The Sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0099c3cf",
   "metadata": {},
   "source": [
    "# Personal simulations before migrating to the full Trachoma model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb80003",
   "metadata": {},
   "source": [
    "## Birth and death model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdba6441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x116e81c9e90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def simulate_outbreak(S0, I0, R0, beta, gamma, mu, nu, total_time):\n",
    "    \n",
    "    S, I, R, t = S0, I0, R0, 0\n",
    "    \n",
    "    S_vals = [S]\n",
    "    I_vals = [I]\n",
    "    R_vals = [R]\n",
    "    times = [t]\n",
    "    \n",
    "    while t < total_time:\n",
    "        \n",
    "        N = S+I+R\n",
    "        \n",
    "        rates = np.array([beta*S*I/N, # Infection\n",
    "                 gamma*I,    # Recovery\n",
    "                 nu*N,       # Birth\n",
    "                 mu*S,       # S dies\n",
    "                 mu*I,       # I dies\n",
    "                 mu*R        # R dies\n",
    "                ])\n",
    "        \n",
    "        total_rate = sum(rates) \n",
    "        if total_rate == 0:\n",
    "            break  \n",
    "        dt = np.random.exponential(1/total_rate)\n",
    "        t += dt\n",
    "        \n",
    "        event = np.random.choice(len(rates), p = rates/total_rate)\n",
    "        \n",
    "        if event == 0:\n",
    "            S, I = S-1, I+1\n",
    "        elif event == 1:\n",
    "            I, R = I-1, R+1\n",
    "        elif event == 2:\n",
    "            S += 1\n",
    "        elif event == 3:\n",
    "            S -= 1\n",
    "        elif event == 4:\n",
    "            I -= 1\n",
    "        elif event == 5:\n",
    "            R -= 1\n",
    "            \n",
    "        S_vals.append(S)\n",
    "        I_vals.append(I)\n",
    "        R_vals.append(R)\n",
    "        times.append(t)\n",
    "        \n",
    "    return S_vals, I_vals, R_vals, times\n",
    "            \n",
    "# rate parameters\n",
    "beta = 0.3\n",
    "gamma = 0.1\n",
    "mu = 0.01\n",
    "nu = 0.01\n",
    "\n",
    "# run parameters\n",
    "S0 = 999\n",
    "I0 = 1\n",
    "R0 = 0\n",
    "total_time = 1000\n",
    "\n",
    "sus, inf, rec, time = simulate_outbreak(S0, I0, R0, beta, gamma, mu, nu, total_time)\n",
    "\n",
    "plt.plot(time, sus, color = 'blue', label = 'Susceptible')\n",
    "plt.plot(time, inf, color = 'red', label = 'Infected')\n",
    "plt.plot(time, rec, color = 'green', label = 'Recovered')\n",
    "plt.xlabel('Time (days)')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Endemic outbreak w/ birth and death')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdd03da",
   "metadata": {},
   "source": [
    "### Determining parameters to achieve a specific equilibrium level of infections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7a09a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I calculated this explicitly for the SIR model with birth and death,\n",
    "# but realised that high endemicity levels are simply not realistic under this model.\n",
    "# This is because people recover too quickly, whereas a model recognising multiple\n",
    "# infectious stages and re-susceptibility allows for higher levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3bd73ca",
   "metadata": {},
   "source": [
    "### First attempts at annual mass vaccination, under a deterministic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "046d259d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Parameters\n",
    "rs = 1/120         # re-susceptibility rate = waning immunity\n",
    "beta = 0.2         # infection rate\n",
    "gamma = 0.1        # recovery rate\n",
    "mu = 0.01          # birth-death rate\n",
    "v_rate = 0.5       # vaccination coverage when administered\n",
    "N = 1000           # population size\n",
    "\n",
    "# Initial conditions\n",
    "S0 = 990           \n",
    "I0 = 10            \n",
    "R0 = 0            \n",
    "\n",
    "# Time parameters\n",
    "T = 365*5            # number of days\n",
    "dt = 0.1           # time step for numerical\n",
    "days_per_year = 365\n",
    "t = np.linspace(0, T, int(T/dt))\n",
    "\n",
    "# compartment populations\n",
    "S = np.zeros(len(t))\n",
    "I = np.zeros(len(t))\n",
    "R = np.zeros(len(t))\n",
    "\n",
    "# initial conditions\n",
    "S[0] = S0\n",
    "I[0] = I0\n",
    "R[0] = R0\n",
    "\n",
    "# function to apply vaccination\n",
