covid19_SEIR.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SEIR model for spread of COVID-19\n",
    "\n",
    "### Dr Wladimir Lyra, New Mexico State University (Department of Astronomy)\n",
    "\n",
    "### Dr Jaber Belkhiria, University of California, Davis  (Center for Animal Disease Modeling and Surveillance, Department of Medicine & Epidemiology, School of Veterinary Medicine)\n",
    "\n",
    "### Leandro de Almeida, Universidade Federal do Rio Grande do Norte (Department of Physics) \n",
    "\n",
    "### Dr Jose Dias do Nascimento, Universidade Federal do Rio Grande do Norte (Department of Physics) \n",
    "\n",
    "\n",
    "Details of the implementation in the publication: \n",
    "\n",
    "PLOS One: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237627\n",
    "\n",
    "medrxiv: https://www.medrxiv.org/content/10.1101/2020.04.09.20060053v1\n",
    "\n",
    "We used a modified version of an 'SIR' type deterministic compartmental model to\n",
    "trace COVID-19 epidemic evolution in an isolated population of N individuals.\n",
    "We assumed that a population could be subdivided into the following compartments:\n",
    "    Susceptible $S$: COV-19 naive individuals, \n",
    "    Exposed ($E$): Suceptible that have been exposed to infective individuals,\n",
    "    Asymptomatic $A$: Infected and infective but showing mild or no symptoms\n",
    "    Symptomatic $I$): Infected and infective but showing symptoms described in the litterature,\n",
    "    Quarantined ($Q$): Symptomatic that are not infective, \n",
    "    Hospitalized ($H$) Symptomatic, not infective, who are being treated, \n",
    "    Removed ($R$) People removed from the epidemic dynamics by\n",
    "    recovering or passing away.\n",
    "    Finally, we add a confined $C$ compartment to model the effect of confinement of a proportion of susceptible. \n",
    "\n",
    "We assumed that the population is subcategories by age (range, 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, and 80+ years old. ) and that rates should vary with age (REF).\n",
    "\n",
    "Taking into consideration the 8 compartments and the 9 age groups, the model is described by a set of 81 coupled non-linear equations: \n",
    "\n",
    "\\begin{eqnarray}\n",
    " \\frac{dS_i}{dt} &=&-\\lambda(t) S_i -\\psi_i(t) S_i + \\phi_i(t) C_i, \\label{eq:dsdt}\\\\\n",
    " \\frac{dC_i}{dt} &=&\\psi_i(t) S_i - \\phi_i(t) C_i, \\label{eq:dcdt}\\\\  \n",
    "\\frac{dE_i}{dt} &=& \\lambda(t) S_i -\\sigma E_i, \\label{eq:dedt}\\\\  \n",
    "\\frac{dA_i}{dt} &=& (1-p)\\sigma E_i - \\theta A_i, \\label{eq:dadt}\\\\\n",
    " \\frac{dI_i}{dt} &=& p\\sigma E_i - (\\gamma+\\xi) I_i +(1-w)\\theta A, \\label{eq:didt}\\\\\n",
    "\\frac{dQ_i}{dt} &=& \\gamma I_i - \\xi Q_i, \\label{eq:dqdt}\\\\  \n",
    "\\frac{dH_i}{dt} &=& q_i\\xi I_i -\\eta H_i, \\label{eq:dhdt}\\\\  \n",
    "\\frac{dR_i}{dt} &=&w\\theta A_i  + (1-q_i)\\xi I_i + \\eta H_i. \\label{eq:drdt}\\\\\n",
    "\\end{eqnarray}\n",
    "\n",
    "For each compartment $X$ the age sub-bins add up to $X = \\sum_i X_i$\n",
    "and compartments are such that $S+C+E+A+I+Q+H+R=N$, with $N=\\sum_i N_i$ being the\n",
    "total population; $N_i$ is the population in each age bin.\n",
    "\n",
    "The equations above describe a compartmentalization of\n",
    "the population and the flow between the compartments. Contact with\n",
    "infected individuals removes a fraction of the susceptible population at a\n",
    "rate given by $\\lambda$, referred to as infection force, placing them\n",
    "at the exposed population. The exposed population becomes infectious\n",
    "at the rate $\\sigma$; a fraction $p$ of them becoming symptomatic and\n",
    "$(1-p)$ not developing symptoms. The symptomatic are removed from the\n",
    "infective force (quarantined) at a rate $\\gamma$, the asymptomatic are\n",
    "removed at a rate $\\theta$, a fraction $w$ of them going in remission. A fraction $q_i$ of the quarantined\n",
    "are hospitalized at a rate $\\xi$. The hospitalized are removed at a\n",
    "rate $\\eta$. The average fatality rate is $\\mu_i$.\n",
    "\n",
    "The timescales corresponding to $\\sigma$, $\\gamma$,\n",
    "$\\theta$, $\\xi$, and $\\eta$ are the incubation time $t_\\sigma=\\sigma^{-1}$ the infectious\n",
    "interval $t_\\gamma=\\gamma^{-1}$, the remission time $t_\\theta=\\theta^{-1}$, the time to\n",
    "hospitalization $t_\\xi=\\xi^{-1}$, and  the average length of\n",
    "hospital stay $t_\\eta=\\eta^{-1}$; $\\gamma^\\prime = \\gamma +\n",
    "(1-q_i)\\xi$. \n",
    "\n",
    "The infection force is driven by the infected, both symptomatic and\n",
    "asymptomatic \n",
    "\n",
    "\\begin{equation}\n",
    "\\lambda(t) = \\beta(t) \\mathcal{I}\n",
    "\\label{eq:lambda}\n",
    "\\end{equation}\n",
    "\n",
    "where we use the shorthand notation\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal{I} = \\sum_i \\frac{\\left(I_i+A_i\\right)}{N_i}\n",
    "\\end{equation}\n",
    "\n",
    "and $\\beta$ is the infection rate, related to the reproduction number $R_t$ via\n",
    "\n",
    "\\begin{equation}\n",
    "R_t=\\frac{\\beta(t)}{\\gamma}. \n",
    "\\end{equation}\n",
    "\n",
    "Lock-down consists of having a fraction of the susceptible population removed from the epidemic dynamic by moving them from  $S_i$ to $C_i$  at a rate $\\psi_i$ .Similarly, lifting the lockdown is done by placing $C_i$ into $S_i$ at the rate $\\phi_i$. We consider these functions to be Dirac deltas\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\psi_i = a_i \\ \\delta(t -t_{\\rm lock})\\\\\n",
    "\\phi_i = b_i \\ \\delta(t -t_{\\rm lift})\n",
    "\\end{eqnarray}\n",
    "\n",
    "\\noindent where $t_{\\rm lock}$ and $t_{\\rm lift}$ are the time (in days) of lock-down and\n",
    "of lifting of the lock-down, respectively. To allow for partial\n",
    "demographic lockdowns (e.g., 80\\% lockdown of the population are in  complete\n",
    "lockdown ), $a_i$ and $b_i$ are allowed to vary by\n",
    "age (e.g., 80\\% lockdown of the 40's age group population are in  complete\n",
    "lockdown ). |\n",
    "\n",
    "\n",
    "Other diagnostic quantities are the numbers $U_i$ of people in need\n",
    "of an intensive care unit (ICU) bed\n",
    "\n",
    "\\begin{equation}\n",
    "U_i = \\zeta_i H_i\n",
    "\\end{equation}\n",
    "\n",
    "\\noindent where $\\zeta_i$ is the fraction of hospitalized patients\n",
    "that need critical care. Both $\\zeta_i$ and the hospitalization fraction $q_i$ are\n",
    "age-stratified. \n",
    "\n",
    "\n",
    "#### Fitting the model to the 2020 Covid-19 epidemic\n",
    "\n",
    "We consider the susceptible population (S) as the total population of a country since at the onset of outbreak no one is immune to the virus yet. Model's parameters were based on previous knwoledge of Coronaviruses and early reports and research on COVID-19 \\cite{Kulkharni} Table~\\ref{table:table1}. \n",
    "The age-dependent parameters are fatality rate $\\mu_i$, fraction of infectious that are\n",
    "hospitalized $q_i$, and fraction of hospitalized that need critical\n",
    "care.\n",
    "\n",
    "Because all these timescales are\n",
    "much smaller than a human lifetime, ageing of the population is\n",
