RARE Diseases in the USA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RARE Diseases USA (1928 - 2011 Vaccination Effort)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Humanity's battles and struggles with the [micro-organisms](https://en.wikipedia.org/wiki/Microorganism) is a continuous one. Our struggles with them dates back to pre-historical times. They have dominated this planet before us, and they continue to dominate.s\n",
    "<br><br>\"Achoo!\", and the thought of the common cold `virus` crosses our mind. A simple wound gets us worried about the `bacteria`. There are innumerous ways they can invade us. Many of them live within us. We have the knowledge that they are around us, everywhere. Thus, it is imperative that ***we should be knowledgeable about the fact that vaccinating against them improves our quality of life and longevity not just for us, but for the humanity as a whole***.\n",
    "<br>Not all viruses are deadly. The same goes for bacteria. Some are even beneficial. This begs us to investigate their [epidemiology](https://en.wikipedia.org/wiki/Epidemiology). Studying them allows us to mine their strengths and weaknesses. This empowers us to be better prepared, now and in the future. This science is what has enabled us to now classify some of the deadly diseases as one of ***rare***, ***very rare***, and ***extremely rare***.\n",
    "<br><br>*But how exactly did we get the idea of tracking it all?*\n",
    "<br>One pivotal time in history (among many others) I would like to mention is the study of [spread of Cholera on Broad Street pump by Dr.John Snow](https://www.ph.ucla.edu/epi/snow/snowcricketarticle.html) at around the year 1850. A simple [graph](https://commons.wikimedia.org/wiki/Category:Epidemiology#/media/File:Broad_Street_graf.jpg) from those times can tell us so much without having to sift through the written text. We learn from our mistakes. We have devised ways to protect ourselves through vaccines, medications, clean water, sanitation and minimizing contact.\n",
    "<br><br>*Did it pay off?*\n",
    "<br>We can assess this with the data collected about the disease occurrence and incidence.\n",
    "\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"toc\">Elements of Analysis</h2>\n",
    "\n",
    "\n",
    "### [DATA](#data) ###\n",
    "\n",
    "\n",
    "In this mission, I will be sourcing the standardized spatio-temporal data from [Contagious Diseases of the USA](https://www.kaggle.com/pitt/contagious-diseases) hosted on Kaggle.\n",
    "\n",
    "<br>\n",
    "\n",
    "### [DISEASE FOCUS](#data) ###\n",
    "\n",
    "\n",
    "In the USA, the nationally notifiable contagious diseaseas have been reported to the health authorities on a weekly basis for over more than a century. The dataset used is a digitized collection of such weekly surveillance reports of \n",
    "[`Hepatitis`](https://en.wikipedia.org/wiki/Hepatitis)\n",
    "[`Measles`](https://en.wikipedia.org/wiki/Measles)\n",
    "[`Mumps`](https://en.wikipedia.org/wiki/Mumps)\n",
    "[`Pertussis`](https://en.wikipedia.org/wiki/Whooping_cough)\n",
    "[`Polio`](https://en.wikipedia.org/wiki/Polio)\n",
    "[`Rubella`](https://en.wikipedia.org/wiki/Rubella) & \n",
    "[`Smallpox`](https://en.wikipedia.org/wiki/Smallpox), \n",
    "that are vaccine-preventable diseases ***spanning 8 decades (from 1928 until 2011), based on historical population estimates, focusing particularly on the effects of vaccination programs (one of the Public Health Programs in the USA).***\n",
    "<br>For some interesting insight, I recommend this published [article from the New England Journal of Medicine](https://www.nejm.org/doi/full/10.1056/NEJMms1215400). <sup>requires registration</sup>\n",
    "\n",
    "<br>\n",
    "\n",
    "### [WHAT YOU GET](#tidy-data) ###\n",
    "\n",
    "\n",
    "I will be ***analysing the dataset*** for possible ***cleaning***, and ***organizing*** it ready for analysis, in one of the more popular programming language, ***[python](https://www.python.org/doc/essays/blurb/)***. \n",
    "<br>No analysis work is complete without ***graphing important findings***. Our eyes are naturally drawn towards pictures and colors first true to the saying, *a picture speaks more than a thousand words*. You may start from the graph and read-on for more.\n",
    "\n",
    "<br>\n",
    "\n",
    "### [TIDY DATA](#tidy-data) ###\n",
    "\n",
    "\n",
    "I will be scrubbing the dataset for possible duplicates, missing data, newly computed columns, and in identifying new categories as applicable, among others.\n",
    "<br>T.1 [Data On Hand](#data-on-hand)\n",
    "<br>T.2 [Duplicate Data](#duplicate-data)\n",
    "<br>T.3 [Numerical Data Only](#numerical-data)\n",
    "<br>T.4 [Missing Values](#missing-values)\n",
    "<br>T.5 [All Zeros!](#all-zeros)\n",
    "<br>T.6 [Redundant Columns](#redundant-columns)\n",
    "<br>T.7 [Merge Data](#merge-data)\n",
    "<br>T.8 [Incidence Per Capita](#incidence-per-capita)\n",
    "<br>T.9 [Identify the Season](#identify-season)\n",
    "<br>T.10 [Sorted Data](#sorted-data)\n",
    "\n",
    "<br>\n",
    "\n",
    "### [FINDINGS](#findings) ###\n",
    "\n",
    "\n",
    "No research is thoroughly exhaustive. With this in mind, my interest is in the bird's-eye view of the following specifics:\n",
    "<br>F.1 [The Big Picture](#big-picture)\n",
    "<br>F.2 [Seasonality in Disease Incidence](#disease-seasonality)\n",
    "<br>F.3 [All cases reported?](#smallpox-3states)\n",
    "\n",
    "<br>\n",
    "\n",
    "### [CONCLUSION](#conclusion) ###\n",
    "\n",
    "\n",
    "A few important conclusions based on the analysis.\n",
    "\n",
    "<br>\n",
    "\n",
    "### [REFERENCES](#references) ###\n",
    "\n",
    "\n",
