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Logistic regression COVID-19.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# README\n",
"\n",
"Based on [Covid-19 infection in Italy. Mathematical models and predictions](https://towardsdatascience.com/covid-19-infection-in-italy-mathematical-models-and-predictions-7784b4d7dd8d)\n",
"\n",
"Data from [CSSEGISandData/COVID-19](https://github.com/CSSEGISandData/COVID-19) / Dashboard [Coronavirus COVID-19 (2019-nCoV)](https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html?fbclid=IwAR3prf7gRuznOnGiv_wZpjhVQ-YZAtQcVJYorx1Yfu3Tutt4nn2dUQaGbyo#/bda7594740fd40299423467b48e9ecf6)\n",
"\n",
"\n",
"## requirements to run the notbook fully\n",
"\n",
" numpy\n",
" pandas\n",
" sklearn\n",
" scipy\n",
" # this is for interactive plots\n",
" hvplot\n",
" holoviews\n"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [],
"source": [
"# useful imports\n",
"import numpy as np\n",
"np.set_printoptions(suppress=True)\n",
"\n",
"import pandas as pd\n",
"# define pandas options\n",
"opts = {\n",
" 'display.width':200,\n",
" 'display.max_colwidth': 100,\n",
" 'display.max_rows': 100\n",
" }\n",
"# set pandas options\n",
"[pd.set_option(n,o) for n,o in opts.items()]\n",
"\n",
"# plotting\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# useful for displaying html, images and so on\n",
"from IPython.display import display\n",
"\n",
"import hvplot.pandas \n",
"import holoviews as hv\n",
"\n",
"from datetime import datetime,timedelta\n",
"from sklearn.metrics import mean_squared_error\n",
"from scipy.optimize import curve_fit\n",
"from scipy.optimize import fsolve\n",
"\n",
"\n",
"doy_to_datetime = lambda x: datetime.strptime( f\"2020 {int(x)}\" , '%Y %j')"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country/Region</th>\n",
" <th>Italy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3/7/20</th>\n",
" <td>5883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3/8/20</th>\n",
" <td>7375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3/9/20</th>\n",
" <td>9172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3/10/20</th>\n",
" <td>10149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3/11/20</th>\n",
" <td>12462</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country/Region Italy\n",
"3/7/20 5883\n",
"3/8/20 7375\n",
"3/9/20 9172\n",
"3/10/20 10149\n",
"3/11/20 12462"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# confirmed infections url\n",
"url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv\"\n",
"# Deaths url\n",
"# url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv\"\n",
"conf_df = pd.read_csv(url)\n",
"# set index as Country/Region\n",
"conf_df.index = conf_df['Country/Region']\n",
"\n",
"conf_df.loc[['Italy']].iloc[:,4:].T.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# Italy vs Country X comparison "
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Country/Region</th>\n",
" <th>Germany</th>\n",
" <th>Italy</th>\n",
" <th>Germany_normalized</th>\n",
" <th>Italy_normalized</th>\n",
" <th>Italy-shift 9 Days</th>\n",
" <th>Italy-shift_normalized 9 Days</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-03-07</th>\n",
" <td>799</td>\n",
" <td>5883.0</td>\n",
" <td>0.000010</td>\n",
" <td>0.000097</td>\n",
" <td>655.0</td>\n",
" <td>0.000011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-08</th>\n",
" <td>1040</td>\n",
" <td>7375.0</td>\n",
" <td>0.000013</td>\n",
" <td>0.000122</td>\n",
" <td>888.0</td>\n",
" <td>0.000015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-09</th>\n",
" <td>1176</td>\n",
" <td>9172.0</td>\n",
" <td>0.000014</td>\n",
" <td>0.000151</td>\n",
" <td>1128.0</td>\n",
" <td>0.000019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-10</th>\n",
" <td>1457</td>\n",
" <td>10817.0</td>\n",
" <td>0.000018</td>\n",
" <td>0.000179</td>\n",
" <td>1694.0</td>\n",
" <td>0.000028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-11</th>\n",
" <td>1908</td>\n",
" <td>12462.0</td>\n",
" <td>0.000023</td>\n",
" <td>0.000206</td>\n",
" <td>2036.0</td>\n",
" <td>0.000034</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Country/Region Germany Italy Germany_normalized Italy_normalized Italy-shift 9 Days Italy-shift_normalized 9 Days\n",
