Ring-Vaccination.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import igraph as ig\n",
    "import math\n",
    "import pandas as pd\n",
    "import random as rd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange, date2num\n",
    "import random\n",
    "font = {'family': 'serif',\n",
    "        'color':  'black',\n",
    "        'weight': 'normal',\n",
    "        'size': 18,\n",
    "        }\n",
    "params = {'legend.fontsize': 16,\n",
    "          'legend.handlelength': 2.}\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import csv\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "class Labeloffset():\n",
    "    def __init__(self,  ax, label=\"\", axis=\"y\"):\n",
    "        self.axis = {\"y\":ax.yaxis, \"x\":ax.xaxis}[axis]\n",
    "        self.label=label\n",
    "        ax.caller_ringcks.connect(axis+'lim_changed', self.update)\n",
    "        ax.figure.canvas.draw()\n",
    "        self.update(None)\n",
    "\n",
    "    def update(self, lim):\n",
    "        fmt = self.axis.get_major_formatter()\n",
    "        self.axis.offsetText.set_visible(False)\n",
    "        self.axis.set_label_text(self.label + \" \"+ fmt.get_offset() )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# In[3]:\n",
    "\n",
    "\n",
    "\n",
    "# In[ ]:\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# In[5]:\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# In[6]:\n",
    "\n",
    "\n",
    "\n",
    "# In[6]:\n",
    "\n",
    "\n",
    "# three_dim = []\n",
    "# for i in lst:\n",
    "#     three_dim.append((30100,i,mean_col[i]))\n",
    "\n",
    "# with open(\"er_ring_three_dim30100.txt\", \"w\") as text_file:\n",
    "#     text_file.write(\"%s\" %three_dim)\n",
    "\n",
    "# with open(\"er_ring_ieach30100.csv\", \"w\", newline=\"\") as f:\n",
    "#     writer = csv.writer(f)\n",
    "#     writer.writerows(i_each)\n",
    "\n",
    "# with open(\"er_ring_sfinalout30100.csv\", \"w\") as text_file:\n",
    "#     wr = csv.writer(text_file)\n",
    "#     wr.writerow(s_final)\n",
    "\n",
    "# with open(\"er_ring_efinalout30100.csv\", \"w\") as text_file:\n",
    "#     wr = csv.writer(text_file)\n",
    "#     wr.writerow(e_final)\n",
    "\n",
    "# with open(\"er_ring_ifinalout30100.csv\", \"w\") as text_file:\n",
    "#     wr = csv.writer(text_file)\n",
    "#     wr.writerow(i_final)\n",
    "\n",
    "# with open(\"er_ring_rfinalout30100.csv\", \"w\") as text_file:\n",
    "#     wr = csv.writer(text_file)\n",
    "#     wr.writerow(r_final)\n",
    "\n",
    "# with open(\"er_ring_vfinalout30100.csv\", \"w\") as text_file:\n",
    "#     wr = csv.writer(text_file)\n",
    "#     wr.writerow(v_final)\n",
    "\n",
    "# peak_max = np.max(mean[\"inf\"])\n",
    "# with open(\"er_ring_peak_max30100.txt\", \"w\") as text_file:\n",
    "#     text_file.write(\"%s\" %peak_max)\n",
    "\n",
    "# with open('er_ring_prop_exp_unvac30100.csv', 'w') as myfile:\n",
    "#     wr = csv.writer(myfile)\n",
    "#     wr.writerow(prop_exp_unvac)\n",
    "\n",
    "# with open(\"er_ring_prop_exp_vac30100.csv\", \"w\") as myfile:\n",
    "#     wr = csv.writer(myfile)\n",
    "#     wr.writerow(prop_exp_vac)\n",
    "\n",
    "# with open('er_ring_all_exp_inf30100.csv', 'w') as myfile:\n",
    "#     wr = csv.writer(myfile)\n",
    "#     wr.writerow(all_exp_inf)\n",
    "\n",
    "# with open(\"er_ring_all_exp_recovered30100.csv\", \"w\") as myfile:\n",
    "#     wr = csv.writer(myfile)\n",
    "#     wr.writerow(all_exp_recovered)\n",
    "                                     \n",
    "# with open(\"er_ring_prop_exp_vac_ft30100.csv\", \"w\") as myfile:\n",
    "#     wr = csv.writer(myfile)\n",
    "#     wr.writerow(prop_exp_vac_ft)  \n",
    "\n",
    "\n",
    "                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel ('Book2.xlsx') #place \"r\" before the path string to address special character, such as '\\'. Don't forget to put the file name at the end of the path + '.xlsx'\n",
    "# print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def double_smoothlog(time, bound1 , bound2, rate1 , rate2 , midpoint1 , midpoint2):\n",