    "Powerful information for the data-hungry.\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "   \n",
    "<h3 id=\"data\"> |1| DATA </h3>\n",
    "\n",
    "\n",
    "<br>Data is available as a single compressed file (easy to maintain) enveloping several files, one for each disease. A brief look at the data files reveals matching column names on `week`, `state` and `state_name` in all the files.\n",
    "\n",
    "To further investigate the need for data cleansing, let's first read all the files into separate dataframes without the need to uncompress. Here, I will not be reading the 2-letter `state` codes as state names are available in `state_name` column which I will be renaming to `State`.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "%matplotlib inline\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")\n",
    "\n",
    "# CUSTOM COLORS\n",
    "colors_line = [\"#DC143C\", \"#008B8B\", \"#8B008B\", \"#C71585\", \"#191990\", \"#FF4500\", \"#808000\"]\n",
    "colors_bar = [\"#E4F132\", \"#9A0794\", \"#FC600A\", \"#1489B8\", \"#C21460\", \"#B2D732\", \"#551130\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": Initial data on hand, by disease :\n",
      "\n",
      "hepatitis          90839 rows of data\n",
      "measles           145167 rows of data\n",
      "mumps              69754 rows of data\n",
      "pertussis         109072 rows of data\n",
      "polio              81531 rows of data\n",
      "rubella            53205 rows of data\n",
      "smallpox           50916 rows of data\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "diseases_dataset = 'usa_contagiousdiseases.zip'\n",
    "diseases = {}\n",
    "\n",
    "with zipfile.ZipFile(diseases_dataset, mode='r') as z:\n",
    "    for csv in [csvs for csvs in z.namelist() if csvs.endswith('.csv')]:\n",
    "        diseases[csv.split('.')[0]] = (\n",
    "            pd.read_csv(z.open(csv, mode='r')\n",
    "                        , usecols=lambda col: col != 'state'\n",
    "                        , converters={'week': str}\n",
    "                       ).rename(columns={'week': 'Week'\n",
    "                                         , 'disease': 'Disease'\n",
    "                                         , 'state_name': 'State'\n",
    "                                         , 'cases': 'Cases'\n",
    "                                         , 'incidence_per_capita': 'Incidence/100,000'}\n",
    "                               )\n",
    "        )\n",
    "\n",
    "print(': Initial data on hand, by disease :', end='\\n\\n')\n",
    "for disease, data in diseases.items():\n",
    "    print(disease.ljust(15, ' '), str(data.shape[0]).rjust(8, ' '), 'rows of data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3 id=\"tidy-data\"> |2| TIDY DATA </h3>\n",
    "\n",
    "<br>\n",
    "\n",
    "Now that the data is loaded, it's time for tidying data by scrubbing it for several factors. I will be doing this in several steps by looking into what kind of information is on hand.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h4 id=\"data-on-hand\"> |T.1| Data On Hand </h4>\n",
    "\n",
    "\n",
    "<br>What data attributes are readily available? I need to know this to make some informed decisions as to whethere what's available suffices or if I will need to deduce anything new that will empower me with a different perspective.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": Information available on hand :\n",
      "\n",
      "hepatitis       ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n",
      "measles         ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n",
      "mumps           ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n",
      "pertussis       ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n",
      "polio           ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n",
      "rubella         ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n",
      "smallpox        ['Week' 'State' 'Disease' 'Cases' 'Incidence/100,000']  information is available.\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "print(': Information available on hand :', end='\\n\\n')\n",
    "for disease, data in diseases.items():\n",
    "    print(disease.ljust(15, ' '), str(data.columns.values), ' information is available.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[Here's a table of data column names and the information they represent](https://www.nejm.org/doi/suppl/10.1056/NEJMms1215400/suppl_file/nejmms1215400_appendix.pdf):\n",
    "\n",
    "|Column|Description|\n",
    "|:--|:--|\n",
    "|`Week`|Week of the year (one of 53 weeks), representing the case reporting period|\n",
    "|`State`|US State (full name)|\n",
    "|`Disease`|Unique **disease name**|\n",
    "|`Cases`|Weekly reported counts of individual **disease cases or deaths**|\n",
    "|[`Incidence/100,000`](https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section2.html)|**Weekly incidence rate** based on the number of reported `Cases` **per 100,000** population-year|\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h4 id=\"duplicate-data\"> |T.2| Duplicate Data </h4>\n",
    "\n",
    "<br>\n",
    "\n",
    "Data sourced from Kaggle is an aggregated data-set at the state level. This means that there should a single entry per state-time period. If there had been any duplicated entries during aggregation, we don't want to count that twice or more. Let's find out if there are any by simply eliminating them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": Eliminating duplicates :\n",
      "\n",
      "hepatitis       BEFORE(90839 rows)    AFTER(90839 rows)   > 100.00% of data remains.\n",
      "measles         BEFORE(145167 rows)   AFTER(145165 rows)  > 100.00% of data remains.\n",