    "ignored and no upward flow between the age subcompartments ($i\\rightarrow i+1$) is considered. \n",
    "\n",
    "We derive $R_0$  from the available statistics since  knowledge on the  real number of infected is not clear. The most reliable indicator in this situation is\n",
    "the number of deaths. Given a fatality rate $\\mu$ and an average time\n",
    "$\\tau$ between exposure and death, we can say that\n",
    "the number of dead at a time $t + \\tau$ will equal the\n",
    "fatality rate times the number of people that got exposed at time\n",
    "$t$. Assuming that confinement dynamics do not play a role, the equation is the following:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Delta D_i (t+\\tau) = -\\mu_i\\Delta S_i (t).\n",
    "\\end{equation}\n",
    "\n",
    "Taking the continuous limit and substituting \\eq{eq:dsdt} \n",
    "\n",
    "\\begin{equation}\n",
    "  \\frac{d}{dt} D_i (t_r) = \\mu_i\\lambda(t) S_i \n",
    "\\end{equation}\n",
    "\n",
    "\\noindent where we also write $t_r = t+\\tau$ for the retarded time. Summing over all age bins $D=\\sum_i D_i$ we have the cumulative death rate on the\n",
    "LHS, which is an observable\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "  \\frac{d}{dt} D (t_r) = \\lambda (t) \\langle \\mu S \\rangle\n",
    "\\end{equation}\n",
    "\n",
    "\\noindent and $\\langle{\\mu S}\\rangle = \\sum_i \\mu_i S_i$.  We can then substitute \\eq{eq:lambda}  and solve for $R_t$ as a function of time \n",
    "\n",
    "\\begin{equation}\n",
    "  R_t =  \\frac{1}{\\gamma\\mathcal{I}\\langle{\\mu S}\\rangle} \\frac{d}{dt} D (t_r).    \n",
    "  \\label{eq:rt}\n",
    "\\end{equation}\n",
    "\n",
    "Since death occurs an average of $\\tau$ days after infections, we setup the integration to  start $\\tau$ days before the first reported COVID-19 death, i.e., $t=0$ means $t_r =\n",
    "\\tau$. Finally, to start the integration we need to define the initial number of infected individuals. This should be \n",
    "\n",
    "\\begin{equation}\n",
    "I_0 (t_0)   = {\\overline{\\mu}}^{-1} D_0(t_0+\\tau)\n",
    "\\end{equation}\n",
    "\n",
    "\\noindent where $t_0$ is the time of the first death and\n",
    "$\\overline{\\mu}=N^{-1}\\sum_i \\mu_i n_i$ is the age-weighted fatality rate. According to current knowledge of the epidemics, $\\tau \\approx 14$ days. \n",
    "\n",
    "For integration, we use a 3rd order Runge-Kutta algorithm, with timesteps $\\Delta t = \\tau_\\beta/2$. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Input parameters\n",
    "'''\n",
    "lockdown='3/01/20' #country-specific overwritten\n",
    "fac1=0.0 # fraction removed at lockdown and confined \n",
    "\n",
    "# confinement factor by age bin, to test vertical confinement hypothesis\n",
    "# age bins = 0-10,10-20,20-30,30-40,40-50,50-60,60-70,70-80,80+\n",
    "factor1 = np.array([fac1,fac1,fac1,fac1,fac1,fac1,fac1,fac1,fac1])   # by age bin\n",
    "\n",
    "release='5/03/20' #release from lockdown m/dd/yy\n",
    "fac2=0.0 # fraction released from confinment\n",
    "factor2 = np.array([fac2,fac2,fac2,fac2,fac2,fac2,fac2,fac2,fac2])   \n",
    "\n",
    "# Virus/Infection dependent parameters\n",
    "\n",
    "Tincubation      = 5.2  # length of incubation period\n",
    "Tinfection       = 2.9  # duration patient is infectious\n",
    "Thospitalization = 5.   # time to hospitalization\n",
    "Thospitalized    = 10.  # length of hospital stay\n",
    "Tdeath           = 14.  # time from exposure to death\n",
    "p                =0.8   # fraction of symptomatic and asymptomatic cases\n",
    "w                =0.8   # asymptomatic that cure on their own\n",
    "\n",
    "# Date to end computations \n",
    "tmax_date = '10/30/20'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#  Country-specific block \n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pylab as plt\n",
    "import sys \n",
    "#\n",
    "# Timescales\n",
    "sigma = 1./Tincubation\n",
    "gamma = 1./Tinfection\n",
    "mu    = 1./Tdeath  \n",
    "eta   = 1./Thospitalized\n",
    "xi    = 1./Thospitalization\n",
    "\n",
    "theta = mu\n",
    "tmu = Tdeath\n",
    "\n",
    "a = 1.575  # empirically determined for dirac delta\n",
    "ampl1 =  factor1*a\n",
    "ampl2 =  factor2*a\n",
    "\n",
    "#\n",
    "# London study breakdown by age\n",
    "#\n",
    "age_bins                     = np.array([10,20,30,40,50,60,70,80,90])\n",
    "fatality_rate_age            = .01*np.array([0.002,0.006,0.03,0.08,0.15,0.60,2.2,5.1,9.3])\n",
    "hospitalization_fraction_age = .01*np.array([0.1,0.3,1.2,3.2,4.9,10.2,16.6,24.3,27.3])\n",
    "critical_care_age            = .01*np.array([5,5,5,5,6.3,12.2,27.4,43.2,70.9])\n",
    "#icu_fraction_age             =  hospitalization_fraction_age*critical_care_age\n",
    "\n",
    "q = hospitalization_fraction_age\n",
    "gammap = gamma + (1-q)*xi\n",
    "    \n",
    "SMALL_SIZE = 16\n",
    "MEDIUM_SIZE = 18\n",
    "BIGGER_SIZE = 20\n",
    "\n",
    "plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes                   \n",
    "plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title               \n",
    "plt.rc('axes', labelsize=BIGGER_SIZE) # fontsize of the x and y labels           \n",
    "plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels             \n",
    "plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels             \n",
    "plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize                         \n",
    "plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title         \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next blocks have funtion definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    " import datetime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_to_time(d):\n",
    "    #from datetime import datetime    \n",
    "\n",
    "    n=len(d)    \n",
    "    \n",
    "    date=np.zeros(n)\n",
    "    for i in range(n):\n",
    "        date_object = datetime.datetime.strptime(d[i], '%m/%d/%y')\n",
    "        date[i] = datetime.datetime.fromisoformat(np.str(date_object)).timestamp()\n",
    "                  \n",
    "    return (date-date[n-1])/86400.                \n",
    "\n",
    "################################################################# \n",
    "\n",
    "def date_to_time_scl(d,d0):  \n",
    "    date1             = datetime.datetime.fromisoformat(\n",
    "                        np.str(datetime.datetime.strptime(d, '%m/%d/%y'))\n",
    "                        ).timestamp()\n",
    "    time              = (date1-d0)/86400.\n",
    "    return time;\n",
    "\n",
    "#################################################################    \n",
    "    \n",
    "def calc_doubling_time(time,cases):\n",
    "\n",
    "    exponential_slope = np.gradient(np.log(1.0*cases+1e-10),time) \n",
    "\n",
    "    n=len(exponential_slope)\n",
    "    doubling_time=np.zeros(n)\n",
    "    for i in range(n):\n",
    "        if (exponential_slope[i] != 0):\n",
    "            doubling_time[i] = np.log(2.)/exponential_slope[i]\n",
    "        else:\n",
    "            doubling_time[i]=0.0\n",
    "    \n",
    "    return doubling_time\n",
    "\n",
    "#################################################################\n",
