diff --git a/R0_COVID_19.ipynb b/R0_COVID_19.ipynb
new file mode 100644
index 0000000..c4d4350
--- /dev/null
+++ b/R0_COVID_19.ipynb
@@ -0,0 +1,279 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "R0_COVID_19.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XuYDyZgzRN9J",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Description\n",
+        "\n",
+        "This Jupyter Notebook contains the code required to load and process an example dataset with the [R<sub>0</sub>](https://cran.r-project.org/web/packages/R0/index.html) package for R.\n",
+        "\n",
+        "This notebook is intended to be run in [Google Colab](https://colab.research.google.com) with an R kernel."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nwKS21RVRFiN",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "ecd48888-d117-4393-cd28-0e7f24b81b0e"
+      },
+      "source": [
+        "install.packages(\"R0\", quiet=TRUE) # Install R0 package\n",
+        "library(R0) # Load R0 package"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading required package: MASS\n",
+            "\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DX8qafzJRIy-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Load dataset as a named vector\n",
+        "incid <- read.table(\"/cases_averaged.csv\", header=T, sep = \",\")\n",
+        "incid <- setNames(incid$x, row.names(incid))\n",
+        "\n",
+        "# Clean Data\n",
+        "first_case_index <- match(incid[incid>0][1], incid) # Find first date that the number of cases was >0\n",
+        "incid <- round(incid[-(1:first_case_index-1)]) # Remove entries before first case\n",
+        "incid <- round(incid) # Round case numbers to the nearest unit\n",
+        "\n",
+        "# Use a not-too-bad generation time\n",
+        "mGT <- generation.time(\"gamma\", val = c(5, 10))\n",
+        "\n",
+        "# Estimate R and GT jointly from data, assuming Poisson distirbution and starting at first non-0 day\n",
+        "est.R <- estimate.R(epid = incid, GT = mGT, methods = c(\"ML\"), checked = T, unknown.GT =T, begin = \"2020-03-14\", end = \"2020-07-16\")"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "M-sVgUZ4UHtC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "d9082071-4765-4dbc-e8bc-b283b7edc04c"
+      },
+      "source": [
+        "est.R"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Reproduction number estimate using  Maximum Likelihood  method.\n",
+              "R :  1.14747[ 1.129204 , 1.165909 ]\n"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MnZuLSniRIt5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 663
+        },
+        "outputId": "f8200553-05c7-4a70-9f7b-c8ab06817e03"
+      },
+      "source": [
+        "print(incid)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 \n",
+            "         5          0          0          6         12         29         39 \n",
+            "2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 \n",
+            "        46         78         83        131          2          3          4 \n",
+            "2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 \n",
+            "         6          8          9         10         12         13         16 \n",
+            "2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 \n",
+            "        18         19         23         25         27         30         33 \n",
+            "2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 \n",
+            "        35         40         45         48         52         55         59 \n",
+            "2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 \n",
+            "        61         64         67         69         72         75         78 \n",
+            "2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 \n",
+            "        82          0         86         89         89         94         96 \n",
+            "2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 \n",
+            "        99        102        104        107        112        116        119 \n",
+            "2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 \n",
+            "       124        128        133        138        142        145        149 \n",
+            "2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 \n",
+            "       154        157        163        167        172        173        178 \n",
+            "2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 \n",
+            "       182        192        195        201        208        213        220 \n",
+            "2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 \n",
+            "       230        236        243        250        255        264        274 \n",
+            "2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 \n",
+            "       276        277        280        285        295        301        308 \n",
+            "2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 \n",
+            "       315        323        336        349        362        377        386 \n",
+            "2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 \n",
+            "       396        402        415        426        435        441        448 \n",
+            "2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 \n",
+            "       451        465        481        495        508        514        539 \n",
+            "2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 \n",
+            "       551        565        581        607        621        637        650 \n",
+            "2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 \n",
+            "       664        681        713        732        753        777        805 \n",
+            "2020-07-15 2020-07-16 \n",
+            "       830        856 \n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2fWlRCs4UFkp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 437
+        },
+        "outputId": "8fd8ebcb-6dc4-439e-8942-ae8e6d024d51"
+      },
+      "source": [
+        "plot(incid)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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GWEkMABjXWd/YLifsnQnuMxxQ5Jc2nz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\ngq9XAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "plot without title"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 420,
+              "height": 420
+            },
+            "text/plain": {
+              "width": 420,
+              "height": 420
+            }
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_xMHq__CRK5p",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 425
+        },
+        "outputId": "015465dc-a816-4246-db0f-639188bf3af7"
+      },
+      "source": [
+        "est.R$GT"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Discretized Generation Time distribution\n",
+              "mean: 8.752925 , sd: 11.63884 \n",
+              "  [1] 0.000000e+00 2.350397e-01 1.265864e-01 8.809366e-02 6.733417e-02\n",
+              "  [6] 5.410405e-02 4.485297e-02 3.798902e-02 3.268218e-02 2.845345e-02\n",
+              " [11] 2.500525e-02 2.214207e-02 1.972970e-02 1.767262e-02 1.590087e-02\n",
+              " [16] 1.436189e-02 1.301538e-02 1.182987e-02 1.078038e-02 9.846845e-03\n",
+              " [21] 9.012912e-03 8.265129e-03 7.592323e-03 6.985126e-03 6.435626e-03\n",
+              " [26] 5.937095e-03 5.483771e-03 5.070693e-03 4.693565e-03 4.348649e-03\n",
+              " [31] 4.032680e-03 3.742791e-03 3.476456e-03 3.231442e-03 3.005768e-03\n",
+              " [36] 2.797671e-03 2.605577e-03 2.428079e-03 2.263913e-03 2.111945e-03\n",
+              " [41] 1.971150e-03 1.840605e-03 1.719472e-03 1.606996e-03 1.502486e-03\n",
+              " [46] 1.405318e-03 1.314922e-03 1.230776e-03 1.152407e-03 1.079379e-03\n",
+              " [51] 1.011295e-03 9.477898e-04 8.885289e-04 8.332045e-04 7.815337e-04\n",
+              " [56] 7.332558e-04 6.881309e-04 6.459376e-04 6.064714e-04 5.695435e-04\n",
+              " [61] 5.349795e-04 5.026179e-04 4.723090e-04 4.439145e-04 4.173059e-04\n",
+              " [66] 3.923641e-04 3.689785e-04 3.470466e-04 3.264728e-04 3.071685e-04\n",
+              " [71] 2.890512e-04 2.720442e-04 2.560759e-04 2.410798e-04 2.269939e-04\n",
+              " [76] 2.137602e-04 2.013249e-04 1.896377e-04 1.786515e-04 1.683225e-04\n",
+              " [81] 1.586098e-04 1.494751e-04 1.408825e-04 1.327987e-04 1.251924e-04\n",
+              " [86] 1.180343e-04 1.112971e-04 1.049550e-04 9.898414e-05 9.336200e-05\n",
+              " [91] 8.806752e-05 8.308099e-05 7.838391e-05 7.395893e-05 6.978981e-05\n",
+              " [96] 6.586129e-05 6.215908e-05 5.866976e-05 5.538072e-05 5.228016e-05\n",
+              "[101] 4.935696e-05 4.660070e-05 4.400160e-05 4.155045e-05 3.923862e-05\n",
+              "[106] 3.705799e-05\n"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rdc7NezUTZtr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}