      "mumps           BEFORE(69754 rows)    AFTER(69754 rows)   > 100.00% of data remains.\n",
      "pertussis       BEFORE(109072 rows)   AFTER(109072 rows)  > 100.00% of data remains.\n",
      "polio           BEFORE(81531 rows)    AFTER(81531 rows)   > 100.00% of data remains.\n",
      "rubella         BEFORE(53205 rows)    AFTER(53205 rows)   > 100.00% of data remains.\n",
      "smallpox        BEFORE(50916 rows)    AFTER(50916 rows)   > 100.00% of data remains.\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "print(\": Eliminating duplicates :\", end='\\n\\n')\n",
    "\n",
    "for disease, data in diseases.items():\n",
    "    print(disease.ljust(15, ' '), end=' ')\n",
    "    \n",
    "    rows_before = data.shape[0]\n",
    "    print(f'BEFORE({rows_before} rows)'.ljust(20, ' '), end=' ')\n",
    "    \n",
    "    data.drop_duplicates(inplace=True)\n",
    "    \n",
    "    rows_after = data.shape[0]\n",
    "    print(f' AFTER({rows_after} rows)'.ljust(20, ' '), f'> {(rows_after/rows_before)*100:.2f}% of data remains.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Fortunately, there are no duplicates.\n",
    "\n",
    "<br>\n",
    "\n",
    "<br>\n",
    "\n",
    "<h4 id=\"numerical-data\"> |T.3| Numerical Data Only </h4>\n",
    "<br>\n",
    "\n",
    "During initial inspection of the files, `Cases` and `Incidence/100,000` were the only columns in each dataset that had numerical data. Let's take a quick look to make sure although the data is visually numerical they are stored as numbers and not as a text as it may happen sometimes while data is written into a file.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hepatitis    dataset's numerical columns : ['Cases' 'Incidence/100,000']\n",
      "measles      dataset's numerical columns : ['Cases' 'Incidence/100,000']\n",
      "mumps        dataset's numerical columns : ['Cases' 'Incidence/100,000']\n",
      "pertussis    dataset's numerical columns : ['Cases' 'Incidence/100,000']\n",
      "polio        dataset's numerical columns : ['Incidence/100,000']\n",
      "rubella      dataset's numerical columns : ['Cases' 'Incidence/100,000']\n",
      "smallpox     dataset's numerical columns : ['Cases' 'Incidence/100,000']\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "for disease, data in diseases.items():\n",
    "    data.Year = data.Week.astype(np.int64)\n",
    "    print(f\"{disease.ljust(12, ' ')} dataset's numerical columns : {data.select_dtypes(include='number').columns.values}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Clearly, `polio` dataset stands out with `non-numerical` data type for its `Cases` data.\n",
    "\n",
    "<br>Let's take a closer look this column to see if atleast its data is all numerical.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Incidence/100,000</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
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       "  Cases  Incidence/100,000   0\n",
       "0    \\N                0.0  47"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "polio_bool = diseases['polio'][diseases['polio'].Cases.map(lambda c: not c.isdigit())]\n",
    "polio_bool[['Cases', 'Incidence/100,000']].value_counts().to_frame().reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**47** entries in `polio` have **non-numerical data**; `\\N  Cases` and `0.0  Incidence/100,000`, which does not yield any meaningful information.\n",
    "\n",
    "<br>I will **replace** `\\N` with `0` for Cases.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "polio: BEFORE Cases dtype = object\n",
      "polio:  AFTER Cases dtype = int64\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "print(f'polio: BEFORE Cases dtype = {diseases[\"polio\"].Cases.dtypes}')\n",
    "\n",
    "polio_cases = diseases['polio'].Cases.map(lambda c: 0 if c == '\\\\N' else int(c))\n",
    "diseases['polio'].Cases = polio_cases\n",
    "\n",
    "print(f'polio:  AFTER Cases dtype = {diseases[\"polio\"].Cases.dtypes}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The **dtype = int64** (numerical data type) listed AFTER conversion means that the conversion was successful and it is stored as a number and not as text.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h4 id=\"missing-values\"> |T.4| Missing Values </h4>\n",
    "\n",
    "<br>\n",
    "\n",
    "Now that we know what data is available, looked for possible duplicates, and have uniform data types across disease datasets, let's take a closer look at how data is presented in each of the columns in all of disease data.\n",
    "\n",
    "<br>First, let's look if there are any missing values (NaN, None) in our numerical columns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": Missing values in `Cases` and `Incidence/100,000` columns :\n",
      "\n",
      "hepatitis  : Cases( 0 ),  Incidence/100,000( 0 )\n",
      "measles    : Cases( 0 ),  Incidence/100,000( 0 )\n",
      "mumps      : Cases( 0 ),  Incidence/100,000( 0 )\n",
      "pertussis  : Cases( 0 ),  Incidence/100,000( 0 )\n",
      "polio      : Cases( 0 ),  Incidence/100,000( 0 )\n",
      "rubella    : Cases( 0 ),  Incidence/100,000( 0 )\n",
      "smallpox   : Cases( 0 ),  Incidence/100,000( 0 )\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "print(\": Missing values in `Cases` and `Incidence/100,000` columns :\", end='\\n\\n')\n",
    "\n",
    "for disease, data in diseases.items():\n",
    "    print(disease.ljust(10, ' '),\n",
    "          ': Cases(', data.Cases.isna().sum(), '), ',\n",
    "          'Incidence/100,000(', data['Incidence/100,000'].isna().sum(), ')'\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "There are no missing values in both of these columns.\n",
    "\n",