"2020-03-07 799 5883.0 0.000010 0.000097 655.0 0.000011\n",
"2020-03-08 1040 7375.0 0.000013 0.000122 888.0 0.000015\n",
"2020-03-09 1176 9172.0 0.000014 0.000151 1128.0 0.000019\n",
"2020-03-10 1457 10817.0 0.000018 0.000179 1694.0 0.000028\n",
"2020-03-11 1908 12462.0 0.000023 0.000206 2036.0 0.000034"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lands = ['Germany','Italy']\n",
"population = {'Germany':82900000.0, 'Italy':60550075.0, 'France': 66990000.0}\n",
"lands_normalized = [f'{l}_normalized' for l in lands]\n",
"\n",
"confirmed = conf_df.loc[lands].iloc[:,4:].T\n",
"confirmed.index = pd.to_datetime(confirmed.index)\n",
"\n",
"# remove outliers : on 2020-03-10 there were uncomplete data.\n",
"confirmed.loc['2020-03-10', 'Italy'] = np.nan\n",
"confirmed['Italy'] = confirmed['Italy'].interpolate()\n",
"\n",
"\n",
"confirmed[lands_normalized] = confirmed[lands].apply(lambda x: x/population[x.name])\n",
"\n",
"days_shift = 9\n",
"confirmed[f'Italy-shift {days_shift} Days'] = confirmed['Italy'].shift(periods=days_shift)\n",
"confirmed[f'Italy-shift_normalized {days_shift} Days'] = confirmed['Italy_normalized'].shift(periods=days_shift)\n",
"\n",
"confirmed.tail()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"hidden": true
},
"outputs": [
{
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"</div>\n",
"<script type=\"application/javascript\">(function(root) {\n",
" function embed_document(root) {\n",
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Days\",\"Italy-shift 9 Days\",\"Italy-shift 9 Days\",\"Italy-shift 9 Days\",\"Italy-shift 9 Days\",\"Italy-shift 9 Days\",\"Italy-shift 9 Days\"],\"index\":{\"__ndarray__\":\"AABAx6j8dkIAAAAt+/x2QgAAwJJN/XZCAACA+J/9dkIAAEBe8v12QgAAAMRE/nZCAADAKZf+dkIAAICP6f52QgAAQPU7/3ZCAAAAW47/dkIAAMDA4P92QgAAgCYzAHdCAABAjIUAd0IAAADy1wB3QgAAwFcqAXdCAACAvXwBd0IAAEAjzwF3QgAAAIkhAndCAADA7nMCd0IAAIBUxgJ3QgAAQLoYA3dCAAAAIGsDd0IAAMCFvQN3QgAAgOsPBHdCAABAUWIEd0IAAAC3tAR3QgAAwBwHBXdCAACAglkFd0IAAEDoqwV3QgAAAE7+BXdCAADAs1AGd0IAAIAZowZ3QgAAQH/1BndCAAAA5UcHd0IAAMBKmgd3QgAAgLDsB3dCAABAFj8Id0IAAAB8kQh3QgAAwOHjCHdCAACARzYJd0IAAECtiAl3QgAAABPbCXdCAADAeC0Kd0IAAIDefwp3QgAAQETSCndCAAAAqiQLd0IAAMAPdwt3QgAAgHXJC3dCAABA2xsMd0IAAABBbgx3Qg==\",\"dtype\":\"float64\",\"shape\":[50]},\"index_dt_strings\":[\"2020-01-22 00:00:00\",\"2020-01-23 00:00:00\",\"2020-01-24 00:00:00\",\"2020-01-25 00:00:00\",\"2020-01-26 00:00:00\",\"2020-01-27 00:00:00\",\"2020-01-28 00:00:00\",\"2020-01-29 00:00:00\",\"2020-01-30 00:00:00\",\"2020-01-31 00:00:00\",\"2020-02-01 00:00:00\",\"2020-02-02 00:00:00\",\"2020-02-03 00:00:00\",\"2020-02-04 00:00:00\",\"2020-02-05 00:00:00\",\"2020-02-06 00:00:00\",\"2020-02-07 00:00:00\",\"2020-02-08 00:00:00\",\"2020-02-09 00:00:00\",\"2020-02-10 00:00:00\",\"2020-02-11 00:00:00\",\"2020-02-12 00:00:00\",\"2020-02-13 00:00:00\",\"2020-02-14 00:00:00\",\"2020-02-15 00:00:00\",\"2020-02-16 00:00:00\",\"2020-02-17 00:00:00\",\"2020-02-18 00:00:00\",\"2020-02-19 00:00:00\",\"2020-02-20 00:00:00\",\"2020-02-21 00:00:00\",\"2020-02-22 00:00:00\",\"2020-02-23 00:00:00\",\"2020-02-24 00:00:00\",\"2020-02-25 00:00:00\",\"2020-02-26 00:00:00\",\"2020-02-27 00:00:00\",\"2020-02-28 00:00:00\",\"2020-02-29 00:00:00\",\"2020-03-01 00:00:00\",\"2020-03-02 00:00:00\",\"2020-03-03 00:00:00\",\"2020-03-04 00:00:00\",\"2020-03-05 00:00:00\",\"2020-03-06 00:00:00\",\"2020-03-07 00:00:00\",\"2020-03-08 00:00:00\",\"2020-03-09 00:00:00\",\"2020-03-10 00:00:00\",\"2020-03-11 00:00:00\"],\"value\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[50]}},\"selected\":{\"id\":\"16398\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"16434\",\"type\":\"UnionRenderers\"}},\"id\":\"16397\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"16620\",\"type\":\"Selection\"},{\"attributes\":{\"overlay\":{\"id\":\"16632\",\"type\":\"BoxAnnotation\"}},\"id\":\"16606\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"source\":{\"id\":\"16369\",\"type\":\"ColumnDataSource\"}},\"id\":\"16376\",\"type\":\"CDSView\"},{\"attributes\":{\"axis_label\":\"\",\"bounds\":\"auto\",\"formatter\":{\"id\":\"16617\",\"type\":\"BasicTickFormatter\"},\"major_label_orientation\":\"horizontal