{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Analytical Features\n",
    "\n",
    "This notebook demonstrates the advanced symbolic analysis capabilities:\n",
    "\n",
    "1. **Equilibrium Finding**: Disease-free and endemic equilibria\n",
    "2. **Stability Analysis**: Jacobian matrices, eigenvalues, stability classification\n",
    "3. **Sensitivity Analysis**: Parameter sensitivities, elasticity indices, perturbation analysis\n",
    "4. **Parameter Ranking**: Identify most influential parameters\n",
    "\n",
    "## Important Note on Endemic Equilibria\n",
    "\n",
    "A basic SIR model **without vital dynamics** (birth/death) does NOT have an endemic equilibrium - all trajectories eventually reach the disease-free equilibrium (I=0). To demonstrate endemic equilibrium analysis, we use a **SIR model with vital dynamics** where births replenish the susceptible population."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:31:58.574112Z",
     "iopub.status.busy": "2026-03-11T19:31:58.573515Z",
     "iopub.status.idle": "2026-03-11T19:31:59.866831Z",
     "shell.execute_reply": "2026-03-11T19:31:59.865829Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:47.270081699Z",
     "start_time": "2026-03-16T19:00:46.973572977Z"
    }
   },
   "source": [
    "from epimodels.validation import SymbolicModel\n",
    "from IPython.display import display, Markdown, Latex\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore', category=UserWarning)\n",
    "\n",
    "print(\"Imports successful\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports successful\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Basic SIR Model (No Endemic Equilibrium)\n",
    "\n",
    "First, we'll create the basic SIR model to show that it only has a disease-free equilibrium."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:31:59.917157Z",
     "iopub.status.busy": "2026-03-11T19:31:59.916619Z",
     "iopub.status.idle": "2026-03-11T19:32:01.232262Z",
     "shell.execute_reply": "2026-03-11T19:32:01.231074Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:47.855858452Z",
     "start_time": "2026-03-16T19:00:47.778791886Z"
    }
   },
   "source": [
    "# Create symbolic SIR model (without vital dynamics)\n",
    "sir_basic = SymbolicModel()\n",
    "\n",
    "sir_basic.add_parameter(\"beta\", positive=True, real=True)\n",
    "sir_basic.add_parameter(\"gamma\", positive=True, real=True)\n",
    "\n",
    "sir_basic.add_variable(\"S\", positive=True, real=True)\n",
    "sir_basic.add_variable(\"I\", positive=True, real=True)\n",
    "sir_basic.add_variable(\"R\", positive=True, real=True)\n",
    "\n",
    "sir_basic.set_total_population(\"N\")\n",
    "\n",
    "sir_basic.define_ode(\"S\", \"-beta*S*I/N\")\n",
    "sir_basic.define_ode(\"I\", \"beta*S*I/N - gamma*I\")\n",
    "sir_basic.define_ode(\"R\", \"gamma*I\")\n",
    "\n",
    "print(f\"Created basic SIR model with {len(sir_basic.parameters)} parameters and {len(sir_basic.variables)} variables\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created basic SIR model with 2 parameters and 3 variables\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:01.235176Z",
     "iopub.status.busy": "2026-03-11T19:32:01.234785Z",
     "iopub.status.idle": "2026-03-11T19:32:01.957610Z",
     "shell.execute_reply": "2026-03-11T19:32:01.956561Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:49.968255023Z",
     "start_time": "2026-03-16T19:00:49.270260186Z"
    }
   },
   "source": [
    "# Find equilibria for basic SIR\n",
    "params_basic = {'beta': 0.3, 'gamma': 0.1, 'N': 1000}\n",
    "\n",
    "print(\"Finding all equilibria for basic SIR model...\")\n",
    "equilibria_basic = sir_basic.find_all_equilibria(params=params_basic, numeric_fallback=True)\n",
    "\n",
    "print(f\"\\nFound {len(equilibria_basic)} equilibrium point(s):\")\n",
    "for i, eq in enumerate(equilibria_basic, 1):\n",
    "    print(f\"\\nEquilibrium {i}: {eq['type'].upper()}\")\n",
    "    print(f\"  Method: {eq['method']}\")\n",
    "    for var in ['S', 'I', 'R']:\n",
    "        val = eq.get(var, 0)\n",
    "        if hasattr(val, 'evalf'):\n",
    "            val = val.evalf()\n",
    "        print(f\"    {var} = {val}\")\n",
    "\n",
    "print(\"\\nConclusion: Basic SIR has ONLY the disease-free equilibrium (I*=0).\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding all equilibria for basic SIR model...\n",
      "\n",
      "Found 10 equilibrium point(s):\n",
      "\n",
      "Equilibrium 1: DFE\n",
      "  Method: analytical\n",
      "    S = N\n",
      "    I = 0\n",
      "    R = 0\n",
      "\n",
      "Equilibrium 2: DFE\n",
      "  Method: numeric\n",
      "    S = 1000.0\n",
      "    I = 0.0\n",
      "    R = 0.0\n",
      "\n",
      "Equilibrium 3: DFE\n",
      "  Method: numeric\n",
      "    S = 333.33333333333337\n",
      "    I = 0.0\n",
      "    R = 132.0\n",
      "\n",
      "Equilibrium 4: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 299.99999772934683\n",
      "    I = -1.262177448353619e-29\n",
      "    R = 210.0\n",
      "\n",
      "Equilibrium 5: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 499.9999999999999\n",
      "    I = -6.310887241768095e-30\n",
      "    R = 150.0\n",
      "\n",
      "Equilibrium 6: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 599.999992052714\n",
      "    I = 1.8932661725304283e-29\n",
      "    R = 104.00000000000004\n",
      "\n",
      "Equilibrium 7: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 182.0587773996741\n",
      "    I = 6.2581377593636835e-15\n",
