model-extended_v5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 10000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>Date</th>\n",
       "      <th>Uncovered</th>\n",
       "      <th>Du</th>\n",
       "      <th>Ru</th>\n",
       "      <th>Exposed</th>\n",
       "      <th>Isolated</th>\n",
       "      <th>Infected</th>\n",
       "      <th>Di</th>\n",
       "      <th>Ri</th>\n",
       "      <th>Apathetic</th>\n",
       "      <th>Da</th>\n",
       "      <th>Ra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24-03-20</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>585</td>\n",
       "      <td>10</td>\n",
       "      <td>575</td>\n",
       "      <td>15</td>\n",
       "      <td>549</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25-03-20</td>\n",
       "      <td>57</td>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>726</td>\n",
       "      <td>12</td>\n",
       "      <td>714</td>\n",
       "      <td>35</td>\n",
       "      <td>656</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26-03-20</td>\n",
       "      <td>71</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>818</td>\n",
       "      <td>20</td>\n",
       "      <td>798</td>\n",
       "      <td>71</td>\n",
       "      <td>693</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27-03-20</td>\n",
       "      <td>83</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>990</td>\n",
       "      <td>21</td>\n",
       "      <td>969</td>\n",
       "      <td>114</td>\n",
       "      <td>817</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28-03-20</td>\n",
       "      <td>97</td>\n",
       "      <td>15</td>\n",
       "      <td>83</td>\n",
       "      <td>1108</td>\n",
       "      <td>24</td>\n",
       "      <td>1084</td>\n",
       "      <td>152</td>\n",
       "      <td>886</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  Uncovered  Du  Ru  Exposed  Isolated  Infected   Di   Ri  \\\n",
       "0  24-03-20         39   6  34      585        10       575   15  549   \n",
       "1  25-03-20         57  10  48      726        12       714   35  656   \n",
       "2  26-03-20         71  13  59      818        20       798   71  693   \n",
       "3  27-03-20         83  11  73      990        21       969  114  817   \n",
       "4  28-03-20         97  15  83     1108        24      1084  152  886   \n",
       "\n",
       "   Apathetic  Da  Ra  \n",
       "0         11   5   6  \n",
       "1         23   8  15  \n",
       "2         34  10  24  \n",
       "3         38   9  29  \n",
       "4         46  12  34  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data feeding\n",
    "df = pd.read_csv(\"Covid_input_data2.csv\") # dataset\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "Uncovered\n",
      "Du\n",
      "Ru\n",
      "Exposed\n",
      "Isolated\n",
      "Infected\n",
      "Di\n",
      "Ri\n",
      "Apathetic\n",
      "Da\n",
      "Ra\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.columns =['Date', 'Uncovered','Du', 'Ru','Exposed', 'Isolated', 'Infected','Di', 'Ri', 'Apathetic','Da', 'Ra']\n",
    "# for col in df.columns:\n",
    "#     print(col)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available 37 days\n"
     ]
    }
   ],
   "source": [
    "available = len(df.index)\n",
    "print(\"Available\", available, \"days\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = df['Date'].iloc[0]   # first index take push korlam array te.\n",
    "uncovered = df['Uncovered'].values.tolist() \n",
    "Du = df['Du'].values.tolist() \n",
    "Ru = df['Ru'].values.tolist() \n",
    "exposed= df['Exposed'].values.tolist()\n",
    "isolated = df['Isolated'].values.tolist()\n",
    "infected = df['Infected'].values.tolist() \n",
    "Di = df['Di'].values.tolist()\n",
    "Ri = df['Ri'].values.tolist()\n",
    "apathetic = df['Apathetic'].values.tolist() \n",
    "Da = df['Da'].values.tolist() \n",
    "Ra = df['Ra'].values.tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24-03-20\n"
     ]
    }
   ],
   "source": [
    "print(start_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-03-24', '2020-03-25', '2020-03-26', '2020-03-27', '2020-03-28', '2020-03-29', '2020-03-30', '2020-03-31', '2020-04-01', '2020-04-02', '2020-04-03', '2020-04-04', '2020-04-05', '2020-04-06', '2020-04-07', '2020-04-08', '2020-04-09', '2020-04-10', '2020-04-11', '2020-04-12', '2020-04-13', '2020-04-14', '2020-04-15', '2020-04-16', '2020-04-17', '2020-04-18', '2020-04-19', '2020-04-20', '2020-04-21', '2020-04-22', '2020-04-23', '2020-04-24', '2020-04-25', '2020-04-26', '2020-04-27', '2020-04-28', '2020-04-29', '2020-04-30', '2020-05-01', '2020-05-02', '2020-05-03', '2020-05-04', '2020-05-05', '2020-05-06', '2020-05-07', '2020-05-08', '2020-05-09', '2020-05-10', '2020-05-11', '2020-05-12', '2020-05-13', '2020-05-14']\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "#Helper Function\n",
    "\n",
    "t_max = available + 15\n",
    "\n",
    "date = np.array('2020-03-24', dtype=np.datetime64) \n",
    "dates = date + np.arange(t_max)\n",
    "str_dates = []\n",
    "for i in dates:\n",
    "    str_dates.append(str(i))\n",
    "print(str_dates)\n",
    "print(len(str_dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seird_model(init_vals, params, t):\n",
    "\n",
    "    DATE_0, S_0, U_0, Du_0, Ru_0, E_0, Is_0, I_0,  Di_0, Ri_0, A_0, Da_0, Ra_0 = init_vals  \n",
    "    DATE, S, U, Du, Ru, E, Is, I, Di, Ri, A, Da, Ra = [DATE_0] , [S_0] , [U_0] , [Du_0] , [Ru_0] , [E_0] , [Is_0] , [I_0] ,  [Di_0] , [Ri_0] , [A_0] , [Da_0] , [Ra_0]\n",
    "    \n",
    "    alpha, beta, lamda, gamma, rho, delta, miu, omega, eta, sigma, tao = params\n",
    "    \n",
    "    ##### Create next t days ######\n",
    "    date = np.array(DATE, dtype=np.datetime64)\n",
    "    dates = date + np.arange(len(t))\n",
    "    str_dates = []\n",
    "    for i in dates:\n",
    "        str_dates.append(str(i))\n",
    "        \n",
    "    ##### End creating t days #####\n",
    "    for tic in t[0:]:                # 1st din er initial value deya ache tai 1 theke shuru kore baki shob add dilam.\n",
    "\n",
    "        DATE.append(str_dates[tic])\n",
    "\n",
    "        \n",
    "        next_S = S[-1] - ( alpha * (S[-1]  * E[-1] / N ) ) - ( beta * (S[-1]  * U[-1] / N ) )  \n",
    "        next_U = U[-1] + ( beta * (S[-1]  * U[-1] / N ) ) - ( sigma * U[-1] ) - ( tao * U[-1] ) \n",
    "        next_Du = Du[-1] + ( sigma * U[-1] )\n",
    "        next_Ru = Ru[-1] + ( tao * U[-1]) \n",
    "        next_E = E[-1] + ( alpha * (S[-1]  * E[-1] / N ) ) - ( gamma * I[-1] ) - ( lamda * A[-1] ) - ( rho * Is[-1] ) \n",
    "        next_Is = Is[-1] + (rho *  Is[-1] )\n",
    "        next_I = I[-1] + ( gamma * I[-1] ) - ( delta * I[-1] ) - ( miu * I[-1] ) \n",
    "        next_Di = Di[-1] + ( delta * I[-1] )  \n",
    "        next_Ri = Ri[-1] + ( miu * I[-1] )  \n",
    "        next_A = A[-1] + ( lamda * A[-1] ) - ( omega * A[-1] ) - ( eta * A[-1] )\n",
    "        next_Da = Da[-1] + ( omega * A[-1] )\n",
    "        next_Ra = Ra[-1] + ( eta * A[-1] )\n",
    "        \n",
    "        \n",
    "        S.append(next_S)\n",
    "        U.append(next_U)\n",
    "        Du.append(next_Du)                   \n",
    "        Ru.append(next_Ru)\n",
    "        E.append(next_E) \n",
    "        Is.append(next_Is)\n",
    "        I.append(next_I)\n",
    "        Di.append(next_Di)\n",
    "        Ri.append(next_Ri)\n",
    "        A.append(next_A) \n",
    "        Da.append(next_Da)                   \n",
    "        Ra.append(next_Ra)\n",
    "                          \n",
    "\n",
    "    return np.stack([DATE, S, U, Du, Ru, E, Is, I, Di, Ri, A, Da, Ra]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the data for one day rolling window approach\n",
    "\n",
    "train_min = 1  # diffrence ber korar jnno ami 1 din er diffrence k niye call kortesi\n",
    "train_max = available + 1\n",
    "\n",
    "uncovered_train = []\n",
    "Du_train = []\n",
    "Ru_train = []\n",
    "exposed_train = []\n",
    "isolated_train = []\n",
    "infected_train = []\n",
    "Di_train = []\n",
    "Ri_train = []\n",
    "apathetic_train = []\n",
    "Da_train = []\n",
    "Ra_train = []\n",
    "\n",
    "\n",
    "for i in range(train_min+1, train_max):\n",
    "    j = i - 2\n",
    "    uncovered_train.append(uncovered[j:i])\n",
    "    Du_train.append(Du[j:i])\n",
    "    Ru_train.append(Ru[j:i])\n",
    "    exposed_train.append(exposed[j:i])\n",
    "    isolated_train.append(isolated[j:i])\n",
    "    infected_train.append(infected[j:i])\n",
    "    Di_train.append(Di[j:i])\n",