\",\"ticker\":{\"id\":\"16599\",\"type\":\"BasicTicker\"}},\"id\":\"16598\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"16434\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"16339\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"16619\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"16622\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"16624\",\"type\":\"Line\"},\"nonselection_glyph\":{\"id\":\"16623\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"16626\",\"type\":\"CDSView\"}},\"id\":\"16625\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"16605\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"callback\":null,\"end\":1583884800000.0,\"reset_end\":1583884800000.0,\"reset_start\":1579651200000.0,\"start\":1579651200000.0,\"tags\":[[[\"index\",\"index\",null]]]},\"id\":\"16580\",\"type\":\"Range1d\"},{\"attributes\":{},\"id\":\"16354\",\"type\":\"PanTool\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#ff7e0e\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"16402\",\"type\":\"Line\"},{\"attributes\":{\"mantissas\":[1,2,5],\"max_interval\":500.0,\"num_minor_ticks\":0},\"id\":\"16383\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"16640\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"16625\",\"type\":\"GlyphRenderer\"},{\"id\":\"16653\",\"type\":\"GlyphRenderer\"}],\"tags\":[\"hv_created\"],\"tooltips\":[[\"Variable\",\"@{Variable}\"],[\"index\",\"@{index_dt_strings}\"],[\"value\",\"@{value}\"]]},\"id\":\"16582\",\"type\":\"HoverTool\"},{\"attributes\":{\"callback\":null,\"end\":2036.0,\"reset_end\":2036.0,\"reset_start\":0.0,\"tags\":[[[\"value\",\"value\",null]]]},\"id\":\"16331\",\"type\":\"Range1d\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"16392\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"16604\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"16648\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"16647\",\"type\":\"ColumnDataSource\"}},\"id\":\"16654\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"16398\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"16661\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"16647\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"16650\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"16652\",\"type\":\"Line\"},\"nonselection_glyph\":{\"id\":\"16651\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"16654\",\"type\":\"CDSView\"}},\"id\":\"16653\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"16879\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
" var render_items = [{\"docid\":\"90ef9a9b-7d74-4d6b-b4cb-e7cd4c014403\",\"roots\":{\"16879\":\"c3f8c1da-e0e4-4319-b263-7b7040132a4c\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" clearInterval(timer);\n",
" embed_document(root);\n",
" } else {\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" clearInterval(timer);\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" }\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);</script>"
],
"text/plain": [
":Layout\n",
" .NdOverlay.I :NdOverlay [Variable]\n",
" :Curve [index] (value)\n",
" .NdOverlay.II :NdOverlay [Variable]\n",
" :Curve [index] (value)"
]
},
"execution_count": 166,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "16879"
}
},
"output_type": "execute_result"
}
],
"source": [
"(\n",
" confirmed.drop(['Italy','Italy_normalized'],axis=1).filter(regex='.*^((?!_normalized).)*$').hvplot()+\\\n",
" confirmed.drop(['Italy','Italy_normalized'],axis=1).filter(regex='.*_normalized').hvplot()\n",
").options(\n",
" hv.opts.Curve(\n",
" axiswise=True\n",
" )\n",
").cols(1)"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1296x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(nrows=2,ncols=1,figsize=[18,9])\n",
"\n",
"confirmed.iloc[-20:].drop(['Italy','Italy_normalized'],axis=1).filter(regex='.*_normalized').plot(subplots=False,ax=axs[0],lw=4)\n",
"axs[0].set_title('Absolute Numbers');\n",
"\n",
"confirmed.iloc[-20:].drop(['Italy','Italy_normalized'],axis=1).filter(regex='.*^((?!_normalized).)*$').plot(subplots=False,ax=axs[1],lw=4)\n",
"axs[1].set_title('Normalised to Total Population');\n",
"\n",
"fig.tight_layout(pad=2.0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Italy models "
