examples/exponential_modeling.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Exponential Modeling of COVID-19 Confirmed Cases\n",
    "This notebook explores modeling the spread of COVID-19 confirmed cases as an exponential function. While this is not a good model for long or even medium-term predictions, it is able to fit initial outbreaks quite well. For a more sophisticated and accurate model, see the [logistic modeling](logistic_modeling.ipynb) notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining our parameters and loading the data\n",
    "Here we are looking at the confirmed and fatal cases for Italy through March 17. To apply the model to other countries or dates, just change the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "ESTIMATE_DAYS = 3\n",
    "data_key = 'IT'\n",
    "date_limit = '2020-03-17'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "df = pd.read_csv(f'https://storage.googleapis.com/covid19-open-data/v3/location/{data_key}.csv').set_index('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at the outbreak\n",
    "There are months of data, but we only care about when the number of cases started to grow. We define *outbreak* as whenever the number of cases exceeded certain threshold. In this case, we are using 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_outbreak_mask(data: pd.DataFrame, threshold: int = 10):\n",
    "    ''' Returns a mask for > N confirmed cases '''\n",
    "    return data['total_confirmed'] > threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['total_confirmed', 'total_deceased']\n",
    "# Get data only for the columns we care about\n",
    "df = df[cols]\n",
    "# Get data only for the selected dates\n",
    "df = df[df.index <= date_limit]\n",
    "# Get data only after the outbreak begun\n",
    "df = df[get_outbreak_mask(df)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the data\n",
    "Let's take a first look at the data. A visual inspection will typically give us a lot of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df.plot(kind='bar', figsize=(16, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling the data\n",
    "The data appears to follow an exponential curve, it looks straight out of a middle school math textbook cover. Let's see if we can model it using some parameter fitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Estimated function: 950.195 * (1.161 ^ X) + -1399.440\n"
    }
   ],
   "source": [
    "from scipy import optimize\n",
    "\n",
    "def exponential_function(x: float, a: float, b: float, c: float):\n",
    "    ''' a * (b ^ x) + c '''\n",
    "    return a * (b ** x) + c\n",
    "\n",
    "X, y = list(range(len(df))), df['total_confirmed'].tolist()\n",
    "params, _ = optimize.curve_fit(exponential_function, X, y)\n",
    "\n",
    "print('Estimated function: {0:.3f} * ({1:.3f} ^ X) + {2:.3f}'.format(*params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "confirmed = df[['total_confirmed']].rename(columns={'total_confirmed': 'Ground Truth'})\n",
    "ax = confirmed.plot(kind='bar', figsize=(16, 8))\n",
    "estimate = [exponential_function(x, *params) for x in X]\n",
    "ax.plot(df.index, estimate, color='red', label='Estimate')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validating the model\n",
    "That curve looks like a very good fit! Even though proper epidemiology models are fundamentally different (because diseases can't grow exponentially indefinitely), the exponential model should be good for short term predictions.\n",
    "\n",
    "To validate our model, let's try to fit it again without looking at the last 3 days of data. Then, we can estimate the missing days using our model, and verify if the results still hold by comparing what the model thought was going to happen with the actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "params_validate, _ = optimize.curve_fit(exponential_function, X[:-ESTIMATE_DAYS], y[:-ESTIMATE_DAYS])\n",
    "\n",
    "# Project zero for all values except for the last ESTIMATE_DAYS\n",
    "projected = [0] * len(X[:-ESTIMATE_DAYS]) + [exponential_function(x, *params_validate) for x in X[-ESTIMATE_DAYS:]]\n",
    "projected = pd.Series(projected, index=df.index, name='Projected')\n",
    "confirmed = pd.DataFrame({'Ground Truth': df['total_confirmed'], 'Projected': projected})\n",
    "ax = confirmed.plot(kind='bar', figsize=(16, 8))\n",
    "estimate = [exponential_function(x, *params_validate) for x in X]\n",
    "ax.plot(df.index, estimate, color='red', label='Estimate')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projecting future data\n",
    "It looks like our exponential model slightly overestimates the confirmed cases. That's a good sign! It means that the disease is slowing down a bit. The numbers are close enough that a 3-day projection is probably an accurate enough estimate.\n",
    "\n",
    "Now, let's use the model we fitted earlier which used all the data, and try to predict what the next 3 days will look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "# Append N new days to our indices\n",
    "date_format = '%Y-%m-%d'\n",
    "date_range = [datetime.datetime.strptime(date, date_format) for date in df.index]\n",
    "for _ in range(ESTIMATE_DAYS): date_range.append(date_range[-1] + datetime.timedelta(days=1))\n",
    "date_range = [datetime.datetime.strftime(date, date_format) for date in date_range]\n",
    "\n",
    "# Perform projection with the previously estimated parameters\n",
    "projected = [0] * len(X) + [exponential_function(x, *params) for x in range(len(X), len(X) + ESTIMATE_DAYS)]\n",
    "projected = pd.Series(projected, index=date_range, name='Projected')\n",
    "df_ = pd.DataFrame({'Confirmed': df['total_confirmed'], 'Projected': projected})\n",
    "ax = df_.plot(kind='bar', figsize=(16, 8))\n",
    "estimate = [exponential_function(x, *params) for x in range(len(date_range))]\n",
    "ax.plot(date_range, estimate, color='red', label='Estimate')\n",
    "ax.legend();"
   ]
  }
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