test-conda/AIcovid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import pandas as pd\n",
    "from scipy.integrate import odeint,quad\n",
    "from scipy.stats import kde,beta\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from importlib import reload\n",
    "pi=np.pi\n",
    "\n",
    "#pour matplotlib\n",
    "font = {'weight' : 'bold',\n",
    "        'size'   : 22}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('xtick',labelsize=22)\n",
    "plt.rc('ytick',labelsize=22)\n",
    "\n",
    "#mardi 31 mars 2020\n",
    "#essayons tout d'abord d'écrire des fonctions qui calculent le rayon spectral\n",
    "#et l'abcisse de convergence d'une matrice\n",
    "\n",
    "from numpy import linalg as LA\n",
    "from scipy.linalg import expm\n",
    "from scipy.optimize import brentq\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more general case that would be worth investigating is the life cycle of Covid19 between Asymptomatic hosts (A) and Symptomatic hosts (S). Only A and S hosts can transmit the infections which may lead to:\n",
    "\\begin{eqnarray}\n",
    "    \\frac{\\mathrm{d}I_A(t)}{\\mathrm{d}t} &=& p_A (\\beta_A(t) I_A(t) + \\beta_S(t) I_S(t))-\\gamma_A(t)I_A(t)\\\\\n",
    "    \\frac{\\mathrm{d}I_S(t)}{\\mathrm{d}t} &=& p_S (\\beta_A(t) I_A(t) + \\beta_S(t) I_S(t))-\\gamma_S(t)I_S(t)\n",
    "\\end{eqnarray}\n",
    "where $p_A+p_S=1$. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ucovid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'ucovid' from '/home/philippe/pca/EPIDEMIE/ucovid.py'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload(ucovid)\n",
    "#ucovid.siraicov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ucovid.bsiraicov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0905f8117a244ed0b2dcf1fcafa0e80d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.25, description='betaA', max=0.75, min=-0.25), FloatSlider(value=0.2…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function ucovid.siraicov>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#interact(ucovid.siraicov,betaS=widgets.FloatSlider(min=0.1, max=0.5, step=0.05, value=0.25),nbpts=fixed(50),\n",
    "#        gammaA=widgets.FloatSlider(min=0.01, max=0.25, step=0.01, value=0.1),\n",
    "#         gammaS=widgets.FloatSlider(min=0.01, max=0.25, step=0.01, value=0.05),\n",
    "#        piS=widgets.FloatSlider(min=0.05, max=0.95, step=0.05, value=0.15),\n",
    "#        T=widgets.IntSlider(min=1, max=100, step=1, value=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ucovid.bsiraicov(T=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ucovid.bsiraicov(T=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reload(ucovid)\n",
    "#ucovid.bipersiraicov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f57784ad1ba249838be2efdb6498b1db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.25, description='betaA', max=0.75, min=-0.25), FloatSlider(value=0.2…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function ucovid.bipersiraicov>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(ucovid.bipersiraicov,betaS=widgets.FloatSlider(min=0.1, max=0.5, step=0.05, value=0.25, continuous_update=False),nbpts=fixed(50),\n",
    "        gammaA=widgets.FloatSlider(min=0.01, max=0.25, step=0.01, value=0.1, continuous_update=False),\n",
    "         gammaS=widgets.FloatSlider(min=0.01, max=0.25, step=0.01, value=0.05, continuous_update=False),\n",
    "        piS=widgets.FloatSlider(min=0.05, max=0.95, step=0.05, value=0.15, continuous_update=False),\n",
    "        T1=widgets.IntSlider(min=1, max=10, step=1, value=7, continuous_update=False),\n",
    "        T2=widgets.IntSlider(min=10, max=400, step=10, value=10, continuous_update=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}