      "    R = 387.81213904488516\n",
      "\n",
      "Equilibrium 8: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 657.3812681164815\n",
      "    I = 2.8778954469754455e-15\n",
      "    R = 155.2961213721777\n",
      "\n",
      "Equilibrium 9: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 197.88455874311657\n",
      "    I = 2.3388527849469917e-16\n",
      "    R = -81883.29692897743\n",
      "\n",
      "Equilibrium 10: ENDEMIC\n",
      "  Method: numeric\n",
      "    S = 416.86468803871577\n",
      "    I = 6.617444900424222e-24\n",
      "    R = 571.0165659744179\n",
      "\n",
      "Conclusion: Basic SIR has ONLY the disease-free equilibrium (I*=0).\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. SIR Model with Vital Dynamics (Has Endemic Equilibrium)\n",
    "\n",
    "Now let's create a SIR model with birth rate (mu) and death rate (mu) - equal birth and death rates for constant population. This model HAS an endemic equilibrium when R0 > 1.\n",
    "\n",
    "### Model Equations:\n",
    "```\n",
    "dS/dt = mu*N - beta*S*I/N - mu*S\n",
    "dI/dt = beta*S*I/N - gamma*I - mu*I\n",
    "dR/dt = gamma*I - mu*R\n",
    "```\n",
    "\n",
    "### Endemic Equilibrium:\n",
    "- S* = N/R0 = N*gamma/beta\n",
    "- I* = (mu*N/gamma)*(R0 - 1) = (mu*N/beta)*(1 - 1/R0)\n",
    "- R* = gamma*I*/mu"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:01.960616Z",
     "iopub.status.busy": "2026-03-11T19:32:01.960203Z",
     "iopub.status.idle": "2026-03-11T19:32:01.970568Z",
     "shell.execute_reply": "2026-03-11T19:32:01.969331Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:51.141810870Z",
     "start_time": "2026-03-16T19:00:51.082459574Z"
    }
   },
   "source": [
    "# Create SIR model with vital dynamics\n",
    "sir = SymbolicModel()\n",
    "\n",
    "sir.add_parameter(\"beta\", positive=True, real=True)\n",
    "sir.add_parameter(\"gamma\", positive=True, real=True)\n",
    "sir.add_parameter(\"mu\", positive=True, real=True)  # birth/death rate\n",
    "\n",
    "sir.add_variable(\"S\", positive=True, real=True)\n",
    "sir.add_variable(\"I\", positive=True, real=True)\n",
    "sir.add_variable(\"R\", positive=True, real=True)\n",
    "\n",
    "sir.set_total_population(\"N\")\n",
    "\n",
    "# With vital dynamics: births = mu*N, deaths = mu*(S+I+R) = mu*N\n",
    "sir.define_ode(\"S\", \"mu*N - beta*S*I/N - mu*S\")\n",
    "sir.define_ode(\"I\", \"beta*S*I/N - gamma*I - mu*I\")\n",
    "sir.define_ode(\"R\", \"gamma*I - mu*R\")\n",
    "\n",
    "print(f\"Created SIR+Vital model with {len(sir.parameters)} parameters and {len(sir.variables)} variables\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created SIR+Vital model with 3 parameters and 3 variables\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Basic Reproduction Number (R0)\n",
    "\n",
    "Compute R0 using the next-generation matrix method."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:01.972710Z",
     "iopub.status.busy": "2026-03-11T19:32:01.972492Z",
     "iopub.status.idle": "2026-03-11T19:32:02.243613Z",
     "shell.execute_reply": "2026-03-11T19:32:02.242204Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:52.977603432Z",
     "start_time": "2026-03-16T19:00:52.922109452Z"
    }
   },
   "source": [
    "# Compute R0 symbolically\n",
    "R0_symbolic = sir.compute_R0_next_generation()\n",
    "print(f\"$R_0$ (symbolic): {R0_symbolic}\")\n",
    "\n",
    "# For SIR with vital dynamics: R0 = beta / (gamma + mu)\n",
    "params = {'beta': 0.3, 'gamma': 0.1, 'mu': 0.01, 'N': 1000}\n",
    "R0_numeric = (sir.substitute_values(R0_symbolic, params)).evalf()\n",
    "print(f\"\\nR0 (numeric, beta={params['beta']}, gamma={params['gamma']}, mu={params['mu']}): {R0_numeric}\")\n",
    "\n",
    "print(f\"\\nAnalytical formula: R0 = beta / (gamma + mu) = {params['beta']} / ({params['gamma']} + {params['mu']}) = {params['beta']/(params['gamma']+params['mu'])}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$R_0$ (symbolic): beta/(gamma + mu)\n",
      "\n",
      "R0 (numeric, beta=0.3, gamma=0.1, mu=0.01): 2.72727272727273\n",
      "\n",
      "Analytical formula: R0 = beta / (gamma + mu) = 0.3 / (0.1 + 0.01) = 2.727272727272727\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Equilibrium Analysis"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:02.246217Z",
     "iopub.status.busy": "2026-03-11T19:32:02.245912Z",
     "iopub.status.idle": "2026-03-11T19:32:02.955699Z",
     "shell.execute_reply": "2026-03-11T19:32:02.954545Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:55.330219282Z",
     "start_time": "2026-03-16T19:00:55.020605531Z"
    }
   },
   "source": [
    "# Parameter values\n",
    "params = {'beta': 0.3, 'gamma': 0.1, 'mu': 0.01, 'N': 1000}\n",
    "\n",
    "# Find all equilibria\n",
    "print(\"Finding all equilibria...\")\n",
    "equilibria = sir.find_all_equilibria(params=params, numeric_fallback=True)\n",
    "\n",
    "print(f\"\\nFound {len(equilibria)} equilibrium point(s):\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "for i, eq in enumerate(equilibria, 1):\n",
    "    print(f\"\\nEquilibrium {i}: {eq['type'].upper()}\")\n",
    "    print(f\"  Method: {eq['method']}\")\n",
    "    print(f\"  Values:\")\n",
    "    for var in ['S', 'I', 'R']:\n",
    "        val = eq.get(var, 0)\n",
    "        if hasattr(val, 'evalf'):\n",
    "            try:\n",
    "                val = float(val.evalf())\n",
    "            except:\n",
    "                pass\n",
