diff --git a/README.md b/README.md
index 0a2a73bf105766c36467d9aa058d14a4156ce035..cd32b4b00f9b6541353d13e25618f57e78513deb 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ Estos ejercicios consisten en notebooks que podran correr localmente o en jupyte
 
 ## Instruciones para correr los notebooks localmente.
 
-Estos ejercicios requieren tener intalado [conda](https://docs.conda.io/projects/conda/en/latest/index.html) y una version de Python 3.7 or mayor. 
+Estos ejercicios requieren tener instalado [conda](https://docs.conda.io/projects/conda/en/latest/index.html) y una version de Python 3.7 or mayor. 
 
 Empecemos creando un nuevo entorno computacional conda de la forma:
 
@@ -28,7 +28,7 @@ tu fork. Esta opción es para preferida para que puedas guardar tu trabajo.
 Entra al repositorio y asegúrate estar en la rama "master"  
 
 ```
-cd ejercicios-clase-13-datos
+cd ejercicios-clase-17-datos
 git checkout master
 ```
 Ahora instala las librarias que usaremos con:
diff --git a/Simulando_una_epidemia.ipynb b/Simulando_una_epidemia.ipynb
index 0a1cc1916f4db6e2bb57ddd0e81620d159039f16..aa354d258837bee94c26831f5b36be60444e5189 100644
--- a/Simulando_una_epidemia.ipynb
+++ b/Simulando_una_epidemia.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Epidemiólogos por un dia: simulando un brote de COVID-19 en una población\n",
+    "# Epidemiólogos por un día: simulando la evolución de una epidemia infecciosa \n",
     "\n",
     "El propósito de esta práctica es demostrar cómo podemos modelar la evolución de una epidemia infecciosa utilizando datos disponibles para COVID-19 en una población susceptible utilizando modelos epidemiológicos estándar. Los parámetros del modelo se toman de una literatura científica en rápida evolución que documenta el brote mundial de COVID-19.\n",
     "\n",
@@ -43,7 +43,7 @@
     "* **Infectada** La subpoblación que se ha vuelto infecciosa.\n",
     "* **Recuperada** La subpoblación que se ha recuperado de la infección y que se supone que ya no es susceptible de contraer la enfermedad.\n",
     "\n",
-    "Sin tener en cuenta los procesos demográficos de nacimiento y muerte por otras causas, y suponiendo una tasa de mortalidad insignificante debida a la enfermedad infecciosa en cuestión, la progresión de una epidemia puede modelarse mediante procesos de tasal\n",
+    "Sin tener en cuenta los procesos demográficos de nacimiento y muerte por otras causas, y suponiendo una tasa de mortalidad insignificante debida a la enfermedad infecciosa en cuestión, la progresión de una epidemia puede modelarse mediante procesos de tasa\n",
     "\n",
     "$$\\text{Susceptible}\n",
     "\\xrightarrow{\\frac{\\beta S I}{N}} \n",
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -145,18 +145,7 @@
     },
     "outputId": "4f36827d-6ff3-4554-df30-3e7e27fbfd36"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "en el dia 0:\n",
-      "suseptibles:  1.0 , infectados:  0.0 , recuperados:  0.0\n",
-      "en el dia 180:\n",
-      "suseptibles:  0.12 , infectados:  0.0 , recuperados:  0.88\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from scipy.integrate import odeint\n",
@@ -199,28 +188,18 @@
     "print(\"en el dia 0:\")\n",
     "print(\"suseptibles: \", round(s[0],2), \", infectados: \",round(i[0],2),\", recuperados: \", round(r[0],2))\n",
     "\n",
+    "print(\"en el dia 50:\")\n",
+    "print(\"suseptibles: \", round(s[49],2), \", infectados: \",round(i[49],2),\", recuperados: \", round(r[49],2))\n",
+    "\n",
     "print(\"en el dia 180:\")\n",
     "print(\"suseptibles: \", round(s[179],2), \", infectados: \",round(i[179],2),\", recuperados: \", round(r[179],2))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def plotdata(t, s, i, e=None):\n",
     "    # plot the data\n",
@@ -269,7 +248,7 @@
    "source": [
     "## Añadiendo incertidumbre al modelo\n",
     "\n",
-    "Los parámetros principales de nuestro modelo son el número básico de reproducción $R0$ y los dias en los cuales una persona es infecciosa $\\tau_{infectividad}$. En el mundo real estos valores tiene su incertidumbre y un modelo realista debe incorporarlas. \n"
+    "Los parámetros principales de nuestro modelo son el número básico de reproducción $R0$ y los días en los cuales una persona es infecciosa $\\tau_{infectividad}$. En el mundo real estos valores tienen su incertidumbre y en un modelo realista deben incorporarse. \n"
    ]
   },
   {
@@ -278,15 +257,12 @@
    "source": [
     "### Incertidumbre en número básico de reproducción $R0$\n",
     "\n",
-    "Podemos usar una distribución log-normal como la PDF que modela nuestro $R0$*\n",
-    ", en este caso usaremos los siguientes parametros escogidos ad-hoc para este ejercicio:\n",
-    "\n",
-    "* [un artículo al respecto](https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0238090/1/pone.0238090.pdf?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa%40plos-prod.iam.gserviceaccount.com%2F20210312%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210312T124607Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)"
+    "Podemos usar una distribución log-normal como la PDF que modela nuestro $R0$ ([leer aquí más al respecto](https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0238090/1/pone.0238090.pdf?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=wombat-sa%40plos-prod.iam.gserviceaccount.com%2F20210312%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210312T124607Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)), en este caso usaremos los siguientes parámetros escogidos ad-hoc para este ejercicio:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -294,20 +270,7 @@
     "id": "z_7OPTr-uLp5",
     "outputId": "6d8dbfa6-a52f-4142-f742-6b6a91e2e6d1"
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUX0lEQVR4nO3df5Bdd3nf8ffHMsYUDJhYSRn9QGoQMA4htbOIdkj57SDGiZwGJpFxMoZCNZlBiVOadEwbHBBJhyQTCNNR0mqME5fEFdSJmU0iUDyxCYUBrBU4gGQEqupUUpOxsE0wbkGIPP3jHqHr9f64K+25Z3fP+zVzZ+8553vvPr629PH3nOd+T6oKSVJ/XdB1AZKkbhkEktRzBoEk9ZxBIEk9ZxBIUs9d2HUBC3XZZZfVhg0bui5DkpaVAwcOfLWqVs90bNkFwYYNG5iamuq6DElaVpL8zWzHPDUkST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPbfsvlms5WnDjX/+3ef3v/vqDiuRNJ0zAknqOYNAknrOIJCknjMIJKnnWg2CJFuSHE5yJMmNMxx/b5J7m8eXk3ytzXokSY/XWtdQklXALuAq4DiwP8lkVR06M6aq/s3Q+J8HrmirHi3cbJ0+w/unH5O0/LQ5I9gMHKmqo1V1CtgDXDPH+GuB/9ZiPZKkGbQZBGuAY0Pbx5t9j5PkWcBG4K5Zjm9PMpVk6uTJk4teqCT12VK5WLwNuL2qvjPTwaraXVUTVTWxevWMt9yUJJ2jNoPgBLBuaHtts28m2/C0kCR1os0g2A9sSrIxyUUM/rKfnD4oyfOAS4FPtViLJGkWrXUNVdXpJDuAfcAq4JaqOphkJzBVVWdCYRuwp6qqrVo0uukdQUvlvSS1p9VF56pqL7B32r6bpm2/o80aJElzWyoXiyVJHTEIJKnnDAJJ6jmDQJJ6ziCQpJ7zVpUr3HK6ReQoi9wt9X8GaTlyRiBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUc641pPO+paRrAUnLmzMCSeq5VoMgyZYkh5McSXLjLGN+KsmhJAeT3NZmPZKkx2vt1FCSVcAu4CrgOLA/yWRVHRoaswl4G/Diqno4yfe2VY8kaWZtzgg2A0eq6mhVnQL2ANdMG/OvgV1V9TBAVT3QYj2SpBm0GQRrgGND28ebfcOeAzwnySeTfDrJlpneKMn2JFNJpk6ePNlSuZLUT113DV0IbAJeBqwFPp7kB6vqa8ODqmo3sBtgYmKixlyjFuB8O5AkjV+bM4ITwLqh7bXNvmHHgcmq+nZV/S/gywyCQZI0Jm0GwX5gU5KNSS4CtgGT08Z8mMFsgCSXMThVdLTFmiRJ07QWBFV1GtgB7APuAz5UVQeT7EyytRm2D3gwySHgbuCXq+rBtmqSJD1eq9cIqmovsHfavpuGnhfw1uYhSeqA3yyWpJ4zCCSp57puH5UWZLb2VBe7k86dMwJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeo5u4a0JHW1eJ233VQfOSOQpJ4zCCSp5wwCSeo5g0CSes4gkKSes2tIY+ftLKWlxRmBJPWcQSBJPWcQSFLPGQSS1HOtBkGSLUkOJzmS5MYZjr8hyckk9zaPN7dZjyTp8VrrGkqyCtgFXAUcB/YnmayqQ9OGfrCqdrRVhyRpbm3OCDYDR6rqaFWdAvYA17T4+yRJ56DNIFgDHBvaPt7sm+61ST6f5PYk61qsR5I0g64vFv8psKGqXgDcCdw606Ak25NMJZk6efLkWAuUpJWuzSA4AQz/H/7aZt93VdWDVfWtZvNm4IdneqOq2l1VE1U1sXr16laKlaS+ajMI9gObkmxMchGwDZgcHpDkmUObW4H7WqxHkjSD1rqGqup0kh3APmAVcEtVHUyyE5iqqkngF5JsBU4DDwFvaKseSdLMWl10rqr2Anun7btp6PnbgLe1WYMWhwvFSStX1xeLJUkdm3NGkOQRoGY7XlVPXfSKJEljNWcQVNUlAEneBfwt8AEgwHXAM+d4qSRpmRj11NDWqvrdqnqkqr5eVb+H3xKWpBVh1CB4NMl1SVYluSDJdcCjbRYmSRqPUbuGXg+8r3kU8Mlmn5apldYFNPzPc/+7rz7nMeczXlquRgqCqrofTwVJ0oo0UhAkuRh4E/ADwMVn9lfVv2qpLknSmIx6jeADwD8GXg38FYN1gx5pqyhJ0viMGgTPrqq3A49W1a3A1cCL2itLkjQuowbBt5ufX0vyfOBpwPe2U5IkaZxG7RraneRS4O0MVhB9CnDT3C+RurfSuqOkNozaNXRz8/SvgH/SXjmSpHGbb62ht851vKres7jlSJLGbb4ZwSXNz+cCL+TsjWV+HLinraIkSeMz36Jz7wRI8nHgyqp6pNl+B+DJV0laAUbtGvo+4NTQ9qlmnyRpmRu1a+i/AvckuaPZ/gngD9ooSJI0XqN2Df16ko8A/6LZ9caq+lx7ZakNtlKe5YJy0lnzdQ09taq+nuQZwP3N48yxZ1TVQ+2WJ0lq23zXCG5rfh4ApoYeZ7bnlGRLksNJjiS5cY5xr01SSSZGrFuStEjm6xr6sebnxoW+cZJVwC7gKuA4sD/JZFUdmjbuEuAG4DML/R2SpPM336mhK+c6XlWfnePwZuBIVR1t3msPg3saHJo27l3AbwC/PG+1kqRFN9/F4t+e41gBr5jj+Brg2ND2caatWNoEzbqq+vMkswZBku3AdoD169fPU7IkaSHmOzX08rZ+cZILgPcAb5hvbFXtBnYDTExMVFs1LTd2vszM7ihpYeY7NfSKqroryU/OdLyq/mSOl58A1g1tr232nXEJ8HzgY0lgcOObySRbq2reC9GSpMUx36mhlwJ3MVhbaLoC5gqC/cCmJBsZBMA2hm54X1V/D1x2ZjvJx4BfMgQkabzmOzX0q83PNy70javqdJIdwD5gFXBLVR1MshOYqqrJud9BkjQOo968/nuAXwV+hMFM4BPAzqp6cK7XVdVeYO+0fTPe0KaqXjZKLZKkxTXqonN7gJPAa4HXNc8/2FZRkqTxGXXRuWdW1buGtn8tyU+3UZDmZkeMpMU26ozgL5JsS3JB8/gpBuf+JUnL3Hzto48wuCYQ4BeBP2wOXQB8A/ilNouTJLVvvq6hS+Y6Lkla/ka9RkCSS4FNwMVn9lXVx9soSpI0PqO2j76ZwQqha4F7gX8GfIq51xqSJC0Do84IbgBeCHy6ql6e5HnAf2yvLGl8RunEcl0nrWSjdg19s6q+CZDkiVX1JeC57ZUlSRqXUWcEx5M8HfgwcGeSh4G/aasoSdL4jHrz+n/ZPH1HkruBpwEfba0qSdLYLKRr6ErOrjX0yao61VpVkqSxGekaQZKbgFuB72GwdPTvJ/mVNguTJI3HqDOC64AfGrpg/G4GbaS/1lJdkqQxGTUI/g+DL5J9s9l+Io+925iWEBemW3psP9VSNt9aQ/+JwTWBvwcOJrmz2b4KuKf98iRJbZtvRnDmtpEHgDuG9n+slWokSWM336Jzt555nuQi4DnN5uGq+nabhUmSxmPUtYZexqBr6H4GS1KvS3K9i85J0vI36hITvw38aFW9tKpeArwaeO98L0qyJcnhJEeS3DjD8Z9L8oUk9yb5RJLLF1a+JOl8jdo19ISqOnxmo6q+nOQJc70gySpgF4MLy8eB/Ukmq+rQ0LDbquo/N+O3Au8BtizkH0Dq0ji6gew4UttGDYIDSW7m7B3KruPsheTZbAaOVNVRgCR7gGuA7wZBVX19aPyTGXQkSZLGaNQg+DngLcAvNNv/A/jdeV6zBjg2tH0ceNH0QUneArwVuIhZ7m+QZDuwHWD9+vUjlixJGsW8QdCc4vnrqnoeg1M3i6qqdgG7krwe+BXg+hnG7AZ2A0xMTDhrkKRFNO/F4qr6DnA4yUL/V/wEsG5oey1zfxt5D/ATC/wdkqTzNOqpoUsZfLP4HuDRMzurauscr9kPbEqykUEAbANePzwgyaaq+kqzeTXwFSRJYzVqELx9oW9cVaeT7AD2AauAW6rqYJKdwFRVTQI7krwK+DbwMDOcFpIktWu+tYYuZnCh+NnAF4D3V9XpUd+8qvYCe6ftu2no+Q0LqlaStOjmu0ZwKzDBIARew+CLZZKkFWS+U0OXV9UPAiR5P644Kkkrznwzgu8uLLeQU0KSpOVjvhnBDyU58+3fAE9qtgNUVT211eokSa2bbxnqVeMqROfHu5JJOlejrj4qSVqhDAJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeq5URedkzSP6S283lZSy4UzAknqOYNAknrOIJCknjMIJKnnDAJJ6jm7hqQOzbZY4CgdR8OvtUNJ58MZgST1nEEgST3XahAk2ZLkcJIjSW6c4fhbkxxK8vkkf5nkWW3WI0l6vNaCIMkqYBeDm95fDlyb5PJpwz4HTFTVC4Dbgd9sqx5J0szanBFsBo5U1dGqOgXsAa4ZHlBVd1fV/202Pw2sbbEeSdIM2uwaWgMcG9o+DrxojvFvAj4y04Ek24HtAOvXr1+s+pY0bz0paVyWxMXiJD8DTAC/NdPxqtpdVRNVNbF69erxFidJK1ybM4ITwLqh7bXNvsdI8irgPwAvrapvtViPJGkGbc4I9gObkmxMchGwDZgcHpDkCuC/AFur6oEWa5EkzaK1IKiq08AOYB9wH/ChqjqYZGeSrc2w3wKeAvz3JPcmmZzl7SRJLWl1iYmq2gvsnbbvpqHnr2rz90uS5rckLhZLkrrjonMdc+Gw5cfWXq00zggkqecMAknqOYNAknrOIJCknjMIJKnn7BqSxqztriM70bRQzggkqecMAknqOYNAknrOIJCknjMIJKnn7Bpq0UK7N1zDZmVp49/nYr3nbO+z0P9O7UpaGZwRSFLPGQSS1HMGgST1nEEgST1nEEhSz7XaNZRkC/A+YBVwc1W9e9rxlwC/A7wA2FZVt7dZT5fstNBCLLQ7aLbxrmukUbQ2I0iyCtgFvAa4HLg2yeXThv1v4A3AbW3VIUmaW5szgs3Akao6CpBkD3ANcOjMgKq6vzn2Dy3WIUmaQ5vXCNYAx4a2jzf7FizJ9iRTSaZOnjy5KMVJkgaWxcXiqtpdVRNVNbF69equy5GkFaXNIDgBrBvaXtvskyQtIW0GwX5gU5KNSS4CtgGTLf4+SdI5aO1icVWdTrID2MegffSWqjqYZCcwVVWTSV4I3AFcCvx4kndW1Q+0VdNS4eJyWm78b3Zla/V7BFW1F9g7bd9NQ8/3MzhlJEnqyLK4WCxJao9BIEk9ZxBIUs8ZBJLUc96qUtKicAG65csZgST1nEEgST1nEEhSzxkEktRzBoEk9VyqqusaFmRiYqKmpqa6LuMxXIdFmt2oHUSjdB3ZmXTukhyoqomZjjkjkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnnerXo3GxtnsNtaHO1go46TtJZ0/+sLPTP0WxjFtpKuljtqeNuYR3H73NGIEk9ZxBIUs+1GgRJtiQ5nORIkhtnOP7EJB9sjn8myYY265EkPV5rQZBkFbALeA1wOXBtksunDXsT8HBVPRt4L/AbbdUjSZpZmzOCzcCRqjpaVaeAPcA108ZcA9zaPL8deGWStFiTJGma1hadS/I6YEtVvbnZ/lngRVW1Y2jMF5sxx5vt/9mM+eq099oObG82nwscbqXoblwGfHXeUf3gZ3GWn8VZfhaPda6fx7OqavVMB5ZF+2hV7QZ2d11HG5JMzbYiYN/4WZzlZ3GWn8VjtfF5tHlq6ASwbmh7bbNvxjFJLgSeBjzYYk2SpGnaDIL9wKYkG5NcBGwDJqeNmQSub56/DrirltsNEiRpmWvt1FBVnU6yA9gHrAJuqaqDSXYCU1U1Cbwf+ECSI8BDDMKib1bkKa9z5Gdxlp/FWX4Wj7Xon8eyu0OZJGlx+c1iSeo5g0CSes4g6ECSdUnuTnIoycEkN3RdU5eSXJzkniR/3Xwe7+y6pq4lWZXkc0n+rOtaupTk/iRfSHJvkqmu6+lSkqcnuT3Jl5Lcl+SfL9Z7L4vvEaxAp4F/W1WfTXIJcCDJnVV1qOvCOvIt4BVV9Y0kTwA+keQjVfXprgvr0A3AfcBTuy5kCXj59C+Z9tT7gI9W1euaTsx/tFhv7IygA1X1t1X12eb5Iwz+wK/ptqru1MA3ms0nNI/edjEkWQtcDdzcdS1aGpI8DXgJg05LqupUVX1tsd7fIOhYs+LqFcBnOi6lU82pkHuBB4A7q6rPn8fvAP8O+IeO61gKCviLJAeapWb6aiNwEvj95pThzUmevFhvbhB0KMlTgD8GfrGqvt51PV2qqu9U1T9l8A30zUme33FJnUjyY8ADVXWg61qWiB+pqisZrGL8liQv6bqgjlwIXAn8XlVdATwKPG5p/3NlEHSkORf+x8AfVdWfdF3PUtFMd+8GtnRcSldeDGxNcj+DFXtfkeQPuy2pO1V1ovn5AHAHg1WN++g4cHxopnw7g2BYFAZBB5qltt8P3FdV7+m6nq4lWZ3k6c3zJwFXAV/qtKiOVNXbqmptVW1g8E37u6rqZzouqxNJntw0U9CcBvlR4IvdVtWNqvo74FiS5za7XgksWnOJXUPdeDHws8AXmvPiAP++qvZ2V1Knngnc2tzM6ALgQ1XV67ZJAfB9wB3NLUouBG6rqo92W1Knfh74o6Zj6CjwxsV6Y5eYkKSe89SQJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgnYMk32lWxPxikj898z2I5tj1Sb7SPK6f422kJcH2UekcJPlGVT2leX4r8OWq+vUkzwCmgAkG6+QcAH64qh7urlppbs4IpPP3Kc6uHvtqBovmPdT85X8n/V0uQ8uEQSCdh+bb0K8EJptda4BjQ0OO0+MlxrU8GATSuXlSszzI3zFYCuHObsuRzp1BIJ2b/9csm/0sIMBbmv0ngHVD49Y2+6Qly4vF0jmYdrH4CuDDwPczuLXkAc4uEfxZBheLH+qiTmkUzgik81RVnwM+D1zb/IX/LmB/89hpCGipc0YgST3njECSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnn/j/SK9mIUktoCgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "s = np.random.lognormal(1.1, 0.21, 1000)\n",
     "plt.hist(s, density=True, bins=100)\n",
@@ -319,31 +282,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Incertidumbre en los dias de infectividad.\n",
+    "### Incertidumbre en los días de infectividad.\n",
     "\n",
-    "Supongamos que lo dias que un individuo es contagioso está en el orden de 8.7 +/- 3 dias. Mas detalles sobre estudios en este tema [aqui](https://www.bdi.ox.ac.uk/news/the-timing-of-covid-19-transmission).\n",
+    "Supongamos que lo días que un individuo es contagioso está en el orden de 8.7 +/- 3 días. Mas detalles sobre estudios en este tema [aquí](https://www.bdi.ox.ac.uk/news/the-timing-of-covid-19-transmission).\n",
     "\n",
-    "Por simplicidad, modelaremos esta incertidumbre con una distribución normal que refleje el rango de 8.7 +/- 3 dias.\n"
+    "Por simplicidad, modelaremos esta incertidumbre con una distribución normal que refleje el rango de 8.7 +/- 3 días.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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su7+afRwR8SKwXpJXQzIzs7qbqkaRPTl+N/CnvsSIOLWUWpmZWduqN3B8ttRamJnZbqPWXFX7knWMHwasBa5I62yYmdkQVeuOYwnwAtkoqpOAKcDHyq6U2a6opyO3jM5es6GmVuf4lIj4x4j4BjATeGuRi0uaLmm9pG5J86ucHyHp+nT+LkkTU/psSatz20uSpqZzd6Zr9p17ZZE6mZnZ4NQKHC9PZFi0iSoN472M7XcqZ0qaUpHtXODxiDgMuAS4OL3WtRExNSKmAu8HHoqI1blys/vOR8QjReplZmaDUytwvFHSk2l7CnhD376kJ2uUnQZ0R8SGtD75UmBGRZ4ZZM1hADcC75CkijxnprJmZtYGak2rPmwQ1x4HbMod9wBv7i9PRGyT9ARwEPBoLs972TngXCnpReC7wIUREZUvLmkuMBfgkEP8CIqZWaPUOxy3JSS9GXgmIu7NJc+OiM2S9icLHO8Hrq4sGxGLgEUAHR0dOwUWM9t1RQcZeFDCnqXe2XF3xWZgQu54PDuvU/5yHknDgQOAx3LnZ5FN3/6yiNicfj4FXEfWJGZmZk1SZuBYCUyWNCmtHjgL6KzI0wnMSfszgdv7mp0k7QWcQa5/Q9JwSWPS/t7AycC9mJlZ05TWVJX6LOYBy4FhwOKIWCdpIdAVEZ3AFcA1krqBrWTBpc+xwKaI2JBLGwEsT0FjGPBvwDfLeg9mZrazUvs4ImIZsKwibUFu/1ng9H7K3gkcU5H2J7zqoJlZS5XZVGVmZnsgBw4zMyvEgcPMzApx4DAzs0IcOMzMrBAHDjMzK6StpxyxoSs/RcXGi95dV77B5LHWqPf3bO3FdxxmZlaIA4eZmRXiwGFmZoU4cJiZWSHuHLfSFO34dCf2nmUwa3a4o7y9+Y7DzMwKceAwM7NCHDjMzKwQBw4zMyuk1MAhabqk9ZK6Jc2vcn6EpOvT+bskTUzpEyX9WdLqtH09V+YoSWtTmUslqcz3YGZmOyptVJWkYcBlwPFAD7BSUmdE3JfLdi7weEQcJmkWcDHw3nTuwYiYWuXSlwMfBO4iW11wOnBrOe/C8soY9eKRVLvOn521Spl3HNOA7ojYEBHPA0uBGRV5ZgBL0v6NwDsGuoOQdDAwMiJWREQAVwOnNbzmZmbWrzIDxzhgU+64J6VVzRMR24AngIPSuUmSfiXpJ5LemsvfU+OaAEiaK6lLUldvb+/g3omZmb2sXTvHtwCHRMSbgPOA6ySNLHKBiFgUER0R0TF27NhSKmlmNhSVGTg2AxNyx+NTWtU8koYDBwCPRcRzEfEYQESsAh4EXpPyj69xTTMzK1GZU46sBCZLmkT2j/ss4H0VeTqBOcAvgZnA7RERksYCWyPiRUmHApOBDRGxVdKTko4h6xw/C/hqie/BSjCYqSjMrPVKCxwRsU3SPGA5MAxYHBHrJC0EuiKiE7gCuEZSN7CVLLgAHAsslPQC8BLwoYjYms59GLgK2I9sNJVHVJmZNVGpkxxGxDKyIbP5tAW5/WeB06uU+y7w3X6u2QUc0diamplZvdq1c9zMzNqUA4eZmRXi9Ths0LyOgrVCf4Mm/B0sn+84zMysEAcOMzMrxIHDzMwKceAwM7NCHDjMzKwQj6qypvC0IbarPGqv/fiOw8zMCnHgMDOzQhw4zMysEAcOMzMrxJ3jQ5g7Hc1sV/iOw8zMCnHgMDOzQkoNHJKmS1ovqVvS/CrnR0i6Pp2/S9LElH68pFWS1qafx+XK3JmuuTptryzzPZiZ2Y5K6+OQNAy4DDge6AFWSuqMiPty2c4FHo+IwyTNAi4G3gs8CpwSEX+QdATZ8rPjcuVmp5UAzcysycrsHJ8GdEfEBgBJS4EZQD5wzAAuSPs3Av8iSRHxq1yedcB+kkZExHMl1tcawE+IWyM06ns00HUGMyBkqA8sKbOpahywKXfcw453DTvkiYhtwBPAQRV5/gG4pyJoXJmaqT4rSY2ttpmZDaStO8clvY6s+eq/5JJnR8Trgbem7f39lJ0rqUtSV29vb/mVNTMbIsoMHJuBCbnj8Smtah5Jw4EDgMfS8XjgJuCsiHiwr0BEbE4/nwKuI2sS20lELIqIjojoGDt2bEPekJmZlRs4VgKTJU2StA8wC+isyNMJzEn7M4HbIyIkHQjcAsyPiJ/3ZZY0XNKYtL83cDJwb4nvwczMKpQWOFKfxTyyEVH3AzdExDpJCyWdmrJdARwkqRs4D+gbsjsPOAxYUDHsdgSwXNIaYDXZHcs3y3oPZma2s1KnHImIZcCyirQFuf1ngdOrlLsQuLCfyx7VyDqa2e6jkaP26hkZNdRHT/WnrTvHzcys/ThwmJlZIQ4cZmZWiAOHmZkV4vU42lRlJ2DZ0yO4E9D2FLvSgV70b6Ro2T2N7zjMzKwQBw4zMyvEgcPMzApx4DAzs0LcOb4bamZnnNfXsKGmjLVA8n+ne0Jnuu84zMysEAcOMzMrxIHDzMwKceAwM7NCFBGtrkPpOjo6oqura5fKFu3IalT+ejvodqWMmbWfRs3q0MjOd0mrIqKjMt13HGZmVogDh5mZFVJq4JA0XdJ6Sd2S5lc5P0LS9en8XZIm5s59OqWvl3Rivdc0M7NylRY4JA0DLgNOAqYAZ0qaUpHtXODxiDgMuAS4OJWdAswCXgdMB74maVid1zQzsxKVeccxDeiOiA0R8TywFJhRkWcGsCTt3wi8Q5JS+tKIeC4iHgK60/XquaaZmZWozClHxgGbcsc9wJv7yxMR2yQ9ARyU0ldUlB2X9mtdEwBJc4G56fBpSet34T3seM2Ldyn/GODRMq6/q2Uq1F2/FnH9Bsf1G5ym1a+ev+UqeQasXwP+fXh1tcQ9dq6qiFgELGp1PSR1VRvO1i5cv8Fx/QbH9RucVtWvzKaqzcCE3PH4lFY1j6ThwAHAYwOUreeaZmZWojIDx0pgsqRJkvYh6+zurMjTCcxJ+zOB2yN7IrETmJVGXU0CJgN313lNMzMrUWlNVanPYh6wHBgGLI6IdZIWAl0R0QlcAVwjqRvYShYISPluAO4DtgEfiYgXAapds6z30CAtby6rwfUbHNdvcFy/wWlJ/YbElCNmZtY4fnLczMwKceAwM7NCHDgaZDDTqzShbhMk3SHpPknrJH2sSp63SXpC0uq0LWhW/dLrb5S0Nr32TlMZK3Np+vzWSDqyiXU7PPe5rJb0pKSPV+Rp6ucnabGkRyTdm0sbLek2SQ+kn6P6KTsn5XlA0pxqeUqq35ck/Sb9/m6SdGA/ZQf8LpRYvwskbc79Dt/VT9nSpz3qp37X5+q2UdLqfsqW/vkREd4GuZF11D8IHArsA/wamFKR58PA19P+LOD6JtbvYODItL8/8Nsq9Xsb8MMWfoYbgTEDnH8XcCsg4Bjgrhb+rv8deHUrPz/gWOBI4N5c2heB+Wl/PnBxlXKjgQ3p56i0P6pJ9TsBGJ72L65Wv3q+CyXW7wLgE3X8/gf8Wy+rfhXnvwwsaNXn5zuOxhjM9Cqli4gtEXFP2n8KuJ/tT+LvLmYAV0dmBXCgpINbUI93AA9GxO9a8Novi4ifko1EzMt/x5YAp1UpeiJwW0RsjYjHgdvI5oMrvX4R8aOI2JYOV5A9h9US/Xx+9WjKtEcD1S/9u3EG8O1Gv269HDgao9r0KpX/MO8wvQrQN71KU6UmsjcBd1U5/TeSfi3pVkmva27NCOBHklal6WIq1fMZN8Ms+v+DbeXnB/CqiNiS9v8deFWVPO3yOX6A7A6ymlrfhTLNS01pi/tp6muHz++twMMR8UA/50v//Bw4hhBJrwC+C3w8Ip6sOH0PWfPLG4GvAt9vcvXeEhFHks18/BFJxzb59WtKD52eCnynyulWf347iKzNoi3H2kv6DNnzWdf2k6VV34XLgb8CpgJbyJqD2tGZDHy3Ufrn58DRGIOZXqUpJO1NFjSujYjvVZ6PiCcj4um0vwzYW9KYZtUvIjann48AN5E1CeS1w3QzJwH3RMTDlSda/fklD/c136Wfj1TJ09LPUdLZwMnA7BTcdlLHd6EUEfFwRLwYES8B3+zndVv9+Q0H3gNc31+eZnx+DhyNMZjpVUqX2kSvAO6PiK/0k+cv+/pcJE0j+240JbBJ+gtJ+/ftk3Wi3luRrRM4K42uOgZ4Itcs0yz9/k+vlZ9fTv47Nge4uUqe5cAJkkalppgTUlrpJE0H/gdwakQ800+eer4LZdUv32f29/28bqunPXon8JuI6Kl2smmfX5k970NpIxv181uyERefSWkLyf5IAPYla+LoJpt369Am1u0tZM0Wa4DVaXsX8CHgQynPPGAd2SiRFcB/amL9Dk2v++tUh77PL18/kS3i9SCwFuho8u/3L8gCwQG5tJZ9fmQBbAvwAlk7+7lkfWY/Bh4A/g0YnfJ2AN/Klf1A+h52A+c0sX7dZP0Dfd/BvlGG/xFYNtB3oUn1uyZ9t9aQBYODK+uXjnf6W29G/VL6VX3fuVzepn9+nnLEzMwKcVOVmZkV4sBhZmaFOHCYmVkhDhxmZlaIA4eZmRXiwGF7PEkvpplC16UpQf67pL3SuQ5Jl7a6jgCSDpT04UFeY6GkdzaqTmbVeDiu7fEkPR0Rr0j7rwSuA34eEee3tmY7SvOI/TAijmh1XcwG4jsOG1Iim4ZhLtlkdlK2jsYPIXviW9IvJf1K0i8kHZ7SXyfp7nTXskbS5MrrpjUa7kl3ND9OaaMlfT+VWSHpDSn9gjSJ3p2SNkj6aLrMRcBfpdf5kqRXSPpxuu5aSTNyr/dZZWtC/D9J35b0iZR+laSZaf8d6b2sTa83IqVfpGxtljWS/ndKmyjp9pT2Y0mHlPMbsD1CmU+3evPWDhvwdJW0P5LNHvs20joawEi2rxfxTuC7af+rZHMrQbYGw34V1xpL9kT0pHQ8Olfu/LR/HLA67V8A/AIYAYwheyJ9b2AiO64PMRwYmfbHkD15LeBosiev9yVbX+UB0joSZE8Wz0znNgGvSelXAx8ne7p8PdtbGw5MP38AzEn7HwC+3+rfm7f23XzHYbbdAcB3lK26dgnQNzX6L4H/KelTZDPg/rmi3DHATyPiIYCI6FtH4S1k01gQEbcDB0kamc7dEhHPRcSjZJMRVpsCXcAXJK0hm0JkXMr3t8DNEfFsZOur/KBK2cOBhyLit+l4CdniQE8AzwJXSHoP0Ddn1N+QNeGR6vyW/j4kMwcOG3IkHQq8yM6zx34OuCOyPoZTyP7XTkRcRzad+p+BZZKOa0A1nsvtv0h2d1FpNtndzFERMRV4uK9OuyqytWCmkS0mdjLwfwdzPRuaHDhsSJE0Fvg68C8RUTky5AC2T5F9dq7MocCGiLiUbMbZN1SUWwEcK2lSyj86pf+M7B9/JL0NeDR2Xgcl7ymypqd8fR6JiBckvR14dUr/OXCKpH2VrbFycpVrrQcmSjosHb8f+EnKf0BkU7//N+CN6fwvyGZ6JdX5ZwPU04a4av/LMdvT7CdpNVk/wjaypphq08t/EVgi6X8Bt+TSzwDeL+kFspX1vpAvFBG9ylZa+14a5vsIcDxZX8bi1NT0DNunPK8qIh6T9PPUVHYr2brcP5C0FugCfpPyrZTUSTaL68NkM7o+UXGtZyWdQ9b0NpxsOvCvk601frOkfcmaws5LRf4rcKWkTwK9wDkD1dWGNg/HNdsNSXpFRDwt6T8APwXmRlpX3qxsvuMw2z0tkjSFrM9jiYOGNZPvOMzMrBB3jpuZWSEOHGZmVogDh5mZFeLAYWZmhThwmJlZIf8fIOmVChHqzmEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "s = np.random.normal(8.7, 3, 1000)\n",
     "plt.hist(s, density=True, bins=100)\n",
@@ -355,25 +305,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Ejercicio 1. \n",
+    "# Ejercicio 1 \n",
     "\n",
     "- Incluir incertidumbres en el modelo, simular 1000 escenarios. \n",
     "- Según este modelo: ¿cuantas personas estiman que se hayan contagiado en el dia 120 de la epidemia? Incluir intervalos de confianza del 95% en el resultado.\n",
-    "- Producir una figura del numero de personas infectadas como funcion de tiempo, la figura debe incluir intervalos de confianza (ejemplo de la figura esperada:https://medium.com/data-for-science/epidemic-modeling-103-adding-confidence-intervals-and-stochastic-effects-to-your-covid-19-models-be618b995d6b)\n"
+    "- Producir una figura del numero de personas infectadas como funcion de tiempo, la figura debe incluir intervalos de confianza ([ejemplo de la figura esperada](https://medium.com/data-for-science/epidemic-modeling-103-adding-confidence-intervals-and-stochastic-effects-to-your-covid-19-models-be618b995d6b)).\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Ejericio 2.\n",
+    "# Ejercicio 2\n",
     "\n",
-    "- Ustedes son asesores cientificos de un gobierno que esta manejando la crisis del Covid19. El gobierno está evaluando implementar uno de estos dos escenarios:\n",
+    "Ustedes son asesores científicos de un gobierno que está manejando la crisis del Covid19. El gobierno está evaluando implementar uno de estos dos escenarios:\n",
     "    \n",
-    "    - Un confinamiento que reduzca R0 a cerca de 1 (el comportamiento del R0 resultante se puede modelar con la distribución np.random.lognormal(-0.1, 0.15, 1000)).\n",
-    "    - Una campaña de seguimiento y localización de casos que reduzca los dias en los que un individuo está en la calle contagioso a 3 +/- 1 dias. \n",
-    "    \n",
-    "Simular ambos escenarios con sus debidas incertidumbres y basado en los resultados aconsejar al gobierno qué hacer. "
+    "1. Un confinamiento que reduzca R0 a cerca de 1.\n",
+    "   \n",
+    "2. Una campaña de seguimiento y localización de casos que reduzca los días en los que un individuo está en la calle contagioso a 3 +/- 1 días. \n",
+    "  \n",
+    "\n",
+    "- Simular ambos escenarios con sus debidas incertidumbres y basado en los resultados hacer una recomendación.\n",
+    "     - *Nota*: El comportamiento del R0 resultante del escenario 1. se puede modelar con la distribución ```np.random.lognormal(-0.1, 0.15, 1000)```)\n",
+    "\n",
+    "\n",
+    "- Discutir las limitaciones de sus modelos, y qué tanta confianza tienen en la recomendación que han dado al gobierno.  "
    ]
   },
   {