affordable-housing-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 0: SETUP\n",
    "The following libraries are used directly. For the full list of installed packages and versions, please see requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 1: DATA ACQUISITION\n",
    "\n",
    "Data is downloaded as csv files, and is already available in this repository in the data folder. See the readme for details on the source of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Data\n",
    "Case data is provided with dates as columns. For processing, the data is melted into a \"date\" column and \"case_count\" column, and unused columns are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>date</th>\n",
       "      <th>Case_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01003</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01005</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01007</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01009</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FIPS       date  Case_Count\n",
       "0  01001 2020-01-22           0\n",
       "1  01003 2020-01-22           0\n",
       "2  01005 2020-01-22           0\n",
       "3  01007 2020-01-22           0\n",
       "4  01009 2020-01-22           0"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get raw data, only keep used columns\n",
    "raw_case_data = pd.read_csv('data/raw/RAW_us_confirmed_cases.csv').drop(columns = [\"Province_State\", \"Admin2\", \"UID\", \"iso2\", \"iso3\", \"Country_Region\", \"Lat\", \"Long_\", \"code3\", \"Combined_Key\"])\n",
    "\n",
    "\n",
    "# Pivot into dat column\n",
    "case_data = raw_case_data.melt(id_vars = \"FIPS\", var_name=\"date\", value_name = \"Case_Count\").dropna(subset=['FIPS'])\n",
    "\n",
    "# Cast types\n",
    "case_data[\"date\"] = pd.to_datetime(case_data[\"date\"])\n",
    "case_data[\"FIPS\"] = case_data[\"FIPS\"].astype(int).astype(str).str.pad(5, 'left', '0')\n",
    "case_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compliance Data\n",
    "Mask compliance is combined into a single score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>mask_compliance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>0.75075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01003</td>\n",
       "      <td>0.74200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01005</td>\n",
       "      <td>0.73200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01007</td>\n",
       "      <td>0.83700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01009</td>\n",
       "      <td>0.72300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FIPS  mask_compliance\n",
       "0  01001          0.75075\n",
       "1  01003          0.74200\n",
       "2  01005          0.73200\n",
       "3  01007          0.83700\n",
       "4  01009          0.72300"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get raw data\n",
    "raw_compliance_data = pd.read_csv('data/raw/mask_use_by_county.csv')\n",
    "raw_compliance_data[\"FIPS\"] = raw_compliance_data['COUNTYFP'].astype(str).str.pad(5, 'left', '0')\n",
    "\n",
    "# Combine into single score\n",
    "raw_compliance_data[\"mask_compliance\"] = np.dot(raw_compliance_data.iloc[:,1:6], [0, 0.25, 0.5, 0.75, 1])\n",
    "\n",
    "# Clean\n",
    "compliance_data = raw_compliance_data[[\"FIPS\", \"mask_compliance\"]]\n",
    "compliance_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Housing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>li_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01001</th>\n",
       "      <td>499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01003</th>\n",
       "      <td>1920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01005</th>\n",
       "      <td>413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01007</th>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01009</th>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       li_units\n",
       "FIPS           \n",
       "01001     499.0\n",
       "01003    1920.0\n",
       "01005     413.0\n",
       "01007     278.0\n",
       "01009     275.0"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get raw data, only keep used columns\n",
    "use_cols = [\"fips2010\",\"li_units\"]\n",
    "raw_housing_data = pd.read_csv('data/raw/LIHTCPUB.CSV', usecols=use_cols)\n",
    "raw_housing_data[\"FIPS\"] = raw_housing_data[\"fips2010\"].str[:5]\n",
    "\n",
    "housing_data = raw_housing_data.drop(columns=[\"fips2010\"]).groupby(\"FIPS\").sum()\n",
    "housing_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demographic Data\n",
    "Census Income and Population data are combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>household_count</th>\n",
       "      <th>mean_income</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13013</td>\n",
       "      <td>27765</td>\n",
       "      <td>77081</td>\n",
       "      <td>83240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13015</td>\n",
       "      <td>39742</td>\n",
       "      <td>70644</td>\n",
       "      <td>107738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13021</td>\n",
       "      <td>56726</td>\n",
       "      <td>67678</td>\n",
       "      <td>153159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13031</td>\n",
       "      <td>28660</td>\n",
       "      <td>61191</td>\n",
       "      <td>79608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13045</td>\n",
       "      <td>42798</td>\n",
       "      <td>69895</td>\n",
       "      <td>119992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    FIPS  household_count  mean_income  population\n",
       "0  13013            27765        77081       83240\n",
       "1  13015            39742        70644      107738\n",
       "2  13021            56726        67678      153159\n",
       "3  13031            28660        61191       79608\n",
       "4  13045            42798        69895      119992"
      ]
     },
     "execution_count": 204,
     "metadata": {},
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   ],
   "source": [
    "# INCOME\n",
    "# Get raw data, only keep used columns\n",
    "use_cols = [\"id\", \"Estimate!!Households!!Total\", \"Estimate!!Households!!Mean income (dollars)\"]\n",
    "raw_income_data = pd.read_csv('data/raw/income_census_data_2019.csv', header=1, usecols=use_cols)\n",
    "raw_income_data.columns= [\"FIPS\", \"household_count\", \"mean_income\"]\n",
    "raw_income_data[\"FIPS\"] = raw_income_data[\"FIPS\"].str[-5:]\n",
    "\n",
    "# POPULATION\n",
    "# Get raw data, only keep used columns\n",
    "use_cols = [\"id\", \"Estimate!!SEX AND AGE!!Total population\"]\n",
    "raw_population_data = pd.read_csv('data/raw/population_census_data_2019.csv', header=1, usecols=use_cols)\n",
    "raw_population_data.columns= [\"FIPS\", \"population\"]\n",
    "raw_population_data[\"FIPS\"] = raw_population_data[\"FIPS\"].str[-5:]\n",
    "\n",
    "# Combine\n",
    "demographic_data = raw_income_data.merge(raw_population_data, on=\"FIPS\", how='inner')\n",
    "demographic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 2: DATA PROCESSING\n",
    "\n",
    "Merge the data on FIPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>date</th>\n",
       "      <th>Case_Count</th>\n",
       "      <th>mask_compliance</th>\n",
       "      <th>li_units</th>\n",
       "      <th>household_count</th>\n",
       "      <th>mean_income</th>\n",
       "      <th>population</th>\n",
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       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>2020-01-22</td>\n",
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       "      <td>499.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>01003</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.74200</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01005</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.73200</td>\n",
       "      <td>413.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01007</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83700</td>\n",
       "      <td>278.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01009</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.72300</td>\n",
       "      <td>275.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FIPS       date  Case_Count  mask_compliance  li_units  household_count  \\\n",
       "0  01001 2020-01-22           0          0.75075     499.0              NaN   \n",
       "1  01003 2020-01-22           0          0.74200    1920.0              NaN   \n",
       "2  01005 2020-01-22           0          0.73200     413.0              NaN   \n",
       "3  01007 2020-01-22           0          0.83700     278.0              NaN   \n",
       "4  01009 2020-01-22           0          0.72300     275.0              NaN   \n",
       "\n",
       "   mean_income  population  \n",
       "0          NaN         NaN  \n",
       "1          NaN         NaN  \n",
       "2          NaN         NaN  \n",
       "3          NaN         NaN  \n",
       "4          NaN         NaN  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine on FIPS\n",
    "combined_data = case_data.merge(compliance_data, on=\"FIPS\", how='left')\n",
    "combined_data = combined_data.merge(housing_data,on=\"FIPS\", how='left')\n",
    "combined_data = combined_data.merge(demographic_data,on=\"FIPS\", how='left')\n",
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize housing data on household count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data[\"li_units_per_household\"] = combined_data[\"li_units\"] / combined_data[\"household_count\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add extra calculations per fip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>date</th>\n",
       "      <th>Case_Count</th>\n",
       "      <th>mask_compliance</th>\n",
       "      <th>li_units</th>\n",
       "      <th>household_count</th>\n",
       "      <th>mean_income</th>\n",
       "      <th>population</th>\n",
       "      <th>li_units_per_household</th>\n",
       "      <th>Case_Count_7da</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>active_cases</th>\n",
       "      <th>vulnerable_pop</th>\n",
       "      <th>case_count_per_capita</th>\n",
       "      <th>new_cases_per_capita</th>\n",
       "      <th>mean_household_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>13135</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8355</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>301471.0</td>\n",
       "      <td>93401.0</td>\n",
       "      <td>936250.0</td>\n",
       "      <td>0.009321</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.105606</td>\n",
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       "    <tr>\n",
       "      <th>3809</th>\n",
       "      <td>13135</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8355</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>301471.0</td>\n",
       "      <td>93401.0</td>\n",
       "      <td>936250.0</td>\n",
       "      <td>0.009321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.105606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7141</th>\n",
       "      <td>13135</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8355</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>301471.0</td>\n",
       "      <td>93401.0</td>\n",
       "      <td>936250.0</td>\n",
       "      <td>0.009321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.105606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10473</th>\n",
       "      <td>13135</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8355</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>301471.0</td>\n",
       "      <td>93401.0</td>\n",
       "      <td>936250.0</td>\n",
       "      <td>0.009321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.105606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13805</th>\n",
       "      <td>13135</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8355</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>301471.0</td>\n",
       "      <td>93401.0</td>\n",
       "      <td>936250.0</td>\n",
       "      <td>0.009321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.105606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        FIPS       date  Case_Count  mask_compliance  li_units  \\\n",
       "477    13135 2020-01-22           0           0.8355    2810.0   \n",
       "3809   13135 2020-01-23           0           0.8355    2810.0   \n",
       "7141   13135 2020-01-24           0           0.8355    2810.0   \n",
       "10473  13135 2020-01-25           0           0.8355    2810.0   \n",
       "13805  13135 2020-01-26           0           0.8355    2810.0   \n",
       "\n",
       "       household_count  mean_income  population  li_units_per_household  \\\n",
       "477           301471.0      93401.0    936250.0                0.009321   \n",
       "3809          301471.0      93401.0    936250.0                0.009321   \n",
       "7141          301471.0      93401.0    936250.0                0.009321   \n",
       "10473         301471.0      93401.0    936250.0                0.009321   \n",
       "13805         301471.0      93401.0    936250.0                0.009321   \n",
       "\n",
       "       Case_Count_7da  new_cases  active_cases  vulnerable_pop  \\\n",
       "477               NaN        NaN           NaN             NaN   \n",
       "3809              NaN        NaN           NaN             NaN   \n",
       "7141              NaN        NaN           NaN             NaN   \n",
       "10473             NaN        NaN           NaN             NaN   \n",
       "13805             NaN        NaN           NaN             NaN   \n",
       "\n",
       "       case_count_per_capita  new_cases_per_capita  mean_household_size  \n",
       "477                      0.0                   NaN             3.105606  \n",
       "3809                     0.0                   NaN             3.105606  \n",
       "7141                     0.0                   NaN             3.105606  \n",
       "10473                    0.0                   NaN             3.105606  \n",
       "13805                    0.0                   NaN             3.105606  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assumed variables\n",
    "active_window = 14 # Days after infection that case is active\n",
    "\n",
    "# Calculations per FIP\n",
    "combined_data.sort_values(by=\"date\", inplace=True)\n",
    "\n",
    "# Do rolling average of cases to account for weekly fluctiation\n",
    "grouped = combined_data.groupby(\"FIPS\")\n",
    "combined_data[\"Case_Count_7da\"] = grouped[\"Case_Count\"].rolling(window=7).mean().reset_index(0,drop=True)\n",
    "grouped = combined_data.groupby(\"FIPS\")\n",
    "combined_data[\"new_cases\"] = grouped[\"Case_Count_7da\"].diff().reset_index(0,drop=True).rolling(window=7).mean()\n",
    "grouped = combined_data.groupby(\"FIPS\")\n",
    "combined_data[\"active_cases\"] = grouped[\"new_cases\"].rolling(window=active_window).sum().reset_index(0,drop=True)\n",
    "combined_data[\"vulnerable_pop\"] = combined_data[\"population\"] - combined_data[\"Case_Count_7da\"]\n",
    "\n",
    "# Add extra data columns\n",
    "combined_data[\"case_count_per_capita\"] = combined_data[\"Case_Count\"] / combined_data[\"population\"]\n",
    "combined_data[\"new_cases_per_capita\"] = combined_data[\"new_cases\"] / combined_data[\"population\"]\n",
    "combined_data[\"mean_household_size\"] = combined_data[\"population\"] / combined_data[\"household_count\"]\n",
    "\n",
    "\n",
    "combined_data[combined_data[\"FIPS\"] == '13135'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter down to jsut the Atlanta area counties, and create a fake FIP for the entire Atlanta area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ccase\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:34: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "C:\\Users\\ccase\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:35: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>date</th>\n",
       "      <th>Case_Count</th>\n",
       "      <th>mask_compliance</th>\n",
       "      <th>li_units</th>\n",
       "      <th>household_count</th>\n",
       "      <th>mean_income</th>\n",
       "      <th>population</th>\n",
       "      <th>li_units_per_household</th>\n",
       "      <th>Case_Count_7da</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>active_cases</th>\n",
       "      <th>vulnerable_pop</th>\n",
       "      <th>case_count_per_capita</th>\n",
       "      <th>new_cases_per_capita</th>\n",
       "      <th>mean_household_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>13247</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773000</td>\n",
       "      <td>180.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.771896</td>\n",
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       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>13089</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.865500</td>\n",
       "      <td>5618.0</td>\n",
       "      <td>289829.0</td>\n",
       "      <td>94336.0</td>\n",
       "      <td>759297.0</td>\n",
       "      <td>0.019384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.619810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>13067</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.811000</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>286952.0</td>\n",
       "      <td>108459.0</td>\n",
       "      <td>760141.0</td>\n",
       "      <td>0.007939</td>\n",
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       "      <td>2.649018</td>\n",
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       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>13097</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.793750</td>\n",
       "      <td>316.0</td>\n",
       "      <td>48899.0</td>\n",
       "      <td>80676.0</td>\n",
       "      <td>146343.0</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.992761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>13113</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.755500</td>\n",
       "      <td>337.0</td>\n",
       "      <td>41253.0</td>\n",
       "      <td>128190.0</td>\n",
       "      <td>114421.0</td>\n",
       "      <td>0.008169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2.773641</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>659457</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>657914.428571</td>\n",
       "      <td>201.938776</td>\n",
       "      <td>2974.938776</td>\n",
       "      <td>4.243213e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-18</td>\n",
       "      <td>660811</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>658924.714286</td>\n",
       "      <td>219.673469</td>\n",
       "      <td>2962.795918</td>\n",
       "      <td>4.242202e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-19</td>\n",
       "      <td>661467</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>659574.000000</td>\n",
       "      <td>191.755102</td>\n",
       "      <td>2934.469388</td>\n",
       "      <td>4.241553e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-20</td>\n",
       "      <td>662153</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>660205.000000</td>\n",
       "      <td>186.530612</td>\n",
       "      <td>2895.755102</td>\n",
       "      <td>4.240922e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-21</td>\n",
       "      <td>662683</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>660783.571429</td>\n",
       "      <td>189.877551</td>\n",
       "      <td>2872.163265</td>\n",
       "      <td>4.240343e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7778 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        FIPS       date  Case_Count  mask_compliance  li_units  \\\n",
       "533    13247 2020-01-22           0         0.773000     180.0   \n",
       "453    13089 2020-01-22           0         0.865500    5618.0   \n",
       "443    13067 2020-01-22           0         0.811000    2278.0   \n",
       "458    13097 2020-01-22           0         0.793750     316.0   \n",
       "466    13113 2020-01-22           0         0.755500     337.0   \n",
       "..       ...        ...         ...              ...       ...   \n",
       "634  Atlanta 2021-10-17      659457         0.811545   15094.0   \n",
       "635  Atlanta 2021-10-18      660811         0.811545   15094.0   \n",
       "636  Atlanta 2021-10-19      661467         0.811545   15094.0   \n",
       "637  Atlanta 2021-10-20      662153         0.811545   15094.0   \n",
       "638  Atlanta 2021-10-21      662683         0.811545   15094.0   \n",
       "\n",
       "     household_count  mean_income  population  li_units_per_household  \\\n",
       "533          32792.0      78651.0     90896.0                0.005489   \n",
       "453         289829.0      94336.0    759297.0                0.019384   \n",
       "443         286952.0     108459.0    760141.0                0.007939   \n",
       "458          48899.0      80676.0    146343.0                0.006462   \n",
       "466          41253.0     128190.0    114421.0                0.008169   \n",
       "..               ...          ...         ...                     ...   \n",
       "634        1792560.0      99644.0   4901127.0                0.011512   \n",
       "635        1792560.0      99644.0   4901127.0                0.011512   \n",
       "636        1792560.0      99644.0   4901127.0                0.011512   \n",
       "637        1792560.0      99644.0   4901127.0                0.011512   \n",
       "638        1792560.0      99644.0   4901127.0                0.011512   \n",
       "\n",
       "     Case_Count_7da   new_cases  active_cases  vulnerable_pop  \\\n",
       "533             NaN         NaN           NaN             NaN   \n",
       "453             NaN         NaN           NaN             NaN   \n",
       "443             NaN         NaN           NaN             NaN   \n",
       "458             NaN         NaN           NaN             NaN   \n",
       "466             NaN         NaN           NaN             NaN   \n",
       "..              ...         ...           ...             ...   \n",
       "634   657914.428571  201.938776   2974.938776    4.243213e+06   \n",
       "635   658924.714286  219.673469   2962.795918    4.242202e+06   \n",
       "636   659574.000000  191.755102   2934.469388    4.241553e+06   \n",
       "637   660205.000000  186.530612   2895.755102    4.240922e+06   \n",
       "638   660783.571429  189.877551   2872.163265    4.240343e+06   \n",
       "\n",
       "     case_count_per_capita  new_cases_per_capita  mean_household_size  \n",
       "533                    0.0                   NaN             2.771896  \n",
       "453                    0.0                   NaN             2.619810  \n",
       "443                    0.0                   NaN             2.649018  \n",
       "458                    0.0                   NaN             2.992761  \n",
       "466                    0.0                   NaN             2.773641  \n",
       "..                     ...                   ...                  ...  \n",
       "634                    NaN                   NaN             2.822757  \n",
       "635                    NaN                   NaN             2.822757  \n",
       "636                    NaN                   NaN             2.822757  \n",
       "637                    NaN                   NaN             2.822757  \n",
       "638                    NaN                   NaN             2.822757  \n",
       "\n",
       "[7778 rows x 16 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter down to Atlanta counties\n",
    "county_fips = [\n",
    "    '13057', #Cherokee\n",
    "    '13063', #Clayton\n",
    "    '13067', #Cobb\n",
    "    '13089', #DeKalb\n",
    "    '13097', #Douglas\n",
    "    '13113', #Fayette\n",
    "    '13117', #Forsyth\n",
    "    '13121', #Fulton\n",
    "    '13135', #Gwinettt\n",
    "    '13151', #Henry\n",
    "    '13247', #Rockdale\n",
    "]\n",
    "combined_data_atl = combined_data[combined_data[\"FIPS\"].isin(county_fips)].copy()\n",
    "combined_data_atl.dropna(subset=[\"active_cases\", \"population\", \"vulnerable_pop\", \"new_cases\", \"FIPS\"])\n",
    "\n",
    "# Add atlanta as its own category\n",
    "funcMap = {\n",
    "    \"Case_Count\": \"sum\",\n",
    "    \"Case_Count_7da\": \"sum\",\n",
    "    \"mask_compliance\": \"mean\", \n",
    "    \"mean_income\": \"mean\", \n",
    "    \"population\": \"max\", \n",
    "    \"household_count\":\"max\", \n",
    "    \"mean_household_size\": \"mean\", \n",
    "    \"li_units\": \"max\", \n",
    "    \"li_units_per_household\": \"mean\",\n",
    "    \"active_cases\": \"sum\",\n",
    "    \"vulnerable_pop\": \"sum\",\n",
    "    \"new_cases\": \"sum\"\n",
    "}\n",
    "atl_data = combined_data_atl.groupby(\"date\").agg(funcMap)\n",
    "atl_data[\"population\"] = combined_data_atl.groupby(\"FIPS\").max().sum()[\"population\"]\n",
    "atl_data[\"household_count\"] = combined_data_atl.groupby(\"FIPS\").max().sum()[\"household_count\"]\n",
    "atl_data[\"FIPS\"] = 'Atlanta'\n",
    "\n",
    "\n",
    "combined_data_atl = pd.concat([combined_data_atl, atl_data.reset_index()])\n",
    "combined_data_atl.head(-10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 3: MODELING\n",
    "\n",
    "Here we model infection rates and case counts. The model is \n",
    "\n",
    "$$\n",
    "C_{new} = P_v(1 - (1 - r)^{\\frac{C_{active}}{P_{total}}x})\n",
    "$$\n",
    "\n",
    "Where $C_{new}$ and $C_{active}$ are new and active cases, $P_v$ and $P_{total}$ are vulnerable and total population, r is the transmission rate and x is the exposure rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transmissionModel(data, r, x):\n",
    "    c_active = data[\"active_cases\"]\n",
    "    p_total = data[\"population\"]\n",
    "    p_vuln = data[\"vulnerable_pop\"]\n",
    "    daily_exposures = c_active / p_total * x\n",
    "    return p_vuln * (1 - pow( 1 - r,daily_exposures))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit r and x to a dataset. r is bound between 0 and 1, since it's a probability, and x is bounded from 0 to 1000 to help in optimizing the curve fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def getTransmissionVariables(data):\n",
    "    y = data[\"new_cases\"]\n",
    "    X = data[[\"active_cases\", \"population\", \"vulnerable_pop\"]]\n",
    "    \n",
    "    #Don't model blank periods\n",
    "    if y.sum() <= 0:\n",
    "        return (np.NaN, np.NaN)\n",
    "    \n",
    "    popt, pcov = curve_fit(transmissionModel, X, y, p0 = [0.03, 3], bounds=(0, [1,1000]))\n",
    "    return popt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simulation confirms that the model creates bell-shaped curves, and that changing the parameters does result in differences in curve characteristics. Changing r and x below, one can expiriment with their impacts on the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulate \n",
    "p_t = 1000000\n",
    "p_v = p_t\n",
    "new_cases = [10]\n",
    "active_cases = new_cases[0]\n",
    "r = 0.001\n",
    "x = 100\n",
    "while p_v > 0 and active_cases >= 1:\n",
    "    active_cases = np.sum(new_cases[-14:])\n",
    "    data = {\"active_cases\": active_cases, \"population\": p_t, \"vulnerable_pop\": p_v}\n",
    "    new_cases.append(transmissionModel(data, r, x))\n",
    "    p_v -= new_cases[-1]\n",
    "\n",
    "#Plot\n",
    "plt.plot(new_cases)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each month in each county, we calculate the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>date</th>\n",
       "      <th>Case_Count</th>\n",
       "      <th>mask_compliance</th>\n",
       "      <th>li_units</th>\n",
       "      <th>household_count</th>\n",
       "      <th>mean_income</th>\n",
       "      <th>population</th>\n",
       "      <th>li_units_per_household</th>\n",
       "      <th>Case_Count_7da</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>active_cases</th>\n",
