuals' transitions across age groups are not relevant.
Use https://github.com/CDCgov/nis-py-api for access to the NIS data.
- Set up a virtual environment with
poetry shell - Installed the required dependencies with
poetry install - Get a Socrata app token and save it in
scripts/socrata_app_token.txt - Cache NIS data with
make cache - Copy the config template in
scripts/config_template.yaml(e.g., toscripts/config.yaml) and fill in the necessary fields- data: specify the type of vaccine data in terms of: rollout dates, grouping factors including geography, demography (
domain_typeanddomain), and metric (indicator_type,indicator). - forecast_timeframe: specify the start and the end of forecast dates, and interval between forecast dates (using the polars string language, e.g.,
7d). - evaluation_timeframe: specify the interval of the forecast dates for evaluation. If blank, no evaluation score will be returned.
- models: specify the name of the model (refer to iup.models), random seed, initial values of parameters, and parameters to use NUTS kernel in MCMC run
- score_funs: specify the evaluation metrics. Can be a list including "mspe", "mean_bias" and "eos_abe".
- data: specify the type of vaccine data in terms of: rollout dates, grouping factors including geography, demography (
make allto get cleaned data "data/nis_raw.parquet", forecasts "data/forecasts.parquet", evaluation scores "data/scores.parquet", forecast plot "output/projections.png", and evaluation score plot "output/scores.png".
- Mean squared prediction error on incident projections ("mspe")
- Mean bias on incident projections ("mean_bias")
- Absolute error of end-of-season uptake on incident projections ("eos_abe")
flowchart TB
nis_data(nis_raw.parquet)
forecast(forecasts.parquet)
scores(scores.parquet)
config{{config.yaml}}
proj_plot[projections.png]
score_plot[scores.png]
subgraph raw data
NIS
end
subgraph clean data with seasons
nis_data
end
subgraph prediction by seasons
forecast
end
subgraph evaluation scores
scores
end
subgraph prediction plots
proj_plot
end
subgraph score plots
score_plot
end
NIS -->preprocess.py --> nis_data
nis_data --> forecast.py --> forecast
forecast --> eval.py --> scores
scores --> postprocess.py --> score_plot
nis_data --> postprocess.py --> proj_plot
forecast --> postprocess.py
config --> preprocess.py
config --> forecast.py
config --> eval.py
style nis_data fill:#7f00ff
style forecast fill:#7f00ff
style scores fill:#7f00ff
style config fill:#f58742
style preprocess.py fill:#0080ff
style forecast.py fill:#0080ff
style eval.py fill:#0080ff
style postprocess.py fill:#0080ff
style proj_plot fill:#ff6666
style score_plot fill: #ff6666
- Edward Schrom (CDC/CFA/Predict) [email protected]
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