Summary of forecasttools-py:
- A Python package.
- Primarily supports the Short Term Forecast’s team.
- Intended to support wider Real Time Monitoring branch operations.
- Has tools for pre- and post-processing.
- Conversion of
az.InferenceDataforecast to Hubverse format. - Addition of time and or dates to
az.InferenceData.
- Conversion of
Notes:
- This repository is a WORK IN PROGRESS.
- For the R version of this toolkit, see forecasttools.
- For CDC project expected to use
forecasttools-py, see pyrenew-hew.
A Tentative Utilities Diagram
%%{init: {"theme": "neutral", "themeVariables": { "fontFamily": "Iosevka", "fontSize": "25px", "lineColor": "#808b96", "arrowheadColor": "#808b96", "edgeStrokeWidth": "10px", "arrowheadLength": "20px"}}}%%
flowchart TD
A1[COVID-19 Data _from forecasttools_] --> A4[NumPyro Model]
A2[Influenza Data _from forecasttools_] --> A4[NumPyro Model]
A3[External Dataset] --> A4[NumPyro Model]
A4[NumPyro Model] -->|_arviz.from_numpyro_| A5[Forecast As InferenceData Object wo/ Dates]
A5[Forecast As InferenceData Object wo/ Dates] -->|_Add Dates To InferenceData_ - done| A6[InferenceData Object w/ Dates]
A6[InferenceData Object w/ Dates] -->|_Convert To Tidy-Like Dataframe_ - done| A7[Polars Forecast Dataframe w/ Draws]
A7[Polars Forecast Dataframe w/ Draws] -->|_Convert To Hubverse Formatted Dataframe_ - done| A8[FluSight Submission Dataframe]
A7[Polars Forecast Dataframe w/ Draws] -->|_Convert To ScoringUtils Formatted Dataframe_ - in progress| A9[ScoringUtils DataFrame]
A7[Polars Forecast Dataframe w/ Draws] -->|_Save_| A10[Parquet File]
A8[FluSight Submission Dataframe] -->|_Save_| A11[Parquet File]
A9[ScoringUtils DataFrame] -->|_Save_| A12[Parquet File]
A8[FluSight Submission Dataframe] -->|_Convert To ScoringUtils Formatted Dataframe_ - in progress| A9[ScoringUtils DataFrame]
A12[Parquet File] -->|_Get scores in R_| A13[Forecast Scores]
A11[Parquet File] -->|_Model Forecast Hypothesis Testing_| A14[Model Comparison Report]
B1[Pulled Parquet Hubverse Submissions] -->|_Model Forecast Hypothesis Testing_| A14[Model Comparison Report]
linkStyle default stroke: #808b96
linkStyle default stroke-width: 2.0px
Install forecasttools-py via:
pip3 install git+https://github.com/CDCgov/forecasttools-py@main
- Format Arviz Forecast Output For FluSight Submission
- Community Meeting Utilities Demonstration (2024-11-19)
Coming soon as webpages, once Issue 26 is completed.
Within forecasttools-py, one finds several packaged datasets. These
datasets can aid with experimentation; some are directly necessary to
other utilities provided by forecasttools-py.
import forecasttoolsSummary of datasets:
forecasttools.location_table- A Polars dataframe of location abbreviations, codes, and names for Hubverse formatted forecast submissions.
forecasttools.example_flusight_submission- An example Hubverse formatted influenza forecast submission (as a Polars dataframe) submitted to the FluSight Hub.
forecasttools.nhsn_hosp_COVID- A Polars dataframe of NHSN COVID hospital admissions data.
forecasttools.nhsn_hosp_flu- A Polars dataframe of NHSN influenza hospital admissions data.
forecasttools.nhsn_flu_forecast_wo_dates- An
az.InferenceDataobject containing a forecast made using NSHN influenza data for Texas.
- An
forecasttools.nhsn_flu_forecast_w_dates- An modified (with dates as coordinates)
az.InferenceDataobject containing a forecast made using NSHN influenza data for Texas.
- An modified (with dates as coordinates)
See below for more information on the datasets.
The location table contains abbreviations, codes, and extended names for the US jurisdictions for which the FluSight and COVID forecasting hubs require users to generate forecasts.
The location table is stored in forecasttools-py as a polars
dataframe and is accessed via:
loc_table = forecasttools.location_table
print(loc_table)shape: (58, 3)
┌───────────────┬────────────┬─────────────────────────────┐
│ location_code ┆ short_name ┆ long_name │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═══════════════╪════════════╪═════════════════════════════╡
│ US ┆ US ┆ United States │
│ 01 ┆ AL ┆ Alabama │
│ 02 ┆ AK ┆ Alaska │
│ 04 ┆ AZ ┆ Arizona │
│ 05 ┆ AR ┆ Arkansas │
│ … ┆ … ┆ … │
│ 66 ┆ GU ┆ Guam │
│ 69 ┆ MP ┆ Northern Mariana Islands │
│ 72 ┆ PR ┆ Puerto Rico │
│ 74 ┆ UM ┆ U.S. Minor Outlying Islands │
│ 78 ┆ VI ┆ U.S. Virgin Islands │
└───────────────┴────────────┴─────────────────────────────┘
Using ./forecasttools/data.py, the location table was created by
running the following:
make_census_dataset(
file_save_path=os.path.join(
os.getcwd(),
"location_table.csv"
),
)The example FluSight submission comes from the following 2023-24 submission.
The example FluSight submission is stored in forecasttools-py as a
polars dataframe and is accessed via:
submission = forecasttools.example_flusight_submission
print(submission)shape: (4_876, 8)
┌────────────┬────────────┬─────────┬────────────┬──────────┬────────────┬────────────┬────────────┐
│ reference_ ┆ target ┆ horizon ┆ target_end ┆ location ┆ output_typ ┆ output_typ ┆ value │
│ date ┆ --- ┆ --- ┆ _date ┆ --- ┆ e ┆ e_id ┆ --- │
│ --- ┆ str ┆ i64 ┆ --- ┆ str ┆ --- ┆ --- ┆ f64 │
│ str ┆ ┆ ┆ str ┆ ┆ str ┆ f64 ┆ │
╞════════════╪════════════╪═════════╪════════════╪══════════╪════════════╪════════════╪════════════╡
│ 2023-10-14 ┆ wk inc flu ┆ -1 ┆ 2023-10-07 ┆ 01 ┆ quantile ┆ 0.01 ┆ 7.670286 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-10-14 ┆ wk inc flu ┆ -1 ┆ 2023-10-07 ┆ 01 ┆ quantile ┆ 0.025 ┆ 9.968043 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-10-14 ┆ wk inc flu ┆ -1 ┆ 2023-10-07 ┆ 01 ┆ quantile ┆ 0.05 ┆ 12.022354 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-10-14 ┆ wk inc flu ┆ -1 ┆ 2023-10-07 ┆ 01 ┆ quantile ┆ 0.1 ┆ 14.497646 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-10-14 ┆ wk inc flu ┆ -1 ┆ 2023-10-07 ┆ 01 ┆ quantile ┆ 0.15 ┆ 16.119813 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-10-14 ┆ wk inc flu ┆ 2 ┆ 2023-10-28 ┆ US ┆ quantile ┆ 0.85 ┆ 2451.87489 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ 9 │
│ 2023-10-14 ┆ wk inc flu ┆ 2 ┆ 2023-10-28 ┆ US ┆ quantile ┆ 0.9 ┆ 2806.92858 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ 8 │
│ 2023-10-14 ┆ wk inc flu ┆ 2 ┆ 2023-10-28 ┆ US ┆ quantile ┆ 0.95 ┆ 3383.74799 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-10-14 ┆ wk inc flu ┆ 2 ┆ 2023-10-28 ┆ US ┆ quantile ┆ 0.975 ┆ 3940.39253 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ 6 │
│ 2023-10-14 ┆ wk inc flu ┆ 2 ┆ 2023-10-28 ┆ US ┆ quantile ┆ 0.99 ┆ 4761.75738 │
│ ┆ hosp ┆ ┆ ┆ ┆ ┆ ┆ 5 │
└────────────┴────────────┴─────────┴────────────┴──────────┴────────────┴────────────┴────────────┘
Using data.py, the example FluSight submission was created by running
the following:
get_and_save_flusight_submission(
file_save_path=os.path.join(
os.getcwd(),
"example_flusight_submission.csv"
),
)NHSN hospital admissions fitting data for COVID and Flu is included in
forecasttools-py as well, for user experimentation.
