{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Graphs of COVID-19 growth and doubling time \n", "\n", "This notebook uses Pandas to retrieve JSON data files created by the [Johns Hopkins Coronavirus Resource Center](https://coronavirus.jhu.edu) (and stored at [The COVID Tracking Project](https://covidtracking.com/)) to make real-time tracking plots of the growth of the COVID-19 pandemic in the US and Michigan, as well as estimate the doubling time of the number of positive cases. These data files are updated daily, so re-running the notebook will get the most current data. It's easy to change the state that is plotted by changing the `my_state` variable. There's also quite a bit more information in the data files!\n", "\n", "**Important note:** The data file tracks the number of people who have _tested positive_ of COVID in the US and Michigan, not an estimate of the total number of positive cases in the US and Michigan.\n", "\n", "Notebook by [Brian O'Shea](https://web.pa.msu.edu/people/osheabr/), last revised 2020-04-12" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Two-letter US state code. Change to the state you're interested in!\n", "my_state = 'MI'\n", "\n", "# If you want a high-resolution copy of this plot, set the following to True\n", "save_plots = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gets the data from [covidtracking.com](https://covidtracking.com) - specifically, the page [https://covidtracking.com/api](https://covidtracking.com/api). This page has historical data for the US as a whole, for states, and for counties. The JSON files have a ton of data in addition to what we're showing.\n", "\n", "Note: the Pandas [`read_json`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html) method is really powerful - it can read both local and remote JSON files!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "us = pd.read_json(\"https://covidtracking.com/api/us/daily\")\n", "all_states = pd.read_json(\"https://covidtracking.com/api/v1/states/daily.json\")\n", "\n", "# select out the state I care about\n", "state = all_states[all_states['state']==my_state]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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The data files we retrieve use an integer YYYYMMDD code for dates, which we convert to something that matplotlib can work with more easily and store it in an array. Also get an array of positive cases." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# US\n", "fixed_US_dates = pd.to_datetime(us['date'], format='%Y%m%d')\n", "\n", "positive_US_cases = us['positive']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# State of choice\n", "fixed_state_dates = pd.to_datetime(state['date'], format='%Y%m%d')\n", "\n", "positive_state_cases = state['positive']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "US: 2020-07-27\n", "State: 2020-07-27\n" ] } ], "source": [ "# get the date\n", "ts=fixed_US_dates[0]\n", "mydate = (str(ts)).split(' ')[0]\n", "print(\"US: \", mydate)\n", "\n", "state_date=fixed_state_dates.iloc[0]\n", "my_state_date = (str(state_date)).split(' ')[0]\n", "print(\"State: \",my_state_date)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "positive US cases: 4,275,188 on 2020-07-27\n", "positive MI cases: 87,173 on 2020-07-27\n" ] } ], "source": [ "print(\"positive US cases: {:,} on {:s}\".format(positive_US_cases[0],mydate))\n", "print(\"positive \"+my_state+\" cases: {:,} on {:s}\".format(int(positive_state_cases.iloc[0]),mydate))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot cumulative positive tests for US and a state\n", "\n", "This plots the cumulative number of positive tests in the US as a whole and a single state, as specified at the top of this file." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "ConversionError", "evalue": "Failed to convert value(s) to axis units: '2020-03-01'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1572\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1573\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1574\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 1920\u001b[0m \"\"\"\n\u001b[0;32m-> 1921\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mdate2num\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_to_ordinalf_np_vectorized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 438\u001b[0m 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\u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1574\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1575\u001b[0m raise munits.ConversionError('Failed to convert value(s) to axis '\n\u001b[0;32m-> 1576\u001b[0;31m f'units: {x!r}') from e\n\u001b[0m\u001b[1;32m 1577\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: '2020-03-01'" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "state_lines=ax.plot(fixed_state_dates,positive_state_cases,'r-',label=my_state,linewidth=2)\n", "us_lines=ax.plot(fixed_US_dates,positive_US_cases,'b-',label='US',linewidth=2)\n", "plt.yscale('log')\n", "plt.ylim(1.0,4.0e+6)\n", "plt.xlim('2020-03-01','2020-06-30')\n", "plt.xticks(rotation=60)\n", "plt.xlabel('Date',size=16)\n", "plt.ylabel('Number of positive tests',size=16)\n", "plt.title(\"Cumulative positive COVID-19\\n tests in US, \"+my_state,size=18)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.grid(zorder=-10,alpha=0.5)\n", "plt.text(\"2020-03-26\",12,\"Last data point:\\n\"+mydate,fontsize=14)\n", "\n", "legend = ax.legend(loc='upper left', shadow=True, fontsize='x-large')\n", "\n", "if save_plots==True:\n", " plt.savefig(\"cum_pos_tests_US_and_\"+my_state+\".png\",dpi=200,bbox_inches='tight',pad_inches=0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Doubling time plot\n", "\n", "Now we calculate an estimate of the [doubling time](https://en.wikipedia.org/wiki/Doubling_time) of positive COVID-19 tests (which gives us a sense of how quickly the virus is spreading). The doubling time is the amount of time it takes the number of cases to increase by a factor of two. We use the instantaneous approximation of the doubling time - for times $t_1$ and $t_2$, and number of cases at those times $n_1$ and $n_2$, we can approximately the doubling time $\\tau_d$ as:\n", "\n", "$\\tau_d \\simeq (t_2 - t_1) \\frac{ln(2)}{ln(n_2/n_1)}$\n", "\n", "A constant instantaneous doubling time as a function of time means that there is perfectly exponential growth of positive COVID-19 cases, which is what one expects for an infectious disease that is spreading without any attempts to control it. The hope with social distancing and other measures is that the doubling time will get _larger_ - that is, the spread of the disease will slow down.