       "      <th>vulnerable_pop</th>\n",
       "      <th>case_count_per_capita</th>\n",
       "      <th>new_cases_per_capita</th>\n",
       "      <th>mean_household_size</th>\n",
       "      <th>month</th>\n",
       "      <th>transmission_chance</th>\n",
       "      <th>exposure_rate</th>\n",
       "      <th>modeled_new_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13247</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32792.0</td>\n",
       "      <td>78651.0</td>\n",
       "      <td>90896.0</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.842171e-13</td>\n",
       "      <td>9.089600e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.771896</td>\n",
       "      <td>20-03</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>5.838598</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13247</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32792.0</td>\n",
       "      <td>78651.0</td>\n",
       "      <td>90896.0</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.842171e-13</td>\n",
       "      <td>9.089600e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.771896</td>\n",
       "      <td>20-03</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>5.838598</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13247</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32792.0</td>\n",
       "      <td>78651.0</td>\n",
       "      <td>90896.0</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.842171e-13</td>\n",
       "      <td>9.089600e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.771896</td>\n",
       "      <td>20-03</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>5.838598</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13247</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32792.0</td>\n",
       "      <td>78651.0</td>\n",
       "      <td>90896.0</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.842171e-13</td>\n",
       "      <td>9.089600e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.771896</td>\n",
       "      <td>20-03</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>5.838598</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13247</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32792.0</td>\n",
       "      <td>78651.0</td>\n",
       "      <td>90896.0</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.842171e-13</td>\n",
       "      <td>9.089600e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.771896</td>\n",
       "      <td>20-03</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>5.838598</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7305</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>659457</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>657914.428571</td>\n",
       "      <td>201.938776</td>\n",
       "      <td>2.974939e+03</td>\n",
       "      <td>4.243213e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>21-10</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>2.573654</td>\n",
       "      <td>172.619303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7306</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-18</td>\n",
       "      <td>660811</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>658924.714286</td>\n",
       "      <td>219.673469</td>\n",
       "      <td>2.962796e+03</td>\n",
       "      <td>4.242202e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>21-10</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>2.573654</td>\n",
       "      <td>171.873802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7307</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-19</td>\n",
       "      <td>661467</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>659574.000000</td>\n",
       "      <td>191.755102</td>\n",
       "      <td>2.934469e+03</td>\n",
       "      <td>4.241553e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>21-10</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>2.573654</td>\n",
       "      <td>170.204539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7308</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-20</td>\n",
       "      <td>662153</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>660205.000000</td>\n",
       "      <td>186.530612</td>\n",
       "      <td>2.895755e+03</td>\n",
       "      <td>4.240922e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>21-10</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>2.573654</td>\n",
       "      <td>167.934098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7309</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>2021-10-21</td>\n",
       "      <td>662683</td>\n",
       "      <td>0.811545</td>\n",
       "      <td>15094.0</td>\n",
       "      <td>1792560.0</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>4901127.0</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>660783.571429</td>\n",
       "      <td>189.877551</td>\n",
       "      <td>2.872163e+03</td>\n",
       "      <td>4.240343e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>21-10</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>2.573654</td>\n",
       "      <td>166.543235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7310 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FIPS       date  Case_Count  mask_compliance  li_units  \\\n",
       "0       13247 2020-03-01           0         0.773000     180.0   \n",
       "1       13247 2020-03-02           0         0.773000     180.0   \n",
       "2       13247 2020-03-03           0         0.773000     180.0   \n",
       "3       13247 2020-03-04           0         0.773000     180.0   \n",
       "4       13247 2020-03-05           0         0.773000     180.0   \n",
       "...       ...        ...         ...              ...       ...   \n",
       "7305  Atlanta 2021-10-17      659457         0.811545   15094.0   \n",
       "7306  Atlanta 2021-10-18      660811         0.811545   15094.0   \n",
       "7307  Atlanta 2021-10-19      661467         0.811545   15094.0   \n",
       "7308  Atlanta 2021-10-20      662153         0.811545   15094.0   \n",
       "7309  Atlanta 2021-10-21      662683         0.811545   15094.0   \n",
       "\n",
       "      household_count  mean_income  population  li_units_per_household  \\\n",
       "0             32792.0      78651.0     90896.0                0.005489   \n",
       "1             32792.0      78651.0     90896.0                0.005489   \n",
       "2             32792.0      78651.0     90896.0                0.005489   \n",
       "3             32792.0      78651.0     90896.0                0.005489   \n",
       "4             32792.0      78651.0     90896.0                0.005489   \n",
       "...               ...          ...         ...                     ...   \n",
       "7305        1792560.0      99644.0   4901127.0                0.011512   \n",
       "7306        1792560.0      99644.0   4901127.0                0.011512   \n",
       "7307        1792560.0      99644.0   4901127.0                0.011512   \n",
       "7308        1792560.0      99644.0   4901127.0                0.011512   \n",
       "7309        1792560.0      99644.0   4901127.0                0.011512   \n",
       "\n",
       "      Case_Count_7da   new_cases  active_cases  vulnerable_pop  \\\n",
       "0           0.000000    0.000000  2.842171e-13    9.089600e+04   \n",
       "1           0.000000    0.000000  2.842171e-13    9.089600e+04   \n",
       "2           0.000000    0.000000  2.842171e-13    9.089600e+04   \n",
       "3           0.000000    0.000000  2.842171e-13    9.089600e+04   \n",
       "4           0.000000    0.000000  2.842171e-13    9.089600e+04   \n",
       "...              ...         ...           ...             ...   \n",
       "7305   657914.428571  201.938776  2.974939e+03    4.243213e+06   \n",
       "7306   658924.714286  219.673469  2.962796e+03    4.242202e+06   \n",
       "7307   659574.000000  191.755102  2.934469e+03    4.241553e+06   \n",
       "7308   660205.000000  186.530612  2.895755e+03    4.240922e+06   \n",
       "7309   660783.571429  189.877551  2.872163e+03    4.240343e+06   \n",
       "\n",
       "      case_count_per_capita  new_cases_per_capita  mean_household_size  month  \\\n",
       "0                       0.0                   0.0             2.771896  20-03   \n",
       "1                       0.0                   0.0             2.771896  20-03   \n",
       "2                       0.0                   0.0             2.771896  20-03   \n",
       "3                       0.0                   0.0             2.771896  20-03   \n",
       "4                       0.0                   0.0             2.771896  20-03   \n",
       "...                     ...                   ...                  ...    ...   \n",
       "7305                    NaN                   NaN             2.822757  21-10   \n",
       "7306                    NaN                   NaN             2.822757  21-10   \n",
       "7307                    NaN                   NaN             2.822757  21-10   \n",
       "7308                    NaN                   NaN             2.822757  21-10   \n",
       "7309                    NaN                   NaN             2.822757  21-10   \n",
       "\n",
       "      transmission_chance  exposure_rate  modeled_new_cases  \n",
       "0                0.034734       5.838598           0.000000  \n",