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {},
"outputs": [],
"source": [
"# ## global no china\n",
"# confirmed = conf_df.loc[[i for i in conf_df.index.tolist() if 'china' not in i.lower()]].reset_index(drop=True).groupby('Country/Region').sum().sum()\n",
"# confirmed = pd.DataFrame(confirmed).iloc[4:,:]\n",
"# confirmed.columns = ['Global no China']\n",
"\n",
"# ## global china\n",
"# confirmed = conf_df.loc[[i for i in conf_df.index.tolist() if 'china' in i.lower()]].iloc[:,4:].sum(axis=0)\n",
"# confirmed = pd.DataFrame(confirmed).iloc[2:,:]\n",
"# confirmed.columns = ['China']\n",
"\n",
"\n",
"# fixed country\n",
"confirmed = conf_df.loc[['Italy']].iloc[:,4:].T\n",
"\n",
"confirmed.index = pd.to_datetime(confirmed.index)\n",
"confirmed.columns = ['cumulative']\n",
"confirmed['daily'] = confirmed.diff().fillna(0)\n",
"\n",
"# remove outliers for italy : on 2020-03-10 there were uncomplete data.\n",
"confirmed.loc['2020-03-10','Italy'] = np.nan\n",
"confirmed = confirmed.interpolate()"
]
},
{
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"execution_count": 254,
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},
"outputs": [
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00:00:00\"],\"value\":[0,0,0,0,0,0,0,0,0,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,20,62,155,229,322,453,655,888,1128,1694,2036,2502,3089,3858,4636,5883,7375,9172,10149,12462]},\"selected\":{\"id\":\"25471\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"25512\",\"type\":\"UnionRenderers\"}},\"id\":\"25470\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"25543\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"25490\",\"type\":\"DaysTicker\"},{\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]},\"id\":\"25488\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"25456\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"25528\",\"type\":\"Selection\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b3\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25474\",\"type\":\"Line\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"25432\",\"type\":\"HoverTool\"},{\"id\":\"25454\",\"type\":\"SaveTool\"},{\"id\":\"25455\",\"type\":\"PanTool\"},{\"id\":\"25456\",\"type\":\"WheelZoomTool\"},{\"id\":\"25457\",\"type\":\"BoxZoomTool\"},{\"id\":\"25458\",\"type\":\"ResetTool\"}]},\"id\":\"25459\",\"type\":\"Toolbar\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"25483\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"25458\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"25499\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"25476\",\"type\":\"GlyphRenderer\"},{\"id\":\"25504\",\"type\":\"GlyphRenderer\"},{\"id\":\"25533\",\"type\":\"GlyphRenderer\"}],\"tags\":[\"hv_created\"],\"tooltips\":[[\"Variable\",\"@{Variable}\"],[\"index\",\"@{index_dt_strings}\"],[\"value\",\"@{value}\"]]},\"id\":\"25432\",\"type\":\"HoverTool\"},{\"attributes\":{\"text\":\"\",\"text_color\":{\"value\":\"black\"},\"text_font_size\":{\"value\":\"12pt\"}},\"id\":\"25436\",\"type\":\"Title\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"25492\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"25568\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"end\":12462.0,\"reset_end\":12462.0,\"reset_start\":0.0,\"tags\":[[[\"value\",\"value\",null]]]},\"id\":\"25431\",\"type\":\"Range1d\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"25489\",\"type\":\"DaysTicker\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"25485\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#ff7e0e\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25502\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#1f77b3\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25475\",\"type\":\"Line\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"25494\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"25454\",\"type\":\"SaveTool\"},{\"attributes\":{\"line_color\":\"#1f77b3\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25473\",\"type\":\"Line\"}],\"root_ids\":[\"25435\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
" var render_items = [{\"docid\":\"8ec1eb7d-3eca-472b-8d39-90e9ca217eeb\",\"roots\":{\"25435\":\"c8d917a5-79e3-4f09-99fc-5c39f8fc6766\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" clearInterval(timer);\n",
" embed_document(root);\n",
" } else {\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" clearInterval(timer);\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" }\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);</script>"
],
"text/plain": [
":NdOverlay [Variable]\n",
" :Curve [index] (value)"
]
},
"execution_count": 254,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "25435"
}
},
"output_type": "execute_result"
}
],
"source": [
"confirmed.hvplot()"
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"confirmed.plot( figsize=[20,5],lw=4);"
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {},
"outputs": [],
"source": [
"def logistic_model(x,a,b,c):\n",
" return c/(1+np.exp(-(x-b)/a))\n",
"\n",
"X = confirmed.index.dayofyear.to_numpy()\n",
"Y = confirmed['cumulative'].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Time series fit\n",
"\n",
"The idea is to fit the curve using only older data , from beginning to the a day N and store the result in the corresponding day N.\n",
"\n",
"Daily evolving situation will update the parameters, hopefully converging at a fix set of paramter."
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 14 days before last data point (26/02/2020): 36 data points \n",
" 13 days before last data point (27/02/2020): 37 data points \n",
" 12 days before last data point (28/02/2020): 38 data points \n",
" 11 days before last data point (29/02/2020): 39 data points \n",
" 10 days before last data point (01/03/2020): 40 data points \n",
" 9 days before last data point (02/03/2020): 41 data points \n",
" 8 days before last data point (03/03/2020): 42 data points \n",
" 7 days before last data point (04/03/2020): 43 data points \n",
" 6 days before last data point (05/03/2020): 44 data points \n",
" 5 days before last data point (06/03/2020): 45 data points \n",
" 4 days before last data point (07/03/2020): 46 data points \n",
" 3 days before last data point (08/03/2020): 47 data points \n",
" 2 days before last data point (09/03/2020): 48 data points \n",
" 1 days before last data point (10/03/2020): 49 data points \n",
" 0 days before last data point (11/03/2020): 50 data points \n"
]
}
],
"source": [
"fit_time_serie = {}\n",
"\n",
"n_data = confirmed.shape[0]\n",
"days_before = 14\n",
"for n in range(n_data-days_before, n_data+1):\n",
" print('{:3} days before last data point ({}): {} data points '.format(X[-1]-X[n-1],doy_to_datetime(X[:n][-1]).strftime('%d/%m/%Y'),X[:n].shape[0]))\n",
" fit = curve_fit(logistic_model,X[:n],Y[:n],p0=[2,80,20000],maxfev=20000)\n",
" fit_time_serie[X[:n][-1]] = fit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The logistic model\n",
"\n",
"(Text from [Covid-19 infection in Italy. Mathematical models and predictions](https://towardsdatascience.com/covid-19-infection-in-italy-mathematical-models-and-predictions-7784b4d7dd8d))\n",
"\n",
"The logistic model has been widely used to describe the growth of a population. An infection can be described as the growth of the population of a pathogen agent, so a logistic model seems reasonable.\n",
"\n",
"\n",
"This formula is very known among data scientists because it’s used in the logistic regression classifier and as an activation function of neural networks.\n",
"\n",
"\n",
"The most generic expression of a logistic function is:\n",
"\n",
"![logistic formula](https://miro.medium.com/max/546/1*bnVnrdWrWxvZfqJ_1bgrUQ.png)\n",
"\n",
"In this formula, we have the variable x that is the time and three parameters: a,b,c.\n",
"\n",
"- a refers to the infection speed\n",
"- b is the day with the maximum infections occurred\n",
"- c is the total number of recorded infected people at the infection’s end\n",
"\n",
"At high time values, the number of infected people gets closer and closer to c and that’s the point at which we can say that the infection has ended.\n",
"\n",
"This function has also an inflection point at b, that is the point at which the first derivative starts to decrease (i.e. the peak after which the infection starts to become less aggressive and decreases).\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model paramter for last day 11/03/2020 :\n",
