Coronavirus_in_Indonesia_Analysis_ver1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Coronavirus in Indonesia Analysis-ver1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNRmyzvvpH5hfQHHtpgqf9G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/IqbalLx/Coronavirus-in-Indonesia-Analysis/blob/master/Coronavirus_in_Indonesia_Analysis_ver1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gTSQdnqLbXX",
        "colab_type": "text"
      },
      "source": [
        "# Preparing the Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eba9qJaXLNGE",
        "colab_type": "code",
        "outputId": "2838d756-6aaf-466b-d93c-cb5ddd422897",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "files = files.upload() #Data from kawalcovid19.id"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3dc0d288-eadb-463c-b2b2-a7fd5016fbe9\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-3dc0d288-eadb-463c-b2b2-a7fd5016fbe9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Kasus Harian 21 Apr.csv to Kasus Harian 21 Apr.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSwklAqvLjX1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.svm import SVR\n",
        "import datetime\n",
        "from scipy.integrate import odeint\n",
        "import plotly.graph_objs as go\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1osisXMSdvLr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "472382d7-9fc8-4182-d337-03aebcd09335"
      },
      "source": [
        "filename = [key for key in files.keys()][0]\n",
        "print(filename)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Kasus Harian 21 Apr.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K57j_wwkLvKr",
        "colab_type": "code",
        "outputId": "e07bd926-83b0-4afb-b727-16bf83432eba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv(filename)\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DT</th>\n",
              "      <th>Kasus (Kumulatif)</th>\n",
              "      <th>Kasus Baru</th>\n",
              "      <th>Sembuh (baru)</th>\n",
              "      <th>Meninggal (baru)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-03 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-04 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-05 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-06 00:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    DT  Kasus (Kumulatif)  ...  Sembuh (baru)  Meninggal (baru)\n",
              "0  2020-03-02 00:00:00                  2  ...            NaN               NaN\n",
              "1  2020-03-03 00:00:00                  2  ...            NaN               NaN\n",
              "2  2020-03-04 00:00:00                  2  ...            NaN               NaN\n",
              "3  2020-03-05 00:00:00                  2  ...            NaN               NaN\n",
              "4  2020-03-06 00:00:00                  4  ...            NaN               NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQhfE8vaLz6L",
        "colab_type": "code",
        "outputId": "4a2c8148-6768-4df5-eb3a-c7b39227e0b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = df.fillna(0)\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DT</th>\n",
              "      <th>Kasus (Kumulatif)</th>\n",
              "      <th>Kasus Baru</th>\n",
              "      <th>Sembuh (baru)</th>\n",
              "      <th>Meninggal (baru)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-03 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-04 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-05 00:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-06 00:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    DT  Kasus (Kumulatif)  ...  Sembuh (baru)  Meninggal (baru)\n",
              "0  2020-03-02 00:00:00                  2  ...            0.0               0.0\n",
              "1  2020-03-03 00:00:00                  2  ...            0.0               0.0\n",
              "2  2020-03-04 00:00:00                  2  ...            0.0               0.0\n",
              "3  2020-03-05 00:00:00                  2  ...            0.0               0.0\n",
              "4  2020-03-06 00:00:00                  4  ...            0.0               0.0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CE2zlJopMWDc",
        "colab_type": "code",
        "outputId": "a0aaf31e-a1b6-4ee4-94c5-72e0f39f922a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51 entries, 0 to 50\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   DT                 51 non-null     object \n",
            " 1   Kasus (Kumulatif)  51 non-null     int64  \n",
            " 2   Kasus Baru         51 non-null     float64\n",
            " 3   Sembuh (baru)      51 non-null     float64\n",
            " 4   Meninggal (baru)   51 non-null     float64\n",
            "dtypes: float64(3), int64(1), object(1)\n",
            "memory usage: 2.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1xK_r13MBWm",
        "colab_type": "code",
        "outputId": "a036d268-f31f-4c1d-cad8-2ed95a747c9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.DT = df.DT.str[:10]\n",
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DT</th>\n",
              "      <th>Kasus (Kumulatif)</th>\n",
              "      <th>Kasus Baru</th>\n",
              "      <th>Sembuh (baru)</th>\n",
              "      <th>Meninggal (baru)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           DT  Kasus (Kumulatif)  Kasus Baru  Sembuh (baru)  Meninggal (baru)\n",
              "0  2020-03-02                  2         2.0            0.0               0.0\n",
              "1  2020-03-03                  2         0.0            0.0               0.0\n",
              "2  2020-03-04                  2         0.0            0.0               0.0\n",
              "3  2020-03-05                  2         0.0            0.0               0.0\n",
              "4  2020-03-06                  4         2.0            0.0               0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NykKc_43MJz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.DT = pd.to_datetime(df.DT, format=\"%Y-%m-%d\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKDZyoflMRRB",
        "colab_type": "code",
        "outputId": "363bb933-8d01-4531-eae0-6ece58da12e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51 entries, 0 to 50\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   DT                 51 non-null     datetime64[ns]\n",
            " 1   Kasus (Kumulatif)  51 non-null     int64         \n",
            " 2   Kasus Baru         51 non-null     float64       \n",
            " 3   Sembuh (baru)      51 non-null     float64       \n",
            " 4   Meninggal (baru)   51 non-null     float64       \n",
            "dtypes: datetime64[ns](1), float64(3), int64(1)\n",
            "memory usage: 2.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVhhEFDRejCg",
        "colab_type": "text"
      },
      "source": [
        "# Overview Cases in Indonesia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sXcnV9fGYgrA",
        "colab": {}
      },
      "source": [
        "x, y = np.array(range(len(df.DT))), np.array(df['Kasus Baru'].values)\n",
        "x, y = x.reshape(-1, 1), y.reshape(-1, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "eae5c4e9-de3e-4e00-ef24-f66fc17665c5",
        "id": "AdWJk1YVYfQZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "model = SVR(kernel='poly')\n",
        "model.fit(x, y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ql1_8NRBYeZK",
        "colab": {}
      },
      "source": [
        "line = model.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkj8TDx-M9v7",
        "colab_type": "code",
        "outputId": "bc54646b-850f-4e65-cfd3-2143edb51205",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "name = ['New Cases', 'Died', 'Recovered']\n",
        "bar = [go.Bar(\n",
        "        x=df.DT,\n",
        "        y=df[column],\n",
        "        name=name[i],\n",
        "    ) for i, column in enumerate(['Kasus Baru', 'Meninggal (baru)', 'Sembuh (baru)'])]\n",
        "\n",
        "reg = go.Scatter(\n",
        "    x=df.DT,\n",
        "    y=line,\n",
        "    line={\n",
        "        'color':'green',\n",
        "        'dash': 'dash',\n",
        "        'width': 3\n",
        "    },\n",
        "    name='Trendline'\n",
        ")\n",
        "\n",
        "bar.append(reg)\n",
        "\n",
        "layout = {\n",
        "    'title':{\n",
        "        'text': 'Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Num of Cases',\n",
        "    },\n",
        "    'legend':{\n",
        "        'orientation': 'h',\n",
        "        'x': 0.5,\n",
        "        'y': 1, #y itu 1 diatas, 0 dibawah\n",
        "        'xanchor': 'center',\n",
        "    }\n",
        "}\n",
        "\n",
        "fig = go.Figure(data=bar, layout=layout)\n",
        "fig.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"320e638f-51d1-444b-93af-e1450afc1107\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oS3eHKd7Tj35",
        "colab_type": "code",
        "outputId": "f3733947-b665-41cc-f4c4-57cc0e3a6b61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "x, y = np.array(range(len(df.DT))), np.array(df['Kasus (Kumulatif)'].values)\n",
        "x, y = x.reshape(-1, 1), y.reshape(-1, 1)\n",
        "\n",
        "model = SVR(kernel='poly')\n",
        "model.fit(x, y)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRFDINNRW5Tq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Extending Current Date for Prediction\n",
        "dt = datetime.datetime(2020, 3, 2)\n",
        "end = datetime.datetime(2020, 12, 30)\n",
        "step = datetime.timedelta(days=1)\n",
        "\n",
        "date_series = []\n",
        "\n",
        "while dt < end:\n",
        "    date_series.append(dt.strftime('%Y-%m-%d'))\n",
        "    dt += step"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmkMq-IRUIol",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "worst_case = date_series[0:100]\n",
        "x_pred = np.array(range((len(worst_case))))\n",
        "\n",
        "line2= model.predict(x_pred.reshape(-1, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5puvW5qNgy_",
        "colab_type": "code",
        "outputId": "2213be85-e7bc-4f51-b567-3857c250a750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "kum = go.Scatter(\n",
        "    x=df.DT,\n",
        "    y=df['Kasus (Kumulatif)'],\n",
        "    line={\n",
        "        'color':'green',\n",
        "        'width': 3\n",
        "    },\n",
        "    mode='markers',\n",
        "    name='Total Infected from kawalcovid.id'\n",
        ")\n",
        "\n",
        "worst_line = go.Scatter(\n",
        "    x=worst_case,\n",
        "    y=line2,\n",
        "    line={\n",
        "        'color':'orange',\n",
        "        'width': 1,\n",
        "        'dash': 'dash'\n",
        "    },\n",
        "    mode='lines',\n",
        "    name='Prediction of Total Infected'\n",
        ")\n",
        "\n",
        "layout2 = {\n",
        "    'title':{\n",
        "        'text': 'Exponential Regression for Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Infected',\n",
        "    },\n",
        "    'legend':{\n",
        "        'orientation': 'h',\n",
        "        'x': 0.5,\n",
        "        'y': 1, #y itu 1 diatas, 0 dibawah\n",
        "        'xanchor': 'center',\n",
        "    }\n",
        "}\n",
        "\n",
        "fig2 = go.Figure([worst_line, kum], layout=layout2)\n",
        "fig2.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ci7sTEB3Zruq",
        "colab_type": "text"
      },
      "source": [
        "# SEIRD Model Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZM1-h478ZuFm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def seird(y, t, N, gamma, beta, delta, rho, alpha):\n",
        "  S, E, I, R, D = y\n",
        "  dS = - beta(t) * I * S / N\n",
        "  dE = -dS - delta * E\n",
        "  dR = gamma * (1- alpha) * I\n",
        "  dD = rho * alpha * I\n",
        "  dI =  delta * E - (dD + dR)\n",
        "  return dS, dE, dI, dR, dD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqtxM0Gjau64",
        "colab_type": "code",
        "outputId": "0340670b-5060-46aa-a7ec-ebae4eddf1d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "N= 40_000_000\n",
        "alpha= 0.2\n",
        "gamma= 0.1\n",
        "delta = 1/14\n",
        "rho= 1/21\n",
        "\n",
        "R_0_start, k, x0, R_0_end = 10, 0.05, 30, 0.05\n",
        "logistic_R_0 = lambda t: (R_0_start-R_0_end) / (1 + np.exp(-k*(-t+x0))) + R_0_end\n",
        "\n",
        "beta = lambda t: logistic_R_0(t) * gamma\n",
        "\n",
        "E0, I0, R0, D0 = 2, 2, 0, 0\n",
        "S0 = N-(E0+I0+R0+D0) \n",
        "\n",
        "t = np.linspace(0, 300, 300)\n",
        "y0 = S0, E0, I0, R0, D0\n",
        "\n",
        "seird_res = odeint(seird, y0, t, args=(N, gamma, beta, delta, rho, alpha))\n",
        "S, E, I, R, D= seird_res.T\n",
        "\n",
        "plt.plot(t, beta(t), label='Beta')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYVA9CCeSocH",
        "colab_type": "code",
        "outputId": "56ca725b-aaf4-4562-8f88-6ae2e923eecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "name = ['Exposed', 'Infected', 'Recovered', 'Death']\n",
        "data = [go.Scatter(\n",
        "    x=date_series,\n",
        "    y=ode,\n",
        "    name=name[i]\n",
        ") for i, ode in enumerate([E, I, R, D])]\n",
        "\n",
        "layout3 = {\n",
        "    'title':{\n",
        "        'text': 'SEIRD Model for Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Cases',\n",
        "    },\n",
        "}\n",
        "\n",
        "fig = go.Figure(data=data, layout=layout3)\n",
        "fig.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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              "            <script type=\"text/javascript\">\n",
              "                \n",
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              "                    Plotly.newPlot(\n",
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              "                        {\"responsive\": true}\n",
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIRIYJ8JmcNe",
        "colab_type": "code",
        "outputId": "37da3911-f09d-4022-a0f6-7aa8f34e9bb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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      },
      "source": [
        "data = go.Scatter(\n",
        "    x=date_series,\n",
        "    y=I,\n",
        "    name='Prediction of Total Infected',\n",
        "    line={'dash':'dash'}\n",
        ")\n",
        "\n",
        "real_data = go.Scatter(\n",
        "    x=date_series,\n",
        "    y=df['Kasus Baru'],\n",
        "    name='Total Infected from kawalcovid.id',\n",
        "    mode='markers',\n",
        "    marker={'symbol':'diamond'}\n",
        ")\n",
        "\n",
        "layout3 = {\n",
        "    'title':{\n",
        "        'text': 'SEIRD Model for Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Infected',\n",
        "        #'range': [-10, 500],\n",
        "    },\n",
        "}\n",
        "\n",
        "fig = go.Figure(data=[data, real_data], layout=layout3)\n",
        "fig.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
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              "        </div>\n",
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          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-rdgkUhS3mR",
        "colab_type": "code",
        "outputId": "a9a0dd35-8322-4c89-9cb9-7cdb2d876cca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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      },
      "source": [
        "data = go.Scatter(\n",
        "    x=date_series[0:120],\n",
        "    y=I,\n",
        "    name='Prediction of Total Infected',\n",
        "    line={\n",
        "        'dash':'dash'\n",
        "    }\n",
        ")\n",
        "\n",
        "real_data = go.Scatter(\n",
        "    x=date_series[0:120],\n",
        "    y=df['Kasus Baru'],\n",
        "    name='Total Infected from kawalcovid.id',\n",
        "    mode='markers',\n",
        "    marker={'symbol':'diamond'}\n",
        ")\n",
        "\n",
        "layout3 = {\n",
        "    'title':{\n",
        "        'text': 'SEIRD Model for Coronavirus in Indonesia',\n",
        "        'x':0.5, #range x axis 0 - 1\n",
        "    },\n",
        "    'xaxis':{\n",
        "        'title': 'Date',\n",
        "        \n",
        "    },\n",
        "    'yaxis':{\n",
        "        'title': 'Total Infected',\n",
        "        #'range': [-10, 500],\n",
        "    },\n",
        "}\n",
        "\n",
        "fig = go.Figure(data=[data, real_data], layout=layout3)\n",
        "fig.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}