       "1                0.034734       5.838598           0.000000  \n",
       "2                0.034734       5.838598           0.000000  \n",
       "3                0.034734       5.838598           0.000000  \n",
       "4                0.034734       5.838598           0.000000  \n",
       "...                   ...            ...                ...  \n",
       "7305             0.025706       2.573654         172.619303  \n",
       "7306             0.025706       2.573654         171.873802  \n",
       "7307             0.025706       2.573654         170.204539  \n",
       "7308             0.025706       2.573654         167.934098  \n",
       "7309             0.025706       2.573654         166.543235  \n",
       "\n",
       "[7310 rows x 20 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by month\n",
    "combined_data_atl[\"month\"] = combined_data_atl[\"date\"].dt.strftime('%y-%m')\n",
    "\n",
    "# Drill down to specific columns and drop na\n",
    "model_data = combined_data_atl[[\"FIPS\", \"month\", \"new_cases\",\"active_cases\", \"population\", \"vulnerable_pop\"]].dropna()\n",
    "\n",
    "#Groupby month and apply\n",
    "model_fits = model_data.groupby([\"FIPS\", \"month\"]).apply(getTransmissionVariables)\n",
    "model_fits.columns = [\"x\"]\n",
    "model_fits = pd.DataFrame(model_fits.tolist(), index=model_fits.index, columns = [\"transmission_chance\", \"exposure_rate\"]).dropna()\n",
    "\n",
    "# Merge results back into combined data\n",
    "modeled_data = combined_data_atl.merge(model_fits, on=[\"FIPS\", \"month\"])\n",
    "modeled_data[\"modeled_new_cases\"] = transmissionModel(modeled_data, modeled_data[\"transmission_chance\"], modeled_data[\"exposure_rate\"])\n",
    "modeled_data.head(-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how close our fit was for Gwinett. The model visually follow the actual cases, and a high R-squared of 89.7 confirms that this is a reasonably good fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        MSE: 0.6442939430453022\n",
      "        Average Error: 0.17853206765687235\n",
      "        R^2: 0.8969792892917416\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "def evaluateFit(y, y_fit):\n",
    "    # residuals\n",
    "    res = (y - y_fit)\n",
    "    ss_res = np.sum(res ** 2)\n",
    "\n",
    "    # total sum of squares\n",
    "    ss_tot = np.sum((y - np.mean(y)) ** 2)\n",
    "\n",
    "    # r-squared\n",
    "    r2 = 1 - (ss_res / ss_tot)\n",
    "    \n",
    "    print(\"\"\"\n",
    "        MSE: {}\n",
    "        Average Error: {}\n",
    "        R^2: {}\n",
    "    \"\"\".format(np.mean(res), np.mean(np.abs(res)/np.mean(y)), r2)\n",
    "    )\n",
    "\n",
    "def plotModel(index, actual, modeled, title=None, showErrors=False):\n",
    "\n",
    "    plt.figure(figsize=(20,8))\n",
    "    plt.plot(index, actual, 'b-', label='Actual')\n",
    "    plt.plot(index, modeled, 'r-', label='Modeled')\n",
    "\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('New Cases')\n",
    "    \n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    if showErrors:\n",
    "        plt.figure(figsize=(20,8))\n",
    "        plt.bar(index, modeled - actual, label='Errors')\n",
    "        plt.show()\n",
    "\n",
    "target_county = modeled_data[modeled_data[\"FIPS\"] == '13135']\n",
    "plotModel(target_county[\"date\"], target_county[\"new_cases\"], target_county[\"modeled_new_cases\"], \"Gwinett County\", True)\n",
    "evaluateFit(target_county[\"new_cases\"], target_county[\"modeled_new_cases\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare Gwinnett and Atlanta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gwinnett\n",
      "\n",
      "        MSE: 0.6442939430453022\n",
      "        Average Error: 0.17853206765687235\n",
      "        R^2: 0.8969792892917416\n",
      "    \n",
      "Atlanta\n",
      "\n",
      "        MSE: 1.4144744259596813\n",
      "        Average Error: 0.11305587098717472\n",
      "        R^2: 0.9576439365254026\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "target_county = modeled_data[modeled_data[\"FIPS\"] == '13135']\n",
    "atl = modeled_data[modeled_data[\"FIPS\"] == 'Atlanta']\n",
    "plotModel(target_county[\"date\"], target_county[\"new_cases\"], target_county[\"modeled_new_cases\"], \"Gwinett County\")\n",
    "plotModel(atl[\"date\"], atl[\"new_cases\"], atl[\"modeled_new_cases\"], \"Atlanta\")\n",
    "\n",
    "print(\"Gwinnett\")\n",
    "evaluateFit(target_county[\"new_cases\"], target_county[\"modeled_new_cases\"])\n",
    "print(\"Atlanta\")\n",
    "evaluateFit(atl[\"new_cases\"], atl[\"modeled_new_cases\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters align closely between Gwinnett and Atlanta, but in areas where the case count curves diverge, a divergence in the parameters is visible as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "\n",
    "def compareParameters(model_data, target_fip, param_name):\n",
    "    \n",
    "    # Filter data\n",
    "    target_county = model_data.loc[target_fip,:].reset_index()\n",
    "    atl_area = model_data.loc['Atlanta',:].reset_index()\n",
    "    xvals = atl_area.index\n",
    "\n",
    "    # Create figure\n",
    "    fig = plt.figure(figsize=(20,8))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    # Add bars\n",
    "    w = 0.4\n",
    "    ax.bar(xvals - w/2, target_county[param_name], width=w, label='Gwinnett')\n",
    "    ax.bar(xvals + w/2, atl_area[param_name], width=w, label='Atlanta')\n",
    "    \n",
    "    # Label months\n",
    "    plt.xlabel('Month')\n",
    "    ax.set_xticks(xvals)\n",
    "    ax.set_xticklabels(target_county[\"month\"])\n",
    "\n",
    "    # Label values\n",
    "    if param_name == 'transmission_chance':\n",
    "        plt.ylabel('Transmission Chance')\n",
    "        ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0, 1))\n",
    "        plt.title('Transmission Chance Comparison')\n",
    "    else:\n",
    "        plt.ylabel('Daily Exposures')\n",
    "        plt.title('Exposure Rate Comparison')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compareParameters(model_fits, '13135', 'transmission_chance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compareParameters(model_fits, '13135', 'exposure_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "October 2020 provides one example, we examine that actual case counts and paramtere variables for this month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_cases</th>\n",
       "      <th>case_count</th>\n",
       "      <th>transmission_chance</th>\n",
       "      <th>exposure_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13057</th>\n",
       "      <td>-8.926301e-06</td>\n",
       "      <td>-0.003249</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.068527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13063</th>\n",
       "      <td>-1.015267e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>-0.032457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13067</th>\n",
       "      <td>-1.260864e-05</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.053550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089</th>\n",
       "      <td>-1.428669e-05</td>\n",
       "      <td>-0.000212</td>\n",
       "      <td>-0.000583</td>\n",
       "      <td>-0.165179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13097</th>\n",
       "      <td>-8.250760e-06</td>\n",
       "      <td>-0.000374</td>\n",
       "      <td>0.003053</td>\n",
       "      <td>0.056162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13113</th>\n",
       "      <td>3.485966e-05</td>\n",
       "      <td>-0.008267</td>\n",
       "      <td>-0.000772</td>\n",
       "      <td>0.063001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13117</th>\n",
       "      <td>3.959484e-06</td>\n",
       "      <td>-0.009360</td>\n",
       "      <td>-0.000586</td>\n",
       "      <td>0.035125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13121</th>\n",
       "      <td>-7.578505e-06</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>-0.000360</td>\n",
       "      <td>-0.004518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13135</th>\n",
       "      <td>5.940374e-07</td>\n",
       "      <td>0.003889</td>\n",
       "      <td>-0.000152</td>\n",
       "      <td>0.006381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13151</th>\n",
       "      <td>2.456872e-05</td>\n",
       "      <td>-0.003184</td>\n",
       "      <td>-0.000154</td>\n",
       "      <td>-0.021424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13247</th>\n",
       "      <td>-2.178340e-06</td>\n",
       "      <td>-0.003851</td>\n",
       "      <td>-0.001241</td>\n",
       "      <td>-0.058145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atlanta</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>-0.001023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            new_cases  case_count  transmission_chance  exposure_rate\n",
       "FIPS                                                                 \n",
       "13057   -8.926301e-06   -0.003249             0.000432       0.068527\n",
       "13063   -1.015267e-05    0.000151             0.000300      -0.032457\n",
       "13067   -1.260864e-05    0.000305             0.000245       0.053550\n",
       "13089   -1.428669e-05   -0.000212            -0.000583      -0.165179\n",