" a: 3.79± 0.10 : infection speed \n",
" b: 72.01± 0.53 : the day with the maximum infections occurred 12/03/2020\n",
" c: 28409.02± 2273.12 : the total number of recorded infected people at the infection’s end \n"
]
}
],
"source": [
"# from IPython.display import Markdown as md\n",
"# model paramter description \n",
"description = {\n",
" 'a':'infection speed',\n",
" 'b':'the day with the maximum infections occurred',\n",
" 'c':'the total number of recorded infected people at the infection’s end'\n",
"}\n",
"\n",
"fit = fit_time_serie.get(list(fit_time_serie.keys())[-1])\n",
"fit[0]\n",
"# model paramter\n",
"param = dict(zip(['a','b','c'],fit[0]))\n",
"\n",
"# model errror\n",
"error =dict(zip(['a','b','c'], [np.sqrt(fit[1][i][i]) for i in [0,1,2]]))\n",
"\n",
"print(f\"model paramter for last day { doy_to_datetime(list(fit_time_serie.keys())[-1]).strftime('%d/%m/%Y') } :\")\n",
"\n",
"for k in param.keys():\n",
" out = f\" {k}: {param[k]:8.2f}±{error[k]:8.2f} : {description[k]} \"\n",
" if k == 'b' :\n",
" out+= doy_to_datetime(param[k]).strftime('%d/%m/%Y')\n",
" print(out)\n"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Day of 0 new infections 05/05/2020\n"
]
}
],
"source": [
"# day of 0 infections\n",
"sol = int(fsolve(lambda x : logistic_model(x,param['a'],param['b'],param['c']) - int(param['c']),param['b']))\n",
"print(f\"Day of 0 new infections {doy_to_datetime(sol).strftime('%d/%m/%Y')}\")\n",
"\n",
"# calculate new dates from last data point to sol\n",
"pred_dates = pd.date_range(doy_to_datetime(list(fit_time_serie.keys())[-1]+1),doy_to_datetime(sol), freq='D')\n",
"# generate a predicted df\n",
"pred_df = pd.DataFrame(index=pred_dates)\n",
"# set daily = np.nan\n",
"pred_df['daily'] = np.nan\n",
"\n",
"# add to original df\n",
"confirmed = confirmed.append(pred_df)\n",
"# calculate logistic_model model on the input days + future forecast\n",
"confirmed[['predicted']] = confirmed[['daily']].apply(lambda x: logistic_model(x.index.dayofyear,fit[0][0],fit[0][1],fit[0][2]) ,axis=0)\n"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"scrolled": true
},
"outputs": [
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00:00:00\"],\"value\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[105]}},\"selected\":{\"id\":\"25986\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"26022\",\"type\":\"UnionRenderers\"}},\"id\":\"25985\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"25999\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"25985\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"25988\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"25990\",\"type\":\"Line\"},\"nonselection_glyph\":{\"id\":\"25989\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"25992\",\"type\":\"CDSView\"}},\"id\":\"25991\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"25941\",\"type\":\"SaveTool\"},{\"attributes\":{\"label\":{\"value\":\"predicted\"},\"renderers\":[{\"id\":\"25963\",\"type\":\"GlyphRenderer\"}]},\"id\":\"25984\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"25957\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"25960\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"25962\",\"type\":\"Line\"},\"nonselection_glyph\":{\"id\":\"25961\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"25964\",\"type\":\"CDSView\"}},\"id\":\"25963\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"overlay\":{\"id\":\"25970\",\"type\":\"BoxAnnotation\"}},\"id\":\"25944\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"25982\",\"type\":\"YearsTicker\"},{\"attributes\":{\"callback\":null,\"end\":28408.998807552292,\"reset_end\":28408.998807552292,\"reset_start\":0.0,\"tags\":[[[\"value\",\"value\",null]]]},\"id\":\"25919\",\"type\":\"Range1d\"},{\"attributes\":{},\"id\":\"25953\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"25979\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"text\":\"\",\"text_color\":{\"value\":\"black\"},\"text_font_size\":{\"value\":\"12pt\"}},\"id\":\"25923\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"26022\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