    "    result = []\n",
    "    mini = 0\n",
    "    maxi = time\n",
    "    for x in range(time):\n",
    "        if (midpoint1 > maxi | midpoint2 > maxi | midpoint1 < mini | midpoint2 < mini | midpoint1 > midpoint2):\n",
    "            stop('midpoints not in range!')\n",
    "        t1 = 1 / (1 + math.exp(-rate1*(x - midpoint1)))\n",
    "        t2 = 1 / (1 + math.exp( rate2*(x - midpoint2)))\n",
    "        out = bound1 + (bound2-bound1) * ((t1 + t2) - 1)\n",
    "        result.append(out)\n",
    "    return(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def secfxn(newg,bett,efficacy):\n",
    "    beta = bett\n",
    "    inf_nodes = []\n",
    "    first_con = []\n",
    "    sec_contacts = []\n",
    "    sec_con = []\n",
    "    vac_eff = efficacy\n",
    "    snbs = []\n",
    "    exposed_susc = []       # susceptibles who are exposed\n",
    "    exposed_vac = []         # vaccinated group exposed after 14 days\n",
    "    exp_inf = []             # exposed who get serious infection after incuer_ringtion period\n",
    "    exp_recovered = []       # exposed who recover after incuer_ringtion period\n",
    "    ini_vac_exp_ft = []      # initial vaccinated group exposed in 14 days\n",
    "    num_vac_after_ft = []        # number of unexposed initial vaccinated after 14 days\n",
    "    \n",
    "    for inf in newg.vs.select(state_eq = \"E\"):\n",
    "        newg.vs[inf.index][\"duration\"] += 1 \n",
    "        if newg.vs[inf.index][\"duration\"] in range(5,19):  #(7,21)\n",
    "            nr = np.random.binomial(1,0.2,1)\n",
    "            if nr == 1:\n",
    "                newg.vs[inf.index][\"state\"] = \"I\"\n",
    "                exp_inf.append(inf)\n",
    "            else:\n",
    "                newg.vs[inf.index][\"state\"] = \"R\"\n",
    "                exp_recovered.append(inf)\n",
    "        for na in newg.neighbors(inf): #iterates through neighbours of that node\n",
    "            if newg.vs[na][\"state\"] == \"E\":    #previously exposed neighbours\n",
    "                newg.vs[na][\"somecol\"] = \"tfirst\"\n",
    "            if newg.vs[na][\"state\"] == \"S\": #if node is not infected...\n",
    "                s=np.random.binomial(1, beta,1)\n",
    "                if s == 1:\n",
    "                    newg.vs[na][\"state\"] = 'E'  # first contacts exposed\n",
    "                    inf_nodes.append(na)\n",
    "                    exposed_susc.append(na)\n",
    "            if (na not in inf_nodes):\n",
    "                newg.vs[na][\"somecol\"] = 'first'\n",
    "            \n",
    "    for ele in newg.vs.select(somecol_eq = 'first'):\n",
    "        newg.vs[ele.index][\"tcount\"] += 1\n",
    "        for elenb in newg.neighbors(ele):         # select neighbour/contact of contact to vaccinate  \n",
    "#             if elenb not in inf_nodes:              # if neighbour is not already exposed\n",
    "            newg.vs[elenb][\"somecol\"] = 'second'\n",
    "    for tse in newg.vs.select(somecol_eq = 'second'):\n",
    "        if newg.vs[tse.index][\"state\"] == \"S\":\n",
    "#             newg.vs[tse.index][\"tcount\"] += 1\n",
    "            newg.vs[tse.index][\"somecol_eq\"] = \"number2\"\n",
    "            sec_contacts.append(tse)\n",
    "                \n",
    "    for sec in newg.vs.select(somecol_eq = 'number2'):\n",
    "        newg.vs[sec.index][\"tcount\"] += 1\n",
    "        \n",
    "    for thefirst in newg.vs.select(somecol_eq = 'first'):\n",
    "        if newg.vs[thefirst.index][\"tcount\"]== 6:\n",
    "            B = np.random.binomial(1, (1-vac_eff)*beta,1)  #vaccinate first contacts after 3 days\n",
    "            if B == 0:\n",
    "                newg.vs[thefirst.index][\"state\"] = \"VS\"\n",
    "\n",
    "    for scon in sec_contacts:\n",
    "        if newg.vs[scon.index][\"tcount\"]== 6:\n",
    "            C = np.random.binomial(1, (1-vac_eff)*beta,1)  # vaccinate contact of contact after 3 days\n",
    "            if C == 0:\n",
    "                newg.vs[scon.index][\"state\"] = \"VS\"\n",
    "\n",
    "    for mn in newg.vs.select(state_eq = \"I\"): #iterates through each node in the network\n",
    "        newg.vs[mn.index][\"duration\"] += 1 #from day 0 to infect_len this node continues to infect                                \n",
    "        for nbm in newg.neighbors(mn): #iterates through neighbours of that node\n",
    "            if newg.vs[nbm][\"state\"] == \"S\": #if node is infected...\n",
    "                j=np.random.binomial(1, beta,1)\n",
    "                if j == 1:\n",
    "                    newg.vs[nbm][\"state\"] = \"E\"\n",
    "                    exposed_susc.append(nbm)\n",
    "        if newg.vs[mn.index][\"duration\"] in range(19,Time):\n",
    "            newg.vs[mn.index][\"state\"] = 'R'\n",
    " \n",
    "    for mv in newg.vs.select(state_eq = \"VS\"):\n",
    "        newg.vs[mv.index][\"duration\"] += 1\n",
    "        if newg.vs[mv.index][\"duration\"] in range (1,14):\n",
    "            mu = np.random.binomial(1, beta,1)\n",
    "            if mu == 1:\n",
    "                newg.vs[mv.index][\"state\"] = \"E\" \n",
    "                ini_vac_exp_ft.append(mv)\n",
    "        if newg.vs[mv.index][\"duration\"] in range (15,Time):\n",
    "            newg.vs[mv.index][\"state\"] = \"V\"\n",
    "    \n",
    "    for mvp in newg.vs.select(state_eq = \"V\"):\n",
    "        jup = np.random.binomial(1, (1-vac_eff)*beta,1)\n",
    "        if jup == 1:\n",
    "            newg.vs[mvp.index][\"state\"] = \"E\" \n",
    "            exposed_vac.append(mvp) \n",
    "    \n",
    "    return(len(newg.vs.select(state_eq = \"S\")),len(newg.vs.select(state_eq = \"E\")),len(newg.vs.select(state_eq = \"I\")),len(newg.vs.select(state_eq = \"R\")),len(newg.vs.select(state_eq = \"V\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simfxn(Time,popul):\n",
    "    vac_eff = 0.8 # vaccine efficacy\n",
    "    expolim = 0.0100000     # 1% and 3%\n",
    "    bound1 = 0.028\n",
    "    bound2 = 0.001\n",
    "    rate1 = 0.09\n",
    "    rate2 = 0.04\n",
    "    midpoint1 = 50\n",
    "    midpoint2 = 126\n",
    "    beta = double_smoothlog(Time, bound1 , bound2, rate1 , rate2 , midpoint1 , midpoint2)\n",
    "    color_dict = {\"S\": \"blue\", \"I\": \"red\", \"R\": \"green\", \"V\":\"orange\"}\n",
    "    g = ig.Graph.Erdos_Renyi(popul, m=5*popul)\n",
    "    pop = g.vcount()\n",
    "    g.vs[\"state\"] = \"S\"\n",
    "    g.vs[\"duration\"] = 0\n",
    "    g.vs[\"somecol\"] = 'THB'\n",
    "    g.vs[\"number\"] = 'random'\n",
    "\n",
    "    #randomly select an infected node to start epidemic\n",
    "    i = rd.randint(0, pop-1)\n",
    "    g.vs[i][\"state\"] = \"A\"  \n",
    "    nb_S = [pop]\n",
    "    nb_E = [1]\n",
    "    nb_I = [0]\n",
    "    nb_R = [0]\n",
    "    nb_V = [0]\n",
    "    exposed_vac = []\n",
    "    Time = Time \n",
    "    count = 0   # count the number of exposed\n",
    "    inf_nodes = []\n",
    "    infec_nodes = []\n",
    "    sec_contacts = []\n",
    "    g.vs[\"tcount\"] = 0 \n",
    "    newtime = 0\n",
    "    new_exposed =[]\n",
    "    allstates = []\n",
    "    allduration = []\n",
    "    thestates = []\n",
    "    saveg = []\n",
    "    exposed_susc = []       # susceptibles who are exposed\n",
    "    exposed_vac = []         # vaccinated group exposed after 14 days\n",
    "    exp_inf = []             # exposed who get serious infection after incuer_ringtion period\n",
    "    exp_recovered = []       # exposed who recover after incuer_ringtion period\n",
    "    ini_vac_exp_ft = []      # initial vaccinated group exposed in 14 days\n",
    "    num_vac_after_ft = []        # number of unexposed initial vaccinated after 14 days\n",
    "    \n",
    "    for time in range(Time): #no. of days \n",
    "        saveg.append(g)        \n",
    "        newbet = beta[time]\n",
    "        if len(infec_nodes) != 0:\n",
    "            newtime = infec_nodes[0][0]    # time at which exposed perc is reached\n",
    "            newg = saveg[newtime]          # the newgraph is the old graph at the time the exposed perc is reached\n",
    "            secfxn(newg,newbet,vac_eff)    # ring vaccinations function\n",
    "        else:\n",
    "            for mv in g.vs.select(state_eq = \"VS\"):\n",
    "                g.vs[mv.index][\"duration\"] += 1\n",
    "                if g.vs[mv.index][\"duration\"] in range (15,Time):\n",
    "                    g.vs[mv.index][\"state\"] = \"V\"\n",
    "            \n",
    "            for n in g.vs.select(state_eq = \"A\"): #iterates through each node in the network\n",
    "                g.vs[n.index][\"duration\"] += 1 \n",
    "                if count == round(expolim*popul):\n",
    "                    break\n",
    "                else:\n",
    "                    for nb in g.neighbors(n): #iterates through neighbours of that node\n",
    "                        if g.vs[nb][\"state\"] == \"S\": #if node is infected...\n",
    "                            s=np.random.binomial(1, beta[time],1)\n",
    "                            if s == 1:\n",
    "                                g.vs[nb][\"state\"] = \"A\" \n",
    "                                exposed_susc.append(nb)\n",
    "                                count = len(g.vs.select(state_eq = \"A\"))\n",
    "                                if count == round(expolim*popul):\n",
    "                                    infec_nodes.append([time,count])\n",
    "                                    break\n",
    "                                else:\n",
    "                                    continue\n",
    "                                break\n",
    "                            else:\n",
    "                                continue\n",
    "                            break\n",
    "                        else:\n",
    "                            continue\n",
    "                        break\n",
    "                        if g.vs[nb][\"state\"] == \"VS\": #if node is vaccinated and within  the 14 days..\n",
    "                            if g.vs[nb][\"duration\"] in range (1,14):\n",
    "                                u = np.random.binomial(1, beta[time],1)\n",
    "                                if u == 1:\n",
    "                                    g.vs[nb][\"state\"] = \"A\" \n",
    "                                    ini_vac_exp_ft.append(nb)       #initial vaccinated group exposed in 14 days\n",
    "                                    count = len(g.vs.select(state_eq = \"A\"))\n",
    "                                    if count == round(expolim*popul):\n",
    "                                        infec_nodes.append([time,count])\n",
    "                                        break\n",
    "                                    else:\n",
    "                                        continue\n",
    "                                    break\n",
    "                            else:\n",
    "                                continue\n",
    "                            break \n",
    "                        else:\n",
    "                            continue\n",
    "                        break \n",
    "                        if g.vs[nb][\"state\"] == \"V\": #if node is vaccinated...\n",
    "                            ju = np.random.binomial(1, (1-vac_eff)*beta[time],1)\n",
    "                            if ju == 1:\n",
    "                                g.vs[nb][\"state\"] = \"A\" \n",
    "                                exposed_vac.append(nb)       #initial vaccinated group exposed in 14 days\n",
    "                                count = len(g.vs.select(state_eq = \"A\"))\n",
    "                                if count == round(expolim*popul):\n",
    "                                    infec_nodes.append([time,count])\n",
    "                                    break\n",
    "                                else:\n",
    "                                    continue\n",
    "                                break\n",
    "                            else:\n",
    "                                continue\n",
    "                            break \n",
    "                        else:\n",
    "                            continue\n",
    "                        break \n",
    "                if g.vs[n.index][\"duration\"] in range(5,19):  #(7,21)\n",
    "                    nr = np.random.binomial(1,0.2,1)\n",
    "                    if nr == 1:\n",
    "                        g.vs[n.index][\"state\"] = \"I\"\n",
    "                        exp_inf.append(n)\n",
    "                    else:\n",
    "                        g.vs[n.index][\"state\"] = \"R\"\n",
    "                        exp_recovered.append(n)\n",
    "       \n",
    "            for m in g.vs.select(state_eq = \"I\"): #iterates through each node in the network\n",
    "                g.vs[m.index][\"duration\"] += 1 #from day 0 to infect_len this node continues to infect                                \n",
    "                if count == round(expolim*popul):\n",
    "                    break\n",
    "                else:\n",
    "                    for nbm in g.neighbors(m): #iterates through neighbours of that node\n",
    "                        if g.vs[nbm][\"state\"] == \"S\": #if node is infected...\n",
    "                            j=np.random.binomial(1, beta[time],1)\n",
    "                            if j == 1:\n",
    "                                g.vs[nbm][\"state\"] = \"A\"\n",
    "                                count = len(g.vs.select(state_eq = \"A\"))\n",
    "                                if count == round(expolim*popul):\n",
    "                                    infec_nodes.append([time,count])\n",
    "                                    break\n",
    "                                else:\n",
    "                                    continue\n",
    "                                break\n",
    "                            else:\n",
    "                                continue\n",
    "                            break\n",
    "                        else:\n",
    "                            continue\n",
    "                        break\n",
    "                if g.vs[m.index][\"duration\"] in range(19,Time):\n",
    "                    g.vs[m.index][\"state\"] = 'R'\n",
    "                    \n",
    "        nb_S.append(len(g.vs.select(state_eq = \"S\"))) #no. of susceptibles in population\n",
    "        nb_E.append(len(g.vs.select(state_eq = \"A\"))) #no. of recovereds in population\n",
    "        nb_I.append(len(g.vs.select(state_eq = \"I\"))) #no. of infecteds in population\n",
    "        nb_R.append(len(g.vs.select(state_eq = \"R\"))) #no. of recovereds in population\n",
    "#         nb_V.append(len(g.vs.select(state_eq = \"V\"))) #no. of vaccinateds in population  \n",
    "        \n",
    "        if len(g.vs.select(state_eq = \"V\"))==0:\n",
    "#             print(\"yebo\")\n",
    "            nb_V.append(0.000000000000000001) #no. of vaccinateds in population  \n",
    "        else:\n",
    "#             print(\"naa\")\n",
    "            nb_V.append(len(g.vs.select(state_eq = \"V\"))) #no. of vaccinateds in population  \n",
    "        \n",
    "        propo_exp_vac_ftdays = (len(ini_vac_exp_ft)/nb_V[-1])*100   # proportion of vaccinated that are exposed within 14 days    \n",
    "        propo_exp_unvac = (len(exposed_susc)/(pop))*100  # proportion of unvaccinated (susceptible) that are exposed\n",
    "        propo_exp_vac = (len(exposed_vac)/nb_V[-1])*100 #no. of recovereds in population  )*100        # proportion of vaccinated that are exposed after 14 days\n",
    "\n",
    "\n",
    "    return(nb_S,nb_E,nb_I,nb_R,nb_V,len(exp_inf), len(exp_recovered), propo_exp_unvac,propo_exp_vac,propo_exp_vac_ftdays)#,count, fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Time = 359\n",
    "popul = 1000\n",
    "rep = 10\n",
    "simout=simfxn(Time,popul)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "datavector = []\n",
    "for i in range(rep):          #repeat simulation 50 times\n",
    "    simu = simfxn(Time,popul)\n",
    "    datavector.append(simu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10.5, 5))\n",
    "\n",
    "plt.plot([x/popul for x in simout[0]], label='S',color = '#1f77b4')\n",
    "plt.plot([x/popul for x in simout[1]], label='E',color = 'yellow')\n",
    "plt.plot([x/popul for x in simout[2]], label='I',color = '#d62728')\n",
    "plt.plot([x/popul for x in simout[3]], label='R',color = '#2ca02c')\n",
    "plt.plot([x/popul for x in simout[4]], label='V',color = '#ff7f0e')\n",
    "plt.legend(loc='right')\n",
    "\n",
    "s_final = []\n",
    "e_final = []\n",
    "i_final = []\n",
    "r_final = []\n",
    "v_final = []\n",
    "\n",
    "i_each = []\n",
    "\n",
    "sList = []\n",
    "eList = []\n",
    "iList = []\n",
    "rList = []\n",
    "vList = []\n",
    "i_max = []\n",
    "\n",
    "prop_exp_vac_ft = []\n",
    "prop_exp_vac = []\n",
    "prop_exp_unvac = []\n",
    "\n",
    "all_exp_inf = []\n",
    "all_exp_recovered = []\n",
    "\n",
    "perc_inf = []\n",
    "for n in datavector:\n",
    "    sList = [x / popul for x in n[0]]\n",
    "    eList = [x / popul for x in n[1]]\n",
    "    iList = [x / popul for x in n[2]]\n",
    "    rList = [x / popul for x in n[3]]\n",
    "    vList = [x / popul for x in n[4]]\n",
    "     \n",
    "    plt.plot(sList,color = '#1f77b4')\n",
    "    plt.plot(eList,color = 'yellow')\n",
    "    plt.plot(iList,color = '#d62728')\n",
    "    plt.plot(rList,color = '#2ca02c')\n",
    "    plt.plot(vList,color = '#ff7f0e')\n",
    "    \n",
    "    s_final.append(n[0][-1])\n",
    "    e_final.append(n[1][-1])\n",
    "    i_final.append(n[2][-1])\n",
    "    r_final.append(n[3][-1])\n",
    "    v_final.append(n[4][-1])\n",
    "    \n",
    "    prop_exp_vac_ft.append(n[-1])\n",
    "    prop_exp_vac.append(n[-2])\n",
    "    prop_exp_unvac.append(n[-3])\n",
    "    \n",
    "    all_exp_inf.append(n[-5])\n",
    "    all_exp_recovered.append(n[-4])        \n",
    "    \n",
    "    i_each.append(iList)\n",
    "    i_max.append(max(iList))\n",
    "   \n",
    "    \n",
    "# plt.axvspan(50, 126, color='gray', alpha=0.5, lw=0)\n",
    "# plt.legend(loc='right')\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 20)\n",
    "plt.ylabel('Population', fontsize=30) \n",
    "plt.xlabel('Time(days)', fontsize=30)\n",
    "x=list(range(Time))\n",
    "plt.xticks(np.arange(min(x), max(x)+20, 50.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "flat_list = [item for sublist in i_each for item in sublist]\n",
    "\n",
    "flat_list = []\n",
    "for sublist in i_each:\n",
    "    for item in sublist:\n",
    "        flat_list.append(100*item)\n",
    "\n",
    "datafr = pd.DataFrame(flat_list)\n",
    "\n",
    "lst = range(0,12)\n",
    "itl = list(itertools.chain.from_iterable(itertools.repeat(x, 30) for x in lst))\n",
    "\n",
    "datafr = datafr.assign(month=itl*rep)\n",
    "datafr.columns=[\"inf\",\"months\"]\n",
    "\n",
    "\n",
    "mean = datafr.groupby(['months'])['inf'].mean().reset_index()\n",
    "mean_col = datafr.groupby(['months'])['inf'].mean() # don't reset the index!\n",
    "datafr = datafr.set_index(['months']) # make the same index here\n",
    "datafr['mean_col'] = mean_col\n",
    "datafr = datafr.reset_index() # \n",
    "                                                                            \n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "qq = sns.violinplot(x=\"months\", y=\"inf\", color=\"skyblue\",data=datafr,scale='count', bw=0.25,cut=0, inner=None)\n",
    "sns.swarmplot(x=\"months\", y=\"inf\", data=mean, color='white',edgecolor=\"black\",linewidth=0.5, size=5)\n",
    "sns.pointplot(x=\"months\", y=\"inf\", data=mean, color='grey',scale=0.5)\n",
    "qq.set_ylabel(\"Infectious cases (%)\",fontsize=30)\n",
    "qq.set_xlabel(\"Month\",fontsize=30)\n",
    "\n",
    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
    "formatter.set_powerlimits((-2,2))\n",
    "qq.yaxis.set_major_formatter(formatter)\n",
    "qq.yaxis.offsetText.set_fontsize(18)\n",
    "plt.setp(qq.collections, alpha=1.0)\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 20)\n",
    "# plt.savefig(\"rev_er_ring30100violin.pdf\", bbox_inches='tight')\n",
    "\n"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}