       "13097   -8.250760e-06   -0.000374             0.003053       0.056162\n",
       "13113    3.485966e-05   -0.008267            -0.000772       0.063001\n",
       "13117    3.959484e-06   -0.009360            -0.000586       0.035125\n",
       "13121   -7.578505e-06    0.001380            -0.000360      -0.004518\n",
       "13135    5.940374e-07    0.003889            -0.000152       0.006381\n",
       "13151    2.456872e-05   -0.003184            -0.000154      -0.021424\n",
       "13247   -2.178340e-06   -0.003851            -0.001241      -0.058145\n",
       "Atlanta           NaN         NaN            -0.000182      -0.001023"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = modeled_data.copy()[modeled_data[\"month\"] == '20-08']\n",
    "df[\"new_cases_per_capita_diff\"] =  df[\"new_cases_per_capita\"] - df[\"new_cases_per_capita\"].mean()\n",
    "net_cases = df.groupby(\"FIPS\")[\"Case_Count\"].max().sum()\n",
    "net_pop = df.groupby(\"FIPS\")[\"population\"].mean().sum()\n",
    "df = pd.DataFrame({\"new_cases\": df.groupby(\"FIPS\")[\"new_cases_per_capita_diff\"].mean(),\n",
    "             \"case_count\": df.groupby(\"FIPS\")[\"case_count_per_capita\"].max() - net_cases/net_pop,\n",
    "             })\n",
    "\n",
    "pd.concat([df, model_fits.groupby(\"FIPS\").mean() - model_fits.mean()], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 4: ANALYSIS\n",
    "\n",
    "We now analyze how our demogrpahic data relates to the modelled parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each month is a data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mask_compliance</th>\n",
       "      <th>li_units_per_household</th>\n",
       "      <th>mean_household_size</th>\n",
       "      <th>mean_income</th>\n",
       "      <th>active_cases</th>\n",
       "      <th>transmission_chance</th>\n",
       "      <th>exposure_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">13057</th>\n",
       "      <th>20-03</th>\n",
       "      <td>0.807250</td>\n",
       "      <td>0.008294</td>\n",
       "      <td>2.769373</td>\n",
       "      <td>101950.0</td>\n",
       "      <td>0.114549</td>\n",
       "      <td>0.032735</td>\n",
       "      <td>5.732114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-04</th>\n",
       "      <td>0.807250</td>\n",
       "      <td>0.008294</td>\n",
       "      <td>2.769373</td>\n",
       "      <td>101950.0</td>\n",
       "      <td>4.278231</td>\n",
       "      <td>0.028814</td>\n",
       "      <td>2.856401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-05</th>\n",
       "      <td>0.807250</td>\n",
       "      <td>0.008294</td>\n",
       "      <td>2.769373</td>\n",
       "      <td>101950.0</td>\n",
       "      <td>7.685978</td>\n",
       "      <td>0.026270</td>\n",
       "      <td>2.526533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-06</th>\n",
       "      <td>0.807250</td>\n",
       "      <td>0.008294</td>\n",
       "      <td>2.769373</td>\n",
       "      <td>101950.0</td>\n",
       "      <td>5.507483</td>\n",
       "      <td>0.031815</td>\n",
       "      <td>2.942137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-07</th>\n",
       "      <td>0.807250</td>\n",
       "      <td>0.008294</td>\n",
       "      <td>2.769373</td>\n",
       "      <td>101950.0</td>\n",
       "      <td>18.071758</td>\n",
       "      <td>0.028166</td>\n",
       "      <td>2.839931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Atlanta</th>\n",
       "      <th>21-06</th>\n",
       "      <td>0.811545</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>407.493878</td>\n",
       "      <td>0.026060</td>\n",
       "      <td>2.617694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-07</th>\n",
       "      <td>0.811545</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>496.279131</td>\n",
       "      <td>0.030263</td>\n",
       "      <td>3.487327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-08</th>\n",
       "      <td>0.811545</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>2019.323239</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>3.071861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-09</th>\n",
       "      <td>0.811545</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>3422.069388</td>\n",
       "      <td>0.029842</td>\n",
       "      <td>3.159403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-10</th>\n",
       "      <td>0.811545</td>\n",
       "      <td>0.011512</td>\n",
       "      <td>2.822757</td>\n",
       "      <td>99644.0</td>\n",
       "      <td>3574.588545</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>2.573654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mask_compliance  li_units_per_household  mean_household_size  \\\n",
       "FIPS    month                                                                 \n",
       "13057   20-03         0.807250                0.008294             2.769373   \n",
       "        20-04         0.807250                0.008294             2.769373   \n",
       "        20-05         0.807250                0.008294             2.769373   \n",
       "        20-06         0.807250                0.008294             2.769373   \n",
       "        20-07         0.807250                0.008294             2.769373   \n",
       "...                        ...                     ...                  ...   \n",
       "Atlanta 21-06         0.811545                0.011512             2.822757   \n",
       "        21-07         0.811545                0.011512             2.822757   \n",
       "        21-08         0.811545                0.011512             2.822757   \n",
       "        21-09         0.811545                0.011512             2.822757   \n",
       "        21-10         0.811545                0.011512             2.822757   \n",
       "\n",
       "               mean_income  active_cases  transmission_chance  exposure_rate  \n",
       "FIPS    month                                                                 \n",
       "13057   20-03     101950.0      0.114549             0.032735       5.732114  \n",
       "        20-04     101950.0      4.278231             0.028814       2.856401  \n",
       "        20-05     101950.0      7.685978             0.026270       2.526533  \n",
       "        20-06     101950.0      5.507483             0.031815       2.942137  \n",
       "        20-07     101950.0     18.071758             0.028166       2.839931  \n",
       "...                    ...           ...                  ...            ...  \n",
       "Atlanta 21-06      99644.0    407.493878             0.026060       2.617694  \n",
       "        21-07      99644.0    496.279131             0.030263       3.487327  \n",
       "        21-08      99644.0   2019.323239             0.030100       3.071861  \n",
       "        21-09      99644.0   3422.069388             0.029842       3.159403  \n",
       "        21-10      99644.0   3574.588545             0.025706       2.573654  \n",
       "\n",
       "[240 rows x 7 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_vars = [\"mask_compliance\", \"li_units_per_household\", \"mean_household_size\", \"mean_income\", \"active_cases\"]\n",
    "y_vars = [\"transmission_chance\", \"exposure_rate\"]\n",
    "monthly = modeled_data.groupby([\"FIPS\", \"month\"])[x_vars + y_vars].mean()\n",
    "X = monthly[x_vars]\n",
    "monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some colinnearity is present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "\n",
    "df = X.copy()\n",
    "df.columns = [\"Mask Compliance\", \"LI Units Per Household\", \"Mean Income\", \"Household Size\", \"Active Cases\"]\n",
    "corrMatrix = df.corr()\n",
    "sn.heatmap(corrMatrix, annot=True, fmt='.2f', center=0, vmin = -1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transmission Chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask_compliance           0.061746\n",
      "li_units_per_household    1.743925\n",
      "mean_household_size       0.673423\n",
      "mean_income               0.999998\n",
      "active_cases              1.000002\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>transmission_chance</td> <th>  No. Observations:  </th>  <td>   240</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>GLM</td>         <th>  Df Residuals:      </th>  <td>   235</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>     <td>NegativeBinomial</td>   <th>  Df Model:          </th>  <td>     4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>           <td>log</td>         <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>IRLS</td>         <th>  Log-Likelihood:    </th> <td> -30.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 13 Dec 2021</td>   <th>  Deviance:          </th> <td> 0.48213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:02:22</td>       <th>  Pearson chi2:      </th>  <td> 0.501</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>           <td>4</td>          <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>HC3</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mask_compliance</th>        <td>   -2.7847</td> <td>    0.477</td> <td>   -5.841</td> <td> 0.000</td> <td>   -3.719</td> <td>   -1.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>li_units_per_household</th> <td>    0.5561</td> <td>    2.267</td> <td>    0.245</td> <td> 0.806</td> <td>   -3.888</td> <td>    5.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_household_size</th>    <td>   -0.3954</td> <td>    0.111</td> <td>   -3.570</td> <td> 0.000</td> <td>   -0.612</td> <td>   -0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_income</th>            <td>-2.154e-06</td> <td> 9.13e-07</td> <td>   -2.359</td> <td> 0.018</td> <td>-3.94e-06</td> <td>-3.65e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>active_cases</th>           <td> 2.285e-06</td> <td> 7.44e-06</td> <td>    0.307</td> <td> 0.759</td> <td>-1.23e-05</td> <td> 1.69e-05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                  Generalized Linear Model Regression Results                  \n",