This data:
- Is current as of
2024-04-27 - Comes from the website HealthData.gov COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries.
For influenza, the previous_day_admission_influenza_confirmed column
is retained and for COVID the
previous_day_admission_adult_covid_confirmed column is retained. As
can be seen in the example below, some early dates for each jurisdiction
do not have data.
The fitting data is stored in forecasttools-py as a polars dataframe
and is accessed via:
# access COVID data
covid_nhsn_data = forecasttools.nhsn_hosp_COVID
# access flu data
flu_nhsn_data = forecasttools.nhsn_hosp_flu
# display flu data
print(flu_nhsn_data)shape: (81_713, 3)
┌───────┬────────────┬──────┐
│ state ┆ date ┆ hosp │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞═══════╪════════════╪══════╡
│ AK ┆ 2020-03-23 ┆ null │
│ AK ┆ 2020-03-24 ┆ null │
│ AK ┆ 2020-03-25 ┆ null │
│ AK ┆ 2020-03-26 ┆ null │
│ AK ┆ 2020-03-27 ┆ null │
│ … ┆ … ┆ … │
│ WY ┆ 2024-04-23 ┆ 1 │
│ WY ┆ 2024-04-24 ┆ 1 │
│ WY ┆ 2024-04-25 ┆ 0 │
│ WY ┆ 2024-04-26 ┆ 0 │
│ WY ┆ 2024-04-27 ┆ 0 │
└───────┴────────────┴──────┘
The data was created by placing a csv file called
NHSN_RAW_20240926.csv (the full NHSN dataset) into ./forecasttools/
and running, in data.py, the following:
# generate COVID dataset
make_nshn_fitting_dataset(
dataset="COVID",
nhsn_dataset_path="NHSN_RAW_20240926.csv",
file_save_path=os.path.join(
os.getcwd(),
"nhsn_hosp_COVID.csv"
)
)
# generate flu dataset
make_nshn_fitting_dataset(
dataset="flu",
nhsn_dataset_path="NHSN_RAW_20240926.csv",
file_save_path=os.path.join(
os.getcwd(),
"nhsn_hosp_flu.csv"
)
)Two example forecasts stored in Arviz InferenceData objects are
included for vignettes and user experimentation. Both are 28 day
influenza hospital admissions forecasts for Texas made using a spline
regression model fitted to NHSN data between 2022-08-08 and 2022-12-08.
The only difference between the forecasts is that
example_flu_forecast_w_dates.nc has had dates added as its coordinates
(this is not a native Arviz feature).
The forecast idatas are accessed via:
# idata with dates as coordinates
idata_w_dates = forecasttools.nhsn_flu_forecast_w_dates
print(idata_w_dates)Inference data with groups:
> posterior
> posterior_predictive
> log_likelihood
> sample_stats
> prior
> prior_predictive
> observed_data
# show dates
print(idata_w_dates["observed_data"]["obs"]["obs_dim_0"][:15])<xarray.DataArray 'obs_dim_0' (obs_dim_0: 15)> Size: 120B
array(['2022-08-08T00:00:00.000000000', '2022-08-09T00:00:00.000000000',
'2022-08-10T00:00:00.000000000', '2022-08-11T00:00:00.000000000',
'2022-08-12T00:00:00.000000000', '2022-08-13T00:00:00.000000000',
'2022-08-14T00:00:00.000000000', '2022-08-15T00:00:00.000000000',
'2022-08-16T00:00:00.000000000', '2022-08-17T00:00:00.000000000',
'2022-08-18T00:00:00.000000000', '2022-08-19T00:00:00.000000000',
'2022-08-20T00:00:00.000000000', '2022-08-21T00:00:00.000000000',
'2022-08-22T00:00:00.000000000'], dtype='datetime64[ns]')
Coordinates:
* obs_dim_0 (obs_dim_0) datetime64[ns] 120B 2022-08-08 ... 2022-08-22
# idata without dates as coordinates
idata_wo_dates = forecasttools.nhsn_flu_forecast_wo_dates
print(idata_wo_dates["observed_data"]["obs"]["obs_dim_0"][:15])<xarray.DataArray 'obs_dim_0' (obs_dim_0: 15)> Size: 120B
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
Coordinates:
* obs_dim_0 (obs_dim_0) int64 120B 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
The forecast was generated following the creation of nhsn_hosp_flu.csv
(see previous section) by running data.py with the following added:
make_forecast(
nhsn_data=forecasttools.nhsn_hosp_flu,
start_date="2022-08-08",
end_date="2022-12-08",
juris_subset=["TX"],
forecast_days=28,
save_path="../forecasttools/example_flu_forecast_w_dates.nc",
save_idata=True,
use_log=False,
)(note: make_forecast is no longer included in forecasttools-py,
given the expectation that no one would ever call it; however, for
reproducibility’s sake, the following is included here)
Some Of The Forecast Code
"""
Creating a new idata object with
dates to change the functionality
of idata_w_dates_to_df.
"""
# %% IMPORTS
import os
from datetime import datetime, timedelta
import arviz as az
import forecasttools
import jax.numpy as jnp
import jax.random as jr
import matplotlib.pyplot as plt
import numpy as np
import numpyro
import numpyro.distributions as dist
import patsy
import polars as pl
from numpy.typing import NDArray
# %% CHECK FILE PATH
def check_file_save_path(
file_save_path: str,
) -> None:
"""
Checks whether a file path is valid.
file_save_path
The file path to be checked.
"""
directory = os.path.dirname(file_save_path)
if not os.path.exists(directory):
raise FileNotFoundError(f"Directory does not exist: {directory}")
if not os.access(directory, os.W_OK):
raise PermissionError(f"Directory is not writable: {directory}")
if os.path.exists(file_save_path):
raise FileExistsError(f"File already exists at: {file_save_path}")
# %% SPLINE REGRESSION MODEL
def model(basis_matrix, y=None):
# priors
shift = numpyro.sample("shift", dist.Normal(0.0, 2.0))
beta_coeffs = numpyro.sample(
"beta_coeffs",
dist.Normal(jnp.zeros(basis_matrix.shape[1]), 2.0),
)
shift_mu = jnp.dot(basis_matrix, beta_coeffs) + shift
mu_exp = jnp.exp(shift_mu)
alpha = numpyro.sample("alpha", dist.Exponential(1.0))
# likelihood
numpyro.sample(
"obs",
dist.NegativeBinomial2(mu_exp, alpha),
obs=y,
)
# %% SPLINE BASIS MATRIX
def spline_basis(X, degree: int = 4, df: int = 8) -> NDArray:
basis = patsy.dmatrix(
"bs(x, df=df, degree=degree, include_intercept=True) - 1",
{"x": X, "df": df, "degree": degree},
return_type="matrix",
)
return np.array(basis)
# %% PLOT AND OR SAVE FORECAST
def plot_and_or_save_forecast(
idata: az.InferenceData,
X: NDArray,
y: NDArray,
title: str,
start_date: str,
end_date: str,
last_fit: int,
X_act: NDArray,
y_act: NDArray,
save_to_pdf: bool = False,
use_log: bool = False,
):
"""
Includes hard-coded variables. For the
author's testing and no more.
"""
x_data = idata.posterior_predictive["obs_dim_0"]
y_data = idata.posterior_predictive["obs"]
fig, axes = plt.subplots(1, 1, figsize=(8, 6))
az.plot_hdi(
x_data,
y_data,
hdi_prob=0.95,
color="skyblue",
smooth=False,
fill_kwargs={
"alpha": 0.2,
"label": "95% Credible",
},
ax=axes,
)
az.plot_hdi(
x_data,
y_data,
hdi_prob=0.75,
color="skyblue",
smooth=False,
fill_kwargs={
"alpha": 0.4,
"label": "75% Credible",
},
ax=axes,
)
az.plot_hdi(
x_data,
y_data,
hdi_prob=0.5,
color="C0",
smooth=False,
fill_kwargs={
"alpha": 0.6,
"label": "50% Credible",
},
ax=axes,
)
axes.plot(
X,
y,
marker="o",
color="black",
linewidth=1.0,
markersize=3.0,
label="Observed",
)
if (X_act is not None) and (y_act is not None):
axes.plot(
X_act,
y_act,
marker="o",
color="red",
linewidth=1.0,
markersize=3.0,
label="Actual",
)
if use_log:
axes.set_yscale("log")
axes.set_ylabel(
"(Log) Hospital Admissions",
fontsize=17.5,
)
if not use_log:
axes.set_ylabel("Hospital Admissions", fontsize=17.5)
median_ts = y_data.median(dim=["chain", "draw"])
axes.plot(
x_data,
median_ts,
color="blue",
label="Median",
)
axes.legend()
axes.axvline(last_fit, color="black", linestyle="--")
axes.set_title(
f"{title}",
fontsize=20,
)
axes.set_xlabel("Time", fontsize=17.5)
plt.show()
# %% ADD DATES TO AN INFERENCE DATA OBJECT
def add_dates_to_idata_object(
idata: az.InferenceData,
start_date: str,
) -> az.InferenceData:
"""
Takes an InferenceData object w/
observed_data and posterior_predictive
groups and adds date indexing
"""
pass
# %% MAKE A FORECAST
def make_forecast(
nhsn_data: str,
start_date: str,
end_date: str,
juris_subset: list[str],
forecast_days: int,
save_path: str = os.path.join(os.getcwd(), "forecast.nc"),
show_plot: bool = True,
save_idata: bool = False,
use_log: bool = False,
) -> None:
"""
Generates a forecast for specified
dates using a spline regression model.
"""
# check dataset path
check_file_save_path(save_path)
# clean data and organize data, cleaning null values
nhsn_data = nhsn_data.with_columns(
pl.col("hosp").cast(pl.Int64),
pl.col("date").str.strptime(pl.Date, "%Y-%m-%d"),
).filter(
pl.col("hosp").is_not_null(),
pl.col("state").is_in(juris_subset),
)
nhsn_data_ready = nhsn_data.filter(
(
pl.col("date")
>= pl.lit(start_date).str.strptime(pl.Date, "%Y-%m-%d")
)
& (
pl.col("date")
<= pl.lit(end_date).str.strptime(pl.Date, "%Y-%m-%d")
)
)
# get the actual values, if they exist
try:
forecast_end_date = datetime.strptime(
end_date, "%Y-%m-%d"
) + timedelta(days=forecast_days)
nhsn_data_actual = nhsn_data.filter(
(
pl.col("date")
>= pl.lit(end_date).str.strptime(pl.Date, "%Y-%m-%d")
)
& (pl.col("date") <= pl.lit(forecast_end_date))
)
except Exception as e:
nhsn_data_actual = None
print(f"The following error occurred: {e}")
# define some shared inference values
random_seed = 2134312
num_samples = 1000
num_warmup = 500
# get posterior samples and make forecasts for each selected state
for state in juris_subset:
# get the state data
state_nhsn = nhsn_data_ready.filter(pl.col("state") == state)
# get observation (fitting) data y, X
y = state_nhsn["hosp"].to_numpy()
X = np.arange(y.shape[0])
# set up inference, NUTS/MCMC
kernel = numpyro.infer.NUTS(
model=model,
max_tree_depth=12,
target_accept_prob=0.85,
init_strategy=numpyro.infer.init_to_uniform(),
)
mcmc = numpyro.infer.MCMC(
kernel,
num_warmup=num_warmup,
num_samples=num_samples,
)
# create spline basis for obs period and forecast period
last = X[-1]
X_future = np.hstack(
(
X,
np.arange(
last + 1,
last + 1 + forecast_days,
),
)
)
sbm = spline_basis(X_future)
# get posterior samples
mcmc.run(
rng_key=jr.key(random_seed),
basis_matrix=sbm[: len(X)],
y=y,
)
posterior_samples = mcmc.get_samples()
# get prior predictive
prior_pred = numpyro.infer.Predictive(model, num_samples=num_samples)(
rng_key=jr.key(random_seed),
basis_matrix=sbm[: len(X)],
)
# get posterior predictive forecast
posterior_pred_for = numpyro.infer.Predictive(
model,
posterior_samples=posterior_samples,
)(
rng_key=jr.key(random_seed),
basis_matrix=sbm,
)
# create initial inference data object(s) and store
idata = az.from_numpyro(
posterior=mcmc,
posterior_predictive=posterior_pred_for,
prior=prior_pred,
)
# get actual data, if it exists
if isinstance(nhsn_data_actual, pl.DataFrame):
actual_data = nhsn_data_actual.filter(pl.col("state") == state)
y_act = actual_data["hosp"].to_numpy()
X_act = np.arange(last - 1, last + forecast_days)
if not isinstance(nhsn_data_actual, pl.DataFrame):
y_act = None
X_act = None
# add dates to idata object
# save idata object(s)
if save_idata:
idata.to_netcdf(save_path)
# plot forecast (if desired) from idata light
if show_plot:
plot_and_or_save_forecast(
idata=idata,
X=X,
y=y,
title=f"Hospital Admissions ({state}, {start_date}-{end_date})",
start_date=start_date,
end_date=end_date,
last_fit=last,
X_act=X_act,
y_act=y_act,
use_log=use_log,
)
# %% EXECUTE MODE
make_forecast(
nhsn_data=forecasttools.nhsn_hosp_flu,
start_date="2022-08-08",
end_date="2022-12-08",
juris_subset=["TX"],
forecast_days=28,
save_path="../forecasttools/example_flu_forecast_w_dates.nc",
save_idata=False,
use_log=True,
)The forecast looks like:
General disclaimer This repository was created for use by CDC programs to collaborate on public health related projects in support of the CDC mission. GitHub is not hosted by the CDC, but is a third party website used by CDC and its partners to share information and collaborate on software. CDC use of GitHub does not imply an endorsement of any one particular service, product, or enterprise.
Rules, Policy, And Collaboration
Public Domain Standard Notice
This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.
License Standard Notice
The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.
This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.
This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.
You should have received a copy of the Apache Software License along with this program. If not, see http://www.apache.org/licenses/LICENSE-2.0.html
The source code forked from other open source projects will inherit its license.
Privacy Standard Notice
This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer and Code of Conduct. For more information about CDC’s privacy policy, please visit http://www.cdc.gov/other/privacy.html.
Contributing Standard Notice
Anyone is encouraged to contribute to the repository by forking and submitting a pull request. (If you are new to GitHub, you might start with a basic tutorial.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the Apache Software License v2 or later.
All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at http://www.cdc.gov/other/privacy.html.
Records Management Standard Notice
This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.
Additional Standard Notices
Please refer to CDC’s Template Repository for more information about contributing to this repository, public domain notices and disclaimers, and code of conduct.