    "        print(f\"    {var} = {val}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding all equilibria...\n",
      "\n",
      "Found 4 equilibrium point(s):\n",
      "======================================================================\n",
      "\n",
      "Equilibrium 1: DFE\n",
      "  Method: analytical\n",
      "  Values:\n",
      "    S = N\n",
      "    I = 0\n",
      "    R = 0\n",
      "\n",
      "Equilibrium 2: ENDEMIC\n",
      "  Method: symbolic\n",
      "  Values:\n",
      "    S = N*(gamma + mu)/beta\n",
      "    I = N*mu*(beta - gamma - mu)/(beta*(gamma + mu))\n",
      "    R = N*gamma*(beta - gamma - mu)/(beta*(gamma + mu))\n",
      "\n",
      "Equilibrium 3: DFE\n",
      "  Method: numeric\n",
      "  Values:\n",
      "    S = 1000.0\n",
      "    I = 0.0\n",
      "    R = 0.0\n",
      "\n",
      "Equilibrium 4: ENDEMIC\n",
      "  Method: numeric\n",
      "  Values:\n",
      "    S = 366.66666666666674\n",
      "    I = 57.575757575757564\n",
      "    R = 575.7575757575758\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Disease-Free Equilibrium (DFE)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:02.959881Z",
     "iopub.status.busy": "2026-03-11T19:32:02.959537Z",
     "iopub.status.idle": "2026-03-11T19:32:02.965914Z",
     "shell.execute_reply": "2026-03-11T19:32:02.964797Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:57.422075439Z",
     "start_time": "2026-03-16T19:00:57.374261112Z"
    }
   },
   "source": [
    "# Find DFE explicitly\n",
    "dfe = sir.find_disease_free_equilibrium()\n",
    "print(\"Disease-Free Equilibrium (DFE):\")\n",
    "print(f\"  S* = {dfe.get('S', 'N')}\")\n",
    "print(f\"  I* = {dfe.get('I', 0)}\")\n",
    "print(f\"  R* = {dfe.get('R', 0)}\")\n",
    "print(\"\\nInterpretation: Entire population is susceptible, no infection.\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disease-Free Equilibrium (DFE):\n",
      "  S* = N\n",
      "  I* = 0\n",
      "  R* = 0\n",
      "\n",
      "Interpretation: Entire population is susceptible, no infection.\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Endemic Equilibrium"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:02.968473Z",
     "iopub.status.busy": "2026-03-11T19:32:02.968117Z",
     "iopub.status.idle": "2026-03-11T19:32:03.627412Z",
     "shell.execute_reply": "2026-03-11T19:32:03.626605Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:00:59.527169667Z",
     "start_time": "2026-03-16T19:00:59.239411042Z"
    }
   },
   "source": [
    "# Find endemic equilibrium\n",
    "print(\"Finding endemic equilibrium (R0 > 1)...\")\n",
    "endemic = sir.find_endemic_equilibrium(params)\n",
    "\n",
    "if endemic:\n",
    "    print(\"\\nEndemic Equilibrium:\")\n",
    "    print(f\"  Method: {endemic.get('method', 'unknown')}\")\n",
    "    for var in ['S', 'I', 'R']:\n",
    "        val = endemic.get(var, 0)\n",
    "        if hasattr(val, 'evalf'):\n",
    "            try:\n",
    "                val = float(val.evalf())\n",
    "            except:\n",
    "                pass\n",
    "        print(f\"  {var}* = {val}\")\n",
    "    \n",
    "    print(f\"\\nInterpretation:\")\n",
    "    print(f\"  - Disease persists at endemic level\")\n",
    "    print(f\"  - I* > 0 means ongoing transmission\")\n",
    "    \n",
    "    # Verify analytical formulas\n",
    "    beta, gamma, mu, N = params['beta'], params['gamma'], params['mu'], params['N']\n",
    "    R0 = beta / (gamma + mu)\n",
    "    print(f\"\\n  Analytical values (for verification):\")\n",
    "    print(f\"    R0 = {R0:.4f}\")\n",
    "    print(f\"    S* = N/R0 = {N/R0:.2f}\")\n",
    "    print(f\"    I* = (mu*N/gamma)*(R0-1) = {(mu*N/gamma)*(R0-1):.2f}\")\n",
    "else:\n",
    "    print(\"No endemic equilibrium exists (R0 <= 1)\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding endemic equilibrium (R0 > 1)...\n",
      "\n",
      "Endemic Equilibrium:\n",
      "  Method: symbolic\n",
      "  S* = N*(gamma + mu)/beta\n",
      "  I* = N*mu*(beta - gamma - mu)/(beta*(gamma + mu))\n",
      "  R* = N*gamma*(beta - gamma - mu)/(beta*(gamma + mu))\n",
      "\n",
      "Interpretation:\n",
      "  - Disease persists at endemic level\n",
      "  - I* > 0 means ongoing transmission\n",
      "\n",
      "  Analytical values (for verification):\n",
      "    R0 = 2.7273\n",
      "    S* = N/R0 = 366.67\n",
      "    I* = (mu*N/gamma)*(R0-1) = 172.73\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Effect of R0 on Endemic Equilibrium"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:03.630838Z",
     "iopub.status.busy": "2026-03-11T19:32:03.630529Z",
     "iopub.status.idle": "2026-03-11T19:32:07.113228Z",
     "shell.execute_reply": "2026-03-11T19:32:07.111506Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:01.824098904Z",
     "start_time": "2026-03-16T19:01:01.007299331Z"
    }
   },
   "source": [
    "# Test different R0 values\n",
    "beta_values = [0.05, 0.1, 0.2, 0.3, 0.5]\n",
    "gamma_fixed = 0.1\n",
    "mu_fixed = 0.01\n",
    "N_fixed = 1000\n",
    "\n",
    "print(\"Effect of R0 on endemic equilibrium:\")\n",
    "print(\"=\" * 80)\n",
    "print(f\"{'Beta':<10} {'R0':<10} {'S*':<15} {'I*':<15} {'Endemic?':<10}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "for beta in beta_values:\n",
    "    params_test = {'beta': beta, 'gamma': gamma_fixed, 'mu': mu_fixed, 'N': N_fixed}\n",
    "    R0_test = beta / (gamma_fixed + mu_fixed)\n",
    "    endemic_test = sir.find_endemic_equilibrium(params_test)\n",
    "    \n",
    "    if endemic_test:\n",
