def METHOD_NAME(dataset: MultiRegionDataset) -> MultiRegionDataset:
    """Applies NYT anomalies data to case and death series.
    NYT Anomalies marked with omit_from_rolling_average are removed and tagged.
    """
    nyt_anomalies = read_nytimes_anomalies()
    new_tags = taglib.TagCollection()
    timeseries_copy = dataset.timeseries_bucketed.copy()
    # Find the applicable anomalies by intersecting indexes.
    assert timeseries_copy.index.names == [
        CommonFields.LOCATION_ID,
        PdFields.DEMOGRAPHIC_BUCKET,
        CommonFields.DATE,
    ]
    nyt_anomalies_indexed = nyt_anomalies.set_index(
        [CommonFields.LOCATION_ID, PdFields.DEMOGRAPHIC_BUCKET, CommonFields.DATE,]
    )
    overlapping_index = timeseries_copy.index.intersection(nyt_anomalies_indexed.index)
    if overlapping_index.size == 0:
        return dataset
    # Get the applicable anomalies.
    applicable_nyt_anomalies = nyt_anomalies_indexed.loc[overlapping_index].reset_index()
    for index, row in applicable_nyt_anomalies.iterrows():
        if row[NYTimesFields.OMIT_FROM_ROLLING_AVERAGE] == "yes":
            location_id = row[CommonFields.LOCATION_ID]
            # NOTE(sean) 03/08/22: ignoring MD anamolies in december 2021 to allow
            # test_patch_maryland_missing_case_data to take effect.
            if "iso1:us#iso2:us-md" in location_id:
                date = row[NYTimesFields.DATE]
                if date >= pd.to_datetime("2021-12-01") and date < pd.to_datetime("2022-01-01"):
                    continue
            bucket = row[PdFields.DEMOGRAPHIC_BUCKET]
            date = row[NYTimesFields.DATE]
            description = "NYTimes: {}".format(row["description"])
            variable = row[PdFields.VARIABLE]
            assert location_id and bucket and date and description and variable
            new_tags.add(
                taglib.KnownIssue(public_note=description, date=date.date()),
                location_id=location_id,
                variable=variable,
                bucket=bucket,
            )
            timeseries_copy.at[(location_id, bucket, date), variable] = np.nan
    # Return dataset with filtered timeseries.
    return dataclasses.replace(dataset, timeseries_bucketed=timeseries_copy).append_tag_df(
        new_tags.as_dataframe()
    )