"25973\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"days\":[1,15]},\"id\":\"25977\",\"type\":\"DaysTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"25920\",\"type\":\"HoverTool\"},{\"id\":\"25941\",\"type\":\"SaveTool\"},{\"id\":\"25942\",\"type\":\"PanTool\"},{\"id\":\"25943\",\"type\":\"WheelZoomTool\"},{\"id\":\"25944\",\"type\":\"BoxZoomTool\"},{\"id\":\"25945\",\"type\":\"ResetTool\"}]},\"id\":\"25946\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"25945\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"25929\",\"type\":\"LinearScale\"},{\"attributes\":{\"line_color\":\"#1f77b3\",\"line_width\":4,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25960\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"25985\",\"type\":\"ColumnDataSource\"}},\"id\":\"25992\",\"type\":\"CDSView\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"25970\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"line_color\":\"#ff7e0e\",\"line_width\":4,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25988\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"25937\",\"type\":\"BasicTicker\"},{\"attributes\":{\"months\":[0,6]},\"id\":\"25981\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"grid_line_color\":null,\"ticker\":{\"id\":\"25932\",\"type\":\"DatetimeTicker\"}},\"id\":\"25935\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"25986\",\"type\":\"Selection\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b3\",\"line_width\":4,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25961\",\"type\":\"Line\"},{\"attributes\":{\"dimension\":1,\"grid_line_color\":null,\"ticker\":{\"id\":\"25937\",\"type\":\"BasicTicker\"}},\"id\":\"25940\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"25957\",\"type\":\"ColumnDataSource\"}},\"id\":\"25964\",\"type\":\"CDSView\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#1f77b3\",\"line_width\":4,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"25962\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"25958\",\"type\":\"Selection\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"25976\",\"type\":\"DaysTicker\"},{\"attributes\":{\"months\":[0,4,8]},\"id\":\"25980\",\"type\":\"MonthsTicker\"},{\"attributes\":{},\"id\":\"25943\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"label\":{\"value\":\"cumulative\"},\"renderers\":[{\"id\":\"25991\",\"type\":\"GlyphRenderer\"}]},\"id\":\"26013\",\"type\":\"LegendItem\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"25974\",\"type\":\"DaysTicker\"}],\"root_ids\":[\"25922\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
" var render_items = [{\"docid\":\"4163f4f9-7ed7-4ec3-b813-40046961773e\",\"roots\":{\"25922\":\"cb7df248-b66b-4307-bde1-6050ece42ddc\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" clearInterval(timer);\n",
" embed_document(root);\n",
" } else {\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" clearInterval(timer);\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" }\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);</script>"
],
"text/plain": [
":NdOverlay [Variable]\n",
" :Curve [index] (value)"
]
},
"execution_count": 259,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "25922"
}
},
"output_type": "execute_result"
}
],
"source": [
"confirmed[['predicted','cumulative']].hvplot().opts(hv.opts.Curve(line_width=4)).opts(width=1000)"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"confirmed[['predicted','cumulative']].plot(lw=4,figsize=[10,5]);"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>speed</th>\n",
" <th>max-day</th>\n",
" <th>total-infected</th>\n",
" <th>speed_error</th>\n",
" <th>max-day_error</th>\n",
" <th>total-infected_error</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-02-26</th>\n",
" <td>1.309834</td>\n",
" <td>55.693830</td>\n",
" <td>609.261292</td>\n",
" <td>0.078752</td>\n",
" <td>0.200212</td>\n",
" <td>39.430029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-27</th>\n",
" <td>1.743626</td>\n",
" <td>57.707102</td>\n",
" <td>1189.801720</td>\n",
" <td>0.109714</td>\n",
" <td>0.434864</td>\n",
" <td>150.510401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-28</th>\n",
