team_ghana.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "team_ghana.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ayebilla/DataScience_Learning/blob/master/team_ghana.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TSvDr5mxp4P",
        "colab_type": "text"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Priesemann-Group/covid19_inference/blob/model_cleanup/scripts/interactive/example_one_bundesland.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAroMl3cxLEU",
        "colab_type": "text"
      },
      "source": [
        "#Extending the Repository"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvzQHHJaxp4S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "7ceb89f9-df04-42e1-f19f-41cc6e532722"
      },
      "source": [
        "!pip install git+https://github.com/Priesemann-Group/covid19_inference.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/Priesemann-Group/covid19_inference.git\n",
            "  Cloning https://github.com/Priesemann-Group/covid19_inference.git to /tmp/pip-req-build-tepu9rvk\n",
            "  Running command git clone -q https://github.com/Priesemann-Group/covid19_inference.git /tmp/pip-req-build-tepu9rvk\n",
            "Requirement already satisfied (use --upgrade to upgrade): covid19-inference==0.1.8a0 from git+https://github.com/Priesemann-Group/covid19_inference.git in /usr/local/lib/python3.6/dist-packages\n",
            "Requirement already satisfied: pymc3 in /usr/local/lib/python3.6/dist-packages (from covid19-inference==0.1.8a0) (3.7)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from covid19-inference==0.1.8a0) (3.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from covid19-inference==0.1.8a0) (1.18.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from covid19-inference==0.1.8a0) (1.0.5)\n",
            "Requirement already satisfied: theano in /usr/local/lib/python3.6/dist-packages (from covid19-inference==0.1.8a0) (1.0.5)\n",
            "Requirement already satisfied: tqdm>=4.8.4 in /usr/local/lib/python3.6/dist-packages (from pymc3->covid19-inference==0.1.8a0) (4.41.1)\n",
            "Requirement already satisfied: h5py>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from pymc3->covid19-inference==0.1.8a0) (2.10.0)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from pymc3->covid19-inference==0.1.8a0) (1.4.1)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from pymc3->covid19-inference==0.1.8a0) (0.5.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->covid19-inference==0.1.8a0) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->covid19-inference==0.1.8a0) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->covid19-inference==0.1.8a0) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->covid19-inference==0.1.8a0) (0.10.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->covid19-inference==0.1.8a0) (2018.9)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from theano->covid19-inference==0.1.8a0) (1.15.0)\n",
            "Building wheels for collected packages: covid19-inference\n",
            "  Building wheel for covid19-inference (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for covid19-inference: filename=covid19_inference-0.1.8a0-cp36-none-any.whl size=103637 sha256=714a20d19c445aa8b0fadaf0a942f490c2ec1b5484a690375bd6858cfeb898ce\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-01ts2kjb/wheels/da/f1/e7/da56ba4fe019b70927b4bb10088f02f672b31b69fd54a6e613\n",
            "Successfully built covid19-inference\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8l03iICxJe-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri7RrIuExp4b",
        "colab_type": "text"
      },
      "source": [
        "#Importing modules\n",
        "*italicized text*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJ8g6dP4xp4c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import datetime\n",
        "import time as time_module\n",
        "import sys\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats\n",
        "import theano\n",
        "import theano.tensor as tt\n",
        "import pymc3 as pm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5jsz1KFxVWS",
        "colab_type": "text"
      },
      "source": [
        "#Importing the Extended Repository"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_HDc3Lqxp4i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Now to the fun stuff, we import our module!\n",
        "try:\n",
        "    import covid19_inference as cov19\n",
        "except ModuleNotFoundError:\n",
        "    sys.path.append(\"../../\")\n",
        "    import covid19_inference as cov19"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OO0Pk2S6xh2v",
        "colab_type": "text"
      },
      "source": [
        "#Task 2.1 Starts here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vxo--KVRxp4q",
        "colab_type": "text"
      },
      "source": [