    "def apply_vaccination(S, R, v_rate):\n",
    "    vaccinated = v_rate * S\n",
    "    S -= vaccinated\n",
    "    R += vaccinated\n",
    "    return S, R\n",
    "\n",
    "# numerical approach to solving the system of ODEs\n",
    "for i in range(1, len(t)):\n",
    "    if i % int(days_per_year/dt) == 0:  # vaccinate each year, excluding day 0\n",
    "        S[i-1], R[i-1] = apply_vaccination(S[i-1], R[i-1], v_rate)\n",
    "    \n",
    "    dSdt = mu * (S[i-1] + I[i-1] + R[i-1]) - beta * S[i-1] * I[i-1] / N - mu * S[i-1] + rs * R[i-1]\n",
    "    dIdt = beta * S[i-1] * I[i-1] / N - gamma * I[i-1] - mu * I[i-1]\n",
    "    dRdt = gamma * I[i-1] - mu * R[i-1] - rs * R[i-1]\n",
    "    \n",
    "    S[i] = S[i-1] + dSdt * dt\n",
    "    I[i] = I[i-1] + dIdt * dt\n",
    "    R[i] = R[i-1] + dRdt * dt\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(t, S, label='Susceptible')\n",
    "plt.plot(t, I, label='Infected')\n",
    "plt.plot(t, R, label='Recovered')\n",
    "plt.xlabel('Time (days)')\n",
    "plt.ylabel('Population')\n",
    "plt.title('SIR Model with Annual Mass Vaccination')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38aa898",
   "metadata": {},
   "source": [
    "## Individual-based SIR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ed241a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# parameters\n",
    "population_size = 1000\n",
    "initial_infected = 10\n",
    "contact_rate = 0.01\n",
    "transmission_probability = 0.1\n",
    "recovery_rate = 0.01\n",
    "simulation_days = 160\n",
    "age_groups = [0, 1, 2, 3, 4, 5]  # arbitrary groups for now\n",
    "infectivity_by_age = [0.35, 0.3, 0.25, 0.2, 0.15, 0.1]  # infectivity decreases with age\n",
    "\n",
    "# states\n",
    "SUSCEPTIBLE = 0\n",
    "INFECTED = 1\n",
    "RECOVERED = 2\n",
    "\n",
    "# initialize population\n",
    "population = []\n",
    "for k in range(population_size):\n",
    "    age = random.choice(age_groups)\n",
    "    state = SUSCEPTIBLE\n",
    "    if initial_infected > 0:\n",
    "        state = INFECTED\n",
    "        initial_infected -= 1\n",
    "    population.append({'state': state, 'age': age, 'infectivity': infectivity_by_age[age]})\n",
    "random.shuffle(population)\n",
    "\n",
    "# store counts after each day\n",
    "susceptible_count = []\n",
    "infected_count = []\n",
    "recovered_count = []\n",
    "\n",
    "# simulate each day of outbreak\n",
    "for day in range(simulation_days):\n",
    "\n",
    "    for i in range(population_size):\n",
    "        if population[i]['state'] == INFECTED:\n",
    "            # Check if the infected person recovers\n",
    "            if random.random() < recovery_rate:\n",
    "                population[i]['state'] = RECOVERED\n",
    "            else:\n",
    "                # Try to infect susceptible individuals\n",
    "                for c in range(int(contact_rate * population_size)):  # number of contacts per day\n",
    "                    contact = random.choice(population)\n",
    "                    if contact['state'] == SUSCEPTIBLE and random.random() < (transmission_probability * population[i]['infectivity']):\n",
    "                        contact['state'] = INFECTED\n",
    "\n",
    "    # Record counts\n",
    "    susceptible_count.append(len([person for person in population if person['state'] == SUSCEPTIBLE]))\n",
    "    infected_count.append(len([person for person in population if person['state'] == INFECTED]))\n",
    "    recovered_count.append(len([person for person in population if person['state'] == RECOVERED]))\n",
    "\n",
    "# Plotting the results\n",
    "plt.plot(susceptible_count, label='Susceptible')\n",
    "plt.plot(infected_count, label='Infected')\n",
    "plt.plot(recovered_count, label='Recovered')\n",
    "plt.xlabel('Time (days)')\n",
    "plt.ylabel('Number of Individuals')\n",
    "plt.title('Individual-based SIR Simulation')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5e9010",
   "metadata": {},
   "source": [
    "# Adaptation of Borlase model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee39a3c1",
   "metadata": {},
   "source": [
    "This model will mimic the approach of the translation of the Borlase R code. At each timestep, the age, infective status, bacterial load etc. of each person (represented by the indices) is stored, and we may retrieve the number of infectives at each time for plotting. Time is in weeks (multiply by 7 to retrieve days) and people are split into three general age groups: young children, old children, and adults\n",
    "\n",
    "The treatment matrix keeps track of who receives MDA treatment and when, allowing for SNC\n",
    "Vaccination treatment should use a different matrix (when incorporated)\n",
    "\n",
    "Simulation parameters: simulation time, burn-in (time until MDA starts), MDA (soon to include vax) frequency, number of rounds of MDA (and vax)\n",
    "\n",
    "Population parametes: N, duration of infection/disease, reduction of infection/diseaes, durations of infection and disease, parameters for lambda function, MDA (soon vax) coverage and efficiency, and rho (ommitted, related to SNC) \n",
    "\n",
    "Demographic parameters: tau (death rate), max age, mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e4c6a05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing relevant libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df07503f",
   "metadata": {},
   "source": [
    "## Calculating infection rate and resetting individuals who die or reach maximum age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49459471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rate depends on parameters and diseased individuals\n",
    "def lambda_rate(params, Age, demog, bact_load, IndD, bet):\n",
    "    v_1, v_2, phi, epsilon = params['v_1'], params['v_2'], params['phi'], params['epsilon']\n",
    "    N, max_age = params['N'], demog['max_age']\n",
    "    \n",
    "    totalLoad = [0,0,0]\n",
    "    y_children = np.where((Age >= 0) & (Age < 9*52))[0] # young children = 0-9\n",
    "    totalLoad[0] = np.sum(bact_load[y_children]) / len(y_children) # average bacterial load?\n",
    "    o_children = np.where((Age >= 9*52) & (Age < 15*52))[0] # older children = 9-15\n",
    "    totalLoad[1] = np.sum(bact_load[o_children]) / len(o_children)\n",
    "    adults = np.where(Age >= 15*52)[0]\n",
    "    totalLoad[2] = np.sum(bact_load[adults]) / len(adults)\n",
    "    \n",
    "    prevLambda = bet * (v_1 * np.array(totalLoad) + v_2 * np.array(totalLoad) ** (phi + 1))\n",
    "    \n",
    "    # tile function repeats proportions, forming 3 identical rows\n",
    "    demog_matrix = np.transpose(np.tile(np.array([len(y_children), len(o_children), len(adults)]) / N, (3, 1)))\n",
    "    social_mixing = (epsilon * np.eye(3) + (1 - epsilon)) * demog_matrix\n",
    "    \n",
    "    lambda_ = np.dot(social_mixing, prevLambda)\n",
    "    \n",
    "    # adjust lambda based on disease status\n",
    "    age_intervals = np.searchsorted([0, 9*52, 15*52, max_age*52], Age)\n",
    "    adjusted_lambda = lambda_[age_intervals - 1] * (0.5 + 0.5 * (1 - IndD))\n",
    "    \n",
    "    return adjusted_lambda # relates to force of infection across the population\n",
    "\n",
    "def reset_age(Age, demog, params):\n",
    "    tau, max_age, N = demog['tau'], demog['max_age'], params['N']\n",
    "    # individuals who die or reach the maximum age will be recorded and reset later\n",
    "    reset_indivs = np.where((np.random.uniform(size=N) < 1 - np.exp(-tau)) | (Age > max_age))[0]\n",
    "    return reset_indivs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8008ebce",
   "metadata": {},
   "source": [
    "## Treatment matrix. Child-only matrix is ignored for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0797f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# decide who gets treatment when\n",
    "def tx_matrix(params, sim_params):\n",
    "    N, N_MDA, MDA_Cov, rho = params['N'], sim_params['N_MDA'], params['MDA_Cov'], params['rho']\n",
    "    ind_treat = np.zeros((N, N_MDA)) # must be an N by N_MDA matrix\n",
    "    \n",
    "    for i in range(N): # work through each individual\n",
    "        z = np.random.uniform(size = N)\n",
    "        ind_treat[:, 0] = z < MDA_Cov # chosen so roughly MDA_Cov% of people get the first treatment\n",
    "        for k in range(1, N_MDA): # determine which rounds each person receives\n",
    "            prob = (MDA_Cov * (1 - rho) + (rho * np.sum(ind_treat[i, :k]))) / (1 + (k - 1) * rho) # decrease with rho, k\n",
    "            ind_treat[i, k] = np.random.binomial(1, prob) # determine if individual i receives round k\n",
    "    \n",
    "    return ind_treat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0689a0b",
   "metadata": {},
   "source": [
    "## Timesteps for MDA and indexing who is cured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3261fa56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# return the timestamps of when MDA occurs\n",
    "def set_t_MDA(sim_params):\n",
