SIRS_simulation_SF.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# SIRS with mobility and network structure: SIMULATION SF vs SF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fixed the epidemic parameters in a \"critical\" point at which most of the times the epidemics does not survive and dies out after some iterations. The topology of the two networks is also kept fixed. We now explore the role of the mobility parameter p_mob. In this case simulation is done coupling two scale free networks."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import os\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import time \n",
    "# to install tqdm\n",
    "#! pip install tqdm\n",
    "from tqdm import tnrange, trange\n",
    "\n",
    "# import custom module\n",
    "import SIRS_twoSF"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# needed only if you made some changes at SIRS_twoSF.py and want to import the updated version of the module\n",
    "from importlib import reload\n",
    "reload(SIRS_twoSF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mobility and topology parameters\n",
    "N = 1000\n",
    "I_sf1_init = 5\n",
    "I_sf2_init = 0\n",
    "mean_degree = 4"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "beta0 = 0.3\n",
    "gamma = 0.016\n",
    "mu = 0.15\n",
    "\n",
    "p_mob_list = np.linspace(0.03,0.2,20)\n",
    "\n",
    "print('Number of travellers:')\n",
    "for p in p_mob_list:\n",
    "    print('p=%.2f    N=%d'%(p, int(p*N)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "beta = beta0/mean_degree\n",
    "infection_params = dict(beta=beta, mu=mu, gamma=gamma)\n",
    "\n",
    "\n",
    "UP_directory_name = 'data_SF_asym/'+str(beta)+\"_\"+str(mu)+\"_\"+str(gamma) \n",
    "os.system(\"mkdir data_SF_asym\")\n",
    "os.system(\"mkdir \"+ UP_directory_name)\n",
    "start = time.time()\n",
    "\n",
    "for j, p_mob in enumerate(p_mob_list):\n",
    "\n",
    "    print('Simulation %d/%d'%(j+1,len(p_mob_list)), end =' - ')\n",
    "    print(\"P_mob: %.3f\"% p_mob)\n",
    "\n",
    "\n",
    "    #saving stuff for future analysis\n",
    "    directory_name = UP_directory_name+\"/pmob_\"+\"{:3f}\".format(p_mob)\n",
    "    os.system(\"mkdir \"+directory_name)\n",
    "\n",
    "    #saving epidemics, topology and mobility parameters\n",
    "\n",
    "    params=dict(N=N, I_sf1_init=I_sf1_init, I_sf2_nit=I_sf2_init, p_mob=p_mob, mean_degree=mean_degree,\n",
    "                eps=eps, **infection_params)\n",
    "\n",
    "    parameters = pd.DataFrame(params, index=[0])\n",
    "    save=directory_name+\"/parameters.csv\"\n",
    "    parameters.to_csv(save)\n",
    "\n",
    "\n",
    "\n",
    "    #STARTING SIMULATION\n",
    "    n_runs = 50\n",
    "    n_iter = 1000\n",
    "\n",
    "\n",
    "\n",
    "    for run in tnrange(n_runs, desc='1st loop', leave=True):\n",
    "\n",
    "\n",
    "        # prepare systems\n",
    "        state_sf1_init, state_sf2_init, variables_net_sf1, variables_net_sf2 = SIRS_twoSF.prepare_two_sys(N, I_sf1_init, I_sf2_init, \n",
    "                                                                                      p_mob, mean_degree)\n",
    "\n",
    "        #saving initial states\n",
    "        save = directory_name + \"/state_sf1_init_\"+str(run)+\".txt\"\n",
    "        np.savetxt(save, state_sf1_init.astype(int), fmt ='%d')\n",
    "\n",
    "        save = directory_name + \"/state_sf2_init_\"+str(run)+\".txt\"\n",
    "        np.savetxt(save, state_sf1_init.astype(int), fmt ='%d')\n",
    "\n",
    "        save = directory_name + \"/travellers_sf1_\"+str(run)+\".txt\"\n",
    "        travellers_sf1 = variables_net_sf1[\"travellers_sf1\"]\n",
    "        np.savetxt(save, travellers_sf1.astype(int), fmt ='%d')\n",
    "        \n",
    "        save = directory_name + \"/travellers_sf2_\"+str(run)+\".txt\"\n",
    "        travellers_sf2 = variables_net_sf2[\"travellers_sf2\"]\n",
    "        np.savetxt(save, travellers_sf2.astype(int), fmt ='%d')\n",
    "        \n",
    "        #saving initial variables: we just save the IDs of the travelling nodes and their degrees\n",
    "        save = directory_name + \"/deg_trav_sf1_\"+str(run)+\".txt\"\n",
    "        deg_sf1 = variables_net_sf1[\"A_sf1\"].sum(axis=1)\n",
    "        deg_trav_sf1 = deg_sf1[travellers_sf1]\n",
    "        np.savetxt(save, deg_trav_sf1.astype(int), fmt ='%d')\n",
    "        \n",
    "        save = directory_name + \"/deg_trav_sf2_\"+str(run)+\".txt\"\n",
    "        deg_sf2 = variables_net_sf2[\"A_sf2\"].sum(axis=1)\n",
    "        deg_trav_sf2 = deg_sf2[travellers_sf2]\n",
    "        np.savetxt(save, deg_trav_sf2.astype(int), fmt ='%d')\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    "\n",
    "        #print(\"Iteration:\", run)\n",
    "\n",
    "        state_sf1 = np.copy(state_sf1_init)\n",
    "        state_sf2 = np.copy(state_sf2_init)\n",
    "\n",
    "        S_sf1 = [N - I_sf1_init]\n",
    "        I_sf1 = [I_sf1_init]\n",
    "        R_sf1 = [0]\n",
    "        S_sf2 = [N - I_sf2_init]\n",
    "        I_sf2 = [I_sf2_init]\n",
    "        R_sf2 = [0]\n",
    "        t_vec = []\n",
    "        for i in tnrange(n_iter, desc='2nd loop', leave=False):\n",
    "            state_sf1, state_sf2 = SIRS_twoSF.two_sys_full_SIRS_step(state_sf1, state_sf2, **variables_net_sf1, \n",
    "                                                    **variables_net_sf2, **infection_params)\n",
    "            S_sf1.append(state_sf1[:,0].sum())\n",
    "            I_sf1.append(state_sf1[:,1].sum())\n",
    "            R_sf1.append(state_sf1[:,2].sum())\n",
    "            S_sf2.append(state_sf2[:,0].sum())\n",
    "            I_sf2.append(state_sf2[:,1].sum())\n",
    "            R_sf2.append(state_sf2[:,2].sum())\n",
    "            #t_vec.append(time.time()-start)\n",
    "\n",
    "        #tot_time = time.time()-start\n",
    "        #print(\"Total time elapsed: %.2f s\"%tot_time)\n",
    "        #print(\"Time per iteration: %.4f s\"%(tot_time/n_iter))\n",
    "\n",
    "        S_sf1 = np.array(S_sf1)\n",
    "        I_sf1 = np.array(I_sf1)\n",
    "        R_sf1 = np.array(R_sf1)\n",
    "        S_sf2 = np.array(S_sf2)\n",
    "        I_sf2 = np.array(I_sf2)\n",
    "        R_sf2 = np.array(R_sf2)\n",
    "        t_vec = np.array(t_vec)\n",
    "\n",
    "        #save timeseries of SIR\n",
    "        SIR_sf1 = np.vstack((S_sf1, I_sf1, R_sf1)).T\n",
    "        save = directory_name+\"/SIR_sf1_\"+str(run)+\".txt\"\n",
    "        np.savetxt(save, SIR_sf1.astype(int), fmt ='%d')\n",
    "        SIR_sf2 = np.vstack((S_sf2, I_sf2, R_sf2)).T\n",
    "        save = directory_name+\"/SIR_sf2_\"+str(run)+\".txt\"\n",
    "        np.savetxt(save, SIR_sf2.astype(int), fmt ='%d')\n",
    "\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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