    "<br>Now, a closer look at all the columns with 2 samples from each disease:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>State</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Incidence/100,000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196601</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>5</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196601</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>11</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192801</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>97</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192801</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>76</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196801</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>MUMPS</td>\n",
       "      <td>7</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>196801</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>MUMPS</td>\n",
       "      <td>39</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>193801</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>PERTUSSIS</td>\n",
       "      <td>22</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>193801</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>PERTUSSIS</td>\n",
       "      <td>22</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>192801</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>POLIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>192801</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>POLIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>196601</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>196601</td>\n",
       "      <td>ARIZONA</td>\n",
       "      <td>RUBELLA</td>\n",
       "      <td>29</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>192801</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>SMALLPOX</td>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>192801</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>SMALLPOX</td>\n",
       "      <td>7</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Week     State      Disease  Cases  Incidence/100,000\n",
       "0   196601   ALABAMA  HEPATITIS A      5               0.14\n",
       "1   196601  ARKANSAS  HEPATITIS A     11               0.58\n",
       "2   192801   ALABAMA      MEASLES     97               3.67\n",
       "3   192801  ARKANSAS      MEASLES     76               4.11\n",
       "4   196801    ALASKA        MUMPS      7               2.46\n",
       "5   196801   ALABAMA        MUMPS     39               1.13\n",
       "6   193801   ALABAMA    PERTUSSIS     22               0.79\n",
       "7   193801  ARKANSAS    PERTUSSIS     22               1.14\n",
       "8   192801   ALABAMA        POLIO      0               0.00\n",
       "9   192801  ARKANSAS        POLIO      0               0.00\n",
       "10  196601   ALABAMA      RUBELLA      7               0.20\n",
       "11  196601   ARIZONA      RUBELLA     29               1.80\n",
       "12  192801   ALABAMA     SMALLPOX      1               0.04\n",
       "13  192801  ARKANSAS     SMALLPOX      7               0.38"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "diseases_head2 = pd.DataFrame()\n",
    "\n",
    "for data in diseases.values():\n",
    "    if diseases_head2.empty:\n",
    "        diseases_head2 = data.head(2)\n",
    "    else:\n",
    "        diseases_head2 = pd.concat([diseases_head2, data.head(2)], ignore_index=True)\n",
    "\n",
    "diseases_head2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A couple of entries from the sample listing reveal `0 Cases` and `0 Incidence/100,000`. Let's get a closer look at all of such entries.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h4 id=\"all-zeros\"> |T.5| All Zeros! </h4>\n",
    "<br>\n",
    "\n",
    "First, let's take a look at how many 0's were reported for `hepatitis` dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20114"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "diseases['hepatitis'][\n",
    "    diseases['hepatitis'][\n",
    "        ['Cases', 'Incidence/100,000']\n",
    "    ].apply(np.sum, axis=1) == 0].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "`20114 rows of no data in hepatitis` alone!\n",
    "\n",
    "<br>I can think of a couple possibilities for this scenario:\n",
    "- No new disease incidence for the period.\n",
    "- No cases were reported / they were missed.\n",
    "\n",
    "<br>**No data, no value!** I wouldn't impute these data any further as [this article](https://www.nejm.org/doi/suppl/10.1056/NEJMms1215400/suppl_file/nejmms1215400_appendix.pdf) from which the disease data has been sourced has **already standardized data**. Anything 0, will be ignored. Next step is to **rid the data of 0's** in both `Cases` & `Incidence/100,000` columns in each of the datasets.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hepatitis       BEFORE(90839 rows)    AFTER(70725 rows)   > 77.86% of data remains.\n",
      "measles         BEFORE(145165 rows)   AFTER(113219 rows)  > 77.99% of data remains.\n",
      "mumps           BEFORE(69754 rows)    AFTER(37498 rows)   > 53.76% of data remains.\n",
      "pertussis       BEFORE(109072 rows)   AFTER(75589 rows)   > 69.30% of data remains.\n",
      "polio           BEFORE(81531 rows)    AFTER(46475 rows)   > 57.00% of data remains.\n",
      "rubella         BEFORE(53205 rows)    AFTER(25693 rows)   > 48.29% of data remains.\n",
      "smallpox        BEFORE(50916 rows)    AFTER(18345 rows)   > 36.03% of data remains.\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "for disease in diseases:\n",
    "    print(disease.ljust(15, ' '), end=' ')\n",
    "    \n",
    "    rows_before = diseases[disease].shape[0]\n",
    "    print(f'BEFORE({rows_before} rows)'.ljust(20, ' '), end=' ')\n",
    "    \n",
    "    diseases[disease] = diseases[disease][\n",
    "        diseases[disease][['Cases', 'Incidence/100,000']].apply(np.sum, axis=1) > 0]\n",
    "    \n",
    "    rows_after = diseases[disease].shape[0]\n",
    "    print(f' AFTER({rows_after} rows)'.ljust(20, ' '), f'> {(rows_after/rows_before)*100:.2f}% of data remains.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We have gotten rid of un-usable data. Next, we can merge, alter, or add new attributes to further polish our datasets.\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<h4 id=\"redundant-columns\"> |T.6| Redundant Columns </h4>\n",
    "\n",
    "<br>\n",
    "\n",
    "A quick look at samples from each of the disease datasets reveals that the column name `disease` is the same in its own dataset as seen in the output of the next code cell. **I will retain this column as it will help identify the disease data when all the datasets are merged**.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique disease names in hepatitis dataset     ['HEPATITIS A']\n",
      "Unique disease names in measles dataset       ['MEASLES']\n",
      "Unique disease names in mumps dataset         ['MUMPS']\n",
      "Unique disease names in pertussis dataset     ['PERTUSSIS']\n",
      "Unique disease names in polio dataset         ['POLIO']\n",
      "Unique disease names in rubella dataset       ['RUBELLA']\n",
      "Unique disease names in smallpox dataset      ['SMALLPOX']\n"
     ]
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Save unique column values for later indexing.\n",
    "disease_unique = {}\n",
    "for disease, data in diseases.items():\n",
    "    disease_unique[disease] = [data.Disease.unique()[0]]\n",
    "    print(f'Unique disease names in {disease} dataset'.ljust(45, ' '), f'{disease_unique[disease]}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h4 id=\"merge-data\"> |T.7| Merge Data </h4>\n",
    "\n",
    "<br>\n",
    "\n",
    "The individual datasets have the same column attributes which makes them a good candidate to merge-all and work on a single dataset which will hence-forth be referred to as `contagious_diseases`.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Week</th>\n",
       "      <th>State</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Incidence/100,000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149893</th>\n",
       "      <td>196249</td>\n",
       "      <td>PENNSYLVANIA</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>271</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339368</th>\n",
       "      <td>195832</td>\n",
       "      <td>MICHIGAN</td>\n",
       "      <td>POLIO</td>\n",
       "      <td>39</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53176</th>\n",
       "      <td>199429</td>\n",
       "      <td>TENNESSEE</td>\n",
       "      <td>HEPATITIS A</td>\n",
       "      <td>3</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245974</th>\n",
       "      <td>194820</td>\n",
       "      <td>PENNSYLVANIA</td>\n",
       "      <td>PERTUSSIS</td>\n",
       "      <td>125</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Week         State      Disease  Cases  Incidence/100,000\n",
       "149893  196249  PENNSYLVANIA      MEASLES    271               2.39\n",
       "339368  195832      MICHIGAN        POLIO     39               0.51\n",
       "53176   199429     TENNESSEE  HEPATITIS A      3               0.06\n",
       "245974  194820  PENNSYLVANIA    PERTUSSIS    125               1.21"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Merge all the datasets into a single dataframe\n",
    "contagious_diseases = pd.concat(diseases.values(), ignore_index=True)\n",
    "contagious_diseases.sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "\n",
    "<h4 id=\"incidence-per-capita\"> |T.8| Incidence Per Capita </h4>\n",
    "\n",
    "<br>\n",
    "\n",
    "`Incidence/100,000` is expressed as a **number (new disease incidences)** per **capita (sample population)**. In this case it is for every 100,000. I will retain this value as small numbers are better represented on a plot.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "\n",
    "<h4 id=\"identify-season\"> |T.9| Identify the Season </h4>\n",
    "\n",
    "<br>\n",
    "\n",
    "In addition, I'm interested in knowing if there's any yearly seasonality to the prevalence of the disease. For this, I will roughly classify the data into a `Season` based on the `Week` of the year:\n",
    "\n",
    "|Season|Week of the year|\n",
    "|:--|:--:|\n",
    "|WINTER|Weeks 1-11, Weeks >= 52|\n",
    "|SPRING|Weeks 12-25|\n",
    "|SUMMER|Weeks 26-38|\n",
    "|FALL|Weeks 39-51|\n",
    "\n",
    "<br>In order to realize this, let's look at the `Week` column:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorized weeks into seasons [WINTER, SPRING, SUMMER, FALL]\n"
     ]
    },
    {
     "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>Week</th>\n",
       "      <th>State</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Incidence/100,000</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109847</th>\n",
       "      <td>34</td>\n",
       "      <td>MAINE</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1945</td>\n",
       "      <td>SUMMER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386902</th>\n",
       "      <td>47</td>\n",
       "      <td>MONTANA</td>\n",
       "      <td>SMALLPOX</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1945</td>\n",
       "      <td>FALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110561</th>\n",
       "      <td>02</td>\n",
       "      <td>NEBRASKA</td>\n",
       "      <td>MEASLES</td>\n",
       "      <td>20</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1946</td>\n",
       "      <td>WINTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281971</th>\n",
       "      <td>51</td>\n",
       "      <td>ILLINOIS</td>\n",
       "      <td>PERTUSSIS</td>\n",
       "      <td>3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1999</td>\n",
       "      <td>FALL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Week     State    Disease  Cases  Incidence/100,000  Year  Season\n",
       "109847   34     MAINE    MEASLES      1               0.12  1945  SUMMER\n",
       "386902   47   MONTANA   SMALLPOX      1               0.10  1945    FALL\n",
       "110561   02  NEBRASKA    MEASLES     20               1.59  1946  WINTER\n",
       "281971   51  ILLINOIS  PERTUSSIS      3               0.02  1999    FALL"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Define Seasons\n",
    "seasons = {52: 'WINTER', 39: 'FALL', 26: 'SUMMER', 12: 'SPRING', 1: 'WINTER'}\n",
    "\n",
    "# Function to classify weeks into seasons\n",
    "def what_season(week):\n",
    "    for week_of_year in seasons.keys():\n",
    "        if int(week) >= week_of_year:\n",
    "            return seasons[week_of_year]\n",
    "        \n",
    "contagious_diseases[['Year', 'Week']] = (\n",
    "    contagious_diseases.Week.apply(lambda wk: wk[:4] + ' ' + wk[-2:]).str.split(' ', expand=True)\n",
    ")\n",
    "\n",
    "contagious_diseases['Season'] = contagious_diseases.Week.map(what_season)\n",
    "contagious_diseases.Season = pd.Categorical(contagious_diseases.Season, ['WINTER', 'SPRING', 'SUMMER', 'FALL'])\n",
    "\n",
    "print('Categorized weeks into seasons [WINTER, SPRING, SUMMER, FALL]')\n",
    "\n",
    "contagious_diseases.sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<h4 id=\"sorted-data\"> |T.| Sorted Data </h4>\n",
    "\n",
    "<br>For my analysis, it's convenient to sort data by `Year`, `Season`, `Disease`, and `State` which will simplify time later on as we won't have the need to sort every time (sorting will consume time) before plotting.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935',\n",
       "       '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943',\n",
       "       '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951',\n",
       "       '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959',\n",
       "       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "contagious_diseases.sort_values(by=['Year', 'Season', 'Disease', 'State'], inplace=True)\n",
    "contagious_diseases.Year.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "\n",
    "<h3 id=\"findings\"> |3| FINDINGS </h3>\n",
    "\n",
    "\n",
    "<br>All the above steps are done for a reason: Sanitized data that can be readily utilized for further visual investigation.\n",
    "<br>After all, every picture is worth more than a thousand words!\n",
    "<br>\n",
    "<br>We now have,\n",
    "* `Year`\n",
    "* `Week` of the year\n",
    "* `Season` of the year\n",
    "* `Disease` name\n",
    "* `State` name\n",
    "* `Cases`\n",
    "* `Incidence/100,000`\n",
    "\n",
    "These can be put to use in a multitude of ways.\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<h4 if=\"big-picture\"> |F.1| The Big Picture </h4>\n",
    "\n",
    "<br>Before we boil down to other facets of visual analysis, it is important to get a big picture of the accrued data. It is important to know if their vaccination efforts paid-off.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "mpl.rcParams.update({'font.family': 'Candara',\n",
    "                     'figure.facecolor': 'ivory',\n",
    "                     'axes.facecolor': 'ivory',\n",
    "                     'savefig.facecolor': 'ivory',\n",
    "                     'savefig.edgecolor': 'none',\n",
    "                     'savefig.dpi': 180,\n",
    "                     'savefig.transparent': True,\n",
    "                    })\n",
    "\n",
    "colors = [[230/255,159/255,0], [0,0,0], [86/255,180/255,233/255], [0,158/255,115/255],\n",
    "          [213/255,94/255,0], [0,114/255,178/255], [204/255, 121/255, 167/255], [240/255, 228/255, 66/255]]\n",
    "\n",
    "fig = plt.figure(figsize=(17,8))\n",
    "\n",
    "plt.style.use('tableau-colorblind10')\n",
    "sns.set_context('talk')\n",
    "plt.grid(which='major', axis='x', linewidth=0)\n",
    "plt.grid(which='major', axis='y', color='cornflowerblue', linewidth=.25)\n",
    "sns.despine(left=True, right=True, top=True, bottom=True)\n",
    "\n",
    "big_picture = sns.lineplot(x=contagious_diseases.Year\n",
    "                           , y=contagious_diseases['Incidence/100,000']\n",
    "                           , hue=contagious_diseases.Disease\n",
    "                           , ci=None\n",
    "                           , sort=False\n",
    "                           , linewidth=4\n",
    "                           , legend=False\n",
    "                           , palette=sns.color_palette(colors, 7))\n",
    "\n",
    "big_picture.tick_params(axis='x', which='major', width=1, color='gray', labelsize=15, labelcolor='gray', grid_alpha=0.3)\n",
    "big_picture.tick_params(axis='y', which='major', width=0, color='gray', labelsize=20, labelcolor='gray', grid_alpha=0.3)\n",
    "big_picture.tick_params(axis='x', which='major', labelrotation=90)\n",
    "big_picture.xaxis.label.set_visible(False)\n",
    "big_picture.tick_params(top=False, right=False)\n",
    "big_picture.axhline(y=0, color='black', linewidth=0.5, alpha=.7)\n",
    "\n",
    "# MEASLES\n",
    "x_measles = 1963-1928\n",
    "y = big_picture.get_yticks()[-1]-3.75\n",
    "c = colors[0]\n",
    "big_picture.axvline(x=x_measles, ls='--', color=c, linewidth=1, alpha=.7)\n",
    "plt.text(x=x_measles+0.5, y=y\n",
    "         , s='MEASLES vaccine licensure'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': colors[0], 'alpha': 1}\n",
    "         , color='black'\n",
    "         , style='italic')\n",
    "\n",
    "# POLIO\n",
    "x_polio = 1955-1928\n",
    "y = big_picture.get_yticks()[-1]-2.75\n",
    "c = colors[1]\n",
    "big_picture.axvline(x=x_polio, ymax=big_picture.get_yticks()[-1]-2.75, ls='--', color=c, linewidth=1, alpha=.9)\n",
    "plt.text(x=x_polio+0.5, y=y\n",
    "         , s='Oral POLIO vaccine licensure'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': colors[1], 'alpha': 1}\n",
    "         , color='white'\n",
    "         , style='italic')\n",
    "\n",
    "# SMALLPOX\n",
    "x_smallpox = 1950-1928\n",
    "y = big_picture.get_yticks()[-1]-1.75\n",
    "c = colors[2]\n",
    "big_picture.axvline(x=x_smallpox, ymax=y, ls='--', color=c, linewidth=1, alpha=.7)\n",
    "plt.text(x=x_smallpox+0.5, y=y\n",
    "         , s='SMALLPOX data not available until 1950'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': c, 'alpha': 1}\n",
    "         , color='black'\n",
    "         , style='italic')\n",
    "\n",
    "# PERTUSSIS\n",
    "x_pertussis = 1948-1928\n",
    "y = big_picture.get_yticks()[-1]-0.75\n",
    "c = colors[3]\n",
    "big_picture.axvline(x=x_pertussis, ymax=y, ls='--', color=c, linewidth=1, alpha=.7)\n",
    "plt.text(x=x_pertussis+0.5, y=y\n",
    "         , s='Combined with Diphtheria & Tetanus as DTP'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': c, 'alpha': 1}\n",
    "         , color='black'\n",
    "         , style='italic')\n",
    "\n",
    "# HEPATITIS A\n",
    "x_hepa = 1995-1928\n",
    "y = big_picture.get_yticks()[-1]-6.75\n",
    "c = colors[4]\n",
    "big_picture.axvline(x=x_hepa, ymax=y, ls='--', color=c, linewidth=1, alpha=.7)\n",
    "plt.text(x=x_hepa+0.5, y=y\n",
    "         , s='HEPATITIS-A vaccine licensure'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': c, 'alpha': 1}\n",
    "         , color='black'\n",
    "         , style='italic')\n",
    "\n",
    "# RUBELLA\n",
    "x_rubella = 1969-1928\n",
    "y = big_picture.get_yticks()[-1]-5.75\n",
    "c = colors[5]\n",
    "big_picture.axvline(x=x_rubella, ymax=y, ls='--', color=c, linewidth=1, alpha=.7)\n",
    "plt.text(x=x_rubella+0.5, y=y\n",
    "         , s='RUBELLA vaccine licensure'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': c, 'alpha': 1}\n",
    "         , color='black'\n",
    "         , style='italic')\n",
    "\n",
    "# MUMPS\n",
    "x_mumps = 1968-1928\n",
    "y = big_picture.get_yticks()[-1]-4.75\n",
    "c = colors[6]\n",
    "big_picture.axvline(x=x_mumps, ymax=y, ls='--', color=c, linewidth=1, alpha=.7)\n",
    "plt.text(x=x_mumps+0.5, y=y\n",
    "         , s='MUMPS vaccine licensure'\n",
    "         , fontsize=15, weight='light', rotation=0\n",
    "         , bbox={'pad': 4, 'edgecolor': c, 'facecolor': c, 'alpha': 1}\n",
    "         , color='black'\n",
    "         , style='italic')\n",
    "\n",
    "\n",
    "fig.suptitle('Efficacy of Vaccination programs in the USA for vaccine-preventable diseases',\n",
    "             x=0.03, y=1,\n",
    "             fontsize=30, fontweight='bold', alpha=0.75,\n",
    "             horizontalalignment='left', verticalalignment='top',)\n",
    "\n",
    "plt.savefig('rare-contagions-usa-the-big-picture.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<h4 if=\"disease-seasonality\"> |F.2| Seasonality in Disease Incidence </h4>\n",
    "\n",
    "<br>Seasonality is any predictable pattern that repeats timely. Although vaccination programs have aided in the decline of disease incidences, I am interested in a particular time of the year a disease is most likely at its peak. Let's draw focus on the peak times of the disease:\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cmap = mpl.colors.LinearSegmentedColormap.from_list(\"\", [\"ivory\",\"firebrick\",\"gold\"])\n",
    "\n",
    "sns.set_context('talk')\n",
    "cd_dysc = contagious_diseases[['Disease', 'Year', 'Season', 'Incidence/100,000']]\n",
    "cd_dysc_subset = cd_dysc.groupby(['Season', 'Year']).sum().reset_index()\n",
    "cd_dysc_subset = cd_dysc_subset.pivot('Season', 'Year', 'Incidence/100,000')\n",
    "\n",
    "plt.figure(figsize=(19,3))\n",
    "ax = sns.heatmap(cd_dysc_subset, robust=True, cmap=cmap)\n",
    "\n",
    "ax_cbar = ax.collections[0].colorbar\n",
    "ax_cbar.ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "\n",
    "ax.tick_params(axis='y', which='major', labelcolor='lightgray', labelsize=20)\n",
    "[y.set_color('gray') for y in ax.get_yticklabels()[:2]]\n",
    "[y.set_fontweight('semibold') for y in ax.get_yticklabels()[:2]]\n",
    "[y.set_size(25) for y in ax.get_yticklabels()[:2]]\n",
    "ax.tick_params(axis='x', which='major', labelcolor='gray', labelsize=15)\n",
    "\n",
    "plt.axhline(y=4, color='black', linewidth=1.5, alpha=0.7)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.savefig('contagiousdiseases-favorable-seasons.png', dpi=300, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As you can see, the **disease incidence is the highest** in the `WINTER` & `SPRING` when the general immunity declines (perhaps due to minimal benefits of sunshine during this time of the year) and **lowest in** `SUMMER` & `FALL` (perhaps due to the highest benefits one can obtain due to optimal sunshine).\n",
    "\n",
    "<br>\n",
    "\n",
    "<h4 if=\"smallpox-3states\"> |F.3| All cases reported? </h4>\n",
    "\n",
    "<br>Although we have sufficient yearly data for every disease, I am curious to know if all the states in the USA have experienced the disease wave. A simple analysis and a visual of the same will make it clear:\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": Diseases (out of 7) reported by US states (Sorted Top 10) :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RHODE ISLAND    6\n",