    "    Ri_train.append(Ri[j:i])\n",
    "    apathetic_train.append(apathetic[j:i])\n",
    "    Da_train.append(Da[j:i])\n",
    "    Ra_train.append(Ra[j:i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "t2 = np.arange(0, 15, 1)   # 0 - 3 PORJNTO JABE.. R PROTTEKBAR 1 KORE KORE BARBE. 0 initial value tar mane 0 theke shuRa hoye aro 2 din porjnto simulation cholbe.\n",
    "print(t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-14\n",
      "2020-04-15\n",
      "2020-04-16\n",
      "2020-04-17\n",
      "2020-04-18\n",
      "2020-04-19\n",
      "2020-04-20\n",
      "2020-04-21\n",
      "2020-04-22\n",
      "2020-04-23\n",
      "2020-04-24\n",
      "2020-04-25\n",
      "2020-04-26\n",
      "2020-04-27\n",
      "2020-04-28\n"
     ]
    }
   ],
   "source": [
    "last5_vals = []\n",
    "last5_params = []\n",
    "\n",
    "\n",
    "t_incub = 15 # Assumption days\n",
    "\n",
    "for sample in range(len(infected_train)):\n",
    "    \n",
    "    u_0 = uncovered_train[sample][0]\n",
    "    u_1 = uncovered_train[sample][1]\n",
    "    u_diff = u_1 - u_0\n",
    "    \n",
    "    Du_0 = Du_train[sample][0]\n",
    "    Du_1 = Du_train[sample][1]\n",
    "    Du_diff = Du_1 - Du_0\n",
    "\n",
    "    Ru_0 = Ru_train[sample][0]\n",
    "    Ru_1 = Ru_train[sample][1]\n",
    "    Ru_diff = Ru_1 - Ru_0\n",
    "    \n",
    "    e_0 = exposed_train[sample][0]\n",
    "    e_1 = exposed_train[sample][1]\n",
    "    e_diff = e_1 - e_0\n",
    "    \n",
    "    is_0 = isolated_train[sample][0]\n",
    "    is_1 = isolated_train[sample][1]\n",
    "    is_diff = is_1 - is_0\n",
    "    \n",
    "    i_0 = infected_train[sample][0]\n",
    "    i_1 = infected_train[sample][1]\n",
    "    i_diff = i_1 - i_0\n",
    "    \n",
    "    Di_0 = Di_train[sample][0]\n",
    "    Di_1 = Di_train[sample][1]\n",
    "    Di_diff = Di_1 - Di_0\n",
    "    \n",
    "    Ri_0 = Ri_train[sample][0]\n",
    "    Ri_1 = Ri_train[sample][1]\n",
    "    Ri_diff = Ri_1 - Ri_0\n",
    "    \n",
    "    a_0 = apathetic_train[sample][0]\n",
    "    a_1 = apathetic_train[sample][1]\n",
    "    a_diff = a_1 - a_0\n",
    "\n",
    "    Da_0 = Da_train[sample][0]\n",
    "    Da_1 = Da_train[sample][1]\n",
    "    Da_diff = Da_1 - Da_0\n",
    "\n",
    "    Ra_0 = Ra_train[sample][0]\n",
    "    Ra_1 = Ra_train[sample][1]\n",
    "    Ra_diff = Ra_1 - Ra_0\n",
    "    \n",
    "    s_0 = N - is_0 - Du_0 - Ru_0 - Di_0 - Ri_0 - Da_0 - Ra_0\n",
    "    \n",
    "    alpha = (( e_diff + i_diff + Di_diff + Ri_diff + a_diff + Da_diff + Ra_diff + is_diff) / (s_0 * e_0 )) * N  \n",
    "    beta =  ( ( u_diff + Du_diff + Ru_diff ) / (s_0 * u_0 ) )* N     \n",
    "    lamda =  ( ( a_diff + Da_diff + Ra_diff ) / a_0 )     \n",
    "    gamma = ( i_diff + Di_diff + Ri_diff) / i_0    \n",
    "    rho = ( is_diff / is_0 )      \n",
    "    delta = ( Di_diff / i_0 )  \n",
    "    miu =  ( Ri_diff / i_0 )     \n",
    "    omega =  ( Da_diff / a_0 )     \n",
    "    eta =  ( Ra_diff / a_0 )     \n",
    "    sigma = ( Du_diff / u_0 )     \n",
    "    tao = ( Ru_diff / u_0 )      \n",
    "    \n",
    "\n",
    "    init_vals = str_dates[sample], s_0, u_0, Du_0, Ru_0, e_0, is_0, i_0, Di_0, Ri_0, a_0, Da_0, Ra_0  \n",
    "    params = alpha, beta, lamda, gamma, rho, delta, miu, omega, eta, sigma, tao\n",
    "    pred = seird_model(init_vals, params, t2) # t2 cz etar upor porer day gula depend kortese.\n",
    "\n",
    "\n",
    "    # Store Last five cases for predecting future\n",
    "    if ((len(infected_train) - sample ) <= 15):\n",
    "        print(str_dates[sample])\n",
    "        last5_vals.append(init_vals)\n",
    "        last5_params.append(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33]\n"
     ]
    }
   ],
   "source": [
    "t7 = np.arange(0, 34, 1)  #13 din er data ashbe\n",
    "print(t7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions using 2020-04-14 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 3288 736 2568 40518 655 41021 8358 30886 1547 511 1035\n",
      "2020-05-01 3461 785 2696 43108 675 43920 9032 33010 1610 540 1069\n",
      "2020-05-02 3644 836 2831 45820 695 47025 9753 35283 1676 571 1104\n",
      "2020-05-03 3835 889 2973 48651 717 50348 10525 37718 1744 603 1140\n",
      "2020-05-04 4037 946 3123 51591 739 53907 11352 40324 1815 637 1178\n",
      "2020-05-05 4248 1005 3280 54630 761 57718 12238 43115 1890 671 1218\n",
      "2020-05-06 4470 1068 3446 57749 784 61797 13186 46103 1967 708 1259\n",
      "2020-05-07 4703 1133 3620 60927 808 66165 14201 49302 2047 745 1301\n",
      "2020-05-08 4947 1203 3803 64132 833 70842 15288 52727 2131 785 1346\n",
      "2020-05-09 5204 1276 3996 67325 859 75850 16451 56394 2218 825 1392\n",
      "2020-05-10 5473 1352 4199 70453 885 81211 17697 60321 2309 868 1440\n",
      "2020-05-11 5755 1433 4413 73452 912 86951 19031 64525 2403 912 1490\n",
      "2020-05-12 6050 1517 4637 76239 940 93097 20459 69026 2501 958 1543\n",
      "2020-05-13 6360 1606 4873 78710 968 99678 21989 73846 2603 1006 1597\n",
      "2020-05-14 6685 1700 5121 80739 998 106723 23626 79006 2710 1056 1653\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-15 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 40660 5178 35889 -412840 2035 67943 5699 50883 93240 10693 82547\n",
      "2020-05-01 50322 6370 44237 -628117 2251 75076 6183 56144 125388 14354 111034\n",
      "2020-05-02 62561 7846 54569 -941317 2490 82958 6718 61957 168620 19277 149343\n",
      "2020-05-03 78331 9681 67413 -1397579 2754 91668 7309 68381 226758 25898 200860\n",
      "2020-05-04 99156 11979 83495 -2066291 3047 101292 7962 75479 304941 34801 270139\n",
      "2020-05-05 127640 14887 103853 -3058747 3370 111926 8684 83322 410080 46774 363306\n",
      "2020-05-06 168572 18631 130058 -4564341 3728 123677 9482 91989 551470 62876 488594\n",
      "2020-05-07 231585 23575 164668 -6932472 4124 136662 10363 101566 741609 84528 657080\n",
      "2020-05-08 338415 30367 212214 -10882150 4562 151010 11337 112148 997305 113647 883658\n",
      "2020-05-09 546230 40293 281693 -18130671 5046 166864 12413 123841 1341162 152805 1188356\n",
      "2020-05-10 1041977 56313 393838 -33733313 5582 184383 13602 136762 1803574 205464 1598110\n",
      "2020-05-11 2674350 86874 607764 -77983879 6175 203741 14915 151039 2425421 276279 2149141\n",
      "2020-05-12 12029436 165312 1156827 -285557638 6831 225131 16367 166815 3261670 371510 2890159\n",
      "2020-05-13 164857862 518131 3626558 -2900895644 7556 248767 17971 184247 4386246 499576 3886669\n",
      "2020-05-14 21300244871 5353347 37473074 -266399569309 8358 274885 19744 203510 5898559 671797 5226762\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-16 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 14977 2224 12913 -6521 1519 47300 5746 34386 18244 2476 15767\n",
      "2020-05-01 17244 2563 14887 -22514 1648 51019 6166 36982 22006 2975 19030\n",
      "2020-05-02 19853 2953 17160 -44530 1788 55031 6620 39782 26543 3578 22965\n",
      "2020-05-03 22857 3403 19776 -74416 1939 59358 7108 42801 32017 4304 27712\n",
      "2020-05-04 26319 3920 22789 -114547 2104 64025 7636 46059 38619 5181 33438\n",
      "2020-05-05 30314 4515 26257 -167986 2282 69059 8204 49572 46583 6238 40345\n",
      "2020-05-06 34935 5201 30252 -238689 2476 74490 8818 53362 56188 7513 48675\n",
      "2020-05-07 40295 5991 34856 -331787 2686 80347 9479 57450 67775 9051 58723\n",
      "2020-05-08 46537 6903 40167 -453968 2914 86664 10193 61859 81750 10906 70844\n",
      "2020-05-09 53847 7956 46300 -614026 3161 93479 10963 66614 98608 13144 85463\n",
      "2020-05-10 62471 9174 53396 -823653 3429 100829 11793 71744 118941 15843 103098\n",
      "2020-05-11 72744 10588 61629 -1098619 3720 108757 12689 77277 143467 19098 124368\n",
      "2020-05-12 85136 12233 71216 -1460613 4035 117308 13655 83245 173051 23025 150025\n",
      "2020-05-13 100333 14160 82436 -1940203 4378 126532 14697 89683 208735 27762 180972\n",
      "2020-05-14 119370 16430 95659 -2581812 4749 136481 15821 96626 251777 33476 218301\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-17 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 27523 4179 23811 -82939 1286 70052 8782 49347 36629 4917 31711\n",
      "2020-05-01 33077 5029 28652 -144610 1379 77710 9714 54561 46437 6219 40217\n",
      "2020-05-02 39775 6050 34470 -234070 1478 86205 10747 60344 58872 7870 51001\n",
      "2020-05-03 47888 7278 41466 -362581 1585 95629 11894 66760 74637 9963 64673\n",
      "2020-05-04 57783 8756 49890 -546056 1699 106083 13165 73877 94623 12617 82005\n",
      "2020-05-05 69972 10540 60054 -807294 1821 117679 14576 81772 119961 15981 103979\n",
      "2020-05-06 85212 12699 72361 -1179669 1952 130543 16141 90530 152083 20246 131837\n",
      "2020-05-07 104672 15330 87350 -1713525 2093 144814 17877 100245 192808 25653 167154\n",
      "2020-05-08 130266 18561 105761 -2488020 2244 160644 19803 111022 244437 32508 211929\n",
      "2020-05-09 165330 22582 128674 -3634715 2405 178205 21939 122978 309891 41198 268693\n",
      "2020-05-10 216148 27685 157755 -5388804 2579 197685 24309 136240 392873 52215 340657\n",
      "2020-05-11 295730 34357 195774 -8212892 2764 219295 26938 150952 498075 66183 431892\n",
      "2020-05-12 434634 43485 247791 -13139636 2963 243267 29854 167273 631448 83890 547557\n",
      "2020-05-13 718312 56901 324241 -22909821 3177 269860 33089 185377 800535 106340 694195\n",
      "2020-05-14 1455290 79073 450588 -46860003 3406 299360 36678 205461 1014899 134801 880098\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-18 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 16274 2354 14222 49594 1301 84663 10389 63982 13883 1967 11915\n",
      "2020-05-01 18750 2717 16442 43459 1395 95297 11656 71971 16335 2307 14028\n",
      "2020-05-02 21588 3136 19000 31593 1497 107266 13081 80963 19220 2707 16513\n",
      "2020-05-03 24840 3618 21945 11386 1607 120738 14686 91085 22615 3177 19437\n",
      "2020-05-04 28568 4173 25334 -20805 1724 135903 16491 102479 26609 3730 22878\n",
      "2020-05-05 32844 4811 29232 -70062 1849 152972 18524 115303 31308 4381 26927\n",
      "2020-05-06 37760 5544 33712 -143474 1984 172185 20812 129738 36838 5147 31690\n",
      "2020-05-07 43430 6387 38863 -250991 2129 193811 23388 145986 43344 6048 37295\n",
      "2020-05-08 50010 7357 44788 -406706 2285 218153 26287 164275 50999 7108 43890\n",
      "2020-05-09 57717 8474 51611 -630897 2451 245553 29550 184861 60006 8356 51649\n",
      "2020-05-10 66866 9763 59484 -953427 2630 276394 33223 208032 70604 9824 60779\n",
      "2020-05-11 77945 11256 68606 -1419733 2822 311109 37357 234113 83073 11551 71522\n",
      "2020-05-12 91739 12996 79240 -2102109 3028 350184 42010 263471 97745 13583 84161\n",
      "2020-05-13 109590 15045 91755 -3122542 3249 394166 47248 296515 115008 15975 99033\n",
      "2020-05-14 133951 17492 106705 -4703227 3486 443672 53144 333710 135320 18788 116531\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-19 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 14602 1641 13077 17661 1112 43881 3250 32156 15766 1538 14228\n",
      "2020-05-01 16708 1861 15001 10391 1178 46760 3312 34173 18748 1802 16946\n",
      "2020-05-02 19113 2113 17202 132 1247 49828 3378 36323 22295 2116 20179\n",
      "2020-05-03 21861 2401 19720 -13978 1321 53098 3449 38614 26512 2489 24023\n",
      "2020-05-04 25001 2730 22600 -33023 1399 56582 3524 41055 31526 2932 28594\n",
      "2020-05-05 28588 3107 25894 -58363 1482 60294 3604 43656 37490 3460 34029\n",
      "2020-05-06 32690 3538 29660 -91707 1569 64250 3690 46428 44581 4087 40493\n",
      "2020-05-07 37384 4030 33967 -135198 1662 68466 3781 49382 53013 4833 48180\n",
      "2020-05-08 42764 4593 38892 -191528 1760 72958 3878 52529 63041 5720 57320\n",
      "2020-05-09 48939 5238 44526 -264087 1864 77745 3981 55883 74965 6775 68189\n",
      "2020-05-10 56046 5975 50974 -357152 1974 82846 4091 59457 89145 8030 81114\n",
      "2020-05-11 64254 6819 58358 -476147 2090 88281 4209 63266 106006 9522 96484\n",
      "2020-05-12 73777 7788 66823 -627997 2214 94073 4334 67324 126057 11296 114761\n",
      "2020-05-13 84890 8899 76543 -821617 2345 100246 4467 71649 149901 13405 136495\n",
      "2020-05-14 97960 10178 87727 -1068618 2483 106823 4609 76258 178254 15914 162340\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-20 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 27108 4576 22942 -15270 1520 64018 9040 40872 31542 4508 27033\n",
      "2020-05-01 32757 5562 27771 -48564 1656 70601 10017 44644 39949 5705 34243\n",
      "2020-05-02 39573 6753 33606 -98434 1804 77861 11095 48803 50596 7222 43374\n",
      "2020-05-03 47811 8192 40655 -171642 1966 85868 12283 53390 64081 9142 54939\n",
      "2020-05-04 57796 9930 49171 -277578 2142 94698 13594 58449 81160 11574 69586\n",
      "2020-05-05 69956 12032 59466 -429342 2334 104436 15040 64028 102792 14654 88137\n",
      "2020-05-06 84876 14575 71927 -645390 2543 115176 16635 70180 130188 18555 111632\n",
      "2020-05-07 103390 17661 87045 -952125 2770 127020 18393 76966 164887 23497 141390\n",
      "2020-05-08 126742 21420 105462 -1388196 3018 140082 20332 84449 208834 29755 179079\n",
      "2020-05-09 156894 26028 128038 -2012053 3289 154487 22471 92701 264493 37680 226812\n",
      "2020-05-10 197121 31733 155984 -2916115 3583 170373 24829 101803 334987 47719 287268\n",
      "2020-05-11 253272 38900 191096 -4255442 3904 187893 27430 111840 424269 60433 363836\n",
      "2020-05-12 336656 48108 236210 -6311198 4253 207215 30299 122909 537348 76535 460812\n",
      "2020-05-13 471502 60348 296177 -9647686 4634 228524 33462 135117 680564 96929 583635\n",
      "2020-05-14 717259 77491 380163 -15560893 5049 252024 36951 148580 861951 122758 739192\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-21 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 10188 1632 8607 44443 1110 47340 6382 33428 8517 1263 7254\n",
      "2020-05-01 11183 1801 9450 46240 1172 50657 6848 35628 9464 1404 8060\n",
      "2020-05-02 12272 1987 10376 47786 1237 54205 7347 37982 10515 1560 8955\n",
      "2020-05-03 13465 2191 11391 48971 1306 58002 7881 40502 11684 1733 9950\n",
      "2020-05-04 14770 2414 12506 49658 1379 62065 8453 43197 12982 1926 11055\n",
      "2020-05-05 16198 2660 13728 49673 1456 66412 9064 46082 14425 2140 12284\n",
      "2020-05-06 17759 2929 15069 48803 1538 71065 9719 49169 16027 2378 13648\n",
      "2020-05-07 19466 3223 16539 46779 1623 76043 10419 52472 17808 2643 15165\n",
      "2020-05-08 21331 3547 18150 43272 1714 81369 11168 56006 19787 2937 16850\n",
      "2020-05-09 23369 3901 19916 37872 1810 87069 11970 59788 21986 3263 18722\n",
      "2020-05-10 25596 4289 21850 30077 1911 93168 12828 63834 24429 3626 20802\n",
      "2020-05-11 28028 4714 23969 19267 2017 99694 13745 68165 27143 4029 23113\n",
      "2020-05-12 30685 5179 26288 4682 2130 106678 14728 72798 30159 4477 25682\n",
      "2020-05-13 33588 5689 28828 -14610 2249 114150 15779 77756 33510 4974 28535\n",
      "2020-05-14 36761 6246 31608 -39757 2374 122146 16903 83061 37233 5527 31706\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-22 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 14850 2269 12700 45981 1138 56488 6983 38914 13431 1835 11596\n",
      "2020-05-01 16982 2603 14549 45510 1205 61643 7593 42236 15698 2136 13562\n",
      "2020-05-02 19410 2983 16663 43282 1275 67268 8258 45862 18348 2488 15860\n",
      "2020-05-03 22175 3419 19079 38588 1350 73407 8985 49819 21445 2899 18546\n",
      "2020-05-04 25322 3916 21840 30491 1430 80106 9778 54136 25065 3380 21684\n",
      "2020-05-05 28903 4484 24992 17760 1514 87417 10643 58848 29296 3942 25353\n",
      "2020-05-06 32977 5132 28590 -1220 1603 95394 11587 63990 34241 4599 29641\n",
      "2020-05-07 37612 5872 32695 -28568 1697 104100 12617 69601 40020 5367 34653\n",
      "2020-05-08 42888 6715 37377 -67046 1796 113600 13742 75724 46775 6264 40510\n",
      "2020-05-09 48901 7677 42716 -120271 1902 123967 14969 82406 54670 7313 47357\n",
      "2020-05-10 55767 8773 48804 -192986 2014 135280 16307 89698 63898 8539 55358\n",
      "2020-05-11 63629 10024 55746 -291434 2132 147626 17769 97655 74684 9972 64711\n",
      "2020-05-12 72674 11451 63667 -423901 2257 161098 19363 106338 87289 11647 75642\n",
      "2020-05-13 83143 13081 72714 -601503 2390 175799 21103 115814 102023 13604 88418\n",
      "2020-05-14 95367 14945 83064 -839372 2530 191843 23002 126154 119243 15892 103350\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-23 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 10464 1258 9244 47106 1352 47913 4634 35199 8703 961 7741\n",
      "2020-05-01 11464 1352 10165 49523 1463 51220 4803 37633 9635 1038 8596\n",
      "2020-05-02 12556 1455 11174 51838 1583 54756 4983 40236 10666 1123 9543\n",
      "2020-05-03 13749 1567 12279 53976 1712 58536 5176 43017 11808 1217 10591\n",
      "2020-05-04 15052 1691 13489 55843 1852 62577 5382 45991 13073 1321 11751\n",
      "2020-05-05 16474 1826 14813 57318 2004 66897 5603 49170 14473 1436 13036\n",
      "2020-05-06 18025 1974 16263 58250 2168 71515 5839 52569 16023 1564 14458\n",
      "2020-05-07 19718 2136 17849 58449 2345 76451 6091 56202 17738 1705 16032\n",
      "2020-05-08 21562 2313 19584 57680 2537 81729 6360 60086 19638 1862 17776\n",
      "2020-05-09 23573 2506 21481 55650 2745 87371 6648 64238 21741 2035 19705\n",
      "2020-05-10 25763 2718 23555 51997 2970 93402 6956 68677 24068 2226 21842\n",
      "2020-05-11 28149 2949 25822 46277 3213 99850 7285 73422 26646 2439 24207\n",
      "2020-05-12 30747 3202 28299 37941 3476 106742 7637 78495 29499 2674 26825\n",
      "2020-05-13 33575 3478 31004 26316 3760 114111 8014 83917 32658 2934 29724\n",
      "2020-05-14 36654 3780 33959 10580 4068 121988 8416 89715 36155 3221 32933\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-24 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 9409 1696 7737 49774 1155 49193 7582 34249 7456 1272 6184\n",
      "2020-05-01 10153 1868 8319 53135 1221 52787 8306 36539 8075 1403 6671\n",
      "2020-05-02 10954 2055 8947 56653 1292 56644 9083 38997 8744 1544 7199\n",
      "2020-05-03 11816 2256 9624 60316 1366 60783 9917 41634 9469 1698 7770\n",
      "2020-05-04 12743 2473 10355 64105 1445 65224 10811 44464 10254 1864 8389\n",
      "2020-05-05 13739 2707 11143 67995 1528 69990 11771 47500 11104 2045 9059\n",
      "2020-05-06 14810 2960 11993 71949 1617 75104 12802 50759 12025 2240 9785\n",
      "2020-05-07 15960 3232 12909 75919 1710 80591 13907 54255 13022 2451 10570\n",
      "2020-05-08 17195 3525 13896 79839 1809 86480 15093 58008 14101 2680 11421\n",
      "2020-05-09 18519 3841 14959 83627 1913 92799 16366 62034 15270 2927 12343\n",
      "2020-05-10 19940 4181 16104 87173 2023 99579 17732 66354 16537 3195 13341\n",
      "2020-05-11 21462 4547 17337 90339 2140 106855 19198 70990 17907 3486 14421\n",
      "2020-05-12 23094 4942 18665 92953 2264 114662 20771 75965 19392 3800 15591\n",
      "2020-05-13 24840 5366 20093 94795 2394 123040 22459 81303 21000 4141 16858\n",
      "2020-05-14 26709 5822 21629 95593 2533 132030 24270 87032 22741 4510 18230\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-25 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 10400 1364 9059 47861 1223 47815 5088 34523 8591 1034 7557\n",
      "2020-05-01 11410 1471 9975 50501 1306 51067 5281 36835 9526 1120 8406\n",
      "2020-05-02 12516 1588 10980 53097 1395 54539 5487 39304 10562 1215 9347\n",
      "2020-05-03 13726 1716 12082 55588 1489 58248 5707 41942 11711 1320 10391\n",
      "2020-05-04 15049 1857 13291 57898 1591 62208 5942 44758 12985 1436 11549\n",
      "2020-05-05 16495 2012 14616 59925 1699 66438 6193 47766 14398 1565 12832\n",
      "2020-05-06 18075 2181 16069 61544 1814 70956 6461 50978 15964 1709 14255\n",
      "2020-05-07 19800 2367 17661 62597 1937 75781 6747 54409 17701 1867 15833\n",
      "2020-05-08 21684 2570 19404 62886 2069 80934 7053 58073 19627 2044 17582\n",
      "2020-05-09 23739 2793 21314 62165 2209 86437 7379 61987 21762 2239 19522\n",
      "2020-05-10 25982 3037 23404 60129 2359 92315 7728 66166 24129 2455 21673\n",
      "2020-05-11 28426 3304 25692 56403 2519 98592 8100 70630 26754 2695 24058\n",
      "2020-05-12 31091 3596 28196 50525 2690 105296 8498 75397 29664 2961 26702\n",
      "2020-05-13 33995 3915 30934 41926 2873 112456 8923 80488 32891 3257 29634\n",
      "2020-05-14 37159 4264 33928 29913 3068 120102 9377 85926 36469 3584 32885\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-26 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 10212 1620 8607 47238 1211 46752 6148 32430 8436 1229 7206\n",
      "2020-05-01 11164 1792 9396 49772 1291 49707 6592 34211 9320 1366 7954\n",
      "2020-05-02 12202 1981 10258 52289 1376 52848 7064 36105 10296 1516 8779\n",
      "2020-05-03 13334 2186 11201 54744 1467 56189 7566 38118 11374 1683 9691\n",
      "2020-05-04 14568 2411 12231 57078 1563 59740 8100 40259 12566 1867 10698\n",
      "2020-05-05 15912 2657 13357 59218 1666 63516 8668 42535 13882 2071 11811\n",
      "2020-05-06 17376 2925 14587 61070 1776 67530 9272 44954 15336 2295 13040\n",
      "2020-05-07 18970 3218 15929 62520 1893 71798 9913 47527 16942 2544 14398\n",
      "2020-05-08 20705 3538 17395 63424 2018 76336 10595 50262 18717 2818 15898\n",
      "2020-05-09 22592 3887 18995 63606 2150 81161 11321 53170 20677 3121 17556\n",
      "2020-05-10 24644 4268 20740 62849 2292 86290 12092 56262 22843 3456 19387\n",
      "2020-05-11 26874 4684 22644 60887 2443 91744 12912 59550 25236 3826 21409\n",
      "2020-05-12 29297 5137 24721 57398 2604 97543 13784 63045 27879 4234 23644\n",
      "2020-05-13 31930 5631 26985 51987 2775 103708 14711 66761 30799 4685 26113\n",
      "2020-05-14 34789 6170 29452 44176 2958 110262 15696 70712 34025 5184 28840\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-27 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 9898 962 8946 48583 1246 47577 3792 35841 8022 718 7303\n",
      "2020-05-01 10737 930 9823 51690 1338 50806 3549 38644 8751 693 8058\n",
      "2020-05-02 11645 896 10776 54919 1436 54254 3289 41636 9547 665 8882\n",
      "2020-05-03 12627 858 11809 58255 1542 57936 3012 44832 10415 634 9780\n",
      "2020-05-04 13689 817 12929 61676 1655 61867 2716 48244 11362 601 10760\n",
      "2020-05-05 14837 773 14143 65151 1777 66066 2399 51888 12395 565 11830\n",
      "2020-05-06 16077 725 15459 68639 1908 70550 2062 55779 13522 526 12996\n",
      "2020-05-07 17416 674 16885 72088 2048 75337 1701 59935 14752 483 14269\n",
      "2020-05-08 18862 617 18430 75428 2198 80450 1316 64372 16093 436 15657\n",
      "2020-05-09 20422 557 20103 78572 2360 85910 905 69110 17557 385 17171\n",
      "2020-05-10 22103 491 21914 81410 2533 91740 466 74170 19153 329 18824\n",
      "2020-05-11 23916 420 23874 83800 2720 97966 -2 79574 20895 268 20626\n",
      "2020-05-12 25869 343 25996 85571 2920 104614 -503 85344 22795 202 22592\n",
      "2020-05-13 27972 259 28290 86506 3134 111714 -1037 91506 24867 129 24738\n",
      "2020-05-14 30234 169 30771 86340 3364 119295 -1608 98085 27128 50 27078\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Predictions using 2020-04-28 parameters\n",
      "\n",
      "------------------------------------------------------------------------------------------------------------------\n",
      "Date Uncovered Du Ru Exposed Isolated Infected Di Ri Apathetic Da Ra\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "2020-04-30 10002 1767 8239 47883 1236 46817 6889 31821 8173 1331 6842\n",
      "2020-05-01 10887 2053 8845 50667 1323 49726 7812 33084 8972 1550 7422\n",
      "2020-05-02 11849 2363 9504 53517 1416 52816 8792 34425 9850 1791 8058\n",
      "2020-05-03 12893 2701 10221 56409 1516 56098 9833 35850 10812 2056 8756\n",
      "2020-05-04 14026 3069 11002 59310 1623 59584 10938 37364 11869 2347 9522\n",
      "2020-05-05 15255 3469 11851 62178 1737 63287 12112 38971 13030 2665 10364\n",
      "2020-05-06 16588 3904 12775 64961 1859 67220 13360 40678 14303 3015 11287\n",
      "2020-05-07 18033 4378 13779 67591 1990 71397 14684 42492 15701 3400 12301\n",
      "2020-05-08 19599 4892 14871 69983 2131 75834 16092 44418 17236 3822 13414\n",
      "2020-05-09 21295 5451 16057 72033 2281 80547 17586 46464 18921 4285 14636\n",
      "2020-05-10 23132 6059 17347 73609 2442 85552 19173 48636 20771 4793 15977\n",
      "2020-05-11 25119 6719 18747 74552 2614 90869 20860 50944 22801 5351 17450\n",
      "2020-05-12 27268 7435 20268 74665 2798 96516 22650 53396 25030 5964 19066\n",
      "2020-05-13 29592 8213 21918 73708 2995 102514 24552 55999 27477 6636 20840\n",
      "2020-05-14 32104 9057 23710 71390 3206 108884 26573 58765 30163 7374 22788\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for z in range(1, len(last5_params)+1):\n",
    "    results = seird_model(last5_vals[z-1], last5_params[z-1], t7)\n",
    "    t_z = np.arange(18 - z, 33 - z, 1)\n",
    "    print(\"Predictions using\", last5_vals[z-1][0], \"parameters\")\n",
    "    print(\"---------------------------------------------------------------------------------------------------\")\n",
    "    print(\"Date\", \"Uncovered\", \"Du\", \"Ru\", \"Exposed\", \"Isolated\", \"Infected\", \"Di\", \"Ri\", \"Apathetic\", \"Da\", \"Ra\" )\n",
    "    print(\"---------------------------------------------------------------------------------------------------\")\n",
    "    for day in t_z:\n",
    "        print(results[day][0],int(float(results[day][2])),int(float(results[day][3])),int(float(results[day][4])), int(float(results[day][5])), int(float(results[day][6])),int(float(results[day][7])),int(float(results[day][8])),int(float(results[day][9])), int(float(results[day][10])), int(float(results[day][11])),int(float(results[day][12])))\n",
    "    print(\"-----------------------------------------------------------------------------------------------------\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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