        "a. **Download the COVID19 case data for your assigned country**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkumjjYMxp4r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "283bbe73-53b7-47ab-ac76-8eca816a96cd"
      },
      "source": [
        "jhu = cov19.data_retrieval.JHU()\n",
        "jhu.download_all_available_data()\n",
        "# One could also parse True to the constructor of the class to force an auto download"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO     [covid19_inference.data_retrieval._JHU] Successfully loaded data from local\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qK6uy-pxp40",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bd = datetime.datetime(2020, 3, 26)  # beggining date retrieved from the report of ifm\n",
        "ed = datetime.datetime.now()        # until today(relative whenever you read this code it is today)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bn7K7UQdxp4-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_cases_obs = jhu.get_total(\n",
        "    value=\"confirmed\", country=\"Ghana\", data_begin=bd, data_end=ed\n",
        ")\n",
        "new_cases_obs = jhu.get_new(\n",
        "    value=\"confirmed\", country=\"Ghana\", data_begin=bd, data_end=ed\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uy5il5WuyORw",
        "colab_type": "text"
      },
      "source": [
        "b. Pre-process the downloaded data such that the starting date of the data is when the number of covid19 cases in your country reaches 100 and dominated by a community transmission."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idah3B4q0Uyz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "42290914-3b0d-4f08-eba6-01947e9b49ea"
      },
      "source": [
        "total_cases_obs =  total_cases_obs[total_cases_obs>=100] # making the first case to be taken from the first 100 case and forward\n",
        "total_cases_obs # in ourcase the first case is 132 and on march 26"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2020-03-26      132\n",
              "2020-03-27      137\n",
              "2020-03-28      141\n",
              "2020-03-29      152\n",
              "2020-03-30      152\n",
              "              ...  \n",
              "2020-08-03    37812\n",
              "2020-08-04    37812\n",
              "2020-08-05    39075\n",
              "2020-08-06    39642\n",
              "2020-08-07    40097\n",
              "Name: confirmed, Length: 135, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nx-xxROznc4",
        "colab_type": "text"
      },
      "source": [
        "plotting to see if the given dataset makes sense\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV_tNjqCzv2E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "95c6992d-a4cd-481c-edfe-74c6d7776970"
      },
      "source": [
        "total_cases_obs.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f162d31f438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LROLLO5nbmfziErq3bcJD44bSrFESyQ0SSWkeX0NRV1a5ioGZNSBcCP7m7q8BuHtuxPIngdnB2xygS8TqqUGMY8R3A63MLCk4OohsLyJSJbkFh3jw3fW8tOgzBqa24tnrv0brprpG5UgnLAYWHqv1aWC1u/85It4x6E8A+CawMng9C3jBzP4MdAL6AAsBA/qYWQ/CX/bjgGvc3c1sHnAVMAOYALxRHR9OROqvUMh57F8bePj99ZSFnOtO787PLu4Xt6OOVlV59sqZwLXACjNbGsR+TvhqoCGETxNtBm4CcPdMM5sJrCJ8JdIt7l4GYGaTgTlAIjDN3TOD7d0GzDCz+4AlhIuPiEil7C8q5acvL+Ptldu5ZGAH7hh9Cl3aNIl2WnWaucfmqfe0tDRPT0+PdhoiUsds3rWfm/+awbrcQn5+ySlMPKtHXE5GU1lmluHuaUfGdbwkInHj70ty+MXrK0hKTOCZG4bz9b4p0U4pZqgYiEhc+PWbq3jyo018rXtrHhw3tF4NMlcdVAxEJOa9nL6VJz/axHdGduXuyweQlKjR+StKe0xEYtrKnHx+8feVnNGrrQpBFWiviUjMyj9Ywk3PZ9CuaUMeHj9UhaAKdJpIRGLWA3PXsS3/IK9+/wzaNtOdxFWhMioiMWl9biHPz9/CuOFd683UlDVJxUBEYo6786vZq2jSMJFbL+wb7XTigoqBiMSc91bv4KP1u/jxN/rq9FA1UTEQkZhSVFrGvW+uoldKU647vVu004kbKgYiElOe+WQzW3Yf4M7LB9BAVw9VG+1JEYkZOwoP8fB767ng5JM01EQ1UzEQkZjxh3+upbgsxC8v63/ixlIhKgYiEhNW5uTzckY23z2zBz3aNY12OnFHxUBEYsKD766jRXISPzivd7RTiUsnLAZm1sXM5pnZKjPLNLMfBfE2ZjbXzNYHz62DuJnZFDPLMrPlZjYsYlsTgvbrzWxCRPw0M1sRrDPFNPi4iERYtjWPd1fvYNI5PWnZuEG004lL5TkyKAVudff+wEjgFjPrD9wOvOfufYD3gvcAowlPddkHmAQ8BuHiAdwFjACGA3cdLiBBmxsj1htV9Y8mIvHiz3PX0apJA64/s0e0U4lbJywG7r7N3RcHrwuB1UBnYAwwPWg2HRgbvB4DPOdh8wlPdt8RuBiY6+573H0vMBcYFSxr4e7zPTzt2nMR2xKRei5jyx7+tW4nN53Ti2aav7jGVKjPwMy6A0OBBUB7d98WLNoOtA9edwa2RqyWHcSOF88+SlxE6rnSshD3zl5N26YNmXCGbjCrSeUuBmbWDHgV+LG7F0QuC37R1/hkymY2yczSzSx9586dNf3nRCTKHnpvPUu35nH3FQNo0lBHBTWpXMXAzBoQLgR/c/fXgnBucIqH4HlHEM8BukSsnhrEjhdPPUr8K9x9qrunuXtaSopuOBGJZ//esJu/zMvi6rRULh/cKdrpxL0Tltrgyp6ngdXu/ueIRbOACcD9wfMbEfHJZjaDcGdxvrtvM7M5wG8iOo0vAu5w9z1mVmBmIwmffroOeLgaPpuIxJjZyz9n9rLw2ef0LXvo0bYpd18xIMpZ1Q/lOe46E7gWWGFmS4PYzwkXgZlmNhHYAlwdLHsLuATIAg4ANwAEX/r3AouCdr9y9z3B6x8AzwKNgbeDh4jUI8WlIe6elUnIIaVZI7q2acJ9Ywfq9FAtOeFedvePgWNd93/BUdo7cMsxtjUNmHaUeDpw6olyEZH49c6q7ezaV8wzN3yN8/qdFO106h3dgSwidcILCz6jc6vGnNNH/YHRoGIgIlG3add+Pt2wm/HDu5CYoAEIokHFQESi7sWFn5GUYFyd1uXEjaVGqGdGRKIiY8te5m/cDcDL6Vv5xintOalFcpSzqr9UDESkVoVCzuMfbuCPc9YSCm5VTUowbjize1Tzqu9UDESk1uQfLOHWmct4d3Uulw3qyK/HDiS5YQIJZprCMspUDESkVmR+ns8P/raYnL0Huevy/lx/Rnc0Wn3doWIgIjVu9vLPuXXmMlo1acBLN43ktG5top2SHEHFQERq1MJNe/jJS0sZnNqKx689jXbNGkU7JTkKFQMRqTFb9xzg5r9m0KV1E56e8DVaNtEsZXWVemxEpEbk5B3ke9PTKS0L8dSENBWCOk5HBiJSrUrKQkz7eBMPvrsegCevS6NnSrMoZyUnomIgItWmpCzEDc8s4uOsXXzjlPbcfUV/Uls3iXZaUg4qBiJSLdydO99YycdZu/jtlQMZP7xrtFOSClCfgYhUi6c/3sSLC7cy+bzeKgQxSEcGIlIlJWUhpry3nr/My+KSgR34nwv7RjslqYQTHhmY2TQz22FmKyNid5tZjpktDR6XRCy7w8yyzGytmV0cER8VxLLM7PaIeA8zWxDEXzKzhtX5AUWk5ny2+wDfevzfPPx+FlcNS+XPVw8hQUNQx6TynCZ6Fhh1lPgD7j4keLwFYGb9gXHAgGCdR80s0cwSgUeA0UB/YHzQFuB3wbZ6A3uBiVX5QCJSO5Z8tpcxj3zMxp37eOSaYfzhW4NJbpAY7bSkkk5YDNz9Q2DPidoFxgAz3L3I3TcRngd5ePDIcveN7l4MzADGWHhgkvOBV4L1pwNjK/gZRKSWvbc6l/FPzqd5cgNmTT6LSwd1jHZKUkVV6UCebGbLg9NIrYNYZ2BrRJvsIHaseFsgz91Lj4iLSB01c9FWJj2fQZ+TmvPq98+ge7um0U5JqkFli8FjQC9gCLAN+FO1ZXQcZjbJzNLNLH3nzp218SdFJODuPPzeev731eWc0astL04aSUpzjTMULypVDNw9193L3D0EPEn4NBBADhA5b11qEDtWfDfQysySjogf6+9Odfc0d09LSdGk2SK1pbg0xM9fX8Gf5q7jyqGdeXrC12jWSBcjxpNKFQMzizxB+E3g8JVGs4BxZtbIzHoAfYCFwCKgT3DlUEPCncyz3N2BecBVwfoTgDcqk5OI1Izd+4r4zlMLeHHhVm45rxd/unowDZN0i1K8OWFpN7MXgXOBdmaWDdwFnGtmQwAHNgM3Abh7ppnNBFYBpcAt7l4WbGcyMAdIBKa5e2bwJ24DZpjZfcAS4Olq+3QiUmGhkDPl/fXMW7MDCA84V3iolIfGDWHMEHXpxSsL/ziPPWlpaZ6enh7tNETiyqGSMn7y0lLeXrmdtG6taZacRKOkBG45rzeDUltFOz2pBmaW4e5pR8Z10k9EAMg7UMwNzy5i6dY8fnnpKUw8q4empaxHVAxEhPyDJVz79ELWbi/k0WuGMXqg7huob1QMROq5gkMlXDdtIWu2F/D4d07jglPaRzsliQIVA5F6KhRy3lyxjT/MWcvneQd59NvDVAjqMRUDkXoo8/N8fv7aCpZl53Nyh+Y8N3E4Z/RqF+20JIpUDETqkZKyEI/O28DD76+nVZOG/PFbg/nm0M4kaqTRek/FQKSe2JZ/kO//dTFLt+YxZkgn7r58AK2basR4CVMxEKkHPt2wix++sIRDJWU8cs0wjTIqX6FiIBLHMrbs5ckPNzJn1XZ6tmvKE9eeTu+TmkU7LamDVAxE4simXfuZMG0hOwuLcJxDJSFaNm7AD87txc1f70Xz5AbRTlHqKBUDkTjh7tw1K5O9+4v5zsiumBld2jThv4Z1pklD/VeX49O/EJE4MSczlw/X7eT/LuvPxLN6RDsdiTEah1YkDhwsLuPe2avo1745E07vFu10JAbpyEAkxu0oPMS9s1eTk3eQlyaNJClRv/Gk4lQMRGJUcWmIB99dxzOfbKa4LMQPz+/NiJ5to52WxCgVA5EYVFRaxi1/W8y7q3cwZkgnfvyNvvTQxPRSBSc8njSzaWa2w8xWRsTamNlcM1sfPLcO4mZmU8wsy8yWm9mwiHUmBO3Xm9mEiPhpZrYiWGeKaQB1keM6VFLGzc9n8O7qHdw7ZgAPjRuqQiBVVp4jg2eBvwDPRcRuB95z9/vN7Pbg/W3AaMLzHvcBRgCPASPMrA3h6TLTCE+VmWFms9x9b9DmRmAB8BYwCni76h9NJPblHShmXe4+APYXl/LRul3MydxOTt5BfvPNgVwzomuUM5R4ccJi4O4fmln3I8JjCM+LDDAd+IBwMRgDPBdMdD/fzFqZWceg7Vx33wNgZnOBUWb2AdDC3ecH8eeAsagYSD13qKSMZz7ZzKPzsigsKv0i3jApgbN6t+PuKwZwYX8NNy3Vp7J9Bu3dfVvwejtw+F9lZ2BrRLvsIHa8ePZR4iL1Uijk/GP55/z+n2vJyTvIN045iW+P7EbDxAQSzBiU2pKmjdTVJ9Wvyv+q3N3NzKsjmRMxs0nAJICuXXV4LPGjpCzEh+t2MuW99SzLzmdApxb84apBnNFbcwxI7ahsMcg1s47uvi04DbQjiOcAXSLapQaxHP5zWulw/IMgnnqU9kfl7lOBqQBpaWm1UoBEalJO3kEe+yCL2cu3kXeghA4tkvnjtwZz5dDOJGiOAalFlS0Gs4AJwP3B8xsR8clmNoNwB3J+UDDmAL85fNURcBFwh7vvMbMCMxtJuAP5OuDhSuYkEjMOFJfy+AcbeOLDjTgwakAHrhjciXP6ptAwSTeNSe07YTEwsxcJ/6pvZ2bZhK8Kuh+YaWYTgS3A1UHzt4BLgCzgAHADQPClfy+wKGj3q8OdycAPCF+x1Jhwx7E6jyVuhULO35fm8Lt/riG3oIjLBnXk9tEnk9q6SbRTk3rOwhf+xJ60tDRPT0+Pdhoi5bZ6WwG3v7aCZVvzGJTakjsv609a9zbRTkvqGTPLcPe0I+O6LEGkhpWUhXjsg/C8wy0bN+BPwbzD6hOQukTFQKSGFJWW8friHJ74cCObdu3n8sGduOeKAbTRvMNSB6kYiFSz/UWlvLjwM576aBPbCw5xaucWPHVdGt/QTWJSh6kYiFTRqs8LmPrhBvYVleIOGZ/tJe9ACSN7tuH3Vw3i7D7t0JBbUtepGIhU0p79xUx5bz3P/XszzRol0aVN+Iqg03u25cZzejKsa+vjb0CkDlExEKmAg8Vl/G3BFt7JzCV9yx4c+PaIrvz0on60aqK+AIldKgYi5TRv7Q7ufGMlW/cc5JSOLbjlvN5cNqgT/To0j3ZqIlWmYiByFKu3FfDa4mzeWZXLgeIy3GHXviJ6pTTlxRtHcnovzSgm8UXFQCRC1o593DVrJZ9k7aZBonFOnxROapEMQK+Uplx7ejcaJSVGOUuR6qdiIPVeaVmIzM8LeGvFNqZ9sonGDRL55aWncOWwVN0TIPWGioHUK0WlZSz5LI+Fm/awadd+tu45wJrthewLJpC5cmhn7rjkFFKaN4pypiK1S8VA4p67s2jzXqZ/upn31uRyqCSEGXRq2ZjU1o0ZO7QTI3q0ZUSPNl+cEhKpb1QMJK6UlIX4dMNu/rHsc9bnFgJQcKiUTbv20yI5iavTunB2nxRG9GxDi+QGUc5WpO5QMZC4MSdzO794fSW79hXRvFESQ7q2IjHBaNesETee3ZOxQzvRpKH+yYscjf5nSEzZV1TKjoJDX4qFHJ7+eBMvLvyMAZ1a8JtvnsrX+6Xoqh+RClAxkDqrpCzEqs8LyNiyl8Wf7SXz8wI2797PsabguOmcntx6UT/NFCZSCVUqBma2GSgEyoBSd08zszbAS0B3YDNwtbvvtfBIXQ8RngntAHC9uy8OtjMB+GWw2fvcfXpV8pLYtWd/MdM/3cz8jbtZlp3HoZIQAJ1aJjMotRVXDu1MlzZNOHLct57tmjEwtWUUMhaJD9VxZHCeu++KeH878J67329mtwfvbwNGA32CxwjgMWBEUDzuAtIABzLMbJa7762G3CRGHCopY/qnm/nLvCz2F5VyaueWjB/eldO6tea0bq3p2LJxtFMUiWs1cZpoDOE5kwGmAx8QLgZjgOc8PM/mfDNrZWYdg7ZzD8+JbGZzgVHAizWQm9RBuQWHmDh9EStzCjivXwo/v+QU+rTXeD8itamqxcCBd8zMgSfcfSrQ3t23Bcu3A4dn9OgMbI1YNzuIHSv+FWY2CZgE0LVr1yqmLnXBqs8LmDh9EQUHS5h67WlcNKBDtFMSqZeqWgzOcvccMzsJmGtmayIXursHhaJaBMVmKkBaWlq1bVei4/01ucSwaQ8AAAzjSURBVPzwhSU0T27AyzefQf9OLaKdkki9VaXLLtw9J3jeAbwODAdyg9M/BM87guY5QJeI1VOD2LHiEsee/WQT35ueTo+Uprwx+UwVApEoq/SRgZk1BRLcvTB4fRHwK2AWMAG4P3h+I1hlFjDZzGYQ7kDOd/dtZjYH+I2ZHZ4W6iLgjsrmJXVPcWmIVxdn8/y/t1BYVEIoBDl5B/nGKe2ZMn6IbgQTqQOq8r+wPfB6MLdrEvCCu//TzBYBM81sIrAFuDpo/xbhy0qzCF9aegOAu+8xs3uBRUG7Xx3uTJbY90nWLv73leXk5B1kYOeWfK1bGwD6dmjOjWf3JDFBcwOL1AXmx7qDp45LS0vz9PT0aKchx3GwuIxz/ziPJg2TuOvy/ny9b4omhheJMjPLcPe0I+M6PpcaM+2TTeQWFDHzpmEM79Em2umIyHHovn2pEbv2FfHYBxu4sH97FQKRGKAjA6k27k7+wRIAHpi7joMlZdw26uQoZyUi5aFiINUiJ+8gP56xhEWb/zOKyLdHdKX3Sc2imJWIlJeKgVTKoZIyMj/Pxx227D7APf/IJORw64V9aZacRHKDRMYOOeqN5CJSB6kYSIUVHCph3BPzWbWt4IvYwM4teXj8ULq3axrFzESkslQMpEIOlZRx4/R01uUWcv+VA+ncujFJCQmc1q215hEQiWEqBvIVB4vLACgJhdiwYx+rthWQmx+eXSx9y14WbNrDQ+OGMEangUTihoqBfCHvQDE/fmkpH6zdedTlZtAwMYF7xwxQIRCJMyoG9Zi7cyA4Cti4cz8/eCGD3Pwibv56L1o1aUCCQbe2TenfsQWprRvr7mGROKZiUE+syM5n/sbdABSXhViRnU/6lj3s2lf8RZsOLZJ56aaRDO3a+libEZE4pWIQh0rKQuwOvuT37C/mkXlZvLli25fadGnTmHP6ptC3fXMSDJISErh8cCdSmjeKRsoiEmUqBjHK3dm8+wCLt+wlJ+8gEB4qell2Hhlb9n5x+gegScNE/vuCPlx3ejeSGySSYGjYaBH5En0j1GGbd+1nxqKt/GPZ5xQeKvnSstKQf+kL/7B+7Ztz1Wmp9OvQnAQzEs049+QUTmqeXFtpi0gMUjGoY4pKy3gnM5cXF37Gpxt2k5hgnNs3hS5tmnypXYIZvU9qxrBureiV0oyEoHNX8wOISGXUmWJgZqOAh4BE4Cl3vz/KKVXJ9vxDzF2dy7rthQCUubMt7yBb9hxg975i3B0HCKaTcMKnfkrKnOKyEJ1bNeanF/XlW2ldaN9Cv+pFpGbViWJgZonAI8CFQDawyMxmufuq6Gb2HweKS/lszwHyDpSw71Ape/YXk733ADl5hygpCwHhjtuCQyXs3lfMmqAItEhOIikxAQNOapFMv/bNSend6Itf8mZgWPAc/mV/Zu92nNW7HQn6lS8itaROFANgOJDl7hsBgnmSxwDHLAa79hXxxL82UOZOKOSUhfjP6y9i/3ldVBoi/2AJBYdKKCl1Qh5+lHn4F3lZyAl96fV/ZoArPFTKjsKir+SQYNC+RTLJDRIBSEowmicn0bFlMpcP7sRF/dvT+6Rmuj5fROq8ulIMOgNbI95nAyOOt8K2/EP89u01X4knWPjXdYIZiQnhDtSEBKNhUgItGzegRXISDZMSaJCQQIIZZkaihc/BJyQYCYdfH/6pDjRpkEj3dk3p1rYJbZo0pGmjJFo3aUiHlskaj0dE4kJdKQblYmaTgEkAXbp2I/Oei7/0xZ9g6Fe4iEgl1JWftTlAl4j3qUHsS9x9qrunuXvaSSntaNooPG5+w6QEEhNMhUBEpJLqSjFYBPQxsx5m1hAYB8yKck4iIvVGnThN5O6lZjYZmEP40tJp7p4Z5bREROqNOlEMANz9LeCtaOchIlIf1ZXTRCIiEkUqBiIiomIgIiIqBiIiAphHDLsQS8ysEFhbjqYtgfwjYu2AXRX4c0fbRmVUx3ZqO5fj7av6vF+Op6L/vmoyl7q0X6rj/2JN5hKt7VRmG0fbb+XdTj93b/6VqLvH5ANIL2e7qZVd93jbqGTOVd5ObedyvH1Vn/dLZfdZPd8vVf6/WF/2SznW+cp+q+r/6fpwmugfdWQb1bUd5VJz26jO7VSHeNsvyqXmtlHl7cTyaaJ0d0+r7XXrG+2ritM+Kz/tq8qpie+/WD4ymBqldesb7auK0z4rP+2ryqn277+YPTIQEZHqE8tHBiIiUk1UDEREJH6LgZmNNTM3s5OjnUtdFOybv0a8TzKznWY2O5p5xRIz2xftHGLJifaXmX1gZupMJjrfX3FbDIDxwMfBc7mZWWLNpFPn7AdONbPGwfsLOcqEQiISFZX6/qqKuCwGZtYMOAuYSHiiHMzsXDP70MzeNLO1Zva4mSUEy/aZ2Z/MbBlwevQyr3VvAZcGr8cDLx5eYGbDzezfZrbEzD41s35B/EMzGxLR7mMzG1yrWdchwb+r2RHv/2Jm1wevN5vZPWa22MxW6Cj1+PtLwo7z/XWsf2eXmNkaM8swsymVPbqPy2IAjAH+6e7rgN1mdloQHw78EOgP9AKuDOJNgQXuPtjdP671bKNnBjDOzJKBQcCCiGVrgLPdfShwJ/CbIP40cD2AmfUFkt19Wa1lHHt2ufsw4DHgp9FORmLCsb6/viL4v/sEMNrdTwNSKvtH47UYjCf8RUfwfPhQa6G7b3T3MsK/gs8K4mXAq7WbYvS5+3KgO+H9c+TEQi2Bl81sJfAAMCCIvwxcZmYNgO8Cz9ZKsrHrteA5g/C+FjmRY31/Hc3JwEZ33xS8f/E4bY+rzsx0Vl3MrA1wPjDQzJzwNJoOvBk8Rzr8/lBQIOqjWcAfgXOBthHxe4F57v5NM+sOfADg7gfMbC7hXy9XA8f81VJPlPLlH1XJRywvCp7LiMP/b5Vwov1Vrx3n++sNani/xeORwVXA8+7ezd27u3sXYBNwNjDczHoEfQX/j3AHTX03DbjH3VccEW/JfzqUrz9i2VPAFGCRu++t2fTqvC1AfzNrZGatgAuinVAdp/11fMf6/krg6PttLdAz+MEG4e+1SonHYjAeeP2I2KtBfBHwF2A14R18ZLt6x92z3X3KURb9HvitmS3hiF+07p4BFADP1EKKdZKZJQFF7r4VmAmsDJ6XRDWxOkr7q9yO9f01jqPsN3c/CPwA+KeZZQCFVHJI7XozHIWZnQv81N0vi3Yusc7MOhE+bXSyu4einE5UBFdQPenuw6OdSyzQ/qo5ZtbM3feZmQGPAOvd/YGKbicejwykBpnZdYSvOvpFPS4ENxPuqPtltHOJBdpfNe5GM1sKZBI+vftEZTZSb44MRETk2OLiyMDMupjZPDNbZWaZZvajIN7GzOaa2frguXUQ/7aZLQ9uBPo08qYpMxsV3JSWZWa3R+sziYjUprg4MjCzjkBHd19sZs0JX9M9lvBVMHvc/f7gi721u99mZmcAq919r5mNBu529xHBUBTrCA/NkE24w3m8u6+KxucSEaktcXFk4O7b3H1x8LqQ8NVCnQlfCz89aDadcIHA3T+NuCRyPpAavB4OZAU3phUTvuFjTO18ChGR6ImLYhApuN52KOFOzvbuvi1YtB1of5RVJgJvB687A1sjlmUHMRGRuBZXd0QGAzy9CvzY3QvCV1qFubsHd/RFtj+PcDE4CxGReixujgyCsXJeBf7m7ofHg8kN+hMO9yvsiGg/iPCdtGPcfXcQzgG6RGw2FQ3rLCL1QFwUg+Bmi6cJdwr/OWLRLGBC8HoC4fE9MLOuhAcQuzYYGfCwRUCfYMiKhoTv+ptV0/mLiERbvFxNdBbwEbACOHwj1M8J9xvMBLoSHhPlanffY2ZPAf8VxABK3T0t2NYlwIOEB4ia5u6/rrUPIiISJXFRDEREpGri4jSRiIhUjYqBiIioGIiIiIqBiIigYiAiIqgYiFSKmd1tZj89zvKxZta/NnMSqQoVA5GaMRZQMZCYofsMRMrJzH5B+E72HYQHNMwgPN/sJKAhkAVcCwwBZgfL8gnf4AjhKQlTgAPAje6+pjbzFzkeFQORcjCz04BngRGEB3hcDDwOPHN4bCszuw/IdfeHzexZYLa7vxIsew+42d3Xm9kI4Lfufn7tfxKRo4urUUtFatDZwOvufgDAzA6PWXVqUARaAc2AOUeuGIymewbwcsRIuo1qPGORClAxEKmaZ4Gx7r7MzK4Hzj1KmwQgz92H1GJeIhWiDmSR8vkQGGtmjYOpVS8P4s2BbcEQ6t+OaF8YLMPdC4BNZvYtCI+yGznvtkhdoGIgUg7BtKovAcsIz4y3KFj0f4RHx/0EiOwQngH8zMyWmFkvwoViopktAzLRdKpSx6gDWUREdGQgIiIqBiIigoqBiIigYiAiIqgYiIgIKgYiIoKKgYiIoGIgIiLA/weBZT2wNUxWKwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXrKWqLez1__",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "4fbfcf85-57c8-4e1e-9156-b1aa72109837"
      },
      "source": [
        "new_cases_obs.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f162d2a7fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KK-IGOfsy8_x",
        "colab_type": "text"
      },
      "source": [
        "c. **It is unlikely but, if there are dates that have NaN values, perform linear regression to fill these missing values**. **Make sure the final data has a continuous date - ensure no date is missing. Zero number of cases for a given date is ok**.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgYut7Nl2TeN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0cd03973-5847-48d2-82ad-8e552c9b8a5c"
      },
      "source": [
        "#checking for missing values\n",
        "total_cases_obs[total_cases_obs.isnull()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: confirmed, dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VifHAcKz2g3T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f6c1b320-326d-4d4f-ca37-f777df4a3380"
      },
      "source": [
        "new_cases_obs[new_cases_obs.isnull()]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: confirmed, dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7U2I8xgv2ifj",
        "colab_type": "text"
      },
      "source": [
        "* From above we can see that there is no missing value for both total confirmed case as well as for new cases since the output from checking the missing value is empty"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2KfG_aM22Ew",
        "colab_type": "text"
      },
      "source": [
        "# d. Split the data into one part used for inference (training set), and an other used for to validate a forecast (validation set):\n",
        "\n",
        "1.  **Training set** includes all dates from the time the community transmission reaches 100 to July 25 2020.\n",
        "2.  Validation set includes dates from 25 July 2020 to one final date in the covid19 cases data.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxeNklsR191n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_cases_obs = new_cases_obs[new_cases_obs.index <= '2020-07-25'] # training set\n",
        "val_set = new_cases_obs[new_cases_obs.index > '2020-07-25'] #validation set"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "286aKjEA39aA",
        "colab_type": "text"
      },
      "source": [
        "# e. Plot the training data together with the model that is sampled from the posterior of the SIR model. The posterior of the SIR model means distributions on the Lambda, Mu, and other parameters. A single model curve means a single sample from the posterior distribution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBO4YcpOxp5Q",
        "colab_type": "text"
      },
      "source": [
        "#### Creating the model\n",
        "\n",
        "First we need to set the priors for the change points and other configs.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "420W2Kdoxp5R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "diff_data_sim = 16  # should be significantly larger than the expected delay, in\n",
        "# order to always fit the same number of data points.\n",
        "num_days_forecast = 15 # we took this 15 becasue from july 26 - august 10 it is 15 days\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Axq7K5LHXJ75",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We set the priors for the changepoints here\n",
        "prior_date_mild_dist_begin = datetime.datetime(2020, 3, 30)\n",
        "prior_date_strong_dist_begin = datetime.datetime(2020, 6, 15)\n",
        "prior_date_contact_ban_begin = datetime.datetime(2020, 7, 26)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Rkd_20Lxp5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "change_points = [\n",
        "    dict(\n",
        "        pr_mean_date_transient=prior_date_mild_dist_begin,\n",
        "        pr_sigma_date_transient=3,\n",
        "        pr_median_lambda=0.2,\n",
        "        pr_sigma_lambda=1,\n",
        "    ),\n",
        "    dict(\n",
        "        pr_mean_date_transient=prior_date_strong_dist_begin,\n",
        "        pr_sigma_date_transient=1.5,\n",
        "        pr_median_lambda=1 / 8,\n",
        "        pr_sigma_lambda=1,\n",
        "    ),\n",
        "    dict(\n",
        "        pr_mean_date_transient=prior_date_contact_ban_begin,\n",
        "        pr_sigma_date_transient=1.5,\n",
        "        pr_median_lambda=1 / 8 / 2,\n",
        "        pr_sigma_lambda=1,\n",
        "    ),\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuV6IYIXxp5f",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "Next, we create the model! There are default values for most of the function arguments,\n",
        "but we will try to explicitly set all kwargs for the sake of this example.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAftcJCXxp5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params_model = dict(\n",
        "    new_cases_obs=new_cases_obs[:],\n",
        "    data_begin=bd,\n",
        "    fcast_len=num_days_forecast,\n",
        "    diff_data_sim=diff_data_sim,\n",
        "    N_population=31072940,\n",
        ")\n",
        "# Median of the prior for the delay in case reporting, we assume 10 days\n",
        "pr_delay = 3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MuExFI-Fxp5l",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "The model is specified in a context. Each function in this context\n",
        "has access to the model parameters set.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ls6H5a4bxp5n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "6331b444-a6fb-4294-eb77-d547431634ed"
      },
      "source": [
        "with cov19.model.Cov19Model(**params_model) as this_model:\n",
        "    # Create the an array of the time dependent infection rate lambda\n",
        "    lambda_t_log = cov19.model.lambda_t_with_sigmoids(\n",
        "        pr_median_lambda_0=0.4,\n",
        "        pr_sigma_lambda_0=0.5,\n",
        "        change_points_list=change_points,  # The change point priors we constructed earlier\n",
        "        name_lambda_t=\"lambda_t\",  # Name for the variable in the trace (see later)\n",
        "    )\n",
        "\n",
        "    # set prior distribution for the recovery rate\n",
        "    mu = pm.Lognormal(name=\"mu\", mu=np.log(1 / 8), sigma=0.2)\n",
        "\n",
        "    # This builds a decorrelated prior for I_begin for faster inference.\n",
        "    # It is not necessary to use it, one can simply remove it and use the default argument\n",
        "    # for pr_I_begin in cov19.SIR\n",
        "    prior_I = cov19.model.uncorrelated_prior_I(\n",
        "        lambda_t_log=lambda_t_log,\n",
        "        mu=mu,\n",
        "        pr_median_delay=pr_delay,\n",
        "        name_I_begin=\"I_begin\",\n",
        "        name_I_begin_ratio_log=\"I_begin_ratio_log\",\n",
        "        pr_sigma_I_begin=2,\n",
        "        n_data_points_used=5,\n",
        "    )\n",
        "\n",
        "    # Use lambda_t_log and mu to run the SIR model\n",
        "    new_cases = cov19.model.SIR(\n",
        "        lambda_t_log=lambda_t_log,\n",
        "        mu=mu,\n",
        "        name_new_I_t=\"new_I_t\",\n",
        "        name_I_t=\"I_t\",\n",
        "        name_I_begin=\"I_begin\",\n",
        "        pr_I_begin=prior_I,\n",
        "    )\n",
        "\n",
        "    # Delay the cases by a lognormal reporting delay\n",
        "    new_cases = cov19.model.delay_cases(\n",
        "        cases=new_cases,\n",
        "        name_cases=\"delayed_cases\",\n",
        "        name_delay=\"delay\",\n",
        "        name_width=\"delay-width\",\n",
        "        pr_mean_of_median=pr_delay,\n",
        "        pr_sigma_of_median=0.2,\n",
        "        pr_median_of_width=0.3,\n",
        "    )\n",
        "\n",
        "    # Modulate the inferred cases by a abs(sin(x)) function, to account for weekend effects\n",
        "    # Also adds the \"new_cases\" variable to the trace that has all model features.\n",
        "    new_cases = cov19.model.week_modulation(\n",
        "        cases=new_cases,\n",
        "        name_cases=\"new_cases\",\n",
        "        name_weekend_factor=\"weekend_factor\",\n",
        "        name_offset_modulation=\"offset_modulation\",\n",
        "        week_modulation_type=\"abs_sine\",\n",
        "        pr_mean_weekend_factor=0.3,\n",
        "        pr_sigma_weekend_factor=0.5,\n",
        "        weekend_days=(6, 7),\n",
        "    )\n",
        "\n",
        "    # Define the likelihood, uses the new_cases_obs set as model parameter\n",
        "    cov19.model.student_t_likelihood(new_cases)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO     [covid19_inference.model.spreading_rate] Lambda_t with sigmoids\n",
            "INFO     [covid19_inference.model.model] pr_median_transient_len was set to default value 4\n",
            "INFO     [covid19_inference.model.model] pr_sigma_transient_len was set to default value 0.5\n",
            "INFO     [covid19_inference.model.model] relative_to_previous was set to default value False\n",
            "INFO     [covid19_inference.model.model] pr_factor_to_previous was set to default value 1\n",
            "INFO     [covid19_inference.model.model] pr_median_transient_len was set to default value 4\n",
            "INFO     [covid19_inference.model.model] pr_sigma_transient_len was set to default value 0.5\n",
            "INFO     [covid19_inference.model.model] relative_to_previous was set to default value False\n",
            "INFO     [covid19_inference.model.model] pr_factor_to_previous was set to default value 1\n",
            "INFO     [covid19_inference.model.model] pr_median_transient_len was set to default value 4\n",
            "INFO     [covid19_inference.model.model] pr_sigma_transient_len was set to default value 0.5\n",
            "INFO     [covid19_inference.model.model] relative_to_previous was set to default value False\n",
            "INFO     [covid19_inference.model.model] pr_factor_to_previous was set to default value 1\n",
            "INFO     [covid19_inference.model.compartmental_models] Uncorrelated prior_I\n",
            "INFO     [covid19_inference.model.compartmental_models] SIR\n",
            "INFO     [covid19_inference.model.delay] Delaying cases\n",
            "INFO     [covid19_inference.model.week_modulation] Week modulation\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zu75Zmdxp5s",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## MCM sampling\n",
        "\n",
        "After the model is built, it is sampled using an MCMC sampler.\n",
        "The number of parallel runs can be set with the argument `cores=`.\n",
        "In particular, due to a bug in Theano, Windows users should set `cores=1`.\n",
        "The sampling can take a long time.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p2UeQDwxp5t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "642d42ca-9af9-4624-b83d-ba4305b69036"
      },
      "source": [
        "trace = pm.sample(model=this_model, cores=4, tune=50, draws=100, init=\"advi+adapt_diag\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Only 100 samples in chain.\n",
            "WARNING  [pymc3] Only 100 samples in chain.\n",
            "Auto-assigning NUTS sampler...\n",
            "INFO     [pymc3] Auto-assigning NUTS sampler...\n",
            "Initializing NUTS using advi+adapt_diag...\n",
            "INFO     [pymc3] Initializing NUTS using advi+adapt_diag...\n",
            "Average Loss = 907.13:  12%|█▏        | 23194/200000 [04:02<30:51, 95.50it/s] \n",
            "Convergence achieved at 23200\n",
            "INFO     [pymc3.variational.inference] Convergence achieved at 23200\n",
            "Interrupted at 23,199 [11%]: Average Loss = 988.66\n",
            "INFO     [pymc3.variational.inference] Interrupted at 23,199 [11%]: Average Loss = 988.66\n",
            "Multiprocess sampling (4 chains in 4 jobs)\n",
            "INFO     [pymc3] Multiprocess sampling (4 chains in 4 jobs)\n",
            "NUTS: [sigma_obs, offset_modulation_rad, weekend_factor_log, delay_log, I_begin_ratio_log, mu, transient_len_3_log_, transient_len_2_log_, transient_len_1_log_, transient_day_3, transient_day_2, transient_day_1, lambda_3_log_, lambda_2_log_, lambda_1_log_, lambda_0_log_]\n",
            "INFO     [pymc3] NUTS: [sigma_obs, offset_modulation_rad, weekend_factor_log, delay_log, I_begin_ratio_log, mu, transient_len_3_log_, transient_len_2_log_, transient_len_1_log_, transient_day_3, transient_day_2, transient_day_1, lambda_3_log_, lambda_2_log_, lambda_1_log_, lambda_0_log_]\n",
            "Sampling 4 chains: 100%|██████████| 600/600 [36:43<00:00,  3.67s/draws]\n",
            "The acceptance probability does not match the target. It is 0.8886217394754838, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "WARNING  [pymc3] The acceptance probability does not match the target. It is 0.8886217394754838, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n",
            "WARNING  [pymc3] The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n",
            "The acceptance probability does not match the target. It is 0.5803599065398277, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "WARNING  [pymc3] The acceptance probability does not match the target. It is 0.5803599065398277, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n",
            "WARNING  [pymc3] The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n",
            "The acceptance probability does not match the target. It is 0.8995343942084186, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "WARNING  [pymc3] The acceptance probability does not match the target. It is 0.8995343942084186, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "The acceptance probability does not match the target. It is 0.6368663727855508, but should be close to 0.8. Try to increase the number of tuning steps.\n",
            "WARNING  [pymc3] The acceptance probability does not match the target. It is 0.6368663727855508, but should be close to 0.8. Try to increase the number of tuning steps.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySG6AvY3S8yZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "df7b6e0a-a34e-42ef-852e-00867be2f8e6"
      },
      "source": [
        "pm.save_trace(trace)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'.pymc_3.trace'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQamiR6cNDSh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " trace1 = pm.load_trace('.pymc_1.trace',model=this_model)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JYZGHHgJcUA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " trace2 = pm.load_trace('.pymc_2.trace',model=this_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TzuyI71NITy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " trace3 = pm.load_trace('.pymc_3.trace',model=this_model)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiRkc3qEkRkU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "st9Qnasckfjv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle_out = open(\"trace1_5400samples.pickle\",\"wb\")\n",
        "pickle.dump(trace1, pickle_out)\n",
        "pickle_out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oAF5bJhMk-Su",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle_out = open(\"trace1_600samples.pickle\",\"wb\")\n",
        "pickle.dump(trace2, pickle_out)\n",
        "pickle_out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bw5d_trklF2W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle_out = open(\"trace2_600samples.pickle\",\"wb\")\n",
        "pickle.dump(trace3, pickle_out)\n",
        "pickle_out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Daah4-a3xp5y",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Plotting\n",
        "Plotting tools are rudimentary right now. But one can always write custom plotting function\n",
        "by accessing the samples stored in the trace.\n",
        "\n",
        "### Distributions\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVoXZf3klFIH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "vbMnwUp6xp5z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "301faebc-c161-4a09-f270-c8b5c9cc8470"
      },
      "source": [
        "fig, axes = plt.subplots(6, 3, figsize=(6, 6.4))\n",
        "varnames = varnames = this_model.free_RVs #this_model.untransformed_freeRVs\n",
        "print(\"Possible varnames are :\")\n",
        "print(varnames)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Possible varnames are :\n",
            "[lambda_0_log_, lambda_1_log_, lambda_2_log_, lambda_3_log_, transient_day_1, transient_day_2, transient_day_3, transient_len_1_log_, transient_len_2_log_, transient_len_3_log_, mu_log__, I_begin_ratio_log, delay_log, weekend_factor_log, offset_modulation_rad_circular__, sigma_obs_log__]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x460.8 with 18 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GwvhiRexp55",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # left column\n",
        "    [\"weekend_factor\", \"mu\", \"lambda_0\", \"lambda_1\", \"lambda_2\", \"lambda_3\"]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace, key, ax=axes[i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUFovkwQNP19",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # left column\n",
        "    [\"weekend_factor\", \"mu\", \"lambda_0\", \"lambda_1\", \"lambda_2\"]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace1, key, ax=axes[i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QApdSe_hNRYx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # left column\n",
        "    [\"weekend_factor\", \"mu\", \"lambda_0\", \"lambda_1\", \"lambda_2\", \"lambda_3\"]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace2, key, ax=axes[i, 0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxBZbnzKxp5_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # mid column\n",
        "    [\n",
        "        \"offset_modulation\",\n",
        "        \"sigma_obs\",\n",
        "        \"I_begin\",\n",
        "        \"transient_day_1\",\n",
        "        \"transient_day_2\",\n",
        "        \"transient_day_3\",\n",
        "    ]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace, key, ax=axes[i, 1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsRRlX4eNWpE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # mid column\n",
        "    [\n",
        "        \"offset_modulation\",\n",
        "        \"sigma_obs\",\n",
        "        \"I_begin\",\n",
        "        \"transient_day_1\",\n",
        "        \"transient_day_2\",\n",
        "        # \"transient_day_3\",\n",
        "    ]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace1, key, ax=axes[i, 1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zhXcECTNWxn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # mid column\n",
        "    [\n",
        "        \"offset_modulation\",\n",
        "        \"sigma_obs\",\n",
        "        \"I_begin\",\n",
        "        \"transient_day_1\",\n",
        "        \"transient_day_2\",\n",
        "        \"transient_day_3\",\n",
        "    ]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace2, key, ax=axes[i, 1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2M_hkb5Zxp6D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i, key in enumerate(\n",
        "    # right column\n",
        "    [\"delay\", \"transient_len_1\", \"transient_len_2\", \"transient_len_3\",]\n",
        "):\n",
        "    cov19.plot._distribution(this_model, trace, key, ax=axes[i + 2, 2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BakaguS1xp6J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "97bd7f64-f6f1-4309-b511-e8175704edd4"
      },
      "source": [
        "fig.tight_layout()\n",
        "fig #To print in jupyter notebook"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x460.8 with 18 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XeyDSY-xp6P",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Timeseries\n",
        "timeseries overview, for now needs an offset variable to get cumulative cases\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxaBRuQXxp6Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "9f55dd41-e276-418c-892c-11a3f8ce259a"
      },
      "source": [
        "fig, axes = cov19.plot.timeseries_overview(this_model, trace, offset=total_cases_obs[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSZrlefBQDXA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "9cb90451-c370-4d02-ac96-ca750ee794e3"
      },
      "source": [
        "fig, axes = cov19.plot.timeseries_overview(this_model, trace1, offset=total_cases_obs[0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtUpnNHzxp6U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}