\n", "\n", "Note that because this value is instantaneous, it's rather noisy (which is to say, it jumps around quite a bit). Look for trends rather than any single day's value. (A smoothed version is at the end of this notebook.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# get just the values for positive cases\n", "us_case_vals = us['positive'].values\n", "us_dates_vals = us['date'].values\n", "\n", "state_case_vals = state['positive'].values\n", "state_dates_vals = state['date'].values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dt=1.0\n", "\n", "# Estimate doubling time using the math above. We're using day N and N-1, so we lose the first data of data.\n", "# Numpy will complain about divide-by-zero errors, but it will still make a plot!\n", "\n", "us_doubling_time = dt * math.log(2) / np.log(us_case_vals[0:-1]/us_case_vals[1:] )\n", "\n", "state_doubling_time = dt * math.log(2) / np.log(state_case_vals[0:-1]/state_case_vals[1:] )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "\n", "us_lines=ax.plot(fixed_US_dates[1:],us_doubling_time,'b-',label='US',linewidth=2)\n", "state_lines=ax.plot(fixed_state_dates[1:],state_doubling_time,'r-',label=my_state,linewidth=2)\n", "plt.xlim('2020-03-01','2020-06-30')\n", "plt.xticks(rotation=60)\n", "plt.xlabel('Date',size=16)\n", "plt.ylim(0,300)\n", "plt.ylabel('Doubling time [days]',size=16)\n", "plt.title(\"COVID-19 positive test doubling \\ntime for US and \"+my_state,size=18)\n", "plt.xticks(fontsize=14)\n", "plt.yticks([0,50,100,150,200,250,300],fontsize=14)\n", "plt.grid(zorder=-10,alpha=0.5)\n", "plt.text(\"2020-03-07\",21,\"Last data point:\\n\"+mydate,fontsize=14)\n", "\n", "legend = ax.legend(loc='upper left', shadow=True, fontsize='x-large')\n", "\n", "if save_plots==True:\n", " plt.savefig(\"doubling_time_pos_tests_US_and_\"+my_state+\".png\",dpi=200,bbox_inches='tight',pad_inches=0.25)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "state_delta=np.copy(state_case_vals)\n", "\n", "state_delta[1:] = state_case_vals[0:-1]-state_delta[1:]\n", "\n", "state_delta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the daily new cases for Michigan\n", "\n", "Also plot the smoothed version of the \"daily new cases\" plot using a [Savitzky–Golay filter](https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter) ([scipy docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html)) so it's easier to see trends!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "\n", "from scipy.signal import savgol_filter\n", "smoothed_state_cases = savgol_filter(state_delta[1:], 15, 2)\n", "\n", "state_lines=ax.plot(fixed_state_dates[1:],state_delta[1:],'r-',label=my_state,linewidth=2)\n", "state_smooth_lines=ax.plot(fixed_state_dates[1:],smoothed_state_cases,'r--',label=my_state,linewidth=2,alpha=0.5)\n", "plt.xticks(rotation=60)\n", "plt.xlabel('Date',size=16)\n", "#plt.ylim(0,2.0e4)\n", "plt.yscale('log')\n", "\n", "plt.ylabel('New cases',size=16)\n", "plt.title(\"COVID-19 daily new cases for \"+my_state,size=18)\n", "plt.xticks(fontsize=14)\n", "#plt.yticks([0,5,10,15,20,25,30,35,40],fontsize=14)\n", "plt.grid(zorder=-10,alpha=0.5)\n", "plt.text(\"2020-04-01\",50,\"Last data point:\\n\"+mydate,fontsize=14)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Smoothed version of doubling time plot\n", "\n", "Just to make it easier to see what's going on, do the same thing for doubling time. This also uses a Savitzky-Golay filter." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.signal import savgol_filter\n", "smoothed_us_doubling_time = savgol_filter(us_doubling_time, 21, 2)\n", "smoothed_state_doubling_time = savgol_filter(state_doubling_time, 11, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "\n", "us_lines=ax.plot(fixed_US_dates[1:],us_doubling_time,'b--',label='US',linewidth=2,alpha=0.5)\n", "state_lines=ax.plot(fixed_state_dates[1:],state_doubling_time,'r--',label=my_state,linewidth=2,alpha=0.5)\n", "\n", "us_lines=ax.plot(fixed_US_dates[1:],smoothed_us_doubling_time,'b-',label='US',linewidth=2)\n", "state_lines=ax.plot(fixed_state_dates[1:],smoothed_state_doubling_time,'r-',label=my_state,linewidth=2)\n", "plt.xticks(rotation=60)\n", "plt.xlabel('Date',size=16)\n", "plt.xlim('2020-03-01','2020-06-30')\n", "plt.ylim(0,250)\n", "plt.ylabel('Doubling time [days]',size=16)\n", "plt.title(\"COVID-19 positive test doubling \\ntime for US and \"+my_state+\" - SMOOTHED\",size=18)\n", "plt.xticks(fontsize=14)\n", "#plt.yticks([0,10,20,30,40,50,60,70,80,90,100,110,120],fontsize=14)\n", "plt.yticks([0,30,60,90,120,150,180,210,240],fontsize=14)\n", "plt.grid(zorder=-10,alpha=0.5)\n", "plt.text(\"2020-03-07\",21,\"Last data point:\\n\"+mydate,fontsize=14)\n", "\n", "legend = ax.legend(loc='upper left', shadow=True, fontsize='x-large')\n", "\n", "if save_plots==True:\n", " plt.savefig(\"doubling_time_pos_tests_US_and_\"+my_state+\".png\",dpi=200,bbox_inches='tight',pad_inches=0.25)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }