COVID_19_SIR_Model_Estimation.ipynb

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      "name": "COVID-19 SIR Model Estimation.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/couedrao/COVID_19_SIR_Model_Estimation/blob/master/COVID_19_SIR_Model_Estimation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "TnxR5gyqUV4T",
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      "source": [
        "# $\\beta$ and $\\gamma$ by Modeling :\n",
        "\n",
        "> In [Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions](https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v2.full.pdf)\n",
        "\n",
        "> In [A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action](https://www.ijidonline.com/article/S1201-9712(20)30117-X/fulltext)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "NotMBJhgH8Dj",
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      "source": [
        "# $\\beta$ and $\\gamma$ by Estimation from previous data as in this [WHO paper](https://www.who.int/bulletin/online_first/20-255158.pdf)\n",
        "\n",
        "> \\begin{align}\n",
        "& \\frac{dS}{dt} = - \\frac{\\beta I S}{N}, \\\\[6pt]\n",
        "& \\frac{dI}{dt} = \\frac{\\beta I S}{N}- \\gamma I, \\\\[6pt]\n",
        "& \\frac{dR}{dt} = \\gamma I,\n",
        "\\end{align} where <math>S</math> is the stock of susceptible population, <math>I</math> is the stock of infected, <math>R</math> is the stock of recovered population, <math>N</math> is the sum of these three, $\\beta$ is the infection rate of covid19, and $\\gamma$ is the recovery rate.\n",
        "\n",
        "> What we are going to do is estimate the β and γ to fit the SIR model with the actual confirmed cases (the number of infectious people). To solve the ordinary differential equation like the SIR model, we can use solve_ivp function in scipy module.\n",
        "\n",
        "\n",
        "Code source based on [1] (https://github.com/Lewuathe/COVID19-SIR)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0q8dJ5aJI13m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.integrate import solve_ivp\n",
        "from scipy.optimize import minimize\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import timedelta, datetime\n",
        "import sys\n",
        "import json\n",
        "import ssl\n",
        "import urllib.request"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaB6STYTJ459",
        "colab_type": "code",
        "outputId": "06d3289d-e9e2-4526-978c-77aa23e49967",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        }
      },
      "source": [
        "! mkdir data \n",
        "%cd data\n",
        "! wget https://raw.githubusercontent.com/Lewuathe/COVID19-SIR/master/data_url.json"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/data\n",
            "--2020-03-31 19:44:42--  https://raw.githubusercontent.com/Lewuathe/COVID19-SIR/master/data_url.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 589 [text/plain]\n",
            "Saving to: ‘data_url.json’\n",
            "\n",
            "data_url.json       100%[===================>]     589  --.-KB/s    in 0s      \n",
            "\n",
            "2020-03-31 19:44:47 (153 MB/s) - ‘data_url.json’ saved [589/589]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GEqeBeiJINK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Learner(object):\n",
        "    def __init__(self, country, loss, start_date, predict_range,s_0, i_0, r_0):\n",
        "        self.country = country\n",
        "        self.loss = loss\n",
        "        self.start_date = start_date\n",
        "        self.predict_range = predict_range\n",
        "        self.s_0 = s_0\n",
        "        self.i_0 = i_0\n",
        "        self.r_0 = r_0\n",
        "\n",
        "\n",
        "    def load_confirmed(self, country):\n",
        "        df = pd.read_csv('time_series_19-covid-Confirmed-country.csv')\n",
        "        country_df = df[df['Country/Region'] == country]\n",
        "        return country_df.iloc[0].loc[self.start_date:]\n",
        "\n",
        "\n",
        "    def load_recovered(self, country):\n",
        "        df = pd.read_csv('time_series_19-covid-Recovered-country.csv')\n",
        "        country_df = df[df['Country/Region'] == country]\n",
        "        return country_df.iloc[0].loc[self.start_date:]\n",
        "\n",
        "\n",
        "    def load_dead(self, country):\n",
        "        df = pd.read_csv('time_series_19-covid-Deaths-country.csv')\n",
        "        country_df = df[df['Country/Region'] == country]\n",
        "        return country_df.iloc[0].loc[self.start_date:]\n",
        "    \n",
        "\n",
        "    def extend_index(self, index, new_size):\n",
        "        values = index.values\n",
        "        current = datetime.strptime(index[-1], '%m/%d/%y')\n",
        "        while len(values) < new_size:\n",
        "            current = current + timedelta(days=1)\n",
        "            values = np.append(values, datetime.strftime(current, '%m/%d/%y'))\n",
        "        return values\n",
        "    \n",
        "    \n",
        "    def predict(self, beta, gamma, data, recovered, death, country, s_0, i_0, r_0):\n",
        "        new_index = self.extend_index(data.index, self.predict_range)\n",
        "        size = len(new_index)\n",
        "        def SIR(t, y):\n",
        "            S = y[0]\n",
        "            I = y[1]\n",
        "            R = y[2]\n",
        "            return [-beta*S*I, beta*S*I-gamma*I, gamma*I]\n",
        "        extended_actual = np.concatenate((data.values, [None] * (size - len(data.values))))\n",
        "        extended_recovered = np.concatenate((recovered.values, [None] * (size - len(recovered.values))))\n",
        "        extended_death = np.concatenate((death.values, [None] * (size - len(death.values))))\n",
        "        return new_index, extended_actual, extended_recovered, extended_death, solve_ivp(SIR, [0, size], [s_0,i_0,r_0], t_eval=np.arange(0, size, 1))\n",
        "\n",
        "    # Find $\\beta$ and $\\gamma$\n",
        "    def train(self):\n",
        "        recovered = self.load_recovered(self.country)\n",
        "        death = self.load_dead(self.country)\n",
        "        data = (self.load_confirmed(self.country) - recovered - death)\n",
        "        optimal = minimize(loss, [0.001, 0.001], args=(data, recovered, self.s_0, self.i_0, self.r_0), method='L-BFGS-B', bounds=[(0.00000001, 0.4), (0.00000001, 0.4)])\n",
        "        #print(optimal)\n",
        "        beta, gamma = optimal.x\n",
        "        new_index, extended_actual, extended_recovered, extended_death, prediction = self.predict(beta, gamma, data, recovered, death, self.country, self.s_0, self.i_0, self.r_0)\n",
        "        df = pd.DataFrame({'Infected data': extended_actual, 'Recovered data': extended_recovered, 'Death data': extended_death, 'Susceptible': prediction.y[0], 'Infected': prediction.y[1], 'Recovered': prediction.y[2]}, index=new_index)\n",
        "        fig, ax = plt.subplots(figsize=(15, 10))\n",
        "        ax.set_title(self.country)\n",
        "        df.plot(ax=ax)\n",
        "        print(f\"country={self.country}, beta={beta:.8f}, gamma={gamma:.8f}, r_0:{(beta/gamma):.8f}\")\n",
        "        fig.savefig(f\"{self.country}.png\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0TB4rkQGT7y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_province(input_file, output_file):\n",
        "    input = open(input_file, \"r\")\n",
        "    output = open(output_file, \"w\")\n",
        "    output.write(input.readline())\n",
        "    for line in input:\n",
        "        if line.lstrip().startswith(\",\"):\n",
        "            output.write(line)\n",
        "    input.close()\n",
        "    output.close()\n",
        "  \n",
        "\n",
        "def download_data(url_dictionary):\n",
        "    #Lets download the files\n",
        "    for url_title in url_dictionary.keys():\n",
        "        urllib.request.urlretrieve(url_dictionary[url_title], url_title)\n",
        "\n",
        "\n",
        "def load_json(json_file_str):\n",
        "    # Loads  JSON into a dictionary or quits the program if it cannot.\n",
        "    try:\n",
        "        with open(json_file_str, \"r\") as json_file:\n",
        "            json_variable = json.load(json_file)\n",
        "            return json_variable\n",
        "    except Exception:\n",
        "        sys.exit(\"Cannot open JSON file: \" + json_file_str)\n",
        "\n",
        "\n",
        "def loss(point, data, recovered, s_0, i_0, r_0):\n",
        "    size = len(data)\n",
        "    beta, gamma = point\n",
        "    def SIR(t, y):\n",
        "        S = y[0]\n",
        "        I = y[1]\n",
        "        R = y[2]\n",
        "        return [-beta*S*I, beta*S*I-gamma*I, gamma*I]\n",
        "    solution = solve_ivp(SIR, [0, size], [s_0,i_0,r_0], t_eval=np.arange(0, size, 1), vectorized=True)\n",
        "    l1 = np.sqrt(np.mean((solution.y[1] - data)**2))\n",
        "    l2 = np.sqrt(np.mean((solution.y[2] - recovered)**2))\n",
        "    alpha = 0.1\n",
        "    return alpha * l1 + (1 - alpha) * l2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "id3soFS1JcwY",
        "colab_type": "code",
        "outputId": "c18b80b0-2081-45aa-8ca9-06108abc8ba6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        }
      },
      "source": [
        "def main():\n",
        "\n",
        "    countries=[\"France\"]#, \"US\", \"Venezuela\"]\n",
        "    download=True\n",
        "    startdate=\"1/22/20\"\n",
        "    predict_range=150\n",
        "    s_0=200000\n",
        "    i_0 = 2\n",
        "    r_0 = 10\n",
        "\n",
        "    if download:\n",
        "        data_d = load_json(\"data_url.json\")\n",
        "        download_data(data_d)\n",
        "\n",
        "    remove_province('time_series_19-covid-Confirmed.csv', 'time_series_19-covid-Confirmed-country.csv')\n",
        "    remove_province('time_series_19-covid-Recovered.csv', 'time_series_19-covid-Recovered-country.csv')\n",
        "    remove_province('time_series_19-covid-Deaths.csv', 'time_series_19-covid-Deaths-country.csv')\n",
        "\n",
        "    for country in countries:\n",
        "        learner = Learner(country, loss, startdate, predict_range, s_0, i_0, r_0)\n",
        "        learner.train()   \n",
        "\n",
        "if __name__ == '__main__':\n",
        "    main()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "country=France, beta=0.00000073, gamma=0.00000015, r_0:4.89027591\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jdBS0g62fFqg0v1rnXTFYbi9/rgBAa+EbFQDQKXgyM5V1552q/miZfMuWOR0H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\ndrfWvmStzbHWTrDW9rfWTjxR5jyzf95lre1rrY2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LP9QWlpoKxlC4/D9NcnwREWnZVABF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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}