    "    burnin, N_MDA, freq_MDA = sim_params['burnin'], sim_params['N_MDA'], sim_params['freq_MDA']\n",
    "    MDA_times = []\n",
    "    for i in range(N_MDA): # add the time of each round\n",
    "        MDA_times.append(burnin + i * 52 * freq_MDA + 52) # add 52 to start 1 year after burnin\n",
    "        \n",
    "    return MDA_times\n",
    "\n",
    "# timestamps for child-only MDA\n",
    "def set_t_MDA_kids(sim_params):\n",
    "    burnin, N_MDA_kids, freq_MDA = sim_params['burnin'], sim_params['N_MDA_kids'], sim_params['freq_MDA']\n",
    "    child_delay, offset = sim_params['child_delay'], sim_params['offset'] # relates child-only times to community-wide ones\n",
    "    MDA_times_kids = []\n",
    "    for i in range(N_MDA_kids):\n",
    "        MDA_times_kids.append(burnin + child_delay + i * 52 * freq_MDA + 52 + offset)\n",
    "    \n",
    "    return MDA_times_kids\n",
    "\n",
    "# decide who is treated during community-wide MDA\n",
    "def doMDA(params, Age, MDA_round, tx_mat):\n",
    "    MDA_eff = params['MDA_eff']\n",
    "    \n",
    "    babies = np.where(Age < 26)[0] # identify who is a baby\n",
    "    treated_b = babies[np.where(tx_mat[babies, MDA_round-1] == 1)[0]] # identify which baby is due treatment\n",
    "    cured_b = treated_b[np.where(np.random.uniform(size = len(treated_b)) < (MDA_eff * 0.5))[0]] # multiplier due to method\n",
    "    \n",
    "    older = np.where(Age > 26)[0]\n",
    "    treated_o = older[np.where(tx_mat[older, MDA_round-1] == 1)[0]]\n",
    "    cured_o = treated_o[np.where(np.random.uniform(size = len(treated_o)) < MDA_eff)[0]]\n",
    "    \n",
    "    all_cured = np.concatenate((cured_b, cured_o))\n",
    "    \n",
    "    return all_cured # returns index of all individuals who are treated and successfully cured\n",
    "\n",
    "# decide who is treated during children-only MDA - ignore matrix and SNC due to complications with ageing/death\n",
    "def doMDAkids(params, Age, MDA_round_kids):\n",
    "    MDA_eff = params['MDA_eff']\n",
    "    MDA_Cov_kids = params['MDA_Cov_kids']\n",
    "    \n",
    "    kids = np.where((Age > 104) & (Age < 10 * 52))[0] # look at 2-10 year olds here (why not 1-9?)\n",
    "    treated_kids = kids[np.where(np.random.uniform(size = len(kids)) < MDA_Cov_kids)[0]] # randomly select enough indiv's\n",
    "    cured_kids = treated_kids[np.where(np.random.uniform(size = len(treated_kids)) < MDA_eff)[0]] # successful treatment\n",
    "    \n",
    "    return cured_kids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b23c2f8",
   "metadata": {},
   "source": [
    "## Function which actually carries out MDA steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "085e4fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# carry out community-wide MDA\n",
    "def MDA_timestep(vals, params, demog, MDA_round):\n",
    "    \n",
    "    IndI = vals['IndI'] # retrieve the variables for each individual\n",
    "    IndD = vals['IndD']\n",
    "    No_Inf = vals['No_Inf']\n",
    "    T_latent = vals['T_latent']\n",
    "    T_ID = vals['T_ID']\n",
    "    T_D = vals['T_D']\n",
    "    Ind_latent = vals['Ind_latent']\n",
    "    Ind_ID_period_base = vals['Ind_ID_period_base']\n",
    "    Ind_D_period_base = vals['Ind_D_period_base']\n",
    "    bact_load = vals['bact_load']\n",
    "    Age = vals['Age']\n",
    "    \n",
    "    all_cured = doMDA(params, Age, MDA_round, tx_mat) # identify who is cured through MDA\n",
    "    \n",
    "    IndI[all_cured] = 0 # reset infection and bacterial load for scuccessfully cured individuals\n",
    "    bact_load[all_cured] = 0\n",
    "    \n",
    "    vals = { # put all the updated variables back together\n",
    "        \"IndI\": IndI,\n",
    "        \"IndD\": IndD,\n",
    "        \"No_Inf\": No_Inf,\n",
    "        \"T_latent\": T_latent,\n",
    "        \"T_ID\": T_ID,\n",
    "        \"T_D\": T_D,\n",
    "        \"Ind_latent\": Ind_latent,\n",
    "        \"Ind_ID_period_base\": Ind_ID_period_base,\n",
    "        \"Ind_D_period_base\": Ind_D_period_base,\n",
    "        \"bact_load\": bact_load,\n",
    "        \"Age\": Age\n",
    "    }\n",
    "    \n",
    "    \n",
    "    return vals\n",
    "\n",
    "# carry out child-only MDA\n",
    "def MDA_timestep_kids(vals, params, demog, MDA_round_kids):\n",
    "    \n",
    "    IndI = vals['IndI']\n",
    "    IndD = vals['IndD']\n",
    "    No_Inf = vals['No_Inf']\n",
    "    T_latent = vals['T_latent']\n",
    "    T_ID = vals['T_ID']\n",
    "    T_D = vals['T_D']\n",
    "    Ind_latent = vals['Ind_latent']\n",
    "    Ind_ID_period_base = vals['Ind_ID_period_base']\n",
    "    Ind_D_period_base = vals['Ind_D_period_base']\n",
    "    bact_load = vals['bact_load']\n",
    "    Age = vals['Age']\n",
    "    \n",
    "    all_cured_kids = doMDAkids(params, Age, MDA_round_kids) # identify which children are cured through MDA\n",
    "    \n",
    "    IndI[all_cured_kids] = 0 # reset infection and bacterial load for scuccessfully cured children\n",
    "    bact_load[all_cured_kids] = 0\n",
    "    \n",
    "    vals = {\n",
    "        \"IndI\": IndI,\n",
    "        \"IndD\": IndD,\n",
    "        \"No_Inf\": No_Inf,\n",
    "        \"T_latent\": T_latent,\n",
    "        \"T_ID\": T_ID,\n",
    "        \"T_D\": T_D,\n",
    "        \"Ind_latent\": Ind_latent,\n",
    "        \"Ind_ID_period_base\": Ind_ID_period_base,\n",
    "        \"Ind_D_period_base\": Ind_D_period_base,\n",
    "        \"bact_load\": bact_load,\n",
    "        \"Age\": Age\n",
    "    }\n",
    "    \n",
    "    \n",
    "    return vals\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ddd8cc9",
   "metadata": {},
   "source": [
    "## Infection and disease periods, used to set duration upon acquiring disease/infection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dfc71dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# give duration of active infection and disease only periods\n",
    "def ID_period_function(Ind_ID_period_base, No_Inf, parameters):\n",
    "    min_ID = parameters['min_ID']\n",
    "    inf_red = parameters['inf_red']\n",
    "    T_ID = np.round((Ind_ID_period_base - min_ID) * np.exp(-inf_red * (No_Inf - 1)) + min_ID)\n",
    "    \n",
    "    return T_ID\n",
    "    \n",
    "# give duration of diseased period\n",
    "def D_period_function(Ind_D_period_base, No_Inf, parameters):\n",
    "    min_D = parameters['min_D']\n",
    "    dis_red = parameters['dis_red']\n",
    "    T_D = np.round((Ind_D_period_base - min_D) * np.exp(-dis_red * (No_Inf - 1)) + min_D)\n",
    "    \n",
    "    return T_D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ce5f92",
   "metadata": {},
   "source": [
    "## Run a non-MDA timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c4360bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transitions which occur during a timestep where MDA does not occur\n",
    "def stepf_fixed(vals, params, demog, bet):\n",
    "    \n",
    "    IndI = vals['IndI']\n",
    "    IndD = vals['IndD']\n",
    "    No_Inf = vals['No_Inf']\n",
    "    T_latent = vals['T_latent']\n",
    "    T_ID = vals['T_ID']\n",
    "    T_D = vals['T_D']\n",
    "    Ind_latent = vals['Ind_latent']\n",
    "    Ind_ID_period_base = vals['Ind_ID_period_base']\n",
    "    Ind_D_period_base = vals['Ind_D_period_base']\n",
    "    bact_load = vals['bact_load']\n",
    "    Age = vals['Age']\n",
    "    \n",
    "    # new I =  Becoming infected, not yet diseased\n",
    "    Ss = np.where(IndI == 0)[0]  # susceptible individuals available for infection\n",
    "    lambda_step = 1 - np.exp(-lambda_rate(params, Age, demog, bact_load, IndD, bet)) # probability of infection\n",
    "    newInf = Ss[np.random.rand(len(Ss)) < lambda_step[Ss]]  # new infections\n",
    "    \n",
    "    # new ID = infected and becoming diseased too\n",
    "    Is = np.where((IndI == 1) & (IndD == 0))[0]\n",
    "    T_latent -= 1  # with each timestep, subtract 1\n",
    "    newDis = np.where(T_latent == 0)[0]  # designated latent period for that individual has expired\n",
    "    T_latent[T_latent < 1] = 0  # reset all not in latent period to 0 at each timestep\n",
    "    \n",
    "    # new D = infectious diseased losing infectiousness\n",
    "    IDs = np.where((IndI == 1) & (IndD == 1))[0]\n",
    "    bact_load[IDs] = bacterialLoad(No_Inf[IDs], params)\n",
    "    T_ID -= 1  # with each timestep, subtract 1\n",
    "    newClearInf = np.where(T_ID == 0)[0]  # designated infectious period for that individual has expired\n",
    "    T_ID[T_ID < 1] = 0  # reset all uninfected to 0 at each timestep\n",
    "    \n",
    "    # new S = Clear disease\n",
    "    Ds = np.where((IndI == 0) & (IndD == 1))[0]\n",
    "    bact_load[Ds] = 0\n",
    "    T_D -= 1  # subtract 1 each timestep\n",
    "    newClearDis = np.where(T_D == 0)[0]  # designated diseased period for that individual has expired\n",
    "    T_D[T_D < 1] = 0  # reset all not in diseased period to 0\n",
    "\n",
    "    No_Inf[newInf] += 1 # tracking infection history\n",
    "\n",
    "    # TRANSITION become infected\n",
    "    IndI[newInf] = 1  # if they've become infected, become I=1\n",
    "    # TRANSITION become diseased (and infected)\n",
    "    IndD[newDis] = 1  # if they've become diseased they become D=1\n",
    "    # TRANSITION Clear infection (remain diseased)\n",
    "    IndI[newClearInf] = 0  # clear infection they become I=0\n",
    "    # TRANSITION Clear disease (become susceptible again)\n",
    "    IndD[newClearDis] = 0  # clear disease they become D=0\n",
    "\n",
    "    # Setting duration of infection/disease for individuals\n",
    "    T_latent[newInf] = Ind_latent[newInf]  # When individual becomes infected, set their latent period\n",
    "    # When individual becomes diseased, set their infected+diseased period\n",
    "    T_ID[newDis] = ID_period_function(Ind_ID_period_base=Ind_ID_period_base[newDis], No_Inf=No_Inf[newDis], parameters=parameters)\n",
    "    # When indiv clears infection, their diseased only period is set\n",
    "    T_D[newClearInf] = D_period_function(Ind_D_period_base=Ind_D_period_base[newClearInf], No_Inf=No_Inf[newClearInf], parameters=parameters)\n",
    "\n",
    "    # Update age, all age by 1w at each timestep, and resetting all \"reset indivs\" age to zero\n",
    "    # Reset_indivs - Identify individuals who die in this timeset\n",
    "    Age += 1\n",
    "    reset_indivs = reset_age(Age=Age, demog=demog, params=parameters)\n",
    "    Age[reset_indivs] = 0\n",
    "\n",
    "    # Resetting new parameters for all new individuals created\n",
    "    IndI[reset_indivs] = 0\n",
    "    IndD[reset_indivs] = 0\n",
    "    No_Inf[reset_indivs] = 0\n",
    "    T_latent[reset_indivs] = 0\n",
    "    T_ID[reset_indivs] = 0\n",
    "    T_D[reset_indivs] = 0\n",
    "\n",
    "    vals = {\n",
    "        \"IndI\": IndI,\n",
    "        \"IndD\": IndD,\n",
    "        \"No_Inf\": No_Inf,\n",
    "        \"T_latent\": T_latent,\n",
    "        \"T_ID\": T_ID,\n",
    "        \"T_D\": T_D,\n",
    "        \"Ind_latent\": Ind_latent,\n",
    "        \"Ind_ID_period_base\": Ind_ID_period_base,\n",
    "        \"Ind_D_period_base\": Ind_D_period_base,\n",
    "        \"bact_load\": bact_load,\n",
    "        \"Age\": Age\n",
    "    }\n",
    "    \n",
    "    return vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf78a60",
   "metadata": {},
   "source": [
    "## Formula for bacterial load and setting the initial ages and infections in the population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96b37b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define bacterial load based on infection history\n",
    "def bacterialLoad(No_Inf, parameters):\n",
    "    b1, ep2 = 1, 0.114\n",
    "    bact_load = b1 * np.exp((No_Inf - 1) * -ep2)\n",
    "    \n",
    "    return bact_load\n",
    "\n",
    "# produce age sample based on exponential distribution and mean age\n",
    "def init_ages(params, demog):\n",
    "    max_age, mean_age, N = demog['max_age'], demog['mean_age'], params['N']\n",
    "    ages = np.arange(1, max_age + 1)\n",
    "    nAges = len(ages)\n",
    "    propAges = np.empty(nAges) # will store the probability of lying in each age group\n",
    "    for i in range(nAges - 1):\n",
    "        propAges[i] = np.exp(-ages[i] / mean_age) - np.exp(-ages[i + 1] / mean_age) # reflects exponential distribution\n",
    "    propAges[-1] = 1 - np.sum(propAges[:-1]) # ensures probabilities sum to 1\n",
    "    sampled_ages = np.random.choice(ages, N, replace=True, p=propAges) # now samples according to distribution\n",
    "    \n",
    "    return sampled_ages\n",
    "\n",
    "# set initial values\n",
    "def set_inits(params, demog):\n",
    "    N = params['N']\n",
    "    av_I_duration = params['av_I_duration']\n",
    "    av_ID_duration = params['av_ID_duration']\n",
    "    av_D_duration = params['av_D_duration']\n",
    "    IndI = np.zeros(N)  # infected status\n",
    "    IndD = np.zeros(N)  # disease status\n",
    "    No_Inf = np.zeros(N)  # total number of infections per individual\n",
    "    T_latent = np.zeros(N)  # duration of latent period (I)\n",
    "    T_ID = np.zeros(N)  # duration of current ID period\n",
    "    T_D = np.zeros(N)  # duration spent in diseased state (post-infection)\n",
    "    Ind_latent = np.full(N, av_I_duration)  # individual's latent period fixed for now\n",
    "    Ind_ID_period_base = np.random.poisson(av_ID_duration, N)  # baseline ID period\n",
    "    Ind_D_period_base = np.random.poisson(av_D_duration, N)  # baseline diseased period\n",
    "    bact_load = np.zeros(N)  # baseline bacterial load set to zero\n",
    "    Age = init_ages(params, demog)  # initial ages\n",
    "\n",
    "    vals = {\n",
    "        \"IndI\": IndI,\n",
    "        \"IndD\": IndD,\n",
    "        \"No_Inf\": No_Inf,\n",
    "        \"T_latent\": T_latent,\n",
    "        \"T_ID\": T_ID,\n",
    "        \"T_D\": T_D,\n",
    "        \"Ind_latent\": Ind_latent,\n",
    "        \"Ind_ID_period_base\": Ind_ID_period_base,\n",
    "        \"Ind_D_period_base\": Ind_D_period_base,\n",
    "        \"bact_load\": bact_load,\n",
    "        \"Age\": Age\n",
    "    }\n",
    "\n",
    "    return vals\n",
    "\n",
    "\n",
    "def seed_infection(params, vals):\n",
    "    N = params['N']\n",
    "    \n",
    "    initial_infected_count = round(N * 0.01) # set 1% of the population to be infected initially\n",
    "    vals['IndI'][:initial_infected_count] = 1\n",
    "    \n",
    "    init_infected = np.where(vals['IndI'] == 1)[0] # get the indices of the initially infected individuals\n",
    "    \n",
    "    vals['T_latent'][init_infected] = vals['Ind_latent'][init_infected] # set latent period for those infected at the start of simulation\n",
    "    \n",
    "    vals['No_Inf'][init_infected] = 1 # number of infections for those initially infected\n",
    "    \n",
    "    return vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b048e3a1",
   "metadata": {},
   "source": [
    "## Parameters (removed 'nsim' from sim_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "06cca778",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_params = {\n",
    "    'timesim': 80 * 52,  # total duration of simulation in weeks\n",
    "    'burnin': 40 * 52,   # burn-in period in weeks\n",
    "    'child_delay': 5 * 52, # begin child-only 5 years after community-wide\n",
    "    'offset': 26,        # weeks between child-only MDA and community-wide. 26 = 6 months\n",
    "    'freq_MDA': 1,       # 1 for annual, 0.5 for biannual, 2 for every 2 years, etc. SAME for community/child-only\n",
    "    'N_MDA': 20,         # number of rounds of MDA to be carried out in simulation\n",
    "    'N_MDA_kids': 10,    # self-explanatory\n",
    "}\n",
    "\n",
    "parameters = {\n",
    "    'N': 3000,                    # population size\n",
    "    'av_I_duration': 2,           # average duration of infection\n",
    "    'av_ID_duration': 200 / 7,    # average duration of ID period\n",
    "    'inf_red': 0.45,              # infection reduction\n",
    "    'min_ID': 11,                 # minimum ID duration\n",
    "    'av_D_duration': 300 / 7,     # average duration of disease period\n",
    "    'dis_red': 0.3,               # disease reduction\n",
    "    'min_D': 1,                   # minimum disease duration\n",
    "    'v_1': 1,                     # parameter for lambda function\n",
    "    'v_2': 2.6,                   # parameter for lambda function\n",
    "    'phi': 1.4,                   # parameter for lambda function\n",
    "    'epsilon': 0.5,               # parameter for lambda function\n",
    "    'MDA_Cov': 0.7,               # MDA coverage\n",
    "    'MDA_Cov_kids': 0,            # MDA coverage during child-only MDA\n",
    "    'MDA_eff': 0.85,              # efficacy of treatment\n",
    "    'rho': 0                      # correlation parameter for systematic non-compliance function\n",
    "}\n",
    "\n",
    "demog = {\n",
    "    'tau': 1 / (40 * 52),   # death rate in weeks^-1\n",
    "    'max_age': 60 * 52,     # maximum age in population in weeks\n",
    "    'mean_age': 20 * 52     # mean age in population in weeks\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fe3574",
   "metadata": {},
   "source": [
    "## Code to run a single simulation, with MDA times specified. Output is times, diseases, and infections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "40a9d7ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def sim_ind_MDA(params, vals, timesim, demog, bet):\n",
    "    \n",
    "    vals = vals\n",
    "    prev_disease = []\n",
    "    prev_infection = []\n",
    "    prev_diseases_1_9 = []\n",
    "    prev_infection_1_9 = []\n",
    "    Ages = []\n",
    "    N_inf = []\n",
    "    Inf_status = []\n",
    "    t = []\n",
    "    MDA_round = []\n",
    "    MDA_round_kids = []\n",
    "    \n",
    "    for i in range(1, timesim+1): # assume timesim is the total number of timesteps\n",
    "        if i in MDA_times:\n",
    "            MDA_round_i = MDA_times.index(i) + 1 # determine round of MDA\n",
    "            MDA_round.append(MDA_round_i)\n",
    "            vals = MDA_timestep(vals, params, demog, MDA_round_i) # update values following this MDA round\n",
    "            \n",
    "            t.append(i/52)\n",
    "            prev_disease.append(sum(vals['IndD']) / params['N']) # add the latest prevalences to each array\n",
    "            prev_infection.append(sum(vals['IndI']) / params['N'])\n",
    "        \n",
    "        elif i in MDA_times_kids:\n",
    "            MDA_round_kids_i = MDA_times_kids.index(i) + 1\n",
    "            MDA_round_kids.append(MDA_round_kids_i)\n",
    "            vals = MDA_timestep_kids(vals, params, demog, MDA_round_kids_i) # ROUND technically not needed as totally random\n",
    "            \n",
    "            t.append(i/52)\n",
    "            prev_disease.append(sum(vals['IndD']) / params['N'])\n",
    "            prev_infection.append(sum(vals['IndI']) / params['N'])\n",
    "        \n",
    "        else:\n",
    "            vals = stepf_fixed(vals, params, demog, bet)\n",
    "            \n",
    "            t.append((i-1)/52)\n",
    "            prev_disease.append(sum(vals['IndD']) / params['N'])\n",
    "            prev_infection.append(sum(vals['IndI']) / params['N'])\n",
    "            \n",
    "    output = {\n",
    "        \"times\": t,\n",
    "        \"infections\": prev_infection,\n",
    "        \"diseases\": prev_disease,\n",
    "        \"infection_1_9\": prev_infection_1_9,\n",
    "        \"diseases_1_9\": prev_diseases_1_9\n",
    "    } \n",
    "            \n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4cdf28e",
   "metadata": {},
   "source": [
    "## Set up initial parameters before running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3646d2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set initial values of each variable and create initial infections\n",
    "def set_initial_conditions(params, demog):\n",
    "    vals1 = set_inits(params, demog)\n",
    "    vals2 = seed_infection(params, vals1)\n",
    "    return vals2\n",
    "\n",
    "vals = set_initial_conditions(parameters, demog)\n",
    "\n",
    "timesim, burnin = sim_params['timesim'], sim_params['burnin']\n",
    "delay, offset = sim_params['child_delay'], sim_params['offset']\n",
    "\n",
    "tx_mat = tx_matrix(parameters, sim_params)\n",
    "# tx_mat_kids = tx_matrix_kids(parameters, sim_params) - AVOIDED until we decide how to address the life-cycle issue\n",
    "\n",
    "bet = np.random.uniform(0.165, 0.175)\n",
    "\n",
    "MDA_times = set_t_MDA(sim_params) # determine times of MDA using simulation parameters\n",
    "\n",
    "MDA_times_kids = set_t_MDA_kids(sim_params) # do this inbetween community times for simplicity (avoids priority issues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277f5ef5",
   "metadata": {},
   "source": [
    "## Run the simulation once and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce5cc2c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x116ef3f6990>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run simulation\n",
    "output = sim_ind_MDA(parameters, vals, timesim, demog, bet)\n",
    "\n",
    "# add extra for child-only prevalences\n",
    "times, infections, diseases = output['times'], output['infections'], output['diseases']\n",
    "\n",
    "# plot time against infections\n",
    "\n",
    "plt.plot(times, infections, color = 'blue', label = 'Infected')\n",
    "plt.plot(times, diseases, color = 'orange', label = 'Diseased')\n",
    "plt.xlabel('Time (years)')\n",
    "plt.ylabel('Prevalences')\n",
    "plt.title('Trachoma prevalence with annual MDA')\n",
    "plt.legend()\n",
    "plt.ylim([-0.05, 1.05])\n",
    "          \n",
    "# lines to indicate the start and end of community-wide intervention          \n",
    "plt.axvline(x=burnin/52, color = 'r', alpha = 0.4)\n",
    "plt.axvline(x=burnin/52 + sim_params['N_MDA']*sim_params['freq_MDA'], color = 'r', alpha = 0.4)\n",
    "\n",
    "# lines to indicate start and end of child-only intervention\n",
    "# plt.axvline(x=(burnin+delay+offset)/52, color = 'c', alpha = 0.4)\n",
    "# plt.axvline(x=(burnin+delay+offset)/52 + sim_params['N_MDA_kids']*sim_params['freq_MDA'], color = 'c', alpha = 0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30549762",
   "metadata": {},
   "source": [
    "## Run many simulations (decide what to do with this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "695a5c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in place of the randomly sampled array of 'bet', just create this before each simulation"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}