       "ALASKA          6\n",
       "HAWAII          6\n",
       "NEBRASKA        7\n",
       "OKLAHOMA        7\n",
       "CALIFORNIA      7\n",
       "VERMONT         7\n",
       "PENNSYLVANIA    7\n",
       "KANSAS          7\n",
       "ILLINOIS        7\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "state_disease = contagious_diseases[['State', 'Disease', 'Cases']]\n",
    "\n",
    "untouched_states = (\n",
    "    state_disease.groupby(['State', 'Disease']).size().index.get_level_values('State')\n",
    "    .value_counts().sort_values()\n",
    ")\n",
    "\n",
    "print(': Diseases (out of 7) reported by US states (Sorted Top 10) :')\n",
    "untouched_states.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": Diseases reported by RHODE ISLAND, HAWAII & ALASKA :\n"
     ]
    },
    {
     "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>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEASLES</th>\n",
       "      <td>3683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEPATITIS A</th>\n",
       "      <td>3074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERTUSSIS</th>\n",
       "      <td>2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MUMPS</th>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUBELLA</th>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLIO</th>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Disease\n",
       "MEASLES         3683\n",
       "HEPATITIS A     3074\n",
       "PERTUSSIS       2051\n",
       "MUMPS           1938\n",
       "RUBELLA         1211\n",
       "POLIO            726"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "print(': Diseases reported by RHODE ISLAND, HAWAII & ALASKA :')\n",
    "state_disease[state_disease.State.isin(['RHODE ISLAND', 'HAWAII', 'ALASKA'])].Disease.value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "One can assume that *`SMALLPOX` never made it to 3 states: `RHODE ISLAND`, `HAWAII`, and `ALASKA`* while **in reality, these 3 states have not reported SMALLPOX data**. [The same has been cited in article listed in REFERENCE & Interesting Reads](#references).\n",
    "\n",
    "<br><br>Here's a visual painting that picture:\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "{\n",
    "    \"tags\": [\n",
    "        \"hide-input\",\n",
    "    ]\n",
    "}\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "plt.figure(figsize=(20,3))\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.grid(False)\n",
    "\n",
    "data = state_disease.sort_values('State', ascending=True, ignore_index=True)\n",
    "untouched_states = sns.scatterplot(x=data.State, y=data.Disease, marker='1', color='lightpink', alpha=.4)\n",
    "\n",
    "untouched_states.tick_params(axis = 'both', which = 'major', labelsize = 20, grid_alpha=0.3, labelcolor='lightgray')\n",
    "untouched_states.tick_params(axis = 'x', which = 'major', labelrotation = 90)\n",
    "\n",
    "sns.despine(left=True, right=True, top=True, bottom=True)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "\n",
    "[x.set_color('gray') for x in untouched_states.get_yticklabels()[6:7]]\n",
    "[x.set_fontweight('semibold') for x in untouched_states.get_yticklabels()[6:7]]\n",
    "[x.set_color('gray') for x in untouched_states.get_xticklabels()[1:2]]\n",
    "[x.set_fontweight('semibold') for x in untouched_states.get_xticklabels()[1:2]]\n",
    "[x.set_color('gray') for x in untouched_states.get_xticklabels()[11:12]]\n",
    "[x.set_fontweight('semibold') for x in untouched_states.get_xticklabels()[11:12]]\n",
    "[x.set_color('gray') for x in untouched_states.get_xticklabels()[39:40]]\n",
    "[x.set_fontweight('semibold') for x in untouched_states.get_xticklabels()[39:40]]\n",
    "\n",
    "plt.text(.45, 5.8, 'no', fontsize=20, weight='bold', color='red', zorder=2)\n",
    "plt.text(10.45, 5.8, 'no', fontsize=20, weight='bold', color='red', zorder=2)\n",
    "plt.text(38.45, 5.8, 'no', fontsize=20, weight='bold', color='red', zorder=2)\n",
    "plt.axvline(x=1, color='lightsalmon', linewidth=2, alpha=.4)    # ALASKA\n",
    "plt.axvline(x=11, color='lightsalmon', linewidth=2, alpha=.4)   # HAWAII\n",
    "plt.axvline(x=39, color='lightsalmon', linewidth=2, alpha=.4)   # RHODE ISLAND\n",
    "plt.axhline(y=6, color='lightsalmon', linewidth=2, alpha=.4);   # SMALLPOX\n",
    "plt.suptitle('SMALLPOX never reported by these states in over 8 decades of recorded history!\\n',\n",
    "             x=0.004, y=1.25,\n",
    "             fontfamily='candara', fontsize=30, fontweight='bold', alpha=0.75,\n",
    "             horizontalalignment='left', verticalalignment='top',)\n",
    "\n",
    "# print(untouched_states.get_xticks())\n",
    "# print(untouched_states.get_yticks())\n",
    "plt.savefig('smallpox-not-reported-states.png', dpi=300, transparent=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "<h3 id=\"conclusion\"> CONCLUSION </h3>\n",
    "<br>\n",
    "\n",
    "The remarkable and down-right reductions in disease incidence rates observed after vaccine licensures strongly support the conclusion that vaccination programs were a leading cause.\n",
    "\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<h3 id=\"references\"> REFERENCES & Interesting Reads </h3>\n",
    "\n",
    "<br>\n",
    "\n",
    "1. [Contagious Diseases in the United States from 1888 to the Present](https://www.nejm.org/doi/full/10.1056/NEJMms1215400)\n",
    "2. [INSIDE the Outbreaks](https://www.amazon.com/Inside-Outbreaks-Detectives-Epidemic-Intelligence/dp/0547520301) by Mark Pendergrast\n",
    "<br>"
   ]
  }
 ],
 "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": "3.8.5"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}