    "\n",
    "def get_fatality_rate(age):\n",
    "    return np.sum(fatality_rate_age*age)/np.sum(age)\n",
    "\n",
    "#################################################################\n",
    "\n",
    "def read_jhu_data(country,mode):\n",
    "    import csv\n",
    "    base='./jhudata/time_series_covid19_' \n",
    "    with open(base+mode+'_global.csv', newline='') as csvfile:    \n",
    "        datareader = csv.reader(csvfile, delimiter=',')#, quotechar='|')\n",
    "        data = {} \n",
    "        for row in datareader:\n",
    "            if (row[0] == 'Province/State'):\n",
    "                n=len(row)\n",
    "                dates = row[4:n]\n",
    "                for i in range(len(dates)):\n",
    "                    data[dates[i]]=0\n",
    "            else:\n",
    "                if (row[1]==country):\n",
    "                    for i in range(len(dates)):\n",
    "                        if (row[i+4]==''): \n",
    "                            row[i+4]='0'                        \n",
    "                        data[dates[i]] += np.int(row[i+4])\n",
    "    \n",
    "    return data,dates                \n",
    "\n",
    "#################################################################    \n",
    "\n",
    "def read_jhu_data_pandas(country,mode):\n",
    "    import csv\n",
    "    import pandas as pd\n",
    "    base='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_'\n",
    "    datareader=pd.read_csv(base+mode+'_global.csv',sep=',',index_col ='Type')\n",
    "    data = {}\n",
    "    #dates = []\n",
    "    for row in datareader:\n",
    "        print(row[0])\n",
    "        \n",
    "        #if (row[0] == 'Province/State'):\n",
    "        #    n=len(row)\n",
    "        #    dates = row[4:n]\n",
    "        #    for i in range(len(dates)):\n",
    "        #        data[dates[i]]=0\n",
    "        #else:\n",
    "        #    if (row[1]==country):\n",
    "        #        for i in range(len(dates)):\n",
    "        #            if (row[i+4]==''): \n",
    "        #                row[i+4]='0'                        \n",
    "        #            data[dates[i]] += np.int(row[i+4])\n",
    "    sys.exit()\n",
    "    return data,dates                \n",
    "\n",
    "#################################################################    \n",
    "\n",
    "def read_population_pyramid_data(country):\n",
    "    import csv\n",
    "    #base='./dat/time_series_19-covid-' \n",
    "    #with open(base+mode+'.csv', newline='') as csvfile:\n",
    "    if (country=='Korea, South'):\n",
    "        file='./popdata/SKorea-2019.csv' \n",
    "    elif (country=='United Kingdom'):\n",
    "        file='./popdata/UK-2019.csv' \n",
    "    elif (country=='Saudi Arabia'):\n",
    "        file='./popdata/KSA-2019.csv'         \n",
    "    else:\n",
    "        file='./popdata/'+country+'-2019.csv' \n",
    "#\n",
    "    with open(file, newline='') as csvfile:    \n",
    "        datareader = csv.reader(csvfile)#, delimiter=',', quotechar='|')\n",
    "        #data = {} \n",
    "        population=[]\n",
    "        for row in datareader:\n",
    "            if (row[0] != 'Age'):\n",
    "                population.append(np.int(row[1])+np.int(row[2]))\n",
    "    \n",
    "    pop=np.array(population)\n",
    "    age_brackets=np.zeros(9)\n",
    "    for i in range(9):\n",
    "        #print(i,2*i,2*i+1)\n",
    "        age_brackets[i] = pop[2*i]+pop[2*i+1]\n",
    "    #add the 90+ to the 80-90    \n",
    "    age_brackets[8]=age_brackets[8] + np.sum(pop[len(pop)-3:len(pop)-1])\n",
    "    \n",
    "    return age_brackets\n",
    "  \n",
    "#################################################################    \n",
    "\n",
    "def appendvalues(S,C,E,A,I,Q,H,U,R,F,D,t,SS,CC,EE,AA,II,QQ,HH,UU,RR,FF,DD,tt):\n",
    "    SS.append(S)\n",
    "    CC.append(C)    \n",
    "    EE.append(E)\n",
    "    AA.append(A)    \n",
    "    II.append(I)\n",
    "    QQ.append(Q)    \n",
    "    HH.append(H)\n",
    "    UU.append(U)    \n",
    "    RR.append(R)\n",
    "    FF.append(F)\n",
    "    DD.append(D)\n",
    "    tt.append(t)\n",
    "    return SS,CC,EE,AA,II,QQ,HH,UU,RR,FF,DD,tt    \n",
    "    \n",
    "#################################################################    \n",
    "    \n",
    "def get_kronecker_delta(time,t0,tt,ampl,dt):    \n",
    "    \n",
    "    if (time-t0 > 0 and tt-t0<0):\n",
    "        delta=1.\n",
    "    else:\n",
    "        delta=0.\n",
    "    psi=delta*ampl/dt\n",
    "    \n",
    "    return psi    \n",
    "        \n",
    "#################################################################    \n",
    "    \n",
    "def write_output(it,t,dt,Rt,g,d):        \n",
    "    \n",
    "    g.write(\"%d %E %E %E %E %E %E %E %E %E %E %E %E\\n\"\n",
    "                %(it,t,dt,Rt,d[0],d[1],d[2],d[3],d[4],d[5],d[6],d[7],d[8]))    \n",
    "    \n",
    "#################################################################\n",
    "    \n",
    "def get_data_country(name):\n",
    "\n",
    "    dict_confirmed,dates = read_jhu_data(name,'confirmed')\n",
    "    dict_recovered,dates = read_jhu_data(name,'recovered')\n",
    "    dict_deaths,dates    = read_jhu_data(name,'deaths')\n",
    "    \n",
    "    n1=len(dict_confirmed.keys())\n",
    "    n2=len(dict_recovered.keys())\n",
    "    if (n1!=n2):\n",
    "        sys.exit()\n",
    "    n3=len(dict_deaths.keys())    \n",
    "    if (n2!=n3):\n",
    "        sys.exit()\n",
    "    \n",
    "    confirmed=np.zeros(len(dates))\n",
    "    recovered=np.zeros(len(dates))\n",
    "    deaths   =np.zeros(len(dates))\n",
    "    for i in range(len(dates)):\n",
    "        confirmed[i] = dict_confirmed[dates[i]]\n",
    "        recovered[i] = dict_recovered[dates[i]]\n",
    "        deaths[i]    = dict_deaths[dates[i]]\n",
    "\n",
    "\n",
    "    country = dict([\n",
    "                    ('name',name),\n",
    "                    ('dates', dates),\n",
    "                    ('confirmed', confirmed),\n",
    "                    ('recovered', recovered),\n",
    "                    ('deaths', deaths),\n",
    "                   ])        \n",
    "        \n",
    "    return country\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Death rate \n",
    "\n",
    "The last parameter in the model is the death rate. The removed are either recovered/cured (C) or dead (D), according to \n",
    "\n",
    "\\begin{eqnarray}\n",
    "D(t) &=& \\mu \\ I(t-\\tau_\\gamma)\n",
    "\\end{eqnarray}\n",
    "\n",
    "where $\\mu$ is the death rate. The cured are supposed to have gained immunity and not added again to the susceptible population. Current knowledge of the epidemic seem to imply $\\mu$=0.01. \n",
    "\n",
    "### Model applied to the current epidemics \n",
    "\n",
    "Given a universal $\\gamma=0.04$, the model input are the doubling times $t_{\\rm double}$ and the initial number of infected $I_0$. Below are the numbers of March 14th for Italy, S. Korea, China, and the USA. The total population is for normalization purposes only. The model assumes N=1 and works with population fractions instead.  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_country(name):\n",
    "    \n",
    "    import csv\n",
    "    #import urllib2\n",
    "    from urllib.request import urlopen\n",
    "    \n",
    "    dict_confirmed,dates = read_jhu_data(name,'confirmed')\n",
    "    dict_deaths,dates    = read_jhu_data(name,'deaths')\n",
    "    \n",
    "    n1=len(dict_confirmed.keys())\n",
    "    n2=len(dict_deaths.keys())    \n",
    "    if (n1!=n2):\n",
    "        sys.exit()\n",
    "    \n",
    "    confirmed=np.zeros(len(dates))\n",
    "    deaths   =np.zeros(len(dates))\n",
    "    for i in range(len(dates)):\n",
    "        confirmed[i] = dict_confirmed[dates[i]]\n",
    "        deaths[i]    = dict_deaths[dates[i]]\n",
    "\n",
    "    age=read_population_pyramid_data(name)    \n",
    "    N=np.sum(age)\n",
    "    fatality_rate            = np.sum(fatality_rate_age           *age)/np.sum(age)\n",
    "\n",
    "    D0=1. #initial number of dead to start computations. Not 1 only for China (overwritten\n",
    "    if (name==\"China\"):\n",
    "        lockdown = '1/23/20'\n",
    "        D0=min(deaths)\n",
    "        median_age=38.4\n",
    "        icu_beds_per_1e5=3.6\n",
    "    elif (name== \"Korea, South\"):\n",
    "        lockdown = '2/18/20'        \n",
    "        median_age=40.8\n",
    "        icu_beds_per_1e5=10.6        \n",
    "    elif (name == 'Iran'):    \n",
    "        lockdown = '2/22/20'\n",
    "        median_age=32.\n",
    "        icu_beds_per_1e5=5.3        \n",
    "    elif (name == 'Italy'):\n",
    "        lockdown = '3/09/20'\n",
    "        median_age=47.3\n",
    "        icu_beds_per_1e5=12.5        \n",
    "    elif (name == 'Denmark'):\n",
    "        lockdown = '3/11/20'        \n",
    "        median_age=41.6\n",
    "        icu_beds_per_1e5=6.7        \n",
    "    elif (name == 'Norway'):\n",
    "        lockdown = '3/12/20'                       \n",
    "        median_age=39.2\n",
    "        icu_beds_per_1e5=8.        \n",
    "    elif (name == 'Poland'):\n",
    "        lockdown = '3/13/20'                               \n",
    "        median_age=39.7\n",
    "        icu_beds_per_1e5=6.9        \n",
    "    elif (name==\"Spain\"):\n",
    "        lockdown = '3/14/20'\n",
    "        median_age=43.1\n",
    "        icu_beds_per_1e5=9.7        \n",
    "    elif (name==\"US\"):\n",
    "        lockdown = '3/19/20'\n",
    "        median_age=38.2\n",
    "        icu_beds_per_1e5=34.7        \n",
    "    elif (name==\"Sweden\"):\n",
    "        lockdown='3/01/20'\n",
    "        median_age=40.9\n",
    "        icu_beds_per_1e5=5.8 \n",
    "    elif (name==\"Brazil\"):\n",
    "        lockdown='3/24/20'\n",
    "        median_age=31.4\n",
    "        icu_beds_per_1e5=18.        \n",
    "    elif (name==\"Tunisia\"):\n",
    "        lockdown ='3/10/20'\n",
    "        median_age=31.3\n",
    "        icu_beds_per_1e5=2.72        \n",
    "    elif (name==\"Germany\"):\n",
    "        lockdown = ''  \n",
    "        median_age=45.9\n",
    "        icu_beds_per_1e5=29.2        \n",
    "    elif (name==\"Japan\"):\n",
    "        lockdown = '' \n",
    "        median_age=47.3\n",
    "        icu_beds_per_1e5=7.3        \n",
    "    elif (name==\"France\"):\n",
    "        lockdown = ''  \n",
    "        median_age=41.2\n",
    "        icu_beds_per_1e5=11.6        \n",
    "    elif (name=='Ireland'):\n",
    "        lockdown = ''  \n",
    "        median_age=36.5\n",
    "        icu_beds_per_1e5=6.5        \n",
    "    elif (name=='Uruguay'):   \n",
    "        lockdown= ''\n",
    "        median_age=34.9\n",
    "        icu_beds_per_1e5=6.        \n",
    "    elif (name=='Chile'):\n",
    "        lockdown= ''\n",
    "        median_age=33.8   \n",
    "        icu_beds_per_1e5=6.        \n",
    "    elif (name=='India'):\n",
    "        lockdown= ''\n",
    "        median_age=26.8\n",
    "        icu_beds_per_1e5=5.2        \n",
    "    elif (name=='United Kingdom'):\n",
    "        lockdown= ''\n",
    "        median_age=26.8\n",
    "        icu_beds_per_1e5=6.6        \n",
    "    elif (name=='Switzerland'):\n",
    "        lockdown= ''\n",
    "        median_age=26.8\n",
    "        icu_beds_per_1e5=11.        \n",
    "    elif (name=='Canada'):\n",
    "        lockdown= ''\n",
    "        median_age=26.8\n",
    "        icu_beds_per_1e5=11.            \n",
    "    elif (name=='Sudan'):\n",
    "        lockdown= ''\n",
    "        median_age=18.9\n",
    "        icu_beds_per_1e5=0.185                    \n",
    "    elif (name=='Netherlands'):\n",
    "        lockdown= ''\n",
    "        median_age=42.1\n",
    "        icu_beds_per_1e5=6.5                    \n",
    "    elif (name=='Saudi Arabia'):\n",
    "        lockdown= ''\n",
    "        median_age=30.0\n",
    "        icu_beds_per_1e5=0.0                            \n",
    "    elif (name=='Russia'):\n",
    "        lockdown= ''\n",
    "        median_age=40.0\n",
    "        icu_beds_per_1e5=1045.67                                    \n",
    "    else:\n",
    "        print(\"choose a valid country\")\n",
    "        sys.exit()    \n",
    "    \n",
    "    number_of_icu_beds=icu_beds_per_1e5 * N / 1e5  \n",
    "\n",
    "    # picks the day of the first death to start the integration; \n",
    "    # it is the best predictor of what is actually happening in a country;\n",
    "    if (deaths.max() >= D0):  \n",
    "        index_D0 = [i for i in range(len(deaths)) if deaths[i] >= D0][0]\n",
    "    else:\n",
    "        index_D0 = 0\n",
    "    date_D0 = dates[index_D0]\n",
    "    \n",
    "    days_past=date_to_time(dates)    \n",
    "    time_D0=days_past[index_D0]\n",
    "    doubling_time=calc_doubling_time(days_past,confirmed)[len(days_past)-1]    \n",
    "\n",
    "    country = dict([\n",
    "                    ('name', name),\n",
    "                    ('D0',D0),\n",
    "                    ('N',N),\n",
    "                    ('days past',days_past),\n",
    "                    ('cases',confirmed),\n",
    "                    ('doubling time',doubling_time),\n",
    "                    ('deaths',deaths),\n",
    "                    ('time_D0',time_D0),\n",
    "                    ('index_D0',index_D0),\n",
    "                    ('lockdown',lockdown),\n",
    "                    ('fatality_rate',fatality_rate),\n",
    "                    ('median_age',median_age),\n",
    "                    ('number_of_icu_beds',number_of_icu_beds),\n",
    "                    ('age',age)\n",
    "                   ])\n",
    "    \n",
    "    return country\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RK3(f):\n",
    "\n",
    "    import os\n",
    "    \n",
    "    N             = f['N']\n",
    "    D0            = f['D0']\n",
    "    time_D0       = f['time_D0']\n",
    "    iD0           = f['index_D0']\n",
    "    days_past     = f['days past']\n",
    "    deaths        = f['deaths']    \n",
    "    name          = f['name']\n",
    "    lockdown      = f['lockdown']\n",
    "    fatality_rate = f['fatality_rate']\n",
    "    #hosp_fraction = f['hospitalization_fraction']\n",
    "    #icu_fraction  = f['icu_fraction']\n",
    "    number_of_icu_beds=f['number_of_icu_beds']\n",
    "    pop               =f['age']\n",
    "    \n",
    "    alpha_ts   = np.double([0.   , -5./9.  ,-153./128.])\n",
    "    beta_ts    = np.double([1./3., 15./16. ,   8./15. ])\n",
    "    Cdt = 0.5\n",
    "\n",
    "    #\n",
    "    # Deaths reflect infections 1/gamma days past\n",
    "    #\n",
    "    E0    = (D0/N)/fatality_rate\n",
    "    nu    = fatality_rate_age\n",
    "#    \n",
    "    cases=np.array(deaths[iD0:len(deaths)])\n",
    "    tpast=days_past[iD0:len(deaths)]\n",
    "    dDdt = np.gradient((1.0*cases/N),tpast)  \n",
    "#\n",
    "# Lists to store the populations \n",
    "#\n",
    "    SS=[]  # susceptible\n",
    "    CC=[]  # confined\n",
    "    EE=[]  # exposed\n",
    "    AA=[]  # assymptomatic    \n",
    "    II=[]  # infected symptomatic\n",
    "    QQ=[]  # isolated\n",
    "    HH=[]  # hospitalized \n",
    "    UU=[]  # ICU patients    \n",
    "    RR=[]  # removed\n",
    "    FF=[]  # dead (fatalities)\n",
    "    DD=[]  # dead -- different array for comparison\n",
    "    tt=[]  # time\n",
    "#\n",
    "# Initial values 1/gamma ago  \n",
    "#\n",
    "    E=np.zeros(9)\n",
    "    E[4]=E0\n",
    "    C=np.zeros(9)\n",
    "    I=np.repeat(1e-30,9)\n",
    "    A=np.zeros(9)\n",
    "    Q=np.zeros(9)\n",
    "    H=np.zeros(9)\n",
    "    R=np.zeros(9)\n",
    "    F=np.zeros(9)    \n",
    "    ni=pop/N\n",
    "    S=(1-C-E-A-I-Q-H-R-F) * ni\n",
    "    U=np.zeros(9)\n",
    "    D=np.zeros(9)\n",
    "    \n",
    "#    \n",
    "    sS=sum(S)\n",
    "    sE=sum(E)\n",
    "    sA=sum(A)\n",
    "    sI=sum(I)\n",
    "#\n",
    "    dSdt=np.zeros(9)\n",
    "    dCdt=np.zeros(9)\n",
    "    dEdt=np.zeros(9)\n",
    "    dAdt=np.zeros(9)\n",
    "    dIdt=np.zeros(9)\n",
    "    dQdt=np.zeros(9)    \n",
    "    dHdt=np.zeros(9)\n",
    "    dRdt=np.zeros(9)\n",
    "    dFdt=np.zeros(9)    \n",
    "    \n",
    "#  Start the integration\n",
    "    itmax=100000   \n",
    "#\n",
    "#  Use t=0 as the time of first death minus 1/gamma\n",
    "#\n",
    "    t= time_D0-tmu\n",
    "    ds=0.\n",
    "#\n",
    "    SS,CC,EE,AA,II,QQ,HH,UU,RR,FF,DD,tt = appendvalues(sS,0.,sE,sA,sI,0.,0.,0.,0.,0.,0.,t,SS,CC,EE,AA,II,QQ,HH,UU,RR,FF,DD,tt)\n",
    "    RRt=[0.]\n",
    "\n",
    "    today        = datetime.datetime.fromisoformat(np.str(datetime.datetime.today())).timestamp() \n",
    "    \n",
    "    if (lockdown!=''):\n",
    "        tlockdown = date_to_time_scl(lockdown,today)\n",
    "    else:\n",
    "        #default to today\n",
    "        tlockdown=0.\n",
    "        \n",
    "    if (release!=''):\n",
    "        trelease=date_to_time_scl(release,today)        \n",
    "    else:\n",
    "        #default to no release\n",
    "        trelease=1e30\n",
    "        \n",
    "    tmax = date_to_time_scl(tmax_date,today)\n",
    "\n",
    "#\n",
    "#  Open file for writing\n",
    "#\n",
    "    dirBase='output'\n",
    "    if not os.path.exists(dirBase):\n",
    "        os.mkdir(dirBase)\n",
    "    dirName=dirBase+'/'+name\n",
    "    if not os.path.exists(dirName):\n",
    "        os.mkdir(dirName)    \n",
    "    fS = open(dirName+'/'+name+'_Sfile.dat','w+')\n",
    "    fC = open(dirName+'/'+name+'_Cfile.dat','w+')\n",
    "    fE = open(dirName+'/'+name+'_Efile.dat','w+')\n",
    "    fA = open(dirName+'/'+name+'_Afile.dat','w+')\n",
    "    fI = open(dirName+'/'+name+'_Ifile.dat','w+')\n",
    "    fQ = open(dirName+'/'+name+'_Qfile.dat','w+')\n",
    "    fH = open(dirName+'/'+name+'_Hfile.dat','w+')\n",
    "    fU = open(dirName+'/'+name+'_Ufile.dat','w+')\n",
    "    fR = open(dirName+'/'+name+'_Rfile.dat','w+')    \n",
    "        \n",
    "    #print 'it --- t (day) --- dt --- Rt --- S'\n",
    "    #f.write(\"%d %E %E %E %E %E %E %E %E %E %E %E %E\\n\"%(it,t,dt,Rt,S[0:9]))\n",
    "    \n",
    "    for it in np.arange(itmax):\n",
    "#                                                                                \n",
    "        tretarded = t + tmu\n",
    "        if (tretarded < 0):    \n",
    "            #beta = np.interp(tretarded,tpast,pastbeta)\n",
    "            dDdt_ = np.interp(tretarded,tpast,dDdt)               \n",
    "            smuS = sum(fatality_rate_age*S)\n",
    "            beta = 1/smuS * 1/(sA+sI) * dDdt_ \n",
    "        else:\n",
    "            beta = beta\n",
    "        Rt = beta/gamma                      \n",
    "            \n",
    "#  Timestep defined by the minimum timescale \n",
    "#\n",
    "        dt = Cdt*np.array([1./beta,1./sigma,1/eta,1/theta,1/gamma,1/xi]).min()\n",
    "#               \n",
    "        dt_beta_ts = [i * dt for i in beta_ts]\n",
    "#                                                                                \n",
    "#  Coefficients for confinement and release\n",
    "#\n",
    "        psi1=get_kronecker_delta(t,tlockdown,np.array(tt)[it-1],ampl1,dt)\n",
    "        psi2=get_kronecker_delta(t,trelease ,np.array(tt)[it-1],ampl2,dt)        \n",
    "#\n",
    "# Starts RK3: advance time\n",
    "#\n",
    "        for itsub in range(3):\n",
    "            ds  = alpha_ts[itsub]*ds\n",
    "            ds  = ds+1.\n",
    "            t   = t + dt_beta_ts[itsub]*ds\n",
    "#\n",
    "# advance quantities\n",
    "#\n",
    "        for itsub in range(3):\n",
    "            sI=sum(I)\n",
    "            sA=sum(A)\n",
    "            Finf=beta*(sI+sA)\n",
    "            for ip in range(9): #subpopulation bins \n",
    "                dSdt[ip]   = alpha_ts[itsub]*dSdt[ip]\n",
    "                dCdt[ip]   = alpha_ts[itsub]*dCdt[ip]            \n",
    "                dEdt[ip]   = alpha_ts[itsub]*dEdt[ip]\n",
    "                dAdt[ip]   = alpha_ts[itsub]*dAdt[ip]            \n",
    "                dIdt[ip]   = alpha_ts[itsub]*dIdt[ip]\n",
    "                dQdt[ip]   = alpha_ts[itsub]*dQdt[ip]                \n",
    "                dHdt[ip]   = alpha_ts[itsub]*dHdt[ip]            \n",
    "                dRdt[ip]   = alpha_ts[itsub]*dRdt[ip]\n",
    "                dFdt[ip]   = alpha_ts[itsub]*dFdt[ip]                \n",
    "                        \n",
    "            #dSdt = dSdt - beta*(I+A)*S - psi*S  + phi*C\n",
    "            #dCdt = dCdt                + psi*S  - phi*C\n",
    "            #dEdt = dEdt + beta*(I+A)*S         -       sigma*E            \n",
    "            #dAdt = dAdt                        + (1-p)*sigma*E - theta*A\n",
    "            #dIdt = dIdt                        +    p *sigma*E            -   gamma*I\n",
    "            #dQdt = dQdt                                                       gamma*I -       xi*Q            \n",
    "            #dHdt = dHdt                                                               +    q *xi*Q - eta*H\n",
    "            #dRdt = dRdt                                         + theta*A             + (1-q)*xi*Q + eta*H\n",
    "\n",
    "                dSdt[ip] = dSdt[ip] - Finf*S[ip] - psi1[ip]*S[ip] + psi2[ip]*C[ip]\n",
    "                dCdt[ip] = dCdt[ip]              + psi1[ip]*S[ip] - psi2[ip]*C[ip] \n",
    "                dEdt[ip] = dEdt[ip] + Finf*S[ip]                             -       sigma*E[ip]            \n",
    "                dAdt[ip] = dAdt[ip]                                          + (1-p)*sigma*E[ip] -       theta*A[ip]\n",
    "                dIdt[ip] = dIdt[ip]                                          +    p *sigma*E[ip] + (1-w)*theta*A[ip] - gamma*I[ip]     \n",
    "                dQdt[ip] = dQdt[ip]                                                                                  + gamma*I[ip] -           xi*Q[ip]               \n",
    "                dHdt[ip] = dHdt[ip]                                                                                                +    q[ip] *xi*Q[ip] - eta*H[ip]\n",
    "                dRdt[ip] = dRdt[ip]                                                              +    w *theta*A[ip]               + (1-q[ip])*xi*Q[ip] + eta*H[ip]\n",
    "                #dFdt[ip] = dFdt[ip]                                                                                                                     +    nu[ip] *eta*H[ip]\n",
    "\n",
    "                # The F bin was originally for tracking a dynamic fatalities compartment. \n",
    "                # but because the dynamics is not fully captured we don't use it. I'm \n",
    "                # leavind this then for diagnostic purposes. \n",
    "                dFdt[ip] = dFdt[ip] + q[ip]*xi*Q[ip]                \n",
    "\n",
    "                \n",
    "                \n",
    "                S[ip] = S[ip] + dt_beta_ts[itsub]*dSdt[ip]\n",
    "                C[ip] = C[ip] + dt_beta_ts[itsub]*dCdt[ip]            \n",
    "                E[ip] = E[ip] + dt_beta_ts[itsub]*dEdt[ip]\n",
    "                A[ip] = A[ip] + dt_beta_ts[itsub]*dAdt[ip]            \n",
    "                I[ip] = I[ip] + dt_beta_ts[itsub]*dIdt[ip]\n",
    "                Q[ip] = Q[ip] + dt_beta_ts[itsub]*dQdt[ip]                \n",
    "                H[ip] = H[ip] + dt_beta_ts[itsub]*dHdt[ip]            \n",
    "                R[ip] = R[ip] + dt_beta_ts[itsub]*dRdt[ip]\n",
    "                F[ip] = F[ip] + dt_beta_ts[itsub]*dFdt[ip]\n",
    "\n",
    "                U[ip] = H[ip]*critical_care_age[ip]\n",
    "                D[ip] = fatality_rate_age[ip]*(ni[ip]-(S[ip]+C[ip]))\n",
    "                #D[ip] = fatality_rate_age[ip]*E[ip]\n",
    "                \n",
    "                \n",
    "        SS,CC,EE,AA,II,QQ,HH,UU,RR,FF,DD,tt = appendvalues(sum(S),sum(C),sum(E),sum(A),sum(I),sum(Q),sum(H),sum(U),sum(R),sum(F),sum(D),\n",
    "                                                                        t,\n",
    "                                                                        SS,CC,EE,AA,II,QQ,HH,UU,RR,FF,DD,tt)\n",
    "        RRt.append(Rt)\n",
    "# \n",
    "        write_output(it,t,dt,Rt,fS,S)\n",
    "        write_output(it,t,dt,Rt,fC,C)\n",
    "        write_output(it,t,dt,Rt,fE,E)\n",
    "        write_output(it,t,dt,Rt,fA,A)\n",
    "        write_output(it,t,dt,Rt,fI,I)\n",
    "        write_output(it,t,dt,Rt,fQ,Q)\n",
    "        write_output(it,t,dt,Rt,fH,H)        \n",
    "        write_output(it,t,dt,Rt,fU,U)\n",
    "        write_output(it,t,dt,Rt,fR,R)        \n",
    "#\n",
    "        if ((it == itmax) or t > tmax):\n",
    "            print(f'End of simulation at t = {np.int(t):d} days \\n')\n",
    "            #\n",
    "            #  Separate the removed into recovered and dead according to death rate\n",
    "            #\n",
    "            print(name)\n",
    "            print('Percentage infected at peak of epidemics: ',   np.int(np.round(100*np.array(II+AA).max())),'%')\n",
    "            print('Number Symptomatic at peak of epidemics: '   ,np.int(np.round(N*np.array(II).max()))) \n",
    "            print('Number Asymptomatic at peak of epidemics: '   ,np.int(np.round(N*np.array(AA).max()))) \n",
    "            print('Number of hospitalized at peak of epidemics: ',np.int(np.round(N*np.array(HH).max())))            \n",
    "            print('Number needing ICU at peak of epidemics: ',    np.int(np.round(N*np.array(UU).max())))\n",
    "            #print('Total number of deaths' = {np.int(np.round(D*N)):d})\n",
    "            #print(f'Total number of deaths averted = {np.int(np.round((D2-D)*N)):d}')\n",
    "\n",
    "            break\n",
    "            \n",
    "    results = dict([('Susceptible', SS),\n",
    "                    ('Confined', CC),\n",
    "                    ('Exposed', EE),\n",
    "                    ('Asymptomatic',AA),\n",
    "                    ('Symptomatic', II), \n",
    "                    ('Quarantined', QQ),                                        \n",
    "                    ('Hospitalized', HH),\n",
    "                    ('ICU', UU),                    \n",
    "                    ('Removed', RR),\n",
    "                    ('Fatalities', FF),                    \n",
    "                    ('Dead',DD),\n",
    "                    ('RRt',RRt),\n",
    "                    ('Time',tt)])\n",
    "\n",
    "    fS.close()\n",
    "    fC.close()\n",
    "    fE.close() \n",
    "    fA.close()\n",
    "    fI.close()    \n",
    "    fQ.close()\n",
    "    fH.close()\n",
    "    fU.close()\n",
    "    fR.close() \n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    " def plottage(country,results):\n",
    "\n",
    "    import matplotlib.dates as mdates\n",
    "    \n",
    "    t=np.array(results['Time'])    \n",
    "    S=np.array(results['Susceptible'])\n",
    "    C=np.array(results['Confined'])    \n",
    "    E=np.array(results['Exposed'])\n",
    "    A=np.array(results['Asymptomatic'])    \n",
    "    I=np.array(results['Symptomatic'])\n",
    "    Q=np.array(results['Quarantined'])    \n",
    "    H=np.array(results['Hospitalized'])\n",
    "    U=np.array(results['ICU'])    \n",
    "    R=np.array(results['Removed'])\n",
    "    F=np.array(results['Fatalities'])    \n",
    "    Dm=np.array(results['Dead'])\n",
    "#   \n",
    "    N             = country['N']\n",
    "    deaths        = country['deaths']\n",
    "    days_past     = country['days past']\n",
    "    D0            = country['D0']\n",
    "    name          = country['name']\n",
    "    lockdown      = country['lockdown']\n",
    "    fatality_rate = country['fatality_rate']    \n",
    "    #icu_fraction  = country['icu_fraction']\n",
    "    number_of_icu_beds=country['number_of_icu_beds']\n",
    "#\n",
    "    strS    = 'Susceptible'\n",
    "    strC    = 'Confined'    \n",
    "    strE    = 'Exposed'    \n",
    "    strA    = 'Asymptomatic'    \n",
    "    strI    = 'Symptomatic'\n",
    "    strR    = 'Removed'\n",
    "    strQ    = 'Quarantined'    \n",
    "    strH    = 'Hospitalized'    \n",
    "    strD    = 'Dead'\n",
    "    strII   = 'Infections'\n",
    "    strDD   = 'Deaths'\n",
    "    strdt   = 'Doubling Time'\n",
    "    strdays = 'days'\n",
    "    strDays = 'Days'\n",
    "    strPop  = '# of Cases'\n",
    "    strCCas  = '# of Cases'\n",
    "    strCas  = 'Cummulative # of Cases'\n",
    "\n",
    "    fig, (ax1,ax2,ax3) = plt.subplots(3,1,figsize=[15,25],sharex=True)\n",
    "    #fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,1,figsize=[15,27],sharex=True)\n",
    "    \n",
    "    #fig, (ax1,ax2) = plt.subplots(2,1,figsize=[15,15],sharex=True)\n",
    "\n",
    "# Evolution of the populations\n",
    "\n",
    "    n=len(S)\n",
    "    \n",
    "    datelist=[]\n",
    "    for i in range(n):\n",
    "        datelist.append(datetime.datetime.today() + datetime.timedelta(days=t[i]))      \n",
    "    date64=np.array(datelist, dtype='datetime64')         \n",
    "    date=date64.astype('O')\n",
    "    #    \n",
    "    #ax4.plot(date,S,color='green'  ,label=strS)\n",
    "    #ax4.plot(date,C,color='grey'   ,label=strC)    \n",
    "    ax1.plot(date,N*E,color='magenta',label=strE)\n",
    "    ax1.plot(date,N*A,color='purple' ,label=strA)    \n",
    "    ax1.plot(date,N*I,color='orange' ,label=strI)\n",
    "    #ax1.plot(date,N*Q,color='grey'   ,label=strQ)    \n",
    "    ax1.plot(date,N*H,color='cyan'   ,label=strH)\n",
    "    \n",
    "    ax2.plot(date,N*H,color='cyan'   ,label=strH)    \n",
    "    ax2.plot(date,N*U,color='green',label='ICU Cases')     \n",
    "    ax2.axhline(number_of_icu_beds,linestyle='--',color='green',label='ICU Capacity')    \n",
    "    \n",
    "    #ax2.plot(date,N*D,color='black'  ,label=strD)\n",
    "    ax2.set_yscale(\"log\")\n",
    "    ax2.set_ylim([1,1.5*N*H.max()])\n",
    "    \n",
    "    #ax1.plot(date,100*R,color='blue'   ,label=strR)\n",
    "    \n",
    "    #ax1.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "    \n",
    "# Cumulative number of infections and deaths \n",
    "    cumm_cases=1-(S+C)  # E+A+I+H+R\n",
    "    ax3.plot(date,N*cumm_cases,color='orange',label=\"# of Cases (Model)\")\n",
    "    tretarted = t + tmu\n",
    "    \n",
    "    D = fatality_rate*np.interp(t,tretarted,cumm_cases)\n",
    "    print(\"Number of dead:\",np.round(N*D[n-1]))\n",
    "    itD0     = [i for i in range(n) if D[i] >= (D0+1)/N][0] \n",
    "    ax3.plot(date[itD0-1:n-1],N*D[itD0-1:n-1],color='black',label=\"Deaths (Model)\")\n",
    "\n",
    "    #integratedD = np.zeros(n)\n",
    "    #dt=np.gradient(t)\n",
    "    #for i in range(n):\n",
    "    #    sumD=0.\n",
    "    #    for j in np.arange(1,i):\n",
    "    #        sumD = sumD + .5*(Dm[j-1]+Dm[j])*dt[j]\n",
    "    #    #integratedD[i]=np.sum(.5*(Dm[0:i-1]+Dm[1:i])*dt[1:i]) * 1/(t[i]-t[0])\n",
    "    #    integratedD[i]=sumD/(t[i]-t[0])\n",
    "    #ax3.plot(date,N*np.interp(t,tretarted,integratedD),color='black')\n",
    "    print(\"Number of dead:\",np.round(N*Dm[n-1]))\n",
    "    \n",
    "    pastdatelist=[]\n",
    "    for i in range(len(days_past)):\n",
    "        pastdatelist.append(datetime.datetime.today() + datetime.timedelta(days=days_past[i]))\n",
    "    pastdate64=np.array(pastdatelist, dtype='datetime64')         \n",
    "    pastdate=pastdate64.astype('O')    \n",
    "    ax3.plot(pastdate,country['cases'],'o',color='orange',label='Confirmed Cases')\n",
    "    ax3.plot(pastdate,country['deaths'],'o',color='black',label='Deaths')\n",
    "    \n",
    "    #integratedH = np.zeros(n)\n",
    "    #dt=np.gradient(t)\n",
    "    #for i in range(n):\n",
    "    #    integratedH[i] = np.trapz(H[0:i],t[0:i])\n",
    "        #sumH=0.\n",
    "        #for j in np.arange(1,i):   \n",
    "        #    sumH = sumH + .5*(H[j-1]+H[j])*dt[j]\n",
    "        #integratedD[i]=np.sum(.5*(Dm[0:i-1]+Dm[1:i])*dt[1:i]) * 1/(t[i]-t[0])\n",
    "        #integratedH[i]=sumH/Thospitalized #/(t[i]-t[0])\n",
    "    #ax3.plot(date,N*np.interp(t,t-Thospitalized,integratedH)/Thospitalized,color='cyan',label='Hospitalized (model)')    \n",
    "    ax3.plot(date,N*F,color='cyan',label='Hospitalized (model)')        \n",
    "    #integratedE = np.zeros(n)\n",
    "    #dt=np.gradient(t)\n",
    "    #for i in range(n):\n",
    "    #    sumE=0.\n",
    "    #    for j in np.arange(1,i):\n",
    "    #        sumE = sumE + .5*(E[j-1]+E[j])*dt[j]\n",
    "    #    #integratedD[i]=np.sum(.5*(Dm[0:i-1]+Dm[1:i])*dt[1:i]) * 1/(t[i]-t[0])\n",
    "    #    integratedE[i]=sumE#/(t[i]-t[0])\n",
    "    #ax3.plot(date,N*integratedE/Tincubation,color='purple',label='Exposed (model)')    \n",
    "\n",
    "    \n",
    "    \n",
    "#    \n",
    "#    ax3.plot(date,N*F,color='black',linestyle=':')\n",
    "\n",
    "#\n",
    "    #S2=np.array(results['S2'])\n",
    "    #E2=np.array(results['E2'])    \n",
    "    #I2=np.array(results['I2'])\n",
    "    #R2=np.array(results['R2'])\n",
    "    #ax1.plot(date,100*S2,color='green'  ,linestyle=':')\n",
    "    #ax1.plot(date,100*E2,color='magenta',linestyle=':')    \n",
    "    #ax1.plot(date,100*I2,color='orange' ,linestyle=':')\n",
    "    #ax1.plot(date,100*R2,color='blue'   ,linestyle=':')\n",
    "    #cumm2_cases=E2+I2+R2    \n",
    "    #D2= np.zeros(n)\n",
    "    #tretarted = t + tmu\n",
    "    #cumm2_cases_prior = np.interp(t,tretarted,cumm2_cases)\n",
    "    #D2 = fatality_rate*cumm2_cases_prior    \n",
    "    #print(\"Number of dead - no intervention:\",np.round(N*D2[n-1]))   \n",
    "    #print(\"Number of dead averted:\",np.round(N*(D2[n-1]-D[n-1])))   \n",
    "    #ax2.plot(date,N*(E2+I2+R2+D2),color='orange',label=\"# of Cases (Model without Intervention)\",linestyle=\":\")\n",
    "    #ax2.plot(date[itD0-1:n-1],N*D2[itD0-1:n-1],color='black',label=\"Deaths (Model without Intervention)\",linestyle=\":\")    \n",
    "    \n",
    "# Annotations and prettifying \n",
    "\n",
    "    ax1.set_ylabel(strPop)\n",
    "    ax1.legend(loc=\"best\",fancybox=True,shadow=True)\n",
    "    title=name+\" Populations\"\n",
    "    \n",
    "    ax1.axvline(datetime.datetime.today(),linestyle='--',color='black')\n",
    "    ax1.annotate('Today', xy=(datetime.datetime.today(), 80),rotation=-90,color='black')\n",
    "    \n",
    "    ax1.set_title(title)\n",
    "    ax1.grid()\n",
    "\n",
    "    ax2.set_ylabel(strCCas)\n",
    "    ax2.legend(loc=\"best\",fancybox=True,shadow=True)\n",
    "    title=name+\" Hospitalizations\"\n",
    "    \n",
    "    ax2.axvline(datetime.datetime.today(),linestyle='--',color='black')\n",
    "    ax2.annotate('Today', xy=(datetime.datetime.today(), 2),rotation=-90,color='black')\n",
    "    \n",
    "    ax2.set_title(title)\n",
    "    ax2.grid()\n",
    "\n",
    "    \n",
    "    ax3.set_yscale(\"log\")\n",
    "    ax3.set_ylim([1,1.5*N])\n",
    "    \n",
    "    #ax2.set_xlim(['2020-03-10','2020-03-30'])\n",
    "    ax3.set_ylabel(strCas)\n",
    "    title=name+\" Cumulative\"#+\", $N$=\"+strlog+\", $I_{tot}$=\"+np.str(np.int(I0))\n",
    "    ax3.set_title(title)\n",
    "\n",
    "    ax3.legend(loc=\"best\",fancybox=True,shadow=True)\n",
    "    ax3.grid()\n",
    "    \n",
    "    #if (lockdown!=''):\n",
    "    #    date_object = datetime.datetime.strptime(lockdown, '%m/%d/%y')\n",
    "    #    ax3.axvline(date_object,linestyle=':',color='red')\n",
    "    #    ax3.annotate('Lockdown', xy=(date_object, 5e1),rotation=-90,color='red')\n",
    "\n",
    "    #    ax3.axvline(date_object+datetime.timedelta(days=+Tdeath),linestyle='--',color='red')\n",
    "    #    ax3.annotate(np.str(np.int(np.round(Tdeath)))+'days after\\nlockdown', \n",
    "    #        xy=(date_object+datetime.timedelta(days=+Tdeath), 5e1),\n",
    "    #        rotation=-90,\n",
    "    #        color='red')    \n",
    " \n",
    "\n",
    "    startdate=date[0]\n",
    "    ##enddate   = datetime.datetime.strptime('5/01/20', '%m/%d/%y')\n",
    "    #if (name==\"China\" or name==\"Denmark\"):\n",
    "    #    endd='5/01/20'\n",
    "    ##elif (name==\"Italy\"):\n",
    "    #else:    \n",
    "    #    endd='6/15/20'\n",
    "    #\n",
    "    enddate   = datetime.datetime.strptime(tmax_date, '%m/%d/%y')\n",
    "    ax1.set_xlim([startdate,enddate])\n",
    "    ax2.set_xlim([startdate,enddate])\n",
    "    ax3.set_xlim([startdate,enddate])    \n",
    "\n",
    "    #fig.autofmt_xdate()\n",
    "    \n",
    "    plt.xticks(rotation=45)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    #plt.show()\n",
    "    plt.savefig(\"./figs/\"+name+\"_\"+np.str(datetime.date.today())+\".png\")\n",
    "\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#import matplotlib.dates as mdates\n",
    "#\n",
    "#fig, (ax1,ax2) = plt.subplots(1,2,figsize=[16,6])\n",
    "#\n",
    "##country_list=['China','Korea, South','Iran','Italy','Denmark','Norway','Poland','Spain','US','Brazil']\n",
    "##country_list=['China','Korea, South','Iran','Italy','US','Brazil','Sweden']\n",
    "#country_list=['US','Brazil','India','Russia','Sweden']\n",
    "##\n",
    "#for i in range(len(country_list)): \n",
    "#    country = select_country(country_list[i])\n",
    "#    N=country['N']\n",
    "#    lockdown=country['lockdown']\n",
    "#    f=RK3(country)\n",
    "#    t=np.array(f['Time'])  \n",
    "#    median_age=country['median_age']\n",
    "#    fatality_rate=country['fatality_rate']    \n",
    "#    \n",
    "#    deaths    = country['deaths']\n",
    "#    tdeaths   = country['days past']\n",
    "#    iD0 = [i for i in range(len(deaths)) if deaths[i] > 10][0]    \n",
    "#    time_D0=tdeaths[iD0] # tretare\n",
    "#    \n",
    "#    Rt=np.array(f['RRt'])   \n",
    "#        \n",
    "#    t                = np.array(f['Time'])\n",
    "#    n=len(t)\n",
    "#    tretarded        = t + tmu\n",
    "#    itD0 = [i for i in range(n) if tretarded[i] >= time_D0][0]\n",
    "#\n",
    "#    datelist=[]\n",
    "#    for k in range(len(t)):\n",
    "#        datelist.append(datetime.datetime.today() + datetime.timedelta(days=t[k]))        \n",
    "#    date64=np.array(datelist, dtype='datetime64')         \n",
    "#    date=date64.astype('O')    \n",
    "#    datelist=[]\n",
    "#    for k in range(len(tdeaths)):\n",
    "#        datelist.append(datetime.datetime.today() + datetime.timedelta(days=tdeaths[k]))        \n",
    "#    date64=np.array(datelist, dtype='datetime64')         \n",
    "#    datedeaths=date64.astype('O')    \n",
    "#    \n",
    "#    ax1.plot(datedeaths,np.gradient(deaths,tdeaths),'-o',label=country_list[i])        \n",
    "#    ax2.plot(date[itD0:n-1],Rt[itD0:n-1],label=country_list[i])    \n",
    "#\n",
    "#ax2.set_ylim([0,6])\n",
    "#\n",
    "#ax1.set_yscale(\"log\")\n",
    "#ax1.legend(loc='best',fancybox='True',shadow='True')\n",
    "#\n",
    "#ax1.set_title('Fatalities (Data)')\n",
    "#ax1.set_ylabel(r'$dD/dt$ (day$^{-1}$)')\n",
    "#\n",
    "#ax2.set_title('Basic Reproduction Number (Model)')\n",
    "#ax2.set_ylabel(r'$R_t$')\n",
    "#\n",
    "#startdate = datetime.datetime.strptime('1/20/20', '%m/%d/%y')\n",
    "#today     = datetime.datetime.today()\n",
    "#ax1.set_xlim([startdate                                ,today                                ])\n",
    "#ax2.set_xlim([startdate-datetime.timedelta(days=Tdeath),today-datetime.timedelta(days=Tdeath)])\n",
    "#\n",
    "#plt.xticks(rotation=45)\n",
    "#fig.autofmt_xdate()\n",
    "#\n",
    "#plt.tight_layout()\n",
    "#plt.show()\n",
    "##plt.savefig(\"Rt_data_model.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#import matplotlib.dates as mdates\n",
    "#\n",
    "#fig, (ax1) = plt.subplots(1,1,figsize=[16,10])\n",
    "#\n",
    "#country_list=['China','Korea, South','Iran','Italy','Denmark','Norway','Poland','Spain','US','India',\n",
    "#              'Sweden','Brazil','Germany','France','Tunisia','Japan','Ireland','Uruguay','Chile','Sudan',\n",
    "#              'Saudi Arabia','Netherlands']\n",
    "#for i in range(len(country_list)): \n",
    "#    country = select_country(country_list[i])\n",
    "#    median_age=country['median_age']\n",
    "#    fatality_rate=country['fatality_rate']    \n",
    "#    \n",
    "#    ax1.plot(median_age,100*fatality_rate,'o')\n",
    "#    ax1.annotate(country_list[i],xy=(median_age+0.1,100*fatality_rate+0.01))\n",
    "#    \n",
    "#ax1.set_title('Median Age vs Fatality Rate')\n",
    "#ax1.set_ylabel('Fatality Rate (%)')\n",
    "#ax1.set_xlabel('Median Age')\n",
    "#\n",
    "#plt.tight_layout()\n",
    "#\n",
    "#plt.show()\n",
    "##plt.savefig(\"MedianAge_FatalityRate.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country(country_name)\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('China')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Korea, South')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Italy')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Iran')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Denmark')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('El Salvador')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Ireland')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Sweden')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Norway')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Poland')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('United Kingdom')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Switzerland')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Spain')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of simulation at t = 51 days \n",
      "\n",
      "Brazil\n",
      "Percentage infected at peak of epidemics:  0 %\n",
      "Number Symptomatic at peak of epidemics:  398387\n",
      "Number Asymptomatic at peak of epidemics:  445054\n",
      "Number of hospitalized at peak of epidemics:  71616\n",
      "Number needing ICU at peak of epidemics:  18081\n",
      "Number of dead: 140301.0\n",
      "Number of dead: 141260.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "country = select_country('Brazil')\n",
    "plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('US')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Germany')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Tunisia')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Canada')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Sudan')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Netherlands')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#country = select_country('Saudi Arabia')\n",
    "#plottage(country,RK3(country))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References \n",
    "\n",
    "More on the model can be found here, and references therein. \n",
    "\n",
    "https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00640-3\n",
    "\n",
    "https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model\n",
    "\n",
    "https://towardsdatascience.com/modelling-the-coronavirus-epidemic-spreading-in-a-city-with-python-babd14d82fa2\n",
    "    \n",
    "    \n",
    "Please feel free to download this code. If it helps on finding solutions for exit strategies on the current pandemics we will be humbled. Please cite as Lyra et al. 2020, submitted (PLOS One)."
   ]
  },
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