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: geography, demography (domain), and vaccine status(indicator).
- timeframe: specify the start and the end of time in predicted projections or evaluation.
- option: specify the returned output. Can be either "prediction" or "evaluation".
- metrics: specify the evaluation metrics. Only valid when option is "evaluation". Can be one of "mspe", "mean_bias","end_of_season_error", or "all".
make run
- Edward Schrom (CDC/CFA/Predict) [email protected]
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