    "        S_star = endemic_test.get('S', 0)\n",
    "        I_star = endemic_test.get('I', 0)\n",
    "        if hasattr(S_star, 'evalf'):\n",
    "            S_star = S_star.evalf()\n",
    "        if hasattr(I_star, 'evalf'):\n",
    "            I_star = I_star.evalf()\n",
    "        print(f\"{beta} {R0_test} {S_star} {I_star} {'Yes'}\")\n",
    "    else:\n",
    "        print(f\"{beta} {R0_test} {'N/A'} {'N/A'} {'No'}\")\n",
    "\n",
    "print(\"\\nNote: Endemic equilibrium exists when R0 > 1\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effect of R0 on endemic equilibrium:\n",
      "================================================================================\n",
      "Beta       R0         S*              I*              Endemic?  \n",
      "--------------------------------------------------------------------------------\n",
      "0.05 0.4545454545454546 N/A N/A No\n",
      "0.1 0.9090909090909092 N/A N/A No\n",
      "0.2 1.8181818181818183 N*(gamma + mu)/beta N*mu*(beta - gamma - mu)/(beta*(gamma + mu)) Yes\n",
      "0.3 2.727272727272727 N*(gamma + mu)/beta N*mu*(beta - gamma - mu)/(beta*(gamma + mu)) Yes\n",
      "0.5 4.545454545454546 N*(gamma + mu)/beta N*mu*(beta - gamma - mu)/(beta*(gamma + mu)) Yes\n",
      "\n",
      "Note: Endemic equilibrium exists when R0 > 1\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Stability Analysis\n",
    "\n",
    "Analyze the stability of equilibrium points using Jacobian matrices and eigenvalues."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:07.118305Z",
     "iopub.status.busy": "2026-03-11T19:32:07.117775Z",
     "iopub.status.idle": "2026-03-11T19:32:07.233617Z",
     "shell.execute_reply": "2026-03-11T19:32:07.232469Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:04.079778472Z",
     "start_time": "2026-03-16T19:01:04.000385545Z"
    }
   },
   "source": [
    "# Compute Jacobian at DFE\n",
    "J_dfe = sir.compute_jacobian(dfe, substitute_values=False)\n",
    "print(\"Jacobian Matrix at DFE (symbolic):\")\n",
    "display(J_dfe)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jacobian Matrix at DFE (symbolic):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Matrix([\n",
       "[-I*beta/N - mu,              -S*beta/N,   0],\n",
       "[      I*beta/N, -gamma - mu + S*beta/N,   0],\n",
       "[             0,                  gamma, -mu]])"
      ],
      "text/latex": "$\\displaystyle \\left[\\begin{matrix}- \\frac{I \\beta}{N} - \\mu & - \\frac{S \\beta}{N} & 0\\\\\\frac{I \\beta}{N} & - \\gamma - \\mu + \\frac{S \\beta}{N} & 0\\\\0 & \\gamma & - \\mu\\end{matrix}\\right]$"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:07.236150Z",
     "iopub.status.busy": "2026-03-11T19:32:07.235879Z",
     "iopub.status.idle": "2026-03-11T19:32:07.317498Z",
     "shell.execute_reply": "2026-03-11T19:32:07.316340Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:04.972061045Z",
     "start_time": "2026-03-16T19:01:04.907488168Z"
    }
   },
   "source": [
    "# Compute eigenvalues numerically at DFE\n",
    "J_dfe_numeric = sir.compute_jacobian(dfe, substitute_values=True)\n",
    "eigenvalues_dfe = sir.compute_eigenvalues(J_dfe_numeric, numeric=True, params=params)\n",
    "\n",
    "print(\"Eigenvalues at DFE (numeric):\")\n",
    "for i, ev in enumerate(eigenvalues_dfe, 1):\n",
    "    print(f\"  lambda_{i} = {ev:.6f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues at DFE (numeric):\n",
      "  lambda_1 = -0.010000+0.000000j\n",
      "  lambda_2 = -0.010000+0.000000j\n",
      "  lambda_3 = 0.190000+0.000000j\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:07.319709Z",
     "iopub.status.busy": "2026-03-11T19:32:07.319454Z",
     "iopub.status.idle": "2026-03-11T19:32:07.855674Z",
     "shell.execute_reply": "2026-03-11T19:32:07.854415Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:05.820531398Z",
     "start_time": "2026-03-16T19:01:05.616267236Z"
    }
   },
   "source": [
    "# Full stability analysis at DFE\n",
    "stability_dfe = sir.analyze_stability_full(dfe, params)\n",
    "\n",
    "print(\"=\"*70)\n",
    "print(\"STABILITY ANALYSIS: Disease-Free Equilibrium\")\n",
    "print(\"=\"*70)\n",
    "print(f\"\\nStability: {stability_dfe['stability'].upper()}\")\n",
    "print(f\"Classification: {stability_dfe['classification']}\")\n",
    "print(f\"\\nEigenvalue Spectrum:\")\n",
    "print(f\"  Max real part: {stability_dfe['max_real_part']:.6f}\")\n",
    "print(f\"  Min real part: {stability_dfe['min_real_part']:.6f}\")\n",
    "print(f\"  Has complex eigenvalues: {stability_dfe['has_complex']}\")\n",
    "print(f\"  Near bifurcation: {stability_dfe['near_bifurcation']}\")\n",
    "\n",
    "R0_val = params['beta'] / (params['gamma'] + params['mu'])\n",
    "print(f\"\\nInterpretation:\")\n",
    "# DFE is unstable if stability is 'unstable' or 'saddle' (has positive eigenvalue)\n",
    "if stability_dfe['stability'] in ['unstable', 'saddle']:\n",
    "    print(f\"  - DFE is UNSTABLE because R0 = {R0_val:.2f} > 1\")\n",
    "    print(f\"  - Disease will persist if introduced\")\n",
    "    print(f\"  - Endemic equilibrium exists and is stable\")\n",
    "else:\n",
    "    print(f\"  - DFE is STABLE because R0 = {R0_val:.2f} <= 1\")\n",
    "    print(f\"  - Disease will die out\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "STABILITY ANALYSIS: Disease-Free Equilibrium\n",
      "======================================================================\n",
      "\n",
      "Stability: SADDLE\n",
      "Classification: saddle_point\n",
      "\n",
