{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# COVID-19 : Exploratory Data Analysis and Exponential Modeling to Predicton of Cases in India" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook contains an exploratory data analysis of the growth of cases of COVID-19 in India, and trying to fit the growth model to an exponential growth curve.\n", "\n", "The dataset comes from: https://github.com/covid19india/api/blob/master/data.json\n", "\n", "Thanks to the work of Guillaume Raille for creation of the Growth Modeling API and laying a foundation to analysis of COVID-19 growth data in Switzerland. The original work can be found here: https://github.com/grll/covid19-cases-prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieve the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Imports\n", "\n", "import csv\n", "import json\n", "import urllib.request\n", "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Define the URL\n", "\n", "url = \"https://raw.githubusercontent.com/covid19india/api/master/data.json\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Write the data in a new CSV file named 'data.csv' and amend the format of dates\n", "# Thanks to my great friend Sahil Gupte (https://github.com/Ovenoboyo) for helping me out with csv and json\n", "\n", "with urllib.request.urlopen(url) as f:\n", " data = json.loads(f.read())['cases_time_series']\n", " writer = csv.writer(open('data.csv', 'w+'))\n", " writer.writerow(list(data[0].keys()))\n", " for items in data:\n", " write_list = []\n", " for key,value in items.items():\n", " if key == \"date\":\n", " d = datetime.strptime(\"2020 \" + value, '%Y %d %B ')\n", " write_list.append(d.strftime(\"%Y-%m-%d\"))\n", " else :\n", " write_list.append(value)\n", " writer.writerow(write_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Imports\n", "\n", "from datetime import date, timedelta\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dailyconfirmeddailydeceaseddailyrecoveredtotalconfirmedtotaldeceasedtotalrecovered
date
2020-01-30100100
2020-01-31000100
2020-02-01000100
2020-02-02100200
2020-02-03100300
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" ], "text/plain": [ " dailyconfirmed dailydeceased dailyrecovered totalconfirmed \\\n", "date \n", "2020-01-30 1 0 0 1 \n", "2020-01-31 0 0 0 1 \n", "2020-02-01 0 0 0 1 \n", "2020-02-02 1 0 0 2 \n", "2020-02-03 1 0 0 3 \n", "\n", " totaldeceased totalrecovered \n", "date \n", "2020-01-30 0 0 \n", "2020-01-31 0 0 \n", "2020-02-01 0 0 \n", "2020-02-02 0 0 \n", "2020-02-03 0 0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retrieve the cases from file\n", "\n", "covid19_cases = pd.read_csv('data.csv', parse_dates=['date'], index_col='date')\n", "covid19_cases.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check if the index is_unique\n", "\n", "covid19_cases.index.is_unique" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(71, 6)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the dimensionality of the data\n", "\n", "covid19_cases.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dailyconfirmeddailydeceaseddailyrecoveredtotalconfirmedtotaldeceasedtotalrecovered
count71.00000071.00000071.00000071.00000071.00000071.000000
mean94.7746483.2253528.943662695.67605619.53521159.450704
std191.6656467.78496220.1486531508.59085446.434849132.504532
min0.0000000.0000000.0000001.0000000.0000000.000000
25%0.0000000.0000000.0000003.0000000.0000002.000000
50%2.0000000.0000000.00000030.0000000.0000003.000000
75%73.5000001.0000004.000000450.0000008.00000024.000000
max813.00000046.00000096.0000006729.000000229.000000635.000000
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" ], "text/plain": [ " dailyconfirmed dailydeceased dailyrecovered totalconfirmed \\\n", "count 71.000000 71.000000 71.000000 71.000000 \n", "mean 94.774648 3.225352 8.943662 695.676056 \n", "std 191.665646 7.784962 20.148653 1508.590854 \n", "min 0.000000 0.000000 0.000000 1.000000 \n", "25% 0.000000 0.000000 0.000000 3.000000 \n", "50% 2.000000 0.000000 0.000000 30.000000 \n", "75% 73.500000 1.000000 4.000000 450.000000 \n", "max 813.000000 46.000000 96.000000 6729.000000 \n", "\n", " totaldeceased totalrecovered \n", "count 71.000000 71.000000 \n", "mean 19.535211 59.450704 \n", "std 46.434849 132.504532 \n", "min 0.000000 0.000000 \n", "25% 0.000000 2.000000 \n", "50% 0.000000 3.000000 \n", "75% 8.000000 24.000000 \n", "max 229.000000 635.000000 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show basic statistics\n", "\n", "covid19_cases.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following observations can be made :**\n", "\n", "- The dataset consists of various columns and the column 'totalconfirmed' is a cumulative count of confirmed cases\n", "- The count is given per day, where each row corrosponds to a single day\n", "- The data starts with the first assumed case on 2020-01-30 and continues till date\n", "\n", "- **The data seemes to be increasing very slowly upto the 75th percentile, and rapidly thereafter; this increase should roughly correspond to the events related to the Tablighi Jamaat in New Delhi, which led to a sudden explosion in the number of cases all over the country**\n", "\n", "### NOTE : We can expect that the sudden rise in numbers could lead to an uneven graph, which can then lead to inaccurate predctions" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the daily cases as a histogram\n", "\n", "fig = plt.figure(figsize=[30,15])\n", "ax = plt.subplot(111)\n", "\n", "ax.stem(covid19_cases.dailyconfirmed.index, covid19_cases.dailyconfirmed.values, label='dailyconfirmed', use_line_collection=True)\n", "\n", "plt.title('new cases per day')\n", "ax.legend()\n", "plt.xticks(covid19_cases['dailyconfirmed'].index, rotation=90)\n", "ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))\n", "plt.grid(True)\n", "\n", "plt.rc('font', size=20)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following obsetvations can be made :**\n", "\n", "- The graph is almost asymptotic to the x-axis in linear scale upto around 60% of its span\n", "- We can conclude that the major rise happened at around 60% of the span of this graph\n", "- Due to this 'uneven' curve, any growth model can't be fit because it would be feeding on irrelevant data\n", "\n", "### Let us now cut down our data to be from 01-03-2020, from where the actual significant increase began" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Write the data in a new CSV file named 'data_new.csv'\n", "# We would only be considering the cases from 01-03-2020 onwards\n", "\n", "with urllib.request.urlopen(url) as f:\n", " data = json.loads(f.read())['cases_time_series']\n", " writer = csv.writer(open('data_new.csv', 'w+'))\n", " writer.writerow(list(data[0].keys()))\n", " for i, items in enumerate(data):\n", " if i not in range(31):\n", " write_list = []\n", " for key,value in items.items():\n", " if key == \"date\":\n", " d = datetime.strptime(\"2020 \" + value, '%Y %d %B ')\n", " write_list.append(d.strftime(\"%Y-%m-%d\"))\n", " else :\n", " write_list.append(value)\n", " writer.writerow(write_list)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# extract cases data from file\n", "path = 'data_new.csv'\n", "covid19_cases_new = pd.read_csv(path, parse_dates=['date'], index_col='date')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the cases on a linear and semi-logarithmic graph\n", "data_conf = covid19_cases_new['totalconfirmed']\n", "\n", "fig = plt.figure(figsize=[30,30])\n", "\n", "ax = plt.subplot(211)\n", "ax.plot(data_conf.index, data_conf.values, marker='x', label='# of positive cases in totalconfirmed')\n", "ax.legend()\n", "ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))\n", "plt.xticks(data_conf.index, rotation=90)\n", "plt.title('# of positive cases in totalconfirmed (linear scale)')\n", "plt.grid(True)\n", "\n", "\n", "ax2 = plt.subplot(212)\n", "ax2.semilogy(data_conf.index, data_conf.values, marker='x', label='# of positive cases in totalconfirmed')\n", "ax2.semilogy(data_conf.index,[1.19 ** i for i in range(1,len(data_conf.index) + 1)], ls='--', c='grey', label='growth rate of R=1.19')\n", "ax2.legend()\n", "ax2.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))\n", "plt.xticks(data_conf.index, rotation=90)\n", "plt.title('# of positive cases in totalconfirmed (semi-log scale)')\n", "plt.grid(True)\n", "\n", "plt.rc('font', size=20)\n", " \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following observations can be made :\"\n", "\n", "- The trend seems to follow an exponential growth on the linear scale\n", "- And thus, it follows a more or less 'linear' growth on the exponential scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exponential Modeling Approach to Cases Prediction" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Imports\n", "\n", "from datetime import timedelta\n", "from math import sqrt\n", "\n", "import pandas as pd\n", "from scipy.stats import poisson\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "\n", "from sklearn.metrics import r2_score\n", "\n", "# Import created Packages by github.com/grll\n", "\n", "from growth_modeling import ExponentialGrowth\n", "from growth_modeling import ExponentialGeneralizedGrowth\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datecumulative_casest
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" ], "text/plain": [ " date cumulative_cases t\n", "0 2020-03-01 3 0\n", "1 2020-03-02 5 1\n", "2 2020-03-03 6 2\n", "3 2020-03-04 28 3\n", "4 2020-03-05 30 4" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dataframe with only the cumulative count of cases (totalconfirmed)\n", "conf_df = covid19_cases_new[['totalconfirmed']].reset_index()\n", "conf_df.rename({'totalconfirmed': 'cumulative_cases'}, axis=1, inplace=True)\n", "\n", "# Create time column in days\n", "starting_date = conf_df.date.iloc[0]\n", "conf_df['t'] = conf_df.date.apply(lambda date: (date - starting_date).days)\n", "\n", "# print head to visualise data\n", "conf_df.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Helper code 1, or some shit like that\n", "\n", "def ax_growth_model(data, predictions, fit_up_to=0, subplot=111, asymptote=None):\n", " r\"\"\"create an matplotlib axes object to ease plotting growth models.\n", " \n", " Parameters\n", " ----------\n", " data : pd.Series\n", " a series with as values the observed cumulative number of cases and as index\n", " the time in days.\n", " predictions : array_like\n", " an array_like object containing predictions `pd.Series` made from a growth models\n", " **warning** the name of the series must be specified and a supplementary attribute\n", " `color` must be set.\n", " fit_up_to : int, optional\n", " the end slice to which the predictions model was fit to.\n", " subplot : int, optional\n", " argument passed to plt.subplot()\n", " asymptote : int, optional\n", " the asymptotic value of the growth model.\n", " \n", " Returns\n", " -------\n", " ax : pyplot.Axes\n", " an axes object with the correspondings plots.\n", " \"\"\"\n", " ax = plt.subplot(subplot)\n", " \n", " days_to_date = lambda x: x.map(lambda item: conf_df.iloc[0].date + timedelta(days=item)).values\n", " \n", " if fit_up_to != 0:\n", " data_fit = data[:fit_up_to]\n", " data_after_fit = data[fit_up_to:]\n", " else:\n", " data_fit = data\n", " data_after_fit = None\n", " \n", " ax.scatter(days_to_date(data_fit.index), data_fit.values, label=\"data used to fit model\", color=\"r\", marker='x')\n", " if data_after_fit is not None:\n", " ax.scatter(days_to_date(data_after_fit.index), data_after_fit.values, label=\"data not used to fit model\", color=\"grey\", marker='x')\n", " \n", " dates_for_axis = conf_df.date.values\n", " for serie in predictions:\n", " d = days_to_date(serie.index)\n", " if max(d) > max(dates_for_axis):\n", " dates_for_axis = d\n", " ax.plot(days_to_date(serie.index), serie.values, label=serie.name, color=serie.color)\n", " \n", " \n", " if asymptote is not None:\n", " ax.hlines(asymptote, dates_for_axis[0], dates_for_axis[-1], color=\"grey\", linestyle=\"--\", label=\"aymptotic value\")\n", " \n", " ax.legend()\n", " ax.set_xlabel('date (month/day)')\n", " ax.set_ylabel('Cumulative Number of Confirmed Cases')\n", " \n", " ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))\n", " plt.xticks(dates_for_axis, rotation=90)\n", " days_to_date(serie.index)\n", " ax.set_xlim((dates_for_axis[0], dates_for_axis[-1]))\n", " ax.set_ylim((0, 7000))\n", " ax.grid(True)\n", "\n", " return ax" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Helper code 2, or some shit like that\n", "\n", "def fit_growth_model(GrowthModel, params, bounds, t, y, name, color, fit_up_to=0, predict_up_to=0):\n", " r\"\"\"Fit a provided GrowthModel and return predicted values series.\n", " \n", " Parameters\n", " ----------\n", " GrowthModel : Growth\n", " one the Growth class implemented in growth_modeling.\n", " params : dict\n", " a dict of params to initialize the model with\n", " bounds : array_like\n", " bounded for each of the parameter (specified similarly as in scipy.optimize.curve_fit)\n", " t : array_like\n", " time values to fit the growth model on.\n", " y : array_like\n", " response values to fit the growth model on.\n", " name: str\n", " name of the model to fit.\n", " color: str\n", " a matplotlib color to put as attribute of the serie.\n", " fit_up_to : int, optional\n", " the end slice to which the predictions model was fit to.\n", " predict_up_to : int, optional\n", " the number of days to predict up to.\n", " \"\"\"\n", " growth = GrowthModel(params.copy(), bounds)\n", " growth.y_0 = y[0]\n", " \n", " # -- FULL DATASET\n", " growth.fit(t, y)\n", " print(\"[OPTIMISED params (full dataset)]\")\n", " print(growth.params)\n", " \n", " y_pred = growth.compute_y(t)\n", " print(\"R2 SCORE (full dataset): {}\".format(r2_score(y, y_pred)))\n", " \n", " t_predict = np.array(range(predict_up_to)) if predict_up_to != 0 else t\n", " growth_serie = pd.Series(growth.compute_y(t_predict), name=name)\n", " growth_serie.color = color\n", " \n", " # -- REDUCED DATASET\n", " if fit_up_to != 0:\n", " growth.params = params.copy()\n", " growth.fit(t[:fit_up_to], y[:fit_up_to])\n", " print(\"\\n[OPTIMISED params (dataset[:{}])]\".format(fit_up_to))\n", " print(growth.params)\n", " \n", " y_pred = growth.compute_y(t)\n", " print(\"R2 SCORE (full dataset): {}\".format(r2_score(y, y_pred)))\n", " \n", " y_pred = growth.compute_y(t[:fit_up_to])\n", " print(\"R2 SCORE (training dataset): {}\".format(r2_score(y[:fit_up_to], y_pred)))\n", " \n", " y_pred = growth.compute_y(t[fit_up_to:])\n", " print(\"R2 SCORE (testing dataset): {}\".format(r2_score(y[fit_up_to:], y_pred)))\n", " \n", " growth_serie_lim = pd.Series(growth.compute_y(t_predict), name=name)\n", " growth_serie_lim.color = color\n", " \n", " return (growth_serie, growth_serie_lim)\n", " \n", " return (growth_serie,)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Fitting Exponential Growth\n", "\n", "**Simple Exponential Model** can be written as :\n", "\n", "$\\frac{\\partial y}{\\partial t} = ay \\Leftrightarrow y = y_0 e^{at}$\n", "\n", "where :\n", "\n", "- $y$ is the observed response (number of cumulative cases)\n", "- $t$ is time step (in days)\n", "- $a$ is the growth rate (relative derivative increase compared to the number of total cases)\n", "- $y_0$ is the observed response at t=0\n", "\n", "**Generalised Exponential Model** can be written as :\n", "\n", "$\\frac{\\partial y}{\\partial t} = ay^p \\Leftrightarrow y = [(1 - p)at + y_0^{1-p}]^{\\frac{1}{1-p}}$\n", "\n", "where all the parameters are identical and :\n", "\n", "- $p$ is a parameter allowing sub-exponential regime ($0" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the results\n", "plt.figure(figsize=[30,30])\n", "\n", "ax = ax_growth_model(conf_df.cumulative_cases, (exponential_predictions[0], exponential_generalized_predictions[0]), subplot=211)\n", "ax.set_title('Exponential Growth Fitted on all dataset.')\n", "\n", "ax = ax_growth_model(conf_df.cumulative_cases, (exponential_predictions[1], exponential_generalized_predictions[1]), fit_up_to=fit_up_to, subplot=212)\n", "ax.set_title('Exponential Growth fitted only on early data points.')\n", "\n", "plt.rc('font', size=20)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following observations can be made :**\n", "\n", "- When the full data is used to fit the models, the generalised exponential growth seems to better fit the data\n", "- When fitted on only the first part of the data, both models seem to fail to accurately predict the growth\n", "\n", "**Conclusion :**\n", "\n", "- The inability of the models to predict the growth on behalf of early data points indicate **a fall in the rate of growth** of this pandemic\n", "- Logistic models may do better than exponential models in the days that are yet to come" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ...to be continued, subject to the availability of data in the days to come" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }