covid-mask-mandate-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": 1,
   "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": 2,
   "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>1001.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009.0</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  1001.0 2020-01-22           0\n",
       "1  1003.0 2020-01-22           0\n",
       "2  1005.0 2020-01-22           0\n",
       "3  1007.0 2020-01-22           0\n",
       "4  1009.0 2020-01-22           0"
      ]
     },
     "execution_count": 2,
     "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\")\n",
    "\n",
    "# Cast types\n",
    "case_data[\"date\"] = pd.to_datetime(case_data[\"date\"])\n",
    "case_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mandate Data\n",
    "The Mandate Data contains separate FIPS_State and FIPS_County columns, that must be combined into a FIPS column during procesinsg. Unused columns are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>mask_mandate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>2020-04-12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001</td>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS       date  mask_mandate\n",
       "0  1001 2020-04-10             0\n",
       "1  1001 2020-04-11             0\n",
       "2  1001 2020-04-12             0\n",
       "3  1001 2020-04-13             0\n",
       "4  1001 2020-04-14             0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get raw data\n",
    "raw_mandate_data = pd.read_csv('data/raw/public_mask_mandates_by_day.csv')\n",
    "\n",
    "# Calcualte FIPS and cast to date\n",
    "raw_mandate_data[\"FIPS\"] = raw_mandate_data[\"FIPS_State\"]*1000 + raw_mandate_data[\"FIPS_County\"]\n",
    "raw_mandate_data[\"date\"] = pd.to_datetime(raw_mandate_data[\"date\"])\n",
    "raw_mandate_data[\"mask_mandate\"] = (raw_mandate_data[\"Face_Masks_Required_in_Public\"] == \"Yes\").astype(int)\n",
    "raw_mandate_data[\"mask_mandate\"].fillna(0, inplace=True)\n",
    "\n",
    "# Remove unused columns\n",
    "mandate_data = raw_mandate_data[[\"FIPS\", \"date\", \"mask_mandate\"]]\n",
    "\n",
    "mandate_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compliance Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>COUNTYFP</th>\n",
       "      <th>NEVER</th>\n",
       "      <th>RARELY</th>\n",
       "      <th>SOMETIMES</th>\n",
       "      <th>FREQUENTLY</th>\n",
       "      <th>ALWAYS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNTYFP  NEVER  RARELY  SOMETIMES  FREQUENTLY  ALWAYS\n",
       "0      1001  0.053   0.074      0.134       0.295   0.444\n",
       "1      1003  0.083   0.059      0.098       0.323   0.436\n",
       "2      1005  0.067   0.121      0.120       0.201   0.491\n",
       "3      1007  0.020   0.034      0.096       0.278   0.572\n",
       "4      1009  0.053   0.114      0.180       0.194   0.459"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get raw data\n",
    "raw_compliance_data = pd.read_csv('data/raw/mask_use_by_county.csv')\n",
    "\n",
    "raw_compliance_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 2: DATA PROCESSING\n",
    "\n",
    "Here we filter down to the target county and combine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine on date and FIPS\n",
    "combined_data = case_data.merge(mandate_data, on=[\"date\", \"FIPS\"], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "# Gwinnet County\n",
    "target_fip = 13135\n",
    "target_pop = 957062\n",
    "\n",
    "# Combine on date\n",
    "combined_data_county = combined_data[combined_data[\"FIPS\"] == target_fip]\n",
    "\n",
    "# Keep only data\n",
    "county_data = combined_data_county[[\"date\", \"Case_Count\", \"mask_mandate\", \"FIPS\"]].copy()\n",
    "county_data[\"Pop\"] = target_pop\n",
    "county_data.set_index(\"date\")[\"Case_Count\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add in neighboring states that had a mask mandate so that we have a comparison\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>date</th>\n",
       "      <th>Case_Count</th>\n",
       "      <th>mask_mandate</th>\n",
       "      <th>Pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15463667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15463667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15463667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15463667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15463667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  Case_Count  mask_mandate       Pop\n",
       "0 2020-01-22           0           NaN  15463667\n",
       "1 2020-01-23           0           NaN  15463667\n",
       "2 2020-01-24           0           NaN  15463667\n",
       "3 2020-01-25           0           NaN  15463667\n",
       "4 2020-01-26           0           NaN  15463667"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neighbor data (pop from US census 2020)\n",
    "neighbors = [\n",
    "    {\"state\":\"NC\", \"FIP\":37, \"Pop\": 10439388 },\n",
    "    {\"state\":\"AL\", \"FIP\":1, \"Pop\": 5024279 }\n",
    "]\n",
    "\n",
    "# Aggregate\n",
    "neighbor_fips = [row[\"FIP\"] for row in neighbors]\n",
    "neighbor_data = combined_data[(combined_data[\"FIPS\"] // 1000).isin(neighbor_fips)].copy()\n",
    "neighbor_data = neighbor_data.groupby(\"date\").aggregate({\"Case_Count\": \"sum\", \"mask_mandate\":\"mean\"}).reset_index()\n",
    "neighbor_data[\"Pop\"] = np.sum([row[\"Pop\"] for row in neighbors])\n",
    "neighbor_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 3: MODELING\n",
    "\n",
    "Here we model infection rates and case counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>Case_Count</th>\n",
       "      <th>mask_mandate</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Pop</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>vulnerable_pop</th>\n",
       "      <th>active_cases</th>\n",
       "      <th>infection_rate</th>\n",
       "      <th>infection_rate_7da</th>\n",
       "      <th>infection_rate_change</th>\n",
       "      <th>infection_rate_change_smooth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2020-01-22</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0</td>\n",
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       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
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       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
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       "      <th>2020-01-25</th>\n",
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       "      <td>13135.0</td>\n",
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       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-27</th>\n",
       "      <td>132879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>142.0</td>\n",
       "      <td>824183</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>1.946932e-07</td>\n",
       "      <td>-0.015387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-28</th>\n",
       "      <td>133024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>145.0</td>\n",
       "      <td>824038</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>6.431613e-06</td>\n",
       "      <td>-0.009097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-29</th>\n",
       "      <td>133120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>96.0</td>\n",
       "      <td>823942</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>-8.300865e-06</td>\n",
       "      <td>-0.008533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-30</th>\n",
       "      <td>133120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>823942</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.008533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>133120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13135.0</td>\n",
       "      <td>957062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>823942</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.008533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Case_Count  mask_mandate     FIPS     Pop  new_cases  \\\n",
       "date                                                               \n",
       "2020-01-22           0           NaN  13135.0  957062        NaN   \n",
       "2020-01-23           0           NaN  13135.0  957062        0.0   \n",
       "2020-01-24           0           NaN  13135.0  957062        0.0   \n",
       "2020-01-25           0           NaN  13135.0  957062        0.0   \n",
       "2020-01-26           0           NaN  13135.0  957062        0.0   \n",
       "...                ...           ...      ...     ...        ...   \n",
       "2021-10-27      132879           NaN  13135.0  957062      142.0   \n",
       "2021-10-28      133024           NaN  13135.0  957062      145.0   \n",
       "2021-10-29      133120           NaN  13135.0  957062       96.0   \n",
       "2021-10-30      133120           NaN  13135.0  957062        0.0   \n",
       "2021-10-31      133120           NaN  13135.0  957062        0.0   \n",
       "\n",
       "            vulnerable_pop  active_cases  infection_rate  infection_rate_7da  \\\n",
       "date                                                                           \n",
       "2020-01-22          957062           NaN             NaN                 NaN   \n",
       "2020-01-23          957062           NaN        0.000000                 NaN   \n",
       "2020-01-24          957062           NaN        0.000000                 NaN   \n",
       "2020-01-25          957062           NaN        0.000000                 NaN   \n",
       "2020-01-26          957062           NaN        0.000000                 NaN   \n",
       "...                    ...           ...             ...                 ...   \n",
       "2021-10-27          824183        1629.0        0.000172            0.000125   \n",
       "2021-10-28          824038        1546.0        0.000176            0.000131   \n",
       "2021-10-29          823942        1471.0        0.000117            0.000123   \n",
       "2021-10-30          823942        1471.0        0.000000            0.000123   \n",
       "2021-10-31          823942        1471.0        0.000000            0.000123   \n",
       "\n",
       "            infection_rate_change  infection_rate_change_smooth  \n",
       "date                                                             \n",
       "2020-01-22                    NaN                           NaN  \n",
       "2020-01-23                    NaN                           NaN  \n",
       "2020-01-24                    NaN                           NaN  \n",
       "2020-01-25                    NaN                           NaN  \n",
       "2020-01-26                    NaN                           NaN  \n",
       "...                           ...                           ...  \n",
       "2021-10-27           1.946932e-07                     -0.015387  \n",
       "2021-10-28           6.431613e-06                     -0.009097  \n",
       "2021-10-29          -8.300865e-06                     -0.008533  \n",
       "2021-10-30           0.000000e+00                     -0.008533  \n",
       "2021-10-31           0.000000e+00                     -0.008533  \n",
       "\n",
       "[649 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculateSpreadRate(df, active_window = 10, smoothing = 14):\n",
    "    df = df.set_index(\"date\")\n",
    "    \n",
    "    # Extra calcs\n",
    "    df[\"new_cases\"] = df[\"Case_Count\"].diff()\n",
    "    df[\"vulnerable_pop\"] = df[\"Pop\"] - df[\"Case_Count\"]\n",
    "    \n",
    "    # Infection rate is new over vulnerable\n",
    "    df[\"active_cases\"] = df[\"new_cases\"].rolling(window=14).sum()\n",
    "    df[\"infection_rate\"] = df[\"new_cases\"] / df[\"vulnerable_pop\"]# / df[\"active_cases\"]\n",
    "    df[\"infection_rate_7da\"] = df[\"infection_rate\"].rolling(window=7).mean()\n",
    "    df[\"infection_rate_change\"] = df[\"infection_rate_7da\"].diff()\n",
    "    df[\"infection_rate_change_smooth\"] = df[\"infection_rate_change\"].rolling(window=smoothing).mean() * 1000000\n",
    "    \n",
    "    \n",
    "    return df\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "calculateSpreadRate(county_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FzQe49YWtALxpai0Ax4yv4qHX9yZkfU21XYW9kSi/fnIjuw71BY+jSdlqjhDkUAfUIig2XtlqlbHf/+oeesJRDnT0MqehAhEGrII9WokmuYYCPk9CAHgoOnoiKesH3DiuoVue35pQWNYTjrpmFPtSViY7BH0eSvxeeiMqBLnGyRCa01DB/IlVAMwdX8nh7nDcuj55Wm08Lbg3EuNgR2JrkK0HOhKeO8FidQ0po8avntwYf7yzpYv97b2Mry5lbHmg6IQglpQ+OqO+gp0tXWnPCh6ootiNUwPw/X+uiWeZQKJFMBRBv5eA10MoUlzB/Hzi+Y0H+PWTG/ntU5sA+N+PnRrfd4yrhuTz583i758+nVJ77vShrlC/m4udhxJTSyPxYPHQQmCM4e5lO9MKQmeCCkERcbg7zJKtLZxzTD0Auw51s7u1mwlVJdRVDNwX52gluaBs3vhKYgZufWErl/7qOV7afHDQ17d2hakawiIYV9VXJ3D9P9bQYjeO6w5HE9pOD0aJz0PA5yF0lLQ8LkS+/vfX+PnjG1i3zwr21riC+fPGV8VTR53/76DP+r916gn8rvhBa1L78Xj6aBoGwYOv7+Wr97zGb5/aOPTBGaBCUERsarZM0osXjAfgI7ct5UBHiOn15VanTFeMYMv+Dt7y6+dy2hHxSJPca+jEqTX4PMJ/PbqetXvbuHPpjgFfa4yVOTJtbPmgn+FuMw3Q1Gb1HuoJxxJ6Cw2GYxGER/guUEmPgx297G7t5sOnN8a3BXx9l87qMj//+tq5PPyFs3jfoqlAnxDsbrUSMM6eXR8//pDdXuSN3YeJxUxfjCANJXCqmfd3jGwnWhWCIsJJXVs8Y2zC9ul1lhAccFkEP3t8A6v3tPH0uv2jusbRJJqUNTShupS/fuI0Fk0fw+TaUl7akmgRbGru4LD9RdzX1kNXKMrMcRWDfsa4ysTK4YMdIXrtC3owXSHwefD7RC2CHPGibRleNL9hwGMm1ZQyb0JV3MJ0gv2/tl2xnz9/Nvd8ajEesSyCdfvaeOtvnufnj28gEks/WOw4B0c6nKBCUKAYY2hu68ko731nSzcegSljyjhzVl8lrCME+9t74wFNx010NKeUmqRgMcCi6WO465OLuXB+A12uLJ3eSJQLfv4vTrjhMR55Yy+bmzsBmFk3uEVQX5loERzs7KXH9hmnGyMoiccIVAhywe+e3sTM+nJObqxN+zXBJLffrHEVLGwcQ3Wpn5auUDwD7LlNB/qCxelcjZ1hSmmvJD1UCAqU+1buZtGPnuSHD61N+zUHO0OMKQ/i9Qg/fudx8e1Tx5RRXxEkFI3R1h3h3hW74tlF2w52jvja84XkND43XkkcZL9kS5+L7M6lO+MZIg3VqXsFOZT4vXz49EZ+9743AZZF0BOO2fvS+/oFfR78Xo0R5IJYzLBlfycXzG8g6PPy7+fN4ssXzhnydUGX6+irFx8TzwqrLQtwqCsc33+oM+RKH00jWGz/HGmLQOsICpQHX9sLwNJt6fvwWzp741ks7jvVEr83/nx/Rw9fumtVfN+OlqN3Vm7MDOyXTZ5NsNGOr1SX+vF5PfHagJrS/sVgyVz/9gXxwLTbIkg7RmAHi9sLsKtlodPU3kMoGmNKrVUX8KWLjknrde46EPd3rbY8wKHOULyaeIeduQd9LSa6Q1HWN7Vz4pSaAd9/pPsSqRAUIN2hKM9vOgBYZeuRaAyfd+i7y5bOULyHTbJbwvFlN7X19nuNMWbAC+b/e2Yz4yqDvOvkyRn/HrnGujin3ucRIea6AY/aTyZUl9AVisSDdtVpCAFYwlJb5qelMxxPJ8w0ffRoGZSe7yzd1kJTWw+1ZYH4LImBekkNhNs15BaChqog6/a2J8ws+OUTVhzB+Y595/43uHv5Ll687ryEeRXQNxVtpFEhKEBe3HyA3kiMy0+cyP2v7mF7Sxcz6wcPWoLlGpo3oSr+/NPnzOSYBisH2mmHvKe1Ox4vuHB+A4+vaaKtO5LQ8sDNTx5ZB8Dps8Ym9NYvBJJ7DbnxCAkWgfO9rSrx09kbpbU7RGXQl5YAO5QGvPSGo64YQYbpoxojOOJ0hSJc+fuX+m2fMUQsKBm3a2icSwim1JbxxJrmBDefiHWBd07FLQcsd+yOlq4UQpB+qmkmaIygAHlibTPlAS//tnAKYBWGpUNzW59rCODrl8zlipMmAcR73u9u7aa61M9bjpvAZcdZaaYHO1PXF7j7sC/+8VOZ/yI5JmoGLuLxehJjBI4oVJb46ApZIySr0rQGHII+Lz2RqCtGkIFFoHUEo8LavX1N4TwC93xqMfd95vSMLYKA6wahwVVLMnlMGaFojL2tfUkY8dnH8eMt4Whq65+oEY8RZLSaoVGLoAD51/pmzp5TH7cCkisVU/Hnl7fT0RuhcYC896DPy7jKIHtau+nsjVAW8DKm3DohWzpDzKjv/5rtBR5INknpo25EBGOIu8WcZnSVJT46e6Mc7gqnbBY3GCV+D73hWMZZQ06wWOsIjjzOXI7vX3EsHzxt2rDfx+1Kdd98Tam17vC/fPeqfq9xcNy0+1Jk7DnnYTo1B5mgFkGBEYrE2HO4h3kTqhhXGSTg8/DfT2/iuY0D5/sbY7jluS2cMLmaDwxyck+sKWVPaw+dvVbrBOcEPmhXw24/2MkfX+jrmbP1QGELwVBZQ9A3WDwSFwI/naEIrd2ZC4FjETiW1FAN62798ELOmzsOn0cKziLY0NRecN1SozHDn1/ezuxxFXzg1Kkj9r7ui/YpjWMSgsDuwjRnFoZThbw9haXfe4RuBlQIRpCecJT7Vu5Ku1fNcHDcNHUVQTweoa48wN7DPXzwlldoauth/b72hOZmq3a2snpPG9sOdnHVoqkJJ14yk2pK2d3aTVcoSlnAG5+b6zTNeu9NL/O9f6xht102v25vYm/1TFo45wOxQYLgjmXvuIRiMasvUXnQR1dvlH2HexhbHkz52oFwLIK9h62/3/ghUk/Pm9vArR8+BREpqDqCtp4wF/3iWT79lxW8sOlAwvmYj/zztT0cd/2jXP6759nU3MGXLpwzYnfc46sS/4/Lgz5+/m8nxJ+74wdOTZDz/7x2b1u/9ztS54AKwQjx389sYu5/PMIX71zFVX94OeWc2pHgQLt1UR5bYV2kb7j82Pi+U3/0JBf/8lmeWW9ZB5ua27n8dy/w1t88D8AZM1OPU3SYWFPC1gOdRGKG8qCP8VUllPq9bGiyshz22KbqqztbAVizt405DRV8aLFlZQwUS8hXYrG+O/9knAuBI25RY/B5hPKAl1A0xu7W7oRmY+ngWAR7WnuoDPrSzjgCCsYiiMYMl/7yOQCe3bCf99+8hD+9tD3Hqxqcnz22gfaeCFv3d/KehVPiLViy5bmvncujXzy733b3zdhUV+zBsTqd/+eVO1r5+O1LufaOlXF3opM5NtIZZCoEWRCJxvjTS9v47F9X8J+PrGfBxCouWTCe13e18svHR7YplINTyFRnT626YH4D2258C585Z2b8mF32HfuqnX0Tx46bVM2UMYNn9bjn55YHvHg9woKJVby++3C82RbA8u2H+PzfVvLUumZOmFwTb1mRb03reiNRlmw5OKCLImbMgNWcjsvIuZmN2WMty1zdRue7MrDSwbEIdrd298sGGQorfTS/76wBXth0IG4xOjy6el+OVpMeQZ+HyqCPV751AT959/F4Rmhm5JQxZSnF3h1I/volc7n1wwuZ01ART1F2d5l9Ym0z//fqnnhhp2MRqBDkEbe/tJ3v3L+a5zce4GNnTufez5zO7z94Mu85ZSq3vrCVr969iu5QlHX72rj1+a0JwZ+9h7v59ZMbEzJv0sFpDFdfkeiW+LeFU5hr36E6F2Tnzh2soqahzF33xcm54M2bUMWGpnZW7jgEWBfIP76wjX+s2gPA+0+bxoKJ1XgE3vLr53nfH17m2Q257U8Uixm+ds8qjvn2I7znppd5/81LUronkucRuHGuBU7mkBNPKA/0BXhPGKTgJxVBn5eNzR08vqYpnq6bLn6vh2jM5L377Ym1TQR8Hn74jj5L9dWdrQl58/lCZ2+EL9yxknX72nn3wslDthQfKdwWwZjyAOfNbWB2Q2U/i8BNJJq4b6RvCkZECETkEhFZLyKbROS6FPuDInKnvX+JiDS69n3D3r5eRC4eifWMBjtbuvj78l3Maahg1Xcv4j/eOj9eTfiVi6wS9LuX7+LaO1dyyS+f44Z/ruG0Hz/JZb96jp89tp7P/GUFP398A9+49/W4b9Caadox4GeC1fhMBOoqE4eeN9aV88i1Z9NQFWSvfUfmfq907l7dKXJl9gWvriJIe0+E3/9rC5UlPk62pzItnjGW579+LidOqWHq2DI+d+4swGrQ9aFbXxnys44k/3htD3ct20V1qZ8TptTw6s5Wth/sH3hL7j7qxpPCNeQV4YxZdfg8wuUnThxw8PxAuOsGTpySft8a6Lt45DJOcKCjlxsfXjfoHf66fe0cN6ma9586jWXfvoBfXXUiXaEoK3a0jt5CB+GOV3bw0OtWVf4Ta5u4/1XrhmZqhumh2eAWAmeqmc81JjUUscZguokmxQ9G2k2YtQSKiBf4HXAhsAtYKiIPGGPWuA77GHDIGDNLRK4CfgK8R0TmA1cBC4CJwBMiMscYk9ejmJZua+FDt7xCTyTKj95xXL/9YyuC/PXjp/K+m5fw6OomJteWcu4x4/jzy9tZs7eNNa4g0D9W7eGZdc2Mry6JtzFY8s3zE3KPHaIxw51Ld3Lx/PGUDZBxMqG6lD12MHLLgQ7GV5Xw/lOnxgdlDIb75HPSTJ3MmN2t3cyoL4+fxOfOrWdybd+X50sXHcNVi6by1t88T0tnyHKljJCJnSn3rthN49gynvryOWze38GFv3iWV7a10JhUFJQ8j8CNIwTGFSz2eIQpY8p47fqLBnzdYLhHGV4wf1yGr7U+b2NzO8dPrsn4s0eCq299hdV72lgwsSqlH/1wd5iNTe1ccqy1r64iyPnzGgh4Pfx9+S4WTR8z2ktO4KHX93Ldva8D1njQvy7ZQXnAy3WX9tXTjAbu88CZc+xNEIIY5QEfX7loDs3tvfzppe39rIWRTiUeCYtgEbDJGLPFGBMC7gAuTzrmcuB2+/E9wPli+SkuB+4wxvQaY7YCm+z3yztaOkOs2dPGn1/axkf+uJQJNSU8+9Vzee+i1Glmi2eO5fPnzeKMWWO57SOn8P0rjk2YagTw6/eexIy6ctp7I3ERACvou8J2xbjZeqCDw91hLhykHe6MunI2N3fS0Ruhqa2XDy6exufPn53W7+juj+K4mdwpkh09EQ7ZQzXmju9vYUysKeWLdkOukZ5/fLgrzA8fXMPHblvKN+97PR48SyYcjbFsWwtnza7H4xFm1ldQFvD2y8D4+O1LeX334QEzd5wLfYJFYG8rC/gS/lbp4vh1P3bmdBZMrM7wtdY6vnbPaxl/7kjQ1NbD6j3W3zBVfvuBjl5O+N5jHOoKM9/1u1UEfbz/tKncuWwnv3riyMTN0iEUifGTR9bFXX6n3/gUS7a28I3L5vHBxY1UlmSWCpwNPtdNhPPYbRGEowa/V/jcebPjgptsEYx0jGAknGKTgJ2u57uAUwc6xhgTEZHDwFh7+8tJrx0Rad51qIvfPb0ZMMRiVmDQYKcDGvdz667PONtc+5wBIi2dVv9wxz07p6GC2z+6aNCWCiLCl5MaVJ0xayyfPXcmF80fT+PYcqrL/Fx27HiWbjvEA6t287dXdnLGrLG8sbuN/356EzdffUrC650vojMrNRXzJ1Zx78rd/PTR9QAsnJaZC+LDpzeyp7U73jrBHeyaUFPKSVNqWL2nbcCMmcl2nGHXoe6UVs1w+cGDa7h7+S4m1ZTy5Lpm6soD/RqAhaMx7lu5m85QlDNnWxlSHo8wbWw5O1yuoZ5wlCfWNgNw6bGpM0Sc76rzfx6NZT9T1kkrzjTIDHDxggZ+8si6BCtsNHnZns3wthMm8o9Ve2hq60n4/73thW3xx1cm9Z365mXz2Ly/k5uf28Inzp4+oDV7JHl8TRPbD3Zxy9ULeWJtE397ZSf/8db5g9bVHCncsTrne+b1ePru+iOxuOXt3HxE4oHkIxMjKJjKYhG5BrgGYOrUoYs92rojPL6mCY9YfTk8IvEvssdjde+z9kl8v2D/dLZjNY8aX13CmbPrOH5yNQsmVtM4tmxYecYiwlcvnpuwzef1sHjmWBZMqqK+Ishnzp3FN+97PeWYxCVbWyj1e5k1yDAU5yJz24vbOHNWHacmDaEZiuvfviDhuXsk3+8/8CbGlAf4wGlTB7zIT7IrJ3e3dnNyhiKUimjMsKe1m/97dTcfWjyNGy4/ls/9dQW/f3YLp8+q47QZY/nHqj2s2HGIZzfsZ/P+TibXlnLe3D7XS+PYMpZvPxR37zxg+4U/tHhawlwGN45bKxYPFscGbFCXLk6zubI03HTJzKivYGZ9eUIPm+HS2hXC65GM7oJf3tJCZdDHu940iX+s2sO37ns9fqOyqbmD3z5tzfJ943sX96uY9ns9XHPWDD6wYQkvbT7I+fMGtmiPBB29EX700FpK/V7ePKee8+aO41NvnjnkdLnRIJVF0BuNUR3wJ+yPxgw/fngtj9lN8PIuRgDsBqa4nk+2t6U6ZpeI+IBq4GCarwXAGHMTcBPAwoULh5TD+ROrWPbtC9L8FXJPVYk/foc7uaaUprYewtFY3J/YG4nyyBv7OH/euAQfYzKnTB/DjLpythzoTLgYDpda2zVUVeKLW0Czxg2cP99gl8c3p+iTMhy+/8813GYPfXfqFb5x2TyWbG3hRw+t5c5rFvP5v60ErAvsf77reM5L+htNG1vOw2/s49/vWMl/vvt4/uux9VQEfXz5omMGbkMtyUIwcM1BunQ7PYaGIQRgXVBHwiVw4g2PM7Y8wPL/uDDt1yzZepCFjbW8eU49bzl+Ak+ubaInHKXE72WVnZ02vqok3nc/mYWNtQR9Hl7YNHwhMMZwuDuccHMSjRlixgz4ndjY1M6H/7iU3a3dXDi/IX4Hng8iAH3xAq9HaOkM0dYTtiwCb6JF0NIZ4o/Pb+P4ydW8tutwXqaPLgVmi8h0EQlgBX8fSDrmAeBq+/G7gaeMFYV7ALjKziqaDswGcptykgdMqi0lZmCbq4XD35bsoKUzxHtOmTLIK60T6+5PLeZH7zhuwPhFJlTZd43HTkrPp11V6iPo89A8AjUFkWgsLgI+j8QFaFJNKR89Yzqv7TrMvO88Ej/+HSdN4t9OmRKvsXD40OJpvHlOPf98bS/zv/Mo+9t7+erFxwxa0OVNyhqKGYPXm50QOIHnsjR7DCUzEkLg/D4H7YB+OjS397BlfyenzRiLiHDFiZPoCcfiAvD67sOU+r28cN15A75Hid/LvAlVbGxuH/CYVKzccYirb32FW57fyvHXP8aJNzzOU+ua4vu/ce9rzP7Wwyz+8ZNcddNLPPKGlRFkjOHnj2/gwl88y+7Wbj755hnc+M7+iR25xrnQ7zpkuS6/fs9rhCJRAj7HUrAu0cu3HyIUjfGNS+dx/txxCefBPct3cTDLmFzWFoHt8/8c8CjgBW41xqwWkRuAZcaYB4BbgD+LyCagBUsssI+7C1gDRIDP5nvG0Gjg5PNf+Itn2frjy9jf0csPH1rLWbPrBnRluBlbEeR9I9QrpbY8wB8/fApvmpqem0dEGFcVTNk5MVOe22jNXKgt8/P7D5ycsO/KhZP5zVMb6bL97jPry/nk2TP7vQdYf8/bPnIKn/vrSh60UweHEjbHNWTiMQKTtUVw47uO55bntrKwcXjZM36vxP3Iw2W3q0Hhv9+xkt/ak9MGY+lWK3HBcTMeP9n6263Z28bT6/dz24vbWDxj7JCZVJNqSlm7r3/bhMG4/h9rWLWzlX9t2G+lMvdG+Ohty5hRX07j2HKeWmfFeipLfKzc0crLW1Zw9px6mtt64kWQX7pwDp87d1bOstjSwZlf7fyeyRZBmzMIqcyP32sVJoLl5vvK3auYO76SR67tX8WcLiMSIzDGPAQ8lLTtO67HPcCVA7z2h8APR2IdRwvHurIulm47xPLthwhHDd9929BFYUeCczN0MTVUltDclr1F8LdXdjCmPMDL3zi/X4+kuoogj157NrsOdbN45tBxEBHhp1ee4BKCwQO28YIyV9ZQtheSSTWlfOdt84f9et8I9BvatL/vjvyFTQfSSvNdsvUgZQEvC+wkhXGVQcaUB3hsdRMv2UHk09P4P5hYU8KT65oGHXTkprmthzd2Hybo83DuMeP4ybuOJxSN8en/Xc76pnYCXg9vP2Ei337rPMZVWgOD/vDsVn779EaqSwPccPkC3rto6qCu1Hzhh1ccS8Dr4b6Vu9l7uJuAz7pZcNJLnel0ZQEvQX9fuxHnxsBd+T8cCiZYXEzUlgd443sXc/z1j/Lo6n3cuXQnZ8+pHzRInE80VJWwes/hoQ8chN2t3TyxtolPvnnmgI3ypowpy6hPfGnAy/fevoDZ4yqGTP/0JgWLYyNgEWRLwOuJB5yHy5b9lrvxukvncuPD65jxzYd45NqzUqYDO7yytYWTp9XGL6giwpum1sQzr752yTF89IzpQ372xJpSesIxDnWFhyzGi8WMVWxpDA9/4WxmuAYv3fXJxfGEDjdlAR9fuGA2HzmzkRKfd9AGi/lGTVmAKxdO5r6VuwlHTb+soT4hsFyvjkWQrntvKArnL1VkVAR9zB1fxS3Pb6WjN8Knzp6R6yWlzcz6cna0dA2Y658Od76yA4D3j2A7YICrT2/k9DTca9IvWDxw8dlo4fNK1jGCLQc6qSnz88439WVp3/HKzgGP7wpFWN/U3s81+I3L5gEwe1wFnzlnVlqzFZy5vw+/sZdzf/pMfO52Kn7xxAaeXNfMty6blyACYLntBrMoqkr8BSUCDgsm9HkCAl7r7+lkDTlNLMsCXoI+L70R67uVravQofD+WkWEkwsP8KYRSMUcLeaMryRmYPP+wdtlDMZT65tZOG1MzvLm+4LF1vPBxlqOFj5P9o3ntu7vZHpdOeMqS3jo38+issTHkq0tAx7/8paDGNM/pjKzvoIV/3Ehf/3EaWl/tpNO/K373mDrgc74mNNkNjV38JunNnHZcePTsjSOFqrL/HZtw1SuXGjVYiRbBKV+r2UR2C7Ckeo9pUKQx3xo8TTqKoJ86cI5aU+zygfm2HOQh+qbNBD723tZvaeNM9K4cz9S9BWU5Y9FEPBlbxFsPdDJjDrrDnv+xCref+o0NjW3p4w9GGP46G3LgNQxlTHlgYTB7ENRWx7glEZLDEr9XvYd7kloRheNGW5+bgs/e2w9Po/wvbcfm9cB3iPBx86czg+uOC4+W9zJGmrrCVPi9+DxCEF/nxC452pn4ybSGEEeM7m2jFe+eX7BfRkcF8CuNEZopuLeFbswhvjM5FzgcRXygGWC5/r/wefxZNXFs7M3wr62noSeUvMmVBKOGjbv74hffBz22ZlfFy9oGLSKPhNu/+gi/F4P/7dyN1+95zXuW7mbK+3Z2799ahO/eGIDYP3fZyIyRytui8CJqwR9XqIxQyQaS7AIwrEYQc/wbhjVIshzcn3xGQ6lAS+1ZX722F1QD3eFeWnzwbTuWNp7wtz8/FZOaaxldkNmg19Gkr6mc9bzmDFkWUaQNf4sZxI4Pe2nu5rvOZXoqaZhORbd1ac3DvszkykL+PB7PRxnp6B+9Z7X+NsrO9jU3M5vn7Z6EU2oLuHaC+aM2GcWMn7XSVdqewWc6vLeSJIQZHFuqEWgHBGs+ceWEPzPs5v572c2c9LUGu7+5OJ4dWdnbwSvRxLcXt+5fzX723v5XlKri9HGyTh0zyPwDTTFZpTwZxks/pc9J2KmK/g6vc7qKJsq/dARgiORrTZ3fBXPfe1cvnzXKn704FpOmlZLic/LS984h7HlgZykSY8mX79kLtsPDj3z2+2OLA+mEAKXaygba1EtAuWIYAmB5VrY0GRdUFbuaOX5TQfixyz+8ZPxMZoOWw90smBiFZcdN2H0FpuC5KyhWIwBp5mNFtlUFhtj+MOzWzhrdh1zGvou7D6vhzkNFQNaBFUlvn5DkEaKKWPKuPFdx9HeG+HZDfs5fdZY6iqCR70IAHz6nJnc+K7jhzzOffNRajfrc4rPeiPRBIsgm/5DKgTKEWF8VQlN7ZYQ7Gjp5Jxj6inxe+LVwqFIjLaeCJuaOxL6Eu093D2s7pwjjZM1FHMVlOU6WOzzSnxSVaZsOdDJoa4wbz1+Qr8L7cz6irjbyM2m5g5mjas4ohfmGfUVcVfVKcOsuD6acZ9zZUmuoVAkRsx17c/GNaRCoBwR6iqCtHaF6Y1E2X6wizkNlUysKY33snebxU5HxXA0RnN7LxMGmBEwmvQVlFnPo7Hcp48GvMMfYP/arlYg9WS0MeUBWjpC/bZv3t8xKkWM37xsHh8+vXFEemMdbfhSuoasn398YRuPr+3ru5SNa0hjBMoRYWyFleGwsamD3kiMybWljK8qYa89Pe1FV5vtZ9bvZ8HEKmrLAhhjzT3INc41P6HpXD5YBMNMETxoX+hTDeKpqwjSGYrGu4mC1cPmQEdoVITgwvkNgw5bKmas4jkracHp7OpYBE5DRods4kcqBMoRwekA+sZuq9VEQ1UJ46tLeGnzQUKRGP/9zCZOaaxlQnUpD6zawxOuO5u5Awy9GU288awhVx1Bji0CZ4D9cMaAtnWHEYHKFG2inbTEls5QvOGhUwxYKG1NjmZ8HiEcNVSUODGC1I6cUCTxJqGlM8QfX9jKtRfMGfImRl1DyhGhvtK6uDgzYhuqSmyLoIc5336YprZe3vWmyf2mrR3TUMmJU2pGe7n9iNcRuIQg16m8Tq+fcCzzO7/D3WEqg76Uv4MjBAdd7iEnY2hmvQpBrnEu4hVBq2264xpK7tcUSTovrn9gNb95ahPP2tlig6FCoBwR6isSXRDjq0qswSCuC9FpM8Zymt3aeLw98ey4ydV5kTXiSZpHkB8WgT22cBhBwbaeCNVlqecvjHWEoLOvY+ym5g4CPk/OWnwofTjX90rbInAy2cYlFdwlu4acanGnL9FgqBAoR4QpY0r59lvmxZ/XVQQ4aWotz3+9b3jJtLFlnDilhg0/uJRHrz2btxw/ga8kzSHOFY5execR5EOMwE4lHI4v+HB3OD5kKBln7Ki7EnxTcwcz6spz/jsrfWmhTozAsQxPSxpDm+wacgYpHeoK81+PruMH/1wz4GdojEA5IogIHz9rBnPHV/HSlgPxIrKGKusuZuqYvrnPAZ+HgM/D79IYkjJaeJNaTMTyoNeQ3+cIwTAsgu7wgBPZJteWUlcRZPn2Q/Fh7pv2d3DC5Jphr1UZeRwhOHlaLbd95BQWzxybEDBOdg05w21uf3Eb6/a1Uz7IiFQVAuWIcubsuoQuqiLC0185Jz4LOV/pN7M4DywCv/35w7UIBvL3iwiLptfy4mZrUE0oGmPXoW7e9abJWa1XGVmcYDHAOcf0HxaVfF44bth1+9o5YUoN93/2DOT7qd9bXUPKqDO9rjxhAHk+kiwEsRi5ryNwtRbIlMODWAQAlx47gaa2Xl7eepDN+zswRjOG8o1UGV9ukl1DPtdktkk1g9fmqEWgKClwKvvdBWW5nnhYafv4O+ze9OlijKGlM8SYioHF12n5vW5ve7wGRIUgv6gcIMbjkOwacidmlAwxkU+FQFFS4E3OGsoD15CTNeJMq0qXw91hIjETr+1IRW2ZH79XaG7vpdWuOWgcWz7g8croM71+8P+PZNeQe1ZzcIh5JioEipICjyfZNZT7FhNO1k97hkJwwK4PqBvEIhAR6iuC7G/vRQQaKksKahhSMVAxhGsonOQacrewLhmgCM1BhUBRUpCPweKqUtsi6M7MNXSgw6oPGMwiAKivDNJsNwpsyIN+T4pF0OeJp/gORnKhoft8HUrUVQgUJQV93Uet5/nQdM7xEWfqGnIqhscOYhEA1FeWsOtQF9GYSZhipuSW166/CGHocy+clETgtg+GihFo1pCipCDedM4YjDGEIrGE4FsuqAz6ELGqhDMhXYtgfHWQPa3d7GvriVd6K7kn6PPGM8YGI7m+xD2rQF1DijIMHLPaGMOfX95ObyTGtLrc3iV7PEJF0Edbd2YWwb62HvxeYcwQKbuz6iviIjN+hGYUK6PHhqZ2Onoj8ViCuxXJUK4htQgUJQWOGygSM3zn/tUAeTEwp6rET3uGFsG+wz2MqywZsmneHFfX1/HVOji+kDhrdh13L9/FmT95Kj6XwJ1OOpRFoEKgKClw6gjcF918aI9dWeLLKEbwP//azONrmtIa9jOnoe/3Syc4qeQP1799Ae980yRau8K02hZjJKYWgaJkhWMROG6Yay+YTfkQ6XujQVWpP23XUCxm+PHD6+jojaSVBVRXEYx3ItUYQWFRXxnkzXPqAWuoEEDU5RoKarBYUTLHyRo6bF9086Uvf1WJL23XkPu4qWPSayc92x5sn2qSmZK/eEVcA4ZSWQTqGlKUjPEkCYG74VcuqSrxp+0aau3uGzRzjn23OBQnTa1lYnUJZYH8+H2V9PB6hFo7GeCQbRG4YwSBIfqj6P+2oqTA77OEwMnBH6rh12iRiWvIEbFJNaUsbByT1mu+cP5sPn7m9GGvT8kNHhFqbYugNS4EfRZBe+/gVmR+nN2KkmeUBXzUVQR4Y481czlfLILKEh8dvZG05ha3dllC8MurTky7KrrE79XWEgWIzyPx1u6Oa8gdIxgq401dQ4oyAJNqy+J+9qH6vIwWVSV+YgY6Q0PHCRyLYLD208rRgccjlPq9BLye+P97JGaYP6GKbTe+hSlDxIhUCBRlANwX0MpgflxMnX5D6QSMVQiKCxEh6PPEZxVHYjF83vQsQRUCRRmAH15xbPxxeTA/3CWZ9BtyfMUqBMVDwOchFLWG1UdjJu22KPlh7ypKHjJlTBlLv3UBb+w+nDDtKZc4MwnSsQi2HOhkXGVQff5FhN/roTccwxhDJGrwedI7b1UIFGUQ6iuDnDu3/3zYXFGZwUyCjU0dHJMH1dDK6BHwebh7+S4iMWNP1VPXkKIcdcSnlA0xk6CtJ8zG5nZmj1MhKCacYTT3rdxNeLRiBCIyRkQeF5GN9s/aAY672j5mo4hcbW8rE5EHRWSdiKwWkRuzWYuiFAN9rqHBLYKfPrqeUCTGO06aNBrLUnKEP+lC7x5PmUmMIFuL4DrgSWPMbOBJ+3kCIjIG+C5wKrAI+K5LMH5qjJkLnAScISKXZrkeRTmqqYoHiwe2CHrCUf735e2855QpHDe5erSWpuSAZ756Lnd9cnHKfZGowZtmjCBbIbgcuN1+fDtwRYpjLgYeN8a0GGMOAY8DlxhjuowxTwMYY0LACmBylutRlKOaEjtXfLBgcXtPhJjJj7bZypFlUk0pi6b3VY0b12ya0bQIGowxe+3H+4CGFMdMAna6nu+yt8URkRrgbVhWhaIog1BZ4uP2F7fx4Gt7U+7vtNsJaL+g4sO4BlT2RqJ4RypGICJPiMgbKf5dnrAAYwyJYzLTQkR8wN+AXxtjtgxy3DUiskxElu3fvz/Tj1GUo4bKEh/d4Sif/euKlPs7bCHIh7bZyujiai/EtoNd+EeqjsAYc8FA+0SkSUQmGGP2isgEoDnFYbuBc1zPJwPPuJ7fBGw0xvxyiHXcZB/LwoULMxYcRTlaqC4LwMGuAfd3hayConxpi6GMHjGXb2hidQmLpo9N63XZnikPAFcDN9o/709xzKPAj1wB4ouAbwCIyA+AauDjWa5DUYoGZ3gMWDOVRRLv+jrjFoEWkhUbjg7c9MGTuWjB+LRfl22M4EbgQhHZCFxgP0dEForIzdbCTAvwfWCp/e8GY0yLiEwGvgXMB1aIyKsiooKgKEPgdvl0pGgvrK6h4sWxCIIZVpNndaYYYw4C56fYvgzXXb4x5lbg1qRjdgHpObAURYnjjv81t/fGq40dOlUIihbHIgj6MrvH18piRSkw3HMInME5bhyLoEKzhooOxyLItL+UCoGiFBheV0ygo7d/hXFnrxUs1hhB8aEWgaIUCR84bVr8cYd90XfT3hMm6PPkTcdUZfQwahEoSnFwwpQaXvrGeQB0pKgwfm3XYeY0aLO5YsSpIwioRaAoRz9OjUBnUtZQTzjKyp2HOH1WevnjytGFEyPwSmZ5OCoEilKAlNuB4PYkITjQ0Us4aphZV5GLZSk55vITJwJ9XWrTRdMKFKUA8XiE8oC3n0XQ2mXPKS7T8ZTFyHWXzuNz583OOHVYLQJFKVDKg75+MQJHCGrLAqleohzleD0yrBnVKgSKUqBUlPjoCCUJQbdVV1CjFoGSASoEilKgVAxiEdQM465QKV5UCBSlQCkP+PrFCA53a4xAyRwVAkUpUEoDXrrDiQVlrV0hygJegj6tKlbSR4VAUQqUVELQ3hPROQRKxqgQKEqBUub30h1KFILucJSygFoDSmaoEChKgZLKIugORTPuM6MoKgSKUqCUBrzxsZQO3eEopWoRKBmiQqAoBUqp30soEiPqmljeHVLXkJI5KgSKUqA4F3y3e6g7HKVUXUNKhqgQKEqB4lzw3QHj7rDGCJTMUSFQlAKl1O5A6haCnpBaBErmqBAoSoEStwhcrqEuTR9VhoEKgaIUKM4Fv8vVeK47FKVEhUDJEBUCRSlQSpIsgljM0BuJqWtIyRgVAkUpUByLoMcWgp5INGG7oqSLCoGiFCilcddQNOGnZg0pmaJCoCgFSnL6aG8kBkCJdh5VMkSFQFEKlNKkgrJe+2fQr19rJTP0jFGUAiVeWWxbBKGoZREEvPq1VjJDzxhFKVAcF5ATGwjZrqGAT7/WSmboGaMoBYrHI5T4PfGsISdGoNPJlExRIVCUAqYs4FOLQMkaPWMUpYAp9fcNp+m16whUCJRM0TNGUQqY0kDfuMpQ3DWkX2slM/SMUZQCJtEiUNeQMjz0jFGUAsYaV2k1nYsLgaaPKhmiZ4yiFDAlfi/dYUsA4q4hLShTMkTPGEUpYAJeD2FbAOLpo15NH1UyQ4VAUQqYoM8TryjW9FFluGR1xojIGBF5XEQ22j9rBzjuavuYjSJydYr9D4jIG9msRVGKEb9XCKsQKFmS7RlzHfCkMWY28KT9PAERGQN8FzgVWAR81y0YIvJOoCPLdShKUeJPcA1F8XkEr0dyvCql0MhWCC4Hbrcf3w5ckeKYi4HHjTEtxphDwOPAJQAiUgF8CfhBlutQlKIkkOQaUmtAGQ7ZnjUNxpi99uN9QEOKYyYBO13Pd9nbAL4P/AzoynIdilKU+L2euEuoNxLTYjJlWPiGOkBEngDGp9j1LfcTY4wREZPuB4vIicBMY8wXRaQxjeOvAa4BmDp1arofoyhHNcnBYrUIlOEwpBAYYy4YaJ+INInIBGPMXhGZADSnOGw3cI7r+WTgGWAxsFBEttnrGCcizxhjziEFxpibgJsAFi5cmLbgKMrRjN/rIRy1vg4doQjlwSG/0orSj2xvHx4AnCygq4H7UxzzKHCRiNTaQeKLgEeNMf/PGDPRGNMInAlsGEgEFEVJjd/rIRozRGOGtu4wVSX+XC9JKUCyFYIbgQtFZCNwgf0cEVkoIjcDGGNasGIBS+1/N9jbFEXJEscVFI7GaOuJUFWqQqBkTlZ2pDHmIHB+iu3LgI+7nt8K3DrI+2wDjs1mLYpSjPi9VqpoKBqjvTvMlNrSHK9IKUQ0sqQoBYxjEYQiMdp6wmoRKMNChUBRChin02goEqOtO6IxAmVYqBAoSgHjt4WgozdCKBqjqlSzhpTMUSFQlALGb7uGPvGnZQBqESjDQoVAUQoYxzW0/aBVnF+tMQJlGKgQKEoBE/AlNpjTYLEyHFQIFKWACSQNoakq0RiBkjkqBIpSwPi8ahEo2aNCoCgFTEtnKOG5BouV4aBCoCgFzBmz6jh5Wt9gwEp1DSnDQIVAUQqY6lI/f//06fHnJX4dXK9kjgqBoihKkaNCoChHARfMa1C3kDJs9MxRlKOAm69emOslKAWMWgSKoihFjgqBoihKkaNCoCiKUuSoECiKohQ5KgSKoihFjgqBoihKkaNCoCiKUuSoECiKohQ5YozJ9RoyRkTagfVJm6uBw8N8y2xeC1AHHMjRZ+dy7SPx+dm8vpDXDrlbf6GueyRen+u1Z/se2a7/GGNMZb+txpiC+wcsS7Htpizeb9ivHWg9o/jZOVt7rtdfyGvP5foLdd0j8fpcrz0P1p/y9UeTa+gfOXpttmT72blc+0h8vv7tR//zC3XdI/X6XH92rv/+/ShU19AyY0zeNFfJt/Vkgq49dxTq+gt13VDYa4fs1z/Q6wvVIrgp1wtIIt/Wkwm69txRqOsv1HVDYa8dsl9/ytcXpEWgKIqijByFahEoiqIoI4QKQZqIyBUiYkRkbq7XMlxEpGOI/c+ISF75T0VksojcLyIbRWSziPxKRAKDHH+tiJSN5hqHYqi/e76i53xuyMU5r0KQPu8Fnrd/po2I6BDZYSIiAtwL/J8xZjYwB6gAfjjIy64F8koIChg950eZXJ3zKgRpICIVwJnAx4Cr7G3niMizIvKgiKwXkd+LiMfe1yEiPxORVcDi3K28P/a6/+l6/lsR+XAOlzQY5wE9xpg/AhhjosAXgY+KSLmI/FRE3hCR10Tk8yLy78BE4GkReTqH6+6HiFSIyJMiskJEXheRy+3tjSKyVkT+ICKrReQxESnNh/Wi53wuyMk5r6Mq0+Ny4BFjzAYROSgiJ9vbFwHzge3AI8A7gXuAcmCJMebLOVnt0cMCYLl7gzGmTUR2AB8HGoETjTERERljjGkRkS8B5xpjsqm+PBL0AO+w118HvCwiD9j7ZgPvNcZ8QkTuAt4F/G+uFmqj53xuyMk5rxZBerwXuMN+fAd9pvIrxpgttmr/DesOCiAK/H10l1h0nAP8jzEmAmCMacntcoZEgB+JyGvAE8AkoMHet9UY86r9eDnWlz3X6Dmff5zDETrn1SIYAhEZg2WuHSciBvACBnjQ/unGed5jf1HykQiJNwAluVpIGqwB3u3eICJVwFRgWy4WlAXvB+qBk40xYRHZRt/fvtd1XBTIqWtIz/mckpNzXi2CoXk38GdjzDRjTKMxZgqwFTgLWCQi020/6XuwAmv5znZgvogERaQGOD/H6xmMJ4EyEfkQxIOQPwNuAx4FPikiPnvfGPs17UD/plq5pxpotkXgXGBarhc0CHrO546cnPMqBEPzXuC+pG1/t7cvBX4LrMX6oiQflzfYJ0+vMWYncBfwhv1zZU4XNgjGqnZ8B3CliGwENmD52r8J3AzsAF6zA5Tvs192E/BIvgSLnb878BdgoYi8DnwIWJfThQ2OnvM5IlfnvFYWDxMROQf4ijHmrTleSlqIyAnAH4wxi3K9lmLiaPq76zl/9KIWQREgIp/CCux9O9drKSb075479G+fGWoRKIqiFDlqESiKohQ5KgSKMkKIyBQReVpE1thVwl+wt48RkcfF6h3zuIjU2tvfb1eIvi4iL9o+bee9bhWRZhF5I1e/j1I8qBAoysgRAb5sjJkPnAZ8VkTmA9cBT9q9Y560n4OVdfNmY8xxwPdJ7BV/G3DJaC1cKW5UCBRlhDDG7DXGrLAft2OlWE7Catdwu33Y7cAV9jEvGmMO2dtfBia73utZIN+rpZWjBBUCRTkCiEgjcBKwBGgwxuy1d+2jr7WEm48BD4/O6hQlEW0xoSgjjN258+/AtXbDsPg+Y4yx2za4jz8XSwjORFFygFoEijKCiIgfSwT+Yoy5197cJCIT7P0TgGbX8cdjVYxebow5ONrrVRRQIVCUEUOsW/9bgLXGmJ+7dj0AXG0/vhq43z5+KtYQkg8aYzaM5loVxY0WlCnKCCEiZwLPAa8DMXvzN7HiBHdhdZDcDvyb3Uf+ZqzZA9vtYyPGmIX2e/0Nq+1wHdAEfNcYc8so/SpKkaFCoCiKUuSoa0hRFKXIUSFQFEUpclQIFEVRihwVAkVRlCJHhUBRFKXIUSFQlAwRketF5CuD7L/CbjanKAWBCoGijDxXACoESsGgdQSKkgYi8i2squBmYCewHDgMXAMEgE3AB4ETgX/a+w5jFYwB/A6oB7qATxhj8nl4vVJkqBAoyhCIyMlY8wFOxWrUuAL4PfBHpz+QiPwAaDLG/EZEbgP+aYy5x973JPApY8xGETkV+LEx5rzR/00UJTXafVRRhuYs4D5jTBeAiDxgbz/WFoAaoAJ4NPmFdifS04G7XV1Ig0d6wYqSCSoEijJ8bgOuMMasEpEPY/UGSsYDtBpjThy9ZSlKZmiwWFGG5lngChEpFZFK4G329kpgr916+v2u49vtfRhj2oCtInIlWB1K3bOJFSUfUCFQlCGwx0/eCazCmiK21N71H1idRV8A3MHfO4CvishKEZmJJRIfE5FVwGqs0ZWKkjdosFhRFKXIUYtAURSlyFEhUBRFKXJUCBRFUYocFQJFUZQiR4VAURSlyFEhUBRFKXJUCBRFUYocFQJFUZQi5/8DXzurRwO0cokAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "county_rates = calculateSpreadRate(county_data)\n",
    "county_rates[\"infection_rate_change_smooth\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 23,
     "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": [
    "neighbor_rates = calculateSpreadRate(neighbor_data, smoothing=30)\n",
    "neighbor_rates[\"infection_rate_change_smooth\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "calculateSpreadRate(neighbor_data)[\"mask_mandate\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig,ax = plt.subplots()\n",
    "fig.set_size_inches(10, 6, forward=True)\n",
    "#fig.autofmt_xdate()\n",
    "\n",
    "# PLot rates\n",
    "county_rates[\"infection_rate_change_smooth\"].plot(label=\"Gwinnet County\", color=\"orange\")\n",
    "neighbor_rates[\"infection_rate_change_smooth\"].plot(label=\"Neighbors\", color=\"slateblue\", alpha=0.6, linestyle='dotted')\n",
    "plt.ylabel(\"Cases per Mil. Vuln. Pop\")\n",
    "plt.xlabel(\"\")\n",
    "plt.legend()\n",
    "\n",
    "#PLot mask usage\n",
    "ax2=ax.twinx()\n",
    "neighbor_rates[\"mask_mandate\"].plot.area(color=\"skyblue\", alpha=0.2, label=\"% Neighbors with Mask Mandate\")\n",
    "plt.axis('off')\n",
    "plt.legend()\n",
    "\n",
    "# Annotation\n",
    "plt.title(label = \"Change in Infection Rate\")\n",
    "#plt.show()\n",
    "plt.savefig('Change_in_infection_rate.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "fig.set_size_inches(10, 6, forward=True)\n",
    "\n",
    "# Find diff\n",
    "infection_rate_diff = (county_rates[\"infection_rate_7da\"] - neighbor_rates[\"infection_rate_7da\"]) * 1000000\n",
    "zero = infection_rate_diff * 0\n",
    "\n",
    "# PLot rates\n",
    "infection_rate_diff.plot(label=\"Infection Rate Difference\", color=\"orange\")\n",
    "zero.plot(color=\"black\", linewidth=0.5, linestyle='dashed', alpha=0.5)\n",
    "plt.ylabel(\"Cases per Mil. Vuln. Pop\")\n",
    "plt.xlabel(\"\")\n",
    "\n",
    "#PLot mask usage\n",
    "ax2=ax.twinx()\n",
    "neighbor_rates[\"mask_mandate\"].plot.area(color=\"skyblue\", alpha=0.2, label=\"% Neighbors with Mask Mandate\")\n",
    "plt.axis('off')\n",
    "plt.legend()\n",
    "\n",
    "#Anotate\n",
    "plt.title(label = \"Diff in Daily New Cases Between Gwinnett County and Neighbors\")\n",
    "#plt.show()\n",
    "plt.savefig('Diff_in_infection_rate.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data-512-a2",
   "language": "python",
   "name": "data-512-a2"
  },
  "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
}