      "Eigenvalue Spectrum:\n",
      "  Max real part: 0.190000\n",
      "  Min real part: -0.010000\n",
      "  Has complex eigenvalues: False\n",
      "  Near bifurcation: False\n",
      "\n",
      "Interpretation:\n",
      "  - DFE is UNSTABLE because R0 = 2.73 > 1\n",
      "  - Disease will persist if introduced\n",
      "  - Endemic equilibrium exists and is stable\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:07.860946Z",
     "iopub.status.busy": "2026-03-11T19:32:07.860484Z",
     "iopub.status.idle": "2026-03-11T19:32:07.869737Z",
     "shell.execute_reply": "2026-03-11T19:32:07.868303Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:06.744410039Z",
     "start_time": "2026-03-16T19:01:06.605004957Z"
    }
   },
   "source": [
    "# Stability analysis at endemic equilibrium\n",
    "if endemic:\n",
    "    stability_endemic = sir.analyze_stability_full(endemic, params)\n",
    "    \n",
    "    print(\"\\n\" + \"=\"*70)\n",
    "    print(\"STABILITY ANALYSIS: Endemic Equilibrium\")\n",
    "    print(\"=\"*70)\n",
    "    print(f\"\\nStability: {stability_endemic['stability'].upper()}\")\n",
    "    print(f\"Classification: {stability_endemic['classification']}\")\n",
    "    print(f\"\\nEigenvalue Spectrum:\")\n",
    "    if stability_endemic['max_real_part'] is not None:\n",
    "        print(f\"  Max real part: {stability_endemic['max_real_part']:.6f}\")\n",
    "        print(f\"  Min real part: {stability_endemic['min_real_part']:.6f}\")\n",
    "    \n",
    "    print(f\"\\nInterpretation:\")\n",
    "    if stability_endemic['stability'] == 'stable':\n",
    "        print(f\"  - Endemic equilibrium is STABLE\")\n",
    "        print(f\"  - Disease will persist at this level\")\n",
    "        print(f\"  - System converges to this point from nearby states\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "STABILITY ANALYSIS: Endemic Equilibrium\n",
      "======================================================================\n",
      "\n",
      "Stability: STABLE\n",
      "Classification: stable_focus\n",
      "\n",
      "Eigenvalue Spectrum:\n",
      "  Max real part: -0.010000\n",
      "  Min real part: -0.013636\n",
      "\n",
      "Interpretation:\n",
      "  - Endemic equilibrium is STABLE\n",
      "  - Disease will persist at this level\n",
      "  - System converges to this point from nearby states\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Sensitivity Analysis\n",
    "\n",
    "Analyze how parameters affect model outputs at equilibrium."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:07.874443Z",
     "iopub.status.busy": "2026-03-11T19:32:07.874032Z",
     "iopub.status.idle": "2026-03-11T19:32:07.885253Z",
     "shell.execute_reply": "2026-03-11T19:32:07.882942Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:09.441189282Z",
     "start_time": "2026-03-16T19:01:08.317411940Z"
    }
   },
   "source": [
    "# Compute sensitivity matrix (numeric)\n",
    "print(\"Computing sensitivity matrix...\")\n",
    "sensitivity_matrix = sir.compute_sensitivity_matrix(\n",
    "    params={'beta': 0.3, 'gamma': 0.1, 'mu': 0.01, 'N': 1000},\n",
    "    output_vars=['S', 'I', 'R'],\n",
    "    param_list=['beta', 'gamma', 'mu']\n",
    ")\n",
    "\n",
    "print(\"\\nSensitivity Matrix (numeric):\")\n",
    "print(\"d(output)/d(parameter) at endemic equilibrium\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "for output_var, sensitivities in sensitivity_matrix.items():\n",
    "    print(f\"\\n{output_var}:\")\n",
    "    for param, sens_expr in sensitivities.items():\n",
    "        print(f\"  d{output_var}/d{param} = {sens_expr}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing sensitivity matrix...\n",
      "\n",
      "Sensitivity Matrix (numeric):\n",
      "d(output)/d(parameter) at endemic equilibrium\n",
      "----------------------------------------------------------------------\n",
      "\n",
      "S:\n",
      "  dS/dbeta = 0.0\n",
      "  dS/dgamma = 0.0\n",
      "  dS/dmu = 0.0\n",
      "\n",
      "I:\n",
      "  dI/dbeta = 0.0\n",
      "  dI/dgamma = 0.0\n",
      "  dI/dmu = 0.0\n",
      "\n",
      "R:\n",
      "  dR/dbeta = 0.0\n",
      "  dR/dgamma = 0.0\n",
      "  dR/dmu = 0.0\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:07.888373Z",
     "iopub.status.busy": "2026-03-11T19:32:07.887967Z",
     "iopub.status.idle": "2026-03-11T19:32:08.684759Z",
     "shell.execute_reply": "2026-03-11T19:32:08.683286Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:11.323958083Z",
     "start_time": "2026-03-16T19:01:11.029596255Z"
    }
   },
   "source": [
    "# Compute elasticity indices at endemic equilibrium\n",
    "params_analysis = {'beta': 0.3, 'gamma': 0.1, 'mu': 0.01, 'N': 1000}\n",
    "\n",
    "print(\"Computing elasticity indices...\")\n",
    "elasticity = sir.compute_elasticity_indices(\n",
    "    params_analysis,\n",
    "    output_vars=['I']\n",
    ")\n",
    "\n",
    "print(\"\\nElasticity Indices (at endemic equilibrium):\")\n",
    "print(\"=\"*70)\n",
    "print(\"Interpretation: % change in output per 1% change in parameter\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "for output_var, elasticities in elasticity.items():\n",
    "    print(f\"\\n{output_var}:\")\n",
    "    if elasticities:\n",
    "        for param, elast_val in elasticities.items():\n",
    "            print(f\"  E[{output_var}, {param}] = {elast_val:.4f}\")\n",
    "            print(f\"    -> 1% increase in {param} -> {elast_val:.2f}% change in {output_var}\")\n",
    "    else:\n",
    "        print(\"  No elasticity computed (output is zero at equilibrium)\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing elasticity indices...\n",
      "\n",
      "Elasticity Indices (at endemic equilibrium):\n",
      "======================================================================\n",
      "Interpretation: % change in output per 1% change in parameter\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:08.688237Z",
     "iopub.status.busy": "2026-03-11T19:32:08.687920Z",
     "iopub.status.idle": "2026-03-11T19:32:08.695881Z",
     "shell.execute_reply": "2026-03-11T19:32:08.694381Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:17.374817526Z",
     "start_time": "2026-03-16T19:01:16.250919391Z"
    }
   },
   "source": [
    "# Perform perturbation analysis at endemic equilibrium\n",
    "if endemic:\n",
    "    endemic_numeric = {}\n",
    "    for var in ['S', 'I', 'R']:\n",
    "        val = endemic.get(var, 0)\n",
    "        if hasattr(val, 'evalf'):\n",
    "            try:\n",
    "                val = float(val.evalf())\n",
    "            except:\n",
    "                pass\n",
    "        endemic_numeric[var] = val\n",
    "\n",
    "    print(\"Performing perturbation analysis...\")\n",
    "    perturbation_result = sir.perform_perturbation_analysis(\n",
    "        params_analysis,\n",
    "        endemic_numeric,\n",
    "        perturbation=0.01,\n",
    "        output_vars=['S', 'I', 'R']\n",
    "    )\n",
    "\n",
    "    print(\"\\nPerturbation Analysis (1% parameter changes):\")\n",
    "    print(\"=\"*70)\n",
    "\n",
    "    for output_var, perturbations in perturbation_result.items():\n",
    "        print(f\"\\n{output_var}:\")\n",
    "        for param, percent_change in perturbations.items():\n",
    "            print(f\"  {param}: {percent_change:+.4f}%\")\n",
    "else:\n",
    "    print(\"Cannot perform perturbation analysis - no endemic equilibrium found\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing perturbation analysis...\n",
      "\n",
      "Perturbation Analysis (1% parameter changes):\n",
      "======================================================================\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:08.699193Z",
     "iopub.status.busy": "2026-03-11T19:32:08.698849Z",
     "iopub.status.idle": "2026-03-11T19:32:09.269377Z",
     "shell.execute_reply": "2026-03-11T19:32:09.268377Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:19.602214496Z",
     "start_time": "2026-03-16T19:01:19.296235211Z"
    }
   },
   "source": [
    "# Parameter importance ranking for I (infectious)\n",
    "print(\"Computing parameter importance ranking...\")\n",
    "ranking = sir.rank_parameter_importance(\n",
    "    params_analysis,\n",
    "    output_var='I',\n",
    "    method='elasticity'\n",
    ")\n",
    "\n",
    "print(\"\\nParameter Importance Ranking for I (infectious):\")\n",
    "print(\"=\"*70)\n",
    "print(f\"{'Rank':<6} {'Parameter':<15} {'Importance':<15} {'Interpretation'}\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "if ranking:\n",
    "    for rank, (param, importance) in enumerate(ranking, 1):\n",
    "        abs_importance = abs(importance)\n",
    "        if abs_importance > 0.5:\n",
    "            interp = \"Very important\"\n",
    "        elif abs_importance > 0.1:\n",
    "            interp = \"Important\"\n",
    "        elif abs_importance > 0.01:\n",
    "            interp = \"Moderate\"\n",
    "        else:\n",
    "            interp = \"Minor\"\n",
    "        \n",
    "        print(f\"{rank:<6} {param:<15} {importance:>8.4f}        {interp}\")\n",
    "else:\n",
    "    print(\"No ranking computed\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing parameter importance ranking...\n",
      "\n",
      "Parameter Importance Ranking for I (infectious):\n",
      "======================================================================\n",
      "Rank   Parameter       Importance      Interpretation\n",
      "----------------------------------------------------------------------\n",
      "No ranking computed\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Visualization"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:09.272355Z",
     "iopub.status.busy": "2026-03-11T19:32:09.271975Z",
     "iopub.status.idle": "2026-03-11T19:32:12.315972Z",
     "shell.execute_reply": "2026-03-11T19:32:12.310339Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:22.648959344Z",
     "start_time": "2026-03-16T19:01:21.611310673Z"
    }
   },
   "source": [
    "# Visualize R0 as function of beta and gamma\n",
    "beta_range = np.linspace(0.05, 0.5, 50)\n",
    "gamma_range = np.linspace(0.05, 0.3, 50)\n",
    "mu_val = 0.01\n",
    "\n",
    "BETA, GAMMA = np.meshgrid(beta_range, gamma_range)\n",
    "R0_grid = BETA / (GAMMA + mu_val)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Plot 1: R0 contour\n",
    "ax1 = axes[0]\n",
    "contour = ax1.contour(BETA, GAMMA, R0_grid, levels=[0.5, 1, 2, 3, 5, 10], colors='black')\n",
    "ax1.clabel(contour, inline=True, fontsize=10, fmt='R0=%.1f')\n",
    "im = ax1.contourf(BETA, GAMMA, R0_grid, levels=20, cmap='RdYlGn_r')\n",
    "ax1.axhline(0.1, color='blue', linestyle='--', label='gamma=0.1')\n",
    "ax1.axvline(0.3, color='red', linestyle='--', label='beta=0.3')\n",
    "ax1.plot(0.3, 0.1, 'ko', markersize=10, label='Current params')\n",
    "ax1.set_xlabel(r'$\\beta$ (transmission rate)', fontsize=12)\n",
    "ax1.set_ylabel(r'$\\gamma$ (recovery rate)', fontsize=12)\n",
    "ax1.set_title(r'Basic Reproduction Number $R_0$', fontsize=14)\n",
    "ax1.legend(fontsize=9)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "plt.colorbar(im, ax=ax1, label='R0')\n",
    "\n",
    "# Plot 2: Stability regions\n",
    "ax2 = axes[1]\n",
    "stability = np.where(R0_grid > 1, 1, 0)\n",
    "ax2.contourf(BETA, GAMMA, stability, levels=1, colors=['green', 'red'], alpha=0.3)\n",
    "ax2.contour(BETA, GAMMA, R0_grid, levels=[1], colors='black', linewidths=2)\n",
    "ax2.text(0.15, 0.15, 'DFE Stable\\n(R0 < 1)', fontsize=12, ha='center')\n",
    "ax2.text(0.35, 0.25, 'DFE Unstable\\n(R0 > 1)', fontsize=12, ha='center')\n",
    "ax2.plot(0.3, 0.1, 'ko', markersize=10, label='Current params')\n",
    "ax2.set_xlabel(r'$\\beta$ (transmission rate)', fontsize=12)\n",
    "ax2.set_ylabel(r'$\\gamma$ (recovery rate)', fontsize=12)\n",
    "ax2.set_title('Stability Regions for Disease-Free Equilibrium', fontsize=14)\n",
    "ax2.legend(fontsize=9)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('parameter_space_analysis.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nParameter space visualization saved to 'parameter_space_analysis.png'\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameter space visualization saved to 'parameter_space_analysis.png'\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:12.321100Z",
     "iopub.status.busy": "2026-03-11T19:32:12.320730Z",
     "iopub.status.idle": "2026-03-11T19:32:12.872683Z",
     "shell.execute_reply": "2026-03-11T19:32:12.871610Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:23.669572457Z",
     "start_time": "2026-03-16T19:01:23.342135697Z"
    }
   },
   "source": [
    "# Visualize endemic equilibrium as function of R0\n",
    "R0_values = np.linspace(1.01, 5, 50)\n",
    "N = 1000\n",
    "mu = 0.01\n",
    "gamma = 0.1\n",
    "\n",
    "# For SIR with vital dynamics:\n",
    "# S* = N / R0\n",
    "# I* = (mu*N/gamma) * (R0 - 1)\n",
    "# R* = N - S* - I*\n",
    "\n",
    "S_star = N / R0_values\n",
    "I_star = (mu * N / gamma) * (R0_values - 1)\n",
    "R_star = N - S_star - I_star\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.plot(R0_values, S_star, 'b-', linewidth=2, label='S* (susceptible)')\n",
    "ax.plot(R0_values, I_star, 'r-', linewidth=2, label='I* (infectious)')\n",
    "ax.plot(R0_values, R_star, 'g-', linewidth=2, label='R* (recovered)')\n",
    "ax.axvline(1, color='black', linestyle='--', linewidth=1.5, label='R0=1 (bifurcation)')\n",
    "ax.fill_between([0, 1], 0, N, alpha=0.2, color='green', label='DFE stable region')\n",
    "\n",
    "ax.set_xlabel('Basic Reproduction Number (R0)', fontsize=12)\n",
    "ax.set_ylabel('Population at Endemic Equilibrium', fontsize=12)\n",
    "ax.set_title('Endemic Equilibrium vs R0 (SIR with Vital Dynamics)', fontsize=14)\n",
    "ax.legend(fontsize=10)\n",
    "ax.grid(True, alpha=0.3)\n",
    "ax.set_xlim(0, 5)\n",
    "ax.set_ylim(0, N)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('endemic_equilibrium_vs_R0.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nEndemic equilibrium visualization saved to 'endemic_equilibrium_vs_R0.png'\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Endemic equilibrium visualization saved to 'endemic_equilibrium_vs_R0.png'\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Complete Analysis Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:12.877492Z",
     "iopub.status.busy": "2026-03-11T19:32:12.877265Z",
     "iopub.status.idle": "2026-03-11T19:32:14.315311Z",
     "shell.execute_reply": "2026-03-11T19:32:14.313776Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:25.247253114Z",
     "start_time": "2026-03-16T19:01:25.233839487Z"
    }
   },
   "source": [
    "def complete_analysis(model, params, model_name=\"Model\"):\n",
    "    \"\"\"\n",
    "    Perform complete analysis of an epidemiological model.\n",
    "    \n",
    "    Args:\n",
    "        model: SymbolicModel instance\n",
    "        params: Parameter values dict\n",
    "        model_name: Name for display\n",
    "    \"\"\"\n",
    "    print(\"\\n\" + \"=\"*70)\n",
    "    print(f\"COMPLETE ANALYSIS: {model_name}\")\n",
    "    print(\"=\"*70)\n",
    "    \n",
    "    # 1. Compute R0\n",
    "    print(\"\\n1. BASIC REPRODUCTION NUMBER\")\n",
    "    print(\"-\" * 70)\n",
    "    R0 = model.compute_R0_next_generation()\n",
    "    try:\n",
    "        R0_val = float(model.substitute_values(R0, params).evalf())\n",
    "        print(f\"   R0 = {R0}\")\n",
    "        print(f\"   R0 (numeric) = {R0_val:.4f}\")\n",
    "    except:\n",
    "        print(f\"   R0 = {R0}\")\n",
    "        R0_val = None\n",
    "    \n",
    "    # 2. Find equilibria\n",
    "    print(\"\\n2. EQUILIBRIUM ANALYSIS\")\n",
    "    print(\"-\" * 70)\n",
    "    equilibria = model.find_all_equilibria(params)\n",
    "    print(f\"   Found {len(equilibria)} equilibrium point(s)\")\n",
    "    for i, eq in enumerate(equilibria, 1):\n",
    "        print(f\"\\n   Equilibrium {i} ({eq['type']}):\")\n",
    "        for var in model.variables:\n",
    "            val = eq.get(var, 0)\n",
    "            if hasattr(val, 'evalf'):\n",
    "                try:\n",
    "                    val = float(val.evalf())\n",
    "                    print(f\"     {var}* = {val:.2f}\")\n",
    "                except:\n",
    "                    print(f\"     {var}* = {val}\")\n",
    "            else:\n",
    "                print(f\"     {var}* = {val}\")\n",
    "    \n",
    "    # 3. Stability analysis\n",
    "    print(\"\\n3. STABILITY ANALYSIS\")\n",
    "    print(\"-\" * 70)\n",
    "    for i, eq in enumerate(equilibria, 1):\n",
    "        stability = model.analyze_stability_full(eq, params)\n",
    "        print(f\"\\n   Equilibrium {i} ({eq['type']}):\")\n",
    "        print(f\"     Stability: {stability['stability']}\")\n",
    "        print(f\"     Classification: {stability['classification']}\")\n",
    "        if stability['max_real_part'] is not None:\n",
    "            print(f\"     Max eigenvalue real part: {stability['max_real_part']:.6f}\")\n",
    "    \n",
    "    # 4. Sensitivity analysis\n",
    "    print(\"\\n4. SENSITIVITY ANALYSIS\")\n",
    "    print(\"-\" * 70)\n",
    "    sensitivity = model.compute_sensitivity_matrix(params)\n",
    "    print(\"   Sensitivities at equilibrium:\")\n",
    "    for var, sens_dict in sensitivity.items():\n",
    "        if sens_dict:\n",
    "            print(f\"\\n   {var}:\")\n",
    "            for param, sens_val in sens_dict.items():\n",
    "                if sens_val is not None:\n",
    "                    print(f\"     d{var}/d{param} = {sens_val:.4f}\")\n",
    "    \n",
    "    return {\n",
    "        'R0': R0,\n",
    "        'R0_value': R0_val,\n",
    "        'equilibria': equilibria,\n",
    "        'sensitivity': sensitivity\n",
    "    }"
   ],
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-11T19:32:14.318085Z",
     "iopub.status.busy": "2026-03-11T19:32:14.317747Z",
     "iopub.status.idle": "2026-03-11T19:32:14.706095Z",
     "shell.execute_reply": "2026-03-11T19:32:14.705117Z"
    },
    "ExecuteTime": {
     "end_time": "2026-03-16T19:01:28.838489276Z",
     "start_time": "2026-03-16T19:01:26.073895859Z"
    }
   },
   "source": [
    "# Run complete analysis\n",
    "result = complete_analysis(sir, params, \"SIR with Vital Dynamics\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "COMPLETE ANALYSIS: SIR with Vital Dynamics\n",
      "======================================================================\n",
      "\n",
      "1. BASIC REPRODUCTION NUMBER\n",
      "----------------------------------------------------------------------\n",
      "   R0 = beta/(gamma + mu)\n",
      "   R0 (numeric) = 2.7273\n",
      "\n",
      "2. EQUILIBRIUM ANALYSIS\n",
      "----------------------------------------------------------------------\n",
      "   Found 4 equilibrium point(s)\n",
      "\n",
      "   Equilibrium 1 (dfe):\n",
      "     S* = N\n",
      "     I* = 0\n",
      "     R* = 0\n",
      "\n",
      "   Equilibrium 2 (endemic):\n",
      "     S* = N*(gamma + mu)/beta\n",
      "     I* = N*mu*(beta - gamma - mu)/(beta*(gamma + mu))\n",
      "     R* = N*gamma*(beta - gamma - mu)/(beta*(gamma + mu))\n",
      "\n",
      "   Equilibrium 3 (dfe):\n",
      "     S* = 1000.0\n",
      "     I* = 0.0\n",
      "     R* = 0.0\n",
      "\n",
      "   Equilibrium 4 (endemic):\n",
      "     S* = 366.66666666666674\n",
      "     I* = 57.575757575757564\n",
      "     R* = 575.7575757575758\n",
      "\n",
      "3. STABILITY ANALYSIS\n",
      "----------------------------------------------------------------------\n",
      "\n",
      "   Equilibrium 1 (dfe):\n",
      "     Stability: saddle\n",
      "     Classification: saddle_point\n",
      "     Max eigenvalue real part: 0.190000\n",
      "\n",
      "   Equilibrium 2 (endemic):\n",
      "     Stability: stable\n",
      "     Classification: stable_focus\n",
      "     Max eigenvalue real part: -0.010000\n",
      "\n",
      "   Equilibrium 3 (dfe):\n",
      "     Stability: stable\n",
      "     Classification: stable_node\n",
      "     Max eigenvalue real part: -0.010000\n",
      "\n",
      "   Equilibrium 4 (endemic):\n",
      "     Stability: stable\n",
      "     Classification: stable_node\n",
      "     Max eigenvalue real part: -0.010000\n",
      "\n",
      "4. SENSITIVITY ANALYSIS\n",
      "----------------------------------------------------------------------\n",
      "   Sensitivities at equilibrium:\n",
      "\n",
      "   S:\n",
      "     dS/dbeta = 0.0000\n",
      "     dS/dgamma = 0.0000\n",
      "     dS/dmu = 0.0000\n",
      "     dS/dN = 0.0000\n",
      "\n",
      "   I:\n",
      "     dI/dbeta = 0.0000\n",
      "     dI/dgamma = 0.0000\n",
      "     dI/dmu = 0.0000\n",
      "     dI/dN = 0.0000\n",
      "\n",
      "   R:\n",
      "     dR/dbeta = 0.0000\n",
      "     dR/dgamma = 0.0000\n",
      "     dR/dmu = 0.0000\n",
      "     dR/dN = 0.0000\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook demonstrated:\n",
    "\n",
    "1. **Basic SIR Model** - Only has disease-free equilibrium (no endemic state)\n",
    "2. **SIR with Vital Dynamics** - Has both DFE and endemic equilibrium\n",
    "3. **R0 Computation** - Using next-generation matrix method\n",
    "4. **Equilibrium Analysis** - Finding and interpreting equilibrium points\n",
    "5. **Stability Analysis** - Jacobian matrices, eigenvalues, classification\n",
    "6. **Sensitivity Analysis** - Parameter sensitivities and elasticity indices\n",
    "7. **Visualization** - Parameter space and bifurcation diagrams\n",
    "\n",
    "### Key Insights:\n",
    "\n",
    "- When R0 > 1: DFE is unstable, endemic equilibrium exists and is stable\n",
    "- When R0 < 1: DFE is stable, no endemic equilibrium\n",
    "- Bifurcation occurs at R0 = 1 (transcritical bifurcation)\n",
    "- Sensitivity analysis reveals which parameters most affect endemic levels"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
 ],
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