def METHOD_NAME(claims_filepath, _end_date, backfill_dir):
    """
    Store county level backfill data into backfill_dir.
    Parameter:
        claims_filepath: str
            path to the aggregated claims data
        _end_date: datetime
            The most recent date when the raw data is received
        backfill_dir: str
            specified path to store backfill files.
    """
    backfilldata = pd.read_csv(
        claims_filepath,
        usecols=Config.CLAIMS_DTYPES.keys(),
        dtype=Config.CLAIMS_DTYPES,
        parse_dates=[Config.CLAIMS_DATE_COL],
    )
    backfilldata.rename({"ServiceDate": "time_value",
                         "PatCountyFIPS": "fips",
                         "Denominator": "den",
                         "Covid_like": "num"},
                        axis=1, inplace=True)
    backfilldata = gmpr.add_geocode(backfilldata, from_code="fips", new_code="state_id",
                           from_col="fips", new_col="state_id")
    #Store one year's backfill data
    _start_date = _end_date.replace(year=_end_date.year-1)
    selected_columns = ['time_value', 'fips', 'state_id',
                        'den', 'num']
    backfilldata = backfilldata.loc[(backfilldata["time_value"] >= _start_date)
                                    & (~backfilldata["fips"].isnull()),
                                    selected_columns]
    backfilldata["lag"] = [(_end_date - x).days for x in backfilldata["time_value"]]
    backfilldata["time_value"] = backfilldata.time_value.dt.strftime("%Y-%m-%d")
    backfilldata["issue_date"] = datetime.strftime(_end_date, "%Y-%m-%d")
    backfilldata = backfilldata.astype({
        "time_value": "string",
        "issue_date": "string",
        "fips": "string",
        "state_id": "string"
    })
    path = backfill_dir + \
        "/claims_hosp_as_of_%s.parquet"%datetime.strftime(_end_date, "%Y%m%d")
    # Store intermediate file into the backfill folder
    backfilldata.to_parquet(path, index=False)