       "===============================================================================\n",
       "Dep. Variable:     transmission_chance   No. Observations:                  240\n",
       "Model:                             GLM   Df Residuals:                      235\n",
       "Model Family:         NegativeBinomial   Df Model:                            4\n",
       "Link Function:                     log   Scale:                          1.0000\n",
       "Method:                           IRLS   Log-Likelihood:                -30.741\n",
       "Date:                 Mon, 13 Dec 2021   Deviance:                      0.48213\n",
       "Time:                         21:02:22   Pearson chi2:                    0.501\n",
       "No. Iterations:                      4                                         \n",
       "Covariance Type:                   HC3                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "mask_compliance           -2.7847      0.477     -5.841      0.000      -3.719      -1.850\n",
       "li_units_per_household     0.5561      2.267      0.245      0.806      -3.888       5.000\n",
       "mean_household_size       -0.3954      0.111     -3.570      0.000      -0.612      -0.178\n",
       "mean_income            -2.154e-06   9.13e-07     -2.359      0.018   -3.94e-06   -3.65e-07\n",
       "active_cases            2.285e-06   7.44e-06      0.307      0.759   -1.23e-05    1.69e-05\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels import graphics\n",
    "\n",
    "y = monthly[\"transmission_chance\"]\n",
    "glm = sm.GLM(y, X, family=sm.families.NegativeBinomial())\n",
    "results = glm.fit(cov_type='HC3')\n",
    "\n",
    "plt.plot(y, results.predict(X), 'o', alpha=0.5);\n",
    "plt.show()\n",
    "\n",
    "print(np.exp(results.params))\n",
    "\n",
    "resid = results.resid_deviance.copy()\n",
    "graphics.gofplots.qqplot(resid, line='r')\n",
    "plt.title('Transmission Chance QQ PLot')\n",
    "\n",
    "results.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask_compliance           1.569414\n",
      "li_units_per_household    2.971750\n",
      "mean_household_size       1.226396\n",
      "mean_income               1.000001\n",
      "active_cases              0.999973\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>exposure_rate</td>  <th>  No. Observations:  </th>  <td>   240</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>   235</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>         <td>Poisson</td>     <th>  Df Model:          </th>  <td>     4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>log</td>       <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -377.98</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 13 Dec 2021</td> <th>  Deviance:          </th> <td>  55.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:02:22</td>     <th>  Pearson chi2:      </th>  <td>  54.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>4</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>HC3</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mask_compliance</th>        <td>    0.4507</td> <td>    0.562</td> <td>    0.802</td> <td> 0.422</td> <td>   -0.650</td> <td>    1.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>li_units_per_household</th> <td>    1.0892</td> <td>    3.255</td> <td>    0.335</td> <td> 0.738</td> <td>   -5.290</td> <td>    7.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_household_size</th>    <td>    0.2041</td> <td>    0.123</td> <td>    1.656</td> <td> 0.098</td> <td>   -0.037</td> <td>    0.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_income</th>            <td> 1.248e-06</td> <td> 1.09e-06</td> <td>    1.147</td> <td> 0.251</td> <td>-8.84e-07</td> <td> 3.38e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>active_cases</th>           <td> -2.74e-05</td> <td>  9.8e-06</td> <td>   -2.797</td> <td> 0.005</td> <td>-4.66e-05</td> <td> -8.2e-06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:          exposure_rate   No. Observations:                  240\n",
       "Model:                            GLM   Df Residuals:                      235\n",
       "Model Family:                 Poisson   Df Model:                            4\n",
       "Link Function:                    log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -377.98\n",
       "Date:                Mon, 13 Dec 2021   Deviance:                       55.081\n",
       "Time:                        21:02:22   Pearson chi2:                     54.7\n",
       "No. Iterations:                     4                                         \n",
       "Covariance Type:                  HC3                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "mask_compliance            0.4507      0.562      0.802      0.422      -0.650       1.552\n",
       "li_units_per_household     1.0892      3.255      0.335      0.738      -5.290       7.469\n",
       "mean_household_size        0.2041      0.123      1.656      0.098      -0.037       0.446\n",
       "mean_income             1.248e-06   1.09e-06      1.147      0.251   -8.84e-07    3.38e-06\n",
       "active_cases            -2.74e-05    9.8e-06     -2.797      0.005   -4.66e-05    -8.2e-06\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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88pcdxifefXfFoj8LF8JGG8EXvpCSw6679tnkkM+Jwsx6rUKzmzrq3x/++Me8BLFsGdxzT0oOV13VXvRn5Mj0s99+FS360wicKMysVyo1uylnwAAYNw5GfTLgHw+mMYcrrkgrtQ4Y0F7059BDq1b0pxE4UZhZr1TOmMQV33ucI59sg2Hj4ZlnUjI4/PD2oj9rrtkzwfZyThRm1isVm+baxLOMZDyfXqWNbc55LK2ndMAB8J3vpKU01l23ZwNtAE4UZtbrtLamz//cVNiNeIERTKCFNnbj7wDMa9oDvnIxHH98GqC2leZEYWa9Sm5sYuCy1/kEV9NCG/txF/1ZzsPsyA8GXMCOY0/kmK821TrUhlHTNW4lHSbpKUnTJZ1T4PVTJM2T9HD287laxGlmdWLhQv7+1f9j/KKjeIkN+S2fZwizGcsYtu/3BI//+WG+/9a3nCQqrGYtCkn9gUuBg4E5wIOSJkbEEx12vSIizujxAM2sPixZArfckqaz3nADFy1axBw25SK+QhstPMTOgFDUQWnTBlXLrqddgOkRMQNA0nhgONAxUZhZX7N0KfzlLyk5XHNNWlJj/fXh059mxLUtXPXSXkSHDpGqFyLqw2qZKDYFnst7PgfYtcB+n5C0D/A0cFZEPFdgHzPr7SLg/vtTcpgwIRX9GThwxaI/q6zCIAGXAdH+1qoVIjKg/gezbwDaImKJpC8AfwQOKLSjpNHAaIAh/mph1jtEwCOPtBf9mTULVlttxaI/a6wBFL8LW0orxrrbqXo6TRSStgTmZB/W+wE7AH+KiPndPPdcYLO854Ozbf8REfn/SfwW+HGxg0XEOGAcQHNzcxTbz8zqwL//3V7051//SktmHHJIWp11+PBUPjRPqbuwI9IaflY95bQorgaaJW1F+iC+Hvg/4IhunvtBYJikzUkJYiTwyfwdJG0cES9kT48GnuzmOc2sVp57LnUptbXB1KmpKbD33qmZcPzxBYv+5EqZdrbgX5drTFiXlJMolkfEUknHAhdHxMWS/tndE2fHPAO4FegP/D4iHpd0HjAlIiYCX5F0NLAUeA04pbvnNbMeNG9eWnivrS0txAfQ3Aw/+1kq+jN4cNG3nn46XHZZajF0xr3N1VVOonhXUgtwMnBUtm2VSpw8IiYBkzps+17e428D367Eucysh7zxBlx3XUoOkyen26e32QbOOy+tzjpsWKeHaG0tP0l4ILv6ykkUpwKnAWMj4tmsq+jy6oZlZr3K4sVw000pOdx0U7r3YehQ+MY30qD0DjuUVdehnGXD8xWsMWEV12miiIgnJH0LGJI9fxa4sNqBmVmde/fdVCa0rS21IN58EzbcMI06t7TAbrt1qejP6afDr39d/unXXx9eeaXrYVvXlTPr6Sjgp8CqwOaSdgLOi4ijqxybmdWb5ctXLPrz6quwzjppvCFX9Od95c+6L3ewuqMBA1JLwnpGOX/Rc0l3Ud8FEBEPS9qiijGZWT2JSLOUcvc6zJ2bPqmPPrq96M9qq3X5sOUUHipk6NA0JuHupp5T1mB2RCzQik3I5VWKx8zqxRNPpOQwfjxMnw6rrAKHHQY/+UlKEt0s+nPmmeUnifeUM7UeVU6ieFzSJ4H+koYBXwHuq25YZlYTM2emxNDWBo8+moo+7L8/nHMOHHdcRYr+tLbCF74Ab71V3v6rrgq//72TRC2Vkyi+DIwBlgBtpPsefljNoMysB734YvuNcA88kLbttlsaBBgxoltFf7o6i6mjtdZK02SdJGqrnFlPi0iJYkz1wzGzHvH662lV1ra2tErr8uVpCuv558OJJ8Lmm3f7FK2tcOqpaXJUV0lw2mnwq191OwyrgKKJQtINrLA+44o868msl3nrLZg4MXUt3Xxz+gTfcstUS7qlBbbdttun6G4LAjxYXY9KtSh+2mNRmFl1LFkCt96aWg4TJ6bR4003hS9/OSWHj360S/c6dFSJxJAjweWXO0HUo6KJIiL+2pOBmFmFLFuWupPGj4err4b589PdaSedlJLD3nunQepu6k7XUiGnneYkUa9KdT1NiIgRkh6jQBdUROxQ1cjMrHwRaSA6V/TnpZfSSHCu6M9BB6XprRV05pmVSRIesK5/pbqezsx+H9kTgZhZF0XAY4+13+swc2a68e3jH0/J4eMf/0/Rn0rp6tTWQvr1S2PnHovoPUp1PeXqQJweEd/Kf03ShcC33vsuM6u66dPbi/48+WS6G+3gg+Hcc+GYY2Dttbt9ikokhI58P0TvVc59FAfz3qRweIFtZlYtc+a03+swZUratvfeaf7o8cfDBht06/DVSAz5vMpr71ZqjOKLwOnAFpIezXtpIPC3agdm1ue98sqKRX8iYOed0xIaJ54Im23W+THoWgGgSnFiaCylWhT/B9wMnA+ck7f9zYh4rapRmfVVb765YtGfpUvhQx9K3UojR8IHP9jpISo5ZbUrPL21cZUao1gALABaJPUHNsz2X0vSWhHR7Sq1kg4DfkkqhfrbiLigw+urAX8CPgq8CpwYETO7e16zurJ4MUya1F705+23U23Pr30tDUrvuGPRex1q0VooxtNbG1c59SjOIC01/hLtq8YG0K3psVnyuZQ0BjIHeFDSxIh4Im+3zwKvR8RWkkaSCiad2J3zmtWFd9+FO+5IyeHaa1NL4gMfgM99rr3oT79+dZUIivH01sZXzmD2V4GtI6LSDdldgOkRMQNA0nhgOJCfKIaTkhTAVcAlkhRRz//bmBWxfDnce2+aynrllWkMYu2102B0SwtnXL0/l17yPrik1oF2zmMQfUs5ieI5UhdUpW2aHTtnDrBrsX0iYqmkBcD6wHsKIEoaDYwGGDJkSBXCNVsJEfDQQ9z+2Ta2fuQKNmMOi1iDiRxNGy3csuAw3vnDavCHWgdamBOCQXmJYgZwl6SbSEuNAxARP69aVCshIsYB4wCam5vd4rCaOf10+Muvn2Qk42mhjQ/yb/ZhFW7lUL7FhUzkaN5irVqHWZATgxVSTqKYnf2smv1Uylwgf37f4GxboX3mSHofsDZpUNusbuRmGa356ixGMp7RtPErHmE54i/sz485m2s4jtdZr6ZxeizBVlY59Sh+UKVzPwgMk7Q5KSGMBD7ZYZ+JwMnA/cDxwJ0en7BaKDao/AFe4gSu5Hra2DMr/PgAu3Im/80ERvAiG9cg2qRfv3QTnWs6WHeVM+tpA+BsYDtg9dz2iDigOyfOxhzOIFXM6w/8PiIel3QeMCUiJgK/Ay6XNB14jZRMzKqmnFlGazOf47iGFto4gDvpz3Ie5cN8h7GMZyTPskXPBZxxa8GqqZyup1bgCtLigKeRvuHPq8TJI2ISMKnDtu/lPX4bOKES5zLrzo1oa7CIo7iBFto4nJtZjXd4hi04n2/TRgtPsF3lA87jRGC1VE6iWD8ififpzKxGxV8lPVjtwMzKVa17DVbhHQ7lVlpo42gmshZvMZdNuJQvMZ6RPMjHgJUv+lOMk4LVm3ISRW7F+RckfRx4Hmo8Kmd9Tk8tS9GPZezLX2mhjU9wNevxOq+yHq2Moo0W7mFvltN/pY/vJGC9UTmJ4keS1ga+DlwMvB84q6pRWZ9Sq7WJ2gW78ndaaGMEE9iYF1nImlzHMbTRwmQO5t2VmPDnpGCNopxZTzdmDxcA+1c3HOsrap8cYHseo4U2RjKeLXiWt1mNSRxBGy3cxMdZzIAuH9P3IVgjKmfW0x8oXAr1M1WJyBpWPSSHLXjmP8lhex5nKf25gwM5j+9xLcfyBqWL/riVYH1ROV1PN+Y9Xh04ljROYQbURwIoZWOe50SuoIU2diHNw7iHvTidS7mK45nHB97zHicEs3bldD1dnf9cUhtwb9Uisl7l9NPh17+udRTvtR6vcjxXMZLx7Mtf6UfwEB/hm/yYKziRResP4Ze/hF85EZh1qpwWRUfDoMBXMOsz6rUFMXS9N2kdcT17zm6D225LRX+23hpavg8jR7Lz1luzM/CTWgdq1suUM0bxJmmMQtnvF3G97D6htRXGjIFZs1LdnHpYPOU9g8Vvvw0335zqOtx4I1y2OJUIPeusVNdhp52KFv0xs/KU0/U0sCcCsfqQnxzy9USSKHvG0NKlcGte0Z833oANNoDPfCaVC91jj7TQkZlVRMlEIWkNYBSwbbZpCnBVRLxT7cCsZ9WqO6ns5LB8Odx3X0oOV14J8+bB+98Pxx2XWg4HHADvW5meVDPrTNH/syR9mLR6693A1GzzocBZkg4GvhER361+iFZtra0wejQsWlTd8/Trlz7vhw6FsWPLSA4R8M9/puRwxRXw3HOw+upw1FEpORx+eHpuZlVV6ivYRcDoiJicv1HSQcA04PFqBmbVVayLqTsqdrPZU0+l5NDWBk8/nVoKhx4K558PRx8NA90bataTSiWKjTsmCYCIuF3Su6T7KaxO5SeC/v1h2bL235UemK7IPQezZ6da0m1t8PDDKch994Wvfx0+8YmUhcysJkolin6SVouIJfkbJa0OvBsRVe6osK4qNktp2bIVf3c3SXS5C6mYl19O4w1tbfC3v6Vtu+wCv/gFjBgBm2zSvUDNrCJKJYo/AVdL+lJEzAKQ1ETqkrq8B2KzLug4zlCJFkPFEkK++fPTTKXx4+GOO1L22m47+NGP0oylLbeswEnMrJKKJoqI+FFWge4eSbnV0d4CfhoRF3fnpJLWIxVDagJmAiMi4vUC+y0DHsuezo6Io7tz3kZUjbGG/v3hj3+s4PIVixalexza2mDSJHjnHdh8c/jWt9Kg9PbbV+hEZlYNKqcEtaSBABHxZkVOKv0YeC0iLpB0DrBuRLznJj5JCyNira4ev7m5OaZMmVKJUOtSNW+EGzAAxo2rQJJ45x2YPDklh+uvh4ULYaON4MQTU3LYZRffCGdWRyRNjYjmQq+VNfG8Ugkiz3Bgv+zxH4G78N3eZanrLqZly+Duu1NyuPpqeO01WHfdlBhaWmCffVJzxcx6lVrdobRhRLyQPX4R2LDIfqtLmgIsBS6IiOuKHVDSaGA0wJAhQyoYan0ZM6b8+x1yrY2Os55yvysy9hABDz7Yfq/DCy/AmmvC8OEpORxyCKza9aI/ZlY/qpYoJN0ObFTgpTH5TyIiJBX7Xjw0IuZK2gK4U9JjEfFMoR0jYhwwDlLXUzdCr2uzZ5e3X0UHoAuZNi0lh/HjYcaMlAyOOCINSB95ZEoWZtYQylkUcACpDOqQiPi8pGHA1nmV7wqKiINKHPMlSRtHxAuSNgZeLnKMudnvGZLuAj4CFEwUfcWQIaUHris2xlDIjBnt9zpMm5b6rA48EL77XTj2WFhnnSqc1MxqrZyV0/4ALAF2z57PBX7UzfNOBE7OHp8MXN9xB0nrSlotezwI2BN4opvn7bVaW6GpqX0AO1/u+dChVUgSL7wA//3fsOuuaerqmDFpjaWLL4bnn0/LeZ96qpOEWQMrp+tpy4g4UVILQEQskro9XeUCYIKkzwKzgBEAkpqB0yLic8A2wP9IWk5KaBdERMMmitxMptmzU6vhiCPSTNLZs2G99eDNN9NEIkjDArnxh6p0Mb32WhqMbmuDu+5KJ9ppJ7jwwjRraejQCp7MzOpdOYninWwV2QCQtCWphbHSIuJV4MAC26cAn8se3wd8uDvnqWf5iaFjIpg1a8WqcYVWdM0liZkzKxTQwoVpGmtbG9x6a1rKe9gw+H//Lw1Kf+hDFTqRmfU25SSK7wO3AJtJaiV1AZ1SzaAaXccpriu7tHe5A9tFLVnSXvTnhhtg8WIYPBi++tWUHD7yEd/rYGZlFS6aLOkhYDdSlbszI+KVqkfWwLoyxbWUlZoFvHQp3HlnGpS+5hpYsAAGDYJTTknJYc89XfTHzFZQqh7Fzh025e57GCJpSEQ8VL2wGlu3WwKk2U1jx5a58/LlcP/97UV/Xn45DUgfe2x70Z9VVul+UGbWkEq1KH5W4rUADqhwLH1GZ1NcC1lllfTZ/tpr6f2dDmBHpOW6czfCzZ6divwceWRKDkcc4aI/ZlaWUosC7t+TgfQlY8e+t6Jcx0SQP+uprMSQ8/TT7UV/nnoqFf055JB0gOHDXfTHzLqsnBvuVgdOB/YitSTuAS6LiLerHFvDyn3g50+H7dYU1+eeS62GtjZ46KE0AL3PPnDWWanoz6BBFYvdzPqeTlePlTQBeBP4c7bpk8A6EXFClWNbaY2+eiwA8+a1F/2599607WMfS91KI0bAppvWNj4z61W6u3rs9hGxbd7zv0hq2Bvf6tqCBXDddSk53H57Wtlv223hhz9MayxttVWtIzSzBlROonhI0m4R8QCApF2BBv+6XkcWL16x6M+SJWktj7PPTsnhwx/2vQ5mVlXlJIqPAvdJyk3qHAI8Jekx0uKvO1Qtur7q3Xfbi/5cd1170Z8vfCF1Le26q5ODmfWYchLFYVWPwtK9DrmiP1ddlaY/rbNOe0W4/fZz0R8zq4ly7syeJWldYLP8/X3DXQVEwJQp7fc6PP98upMuV/Tn0ENd9MfMaq6c6bE/JK3t9AzZwoD4hrvuefzx9qI/zzyTksHhh6cxh6OOctEfM6sr5XQ9jSAtNf5OtYNpaM8+217057HH0npKBxwA3/lOWkpj3XVrHaGZWUHlJIppwDoUqUJnJbzwAkyYkBLEAw+kbbvvDhddBCeckAaozczqXDmJ4nzgn5KmkVeHIiKOrlpUvdnrr69Y9Gf5cthxR7jggjQw3dRU6wjNzLqknETxR+BC4DFgeSVOKukE4FxSFbtdsoJFhfY7DPgl0B/4bURcUInzV9zChTBxYmo53HJLmt661VZpjY6WFthmm1pHaGa20spJFIsi4qIKn3cacBzwP8V2kNQfuBQ4GJgDPChpYt2UQ12yJCWFXNGfRYvSshlf+UoalP7oR32vg5k1hHISxT2SzgcmsmLX00pPj42IJwE6Kb29CzA9ImZk+44HhgO1SxRLl8Jf/tJe9Gf+fFh/ffj0p1PLYa+9XPTHzBpOOYniI9nv3fK29cT02E2B5/KezwF2LbazpNHAaIAhK1X6rYiI9qI/Eyakoj8DB6aZSiNHwkEHueiPmTW0cm64W6m6FJJuBwpN6xkTEdevzDFLiYhxwDhIq8d282Dw6KPt9zrMmgWrrbZi0Z811qhE2GZmda+cFgWSPg5sB/ynJFpEnFfqPRFxUPdCYy7pbvCcwdm26vn3v9uL/vzrX2nJjEMOgfPOg2OOSZWFzMz6mHLuzL4MGADsD/wWOB74R5XjAngQGCZpc1KCGEmqhVFZc+a0F/2ZOjVt22cfOPNMOP54F/0xsz6vnBbFHhGxg6RHI+IHkn4G3Nydk0o6FrgY2AC4SdLDEXGopE1I02CPiIilks4AbiVNj/19RDzenfP+x7x5aeG98ePhnntSV9NHPwo//Wm612Hw4IqcxsysEZSTKBZnvxdlH+SvAht356QRcS1wbYHtzwNH5D2fBEzqzrn+44032ov+TJ6civ5ssw384AdpUHrYsIqcxsys0ZSTKG6UtA7wE+Ah0oyn31QzqKo46CB48EEYOhS+8Y00KL3DDr7XwcysE+XMevph9vBqSTcCq0fEguqGVQVjx6ZVWXff3cnBzKwLiiYKSR8DnouIF7PnnwY+AcySdG5EvNZDMVbGwQfXOgIzs16p1G3E/wO8AyBpH+AC4E/AArL7FczMrPGV6nrqn9dqOBEYFxFXk7qgHq56ZGZmVhdKtSj6S8olkgOBO/NeK+tGPTMz6/1KJYo24K+SridNkb0HQNJWpO4nK0NraypB0a9f+t3aWuuIzMy6pmjLICLGSrqDdM/EbRGRWz+pH/Dlngiut2tthdGj0wrkkJaMGj06PR41qnZxmZl1hdo//xtHc3NzTJlSsBZSj2pqSsmho6FDYebMno7GzKw4SVMjornQay6eUEWzZ3dtu5lZPXKiqKJiZTEqWS7DzKzanCiqaOxYGDBgxW0DBqTtZma9hRNFFY0aBePGpTEJKf0eN84D2WbWu/h+iCobNcqJwcx6N7cozMysJCcKMzMrqSaJQtIJkh6XtFxSwXm72X4zJT0m6WFJtb8xwsysD6rVGMU04DjSCrWd2T8iXqlyPGZmVkRNEkVEPAkgFxAyM6t79T5GEcBtkqZKGl1qR0mjJU2RNGXevHk9FJ6ZWeOrWotC0u3ARgVeGhMR15d5mL0iYq6kDwCTJf0rIu4utGNEjCMrqNTc3Nx4C1iZmdVI1RJFRBxUgWPMzX6/LOlaYBegYKIwM7PqqNuuJ0lrShqYewwcQhoENzOzHlSr6bHHSpoD7A7cJOnWbPsmkiZlu20I3CvpEeAfwE0RcUst4jUz68tqNevpWuDaAtufB47IHs8Aduzh0MzMrIO67XoyM7P64ERhZmYlOVGYmVlJThRmZlaSE4WZmZXkRGFmZiU5UZiZWUlOFGZmVpIThZmZleREYWZmJTlRmJlZSU4UZmZWkhOFmZmV5ERhZmYlOVGYmVlJThRmZlZSrSrc/UTSvyQ9KulaSesU2e8wSU9Jmi7pnB4O08zMqF2LYjKwfUTsADwNfLvjDpL6A5cChwPbAi2Stu3RKM3MrDaJIiJui4il2dMHgMEFdtsFmB4RMyLiHWA8MLynYjQzs6Qexig+A9xcYPumwHN5z+dk2wqSNFrSFElT5s2bV+EQzcz6rvdV68CSbgc2KvDSmIi4PttnDLAUaO3u+SJiHDAOoLm5Obp7PDMzS6qWKCLioFKvSzoFOBI4MCIKfbDPBTbLez4422ZmZj2oVrOeDgPOBo6OiEVFdnsQGCZpc0mrAiOBiT0VY1e0tkJTE/Trl363drt9ZGZWP2o1RnEJMBCYLOlhSZcBSNpE0iSAbLD7DOBW4ElgQkQ8XqN4i2pthdGjYdYsiEi/R492sjCzxqHCvT69W3Nzc0yZMqVHztXUlJJDR0OHwsyZPRKCmVm3SZoaEc2FXquHWU+92uzZXdtuZtbbOFF005AhXdtuZtbbOFF0orOB6rFjYcCAFbcNGJC2m5k1AieKEsoZqB41CsaNS2MSUvo9blzabmbWCDyYXYIHqs2sr/Bg9kryQLWZmRNFSR6oNjNzoijJA9VmZn0kUazsEhseqDYzq+KigPUiN3NpUbaiVG7mEpT3gT9qlBODmfVtDd+iGDOmPUnkLFqUtpuZWecaPlF45pKZWfc0fKLwzCUzs+5p+EThmUtmZt3T8InCM5fMzLqn4Wc9gWcumZl1R00ShaSfAEcB7wDPAKdGxPwC+80E3gSWAUuLrUNiZmbVU6uup8nA9hGxA/A08O0S++4fETs5SZiZ1UZNEkVE3JbVxAZ4ABhcizjMzKxz9TCY/Rng5iKvBXCbpKmSRpc6iKTRkqZImjJv3ryKB2lm1ldVbYxC0u3ARgVeGhMR12f7jAGWAsVWX9orIuZK+gAwWdK/IuLuQjtGxDhgHKR6FN2+ADMzA2pYuEjSKcAXgAMjYlEnuyPpXGBhRPy0jH3nAQVKDtW1QcArtQ6ih/ma+wZfc+8wNCI2KPRCrWY9HQacDexbLElIWhPoFxFvZo8PAc4r5/jFLraeSZrS1wbsfc19g6+596vVGMUlwEBSd9LDki4DkLSJpEnZPhsC90p6BPgHcFNE3FKbcM3M+q6atCgiYqsi258HjsgezwB27Mm4zMzsveph1pMl42odQA34mvsGX3MvV7PBbDMz6x3cojAzs5KcKMzMrCQnijoi6SeS/iXpUUnXSlqn1jFVm6QTJD0uabmkhplO2JGkwyQ9JWm6pHNqHU9PkPR7SS9LmlbrWHqCpM0k/UXSE9l/02fWOqZKcaKoL11ZLLFRTAOOAwrecd8IJPUHLgUOB7YFWiRtW9uoesT/AofVOogetBT4ekRsC+wGfKlR/s5OFHWkLy6WGBFPRsRTtY6jynYBpkfEjIh4BxgPDK9xTFWXLbfzWq3j6CkR8UJEPJQ9fhN4Eti0tlFVhhNF/Sq1WKL1LpsCz+U9n0ODfIBYYZKagI8Af69xKBXRJyrc1ZMKLZbYq5RzzWaNQtJawNXAVyPijVrHUwlOFD0sIg4q9Xq2WOKRpMUSG+Iml86uuQ+YC2yW93xwts0ajKRVSEmiNSKuqXU8leKupzqSt1ji0eWsqGu9xoPAMEmbS1oVGAlMrHFMVmGSBPwOeDIifl7reCrJiaK+FFwssZFJOlbSHGB34CZJt9Y6pkrLJiicAdxKGuCcEBGP1zaq6pPUBtwPbC1pjqTP1jqmKtsTOAk4IPv/92FJR9Q6qErwEh5mZlaSWxRmZlaSE4WZmZXkRGFmZiU5UZiZWUlOFGZmVpIThdUtSevnTTN8UdLc7PF8SU/0cCzH5C/wJuk8SV2+kVBSU7HVVCVtJ+nObJXZZyT9QFLF/x8tdS2S7mrkVXxt5ThRWN2KiFcjYqeI2Am4DPhF9ngnYHmlzyep1EoFx5BWfs3F9r2IuL2C516DdBPeBRGxNfBh0mKC1Viq+hiqeC3WeJworLfqL+k32br/t2UftEjaUtItkqZKukfSh7LtTdm39Ucl3SFpSLb9fyVdJunvwI8LvV/SHsDRwE+yFs2W2fuOz47xMUn3SXpE0j8kDczOd4+kh7KfPTq5nk8Cf4uI2wCyO/PPAL6ZneNcSd/I7SxpWrbwHJKuy+J9XNLovH0WShqbxfWApA07u5Z8kg6RdH8W/5XZGkZIuiCrufCopJ92/U9nvY0ThfVWw4BLI2I7YD7wiWz7OODLEfFR4BvAr7LtFwN/zGp9tAIX5R1rMLBHRHyt0Psj4j7St/1vZi2cZ3JvzJbkuAI4MyJ2BA4CFgMvAwdHxM7AiR3OV8h2wNT8Ddl51lDnBaw+k8XbDHxF0vrZ9jWBB7K47gY+X+pa8kkaBHwXOCi7hinA17JjHwtsl/1b/qiT2KwBeFFA662ejYiHs8dTgabsG+8ewJVp2R0AVst+704qkARwOfDjvGNdGRHLOnl/MVsDL0TEgwC51UIlrQlcImknYBnwwa5eYBd8RdKx2ePNSEn0VeAd4MZs+1Tg4C4cczdS99Tfsn+LVUnLcSwA3gZ+J+nGvONbA3OisN5qSd7jZcAapBby/Gwcoyveyn6v7PsLOQt4CdgxO+7bnez/BLBP/gZJWwCvRsR8SUtZsQdg9Wyf/UitmN0jYpGku3KvAe/mrUC8jK79/y5gckS0vOcFaRfgQOB4UvfYAV04rvVC7nqyhpF9m39W0gmQVvOUtGP28n2kVVsBRgH3dPH9b5IWbOzoKWBjSR/L3jMwGxRfm9TSWE5aKK5/J+G3AnvlzT5ag9Rd9f3s9ZnAztlrOwObZ9vXBl7PksSHSC2BzhS7lnwPAHtK2io755qSPpi1utaOiEmkZLhjqYNYY3CisEYzCvispEeAx2kvOfpl4FRJj5I+uIvNJir2/vHANyX9U9KWuZ2z0qYnAhdn75lM+kb/K+DkbNuHaG+1FBQRi0mDzGMkPQ28QhrczhWvuhpYT9LjpG/xT2fbbwHeJ+lJ4ALSB3xnCl5Lh3jmAacAbdm/2f3ZdQwEbsy23Qt8rYzzWS/n1WPN6pCkY4CfA/tHxKwah2N9nBOFmZmV5K4nMzMryYnCzMxKcqIwM7OSnCjMzKwkJwozMyvJicLMzEr6/64E2mL7thxfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = monthly[\"exposure_rate\"]\n",
    "glm = sm.GLM(y, X, family=sm.families.Poisson())\n",
    "results = glm.fit(cov_type='HC3')\n",
    "\n",
    "plt.plot(y, results.predict(X), 'o', alpha=0.5);\n",
    "plt.show()\n",
    "\n",
    "print(np.exp(results.params))\n",
    "\n",
    "resid = results.resid_deviance.copy()\n",
    "graphics.gofplots.qqplot(resid, line='r')\n",
    "plt.title('Exposure Rate QQ PLot')\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the difference between Atlanta and Gwinnett, we see the changes attributable to them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FIPS                           NaN\n",
       "active_cases              -0.62105\n",
       "household_count                NaN\n",
       "li_units                       NaN\n",
       "li_units_per_household   -0.565532\n",
       "mask_compliance           0.046325\n",
       "mean_household_size       0.122889\n",
       "mean_income              -0.062653\n",
       "new_cases                      NaN\n",
       "population                     NaN\n",
       "vulnerable_pop                 NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(results.params) * comparison_df[\"Diff\"] / comparison_df[\"Atlanta\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}