" <td>1.943929</td>\n",
" <td>58.910792</td>\n",
" <td>1723.048764</td>\n",
" <td>0.093539</td>\n",
" <td>0.400744</td>\n",
" <td>185.811471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-29</th>\n",
" <td>2.005056</td>\n",
" <td>59.259494</td>\n",
" <td>1901.663145</td>\n",
" <td>0.070119</td>\n",
" <td>0.252195</td>\n",
" <td>116.929172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-01</th>\n",
" <td>2.655139</td>\n",
" <td>64.809208</td>\n",
" <td>8633.662519</td>\n",
" <td>0.152577</td>\n",
" <td>1.773284</td>\n",
" <td>4162.951183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-02</th>\n",
" <td>2.420230</td>\n",
" <td>62.322170</td>\n",
" <td>4403.646331</td>\n",
" <td>0.106547</td>\n",
" <td>0.515072</td>\n",
" <td>513.774558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-03</th>\n",
" <td>2.413792</td>\n",
" <td>62.282996</td>\n",
" <td>4362.750346</td>\n",
" <td>0.079730</td>\n",
" <td>0.302469</td>\n",
" <td>273.177334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-04</th>\n",
" <td>2.565731</td>\n",
" <td>63.080999</td>\n",
" <td>5194.908294</td>\n",
" <td>0.073382</td>\n",
" <td>0.270695</td>\n",
" <td>270.947154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-05</th>\n",
" <td>2.844130</td>\n",
" <td>64.607911</td>\n",
" <td>7096.251042</td>\n",
" <td>0.087697</td>\n",
" <td>0.367248</td>\n",
" <td>476.059149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-06</th>\n",
" <td>3.013845</td>\n",
" <td>65.609076</td>\n",
" <td>8615.774474</td>\n",
" <td>0.082460</td>\n",
" <td>0.348258</td>\n",
" <td>519.026006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-07</th>\n",
" <td>3.395786</td>\n",
" <td>68.208156</td>\n",
" <td>13980.824324</td>\n",
" <td>0.113238</td>\n",
" <td>0.640756</td>\n",
" <td>1533.105666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-08</th>\n",
" <td>3.726808</td>\n",
" <td>71.307435</td>\n",
" <td>24899.410306</td>\n",
" <td>0.123445</td>\n",
" <td>0.992144</td>\n",
" <td>4326.747561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-09</th>\n",
" <td>3.937285</td>\n",
" <td>74.027368</td>\n",
" <td>41671.508778</td>\n",
" <td>0.114046</td>\n",
" <td>1.210745</td>\n",
" <td>9049.531208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-10</th>\n",
" <td>3.627592</td>\n",
" <td>70.807588</td>\n",
" <td>23262.192920</td>\n",
" <td>0.106116</td>\n",
" <td>0.556018</td>\n",
" <td>2016.005227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-11</th>\n",
" <td>3.789576</td>\n",
" <td>72.007238</td>\n",
" <td>28409.017247</td>\n",
" <td>0.098433</td>\n",
" <td>0.530718</td>\n",
" <td>2273.121229</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" speed max-day total-infected speed_error max-day_error total-infected_error\n",
"2020-02-26 1.309834 55.693830 609.261292 0.078752 0.200212 39.430029\n",
"2020-02-27 1.743626 57.707102 1189.801720 0.109714 0.434864 150.510401\n",
"2020-02-28 1.943929 58.910792 1723.048764 0.093539 0.400744 185.811471\n",
"2020-02-29 2.005056 59.259494 1901.663145 0.070119 0.252195 116.929172\n",
"2020-03-01 2.655139 64.809208 8633.662519 0.152577 1.773284 4162.951183\n",
"2020-03-02 2.420230 62.322170 4403.646331 0.106547 0.515072 513.774558\n",
"2020-03-03 2.413792 62.282996 4362.750346 0.079730 0.302469 273.177334\n",
"2020-03-04 2.565731 63.080999 5194.908294 0.073382 0.270695 270.947154\n",
"2020-03-05 2.844130 64.607911 7096.251042 0.087697 0.367248 476.059149\n",
"2020-03-06 3.013845 65.609076 8615.774474 0.082460 0.348258 519.026006\n",
"2020-03-07 3.395786 68.208156 13980.824324 0.113238 0.640756 1533.105666\n",
"2020-03-08 3.726808 71.307435 24899.410306 0.123445 0.992144 4326.747561\n",
"2020-03-09 3.937285 74.027368 41671.508778 0.114046 1.210745 9049.531208\n",
"2020-03-10 3.627592 70.807588 23262.192920 0.106116 0.556018 2016.005227\n",
"2020-03-11 3.789576 72.007238 28409.017247 0.098433 0.530718 2273.121229"
]
},
"execution_count": 261,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fit_time_serie_df = pd.DataFrame.from_dict(\n",
" {k:(*v[0],*[np.sqrt(v[1][i][i]) for i in [0,1,2]]) for k,v in fit_time_serie.items()},\n",
" orient='index')\n",
"\n",
"fit_time_serie_df.columns = ['speed','max-day','total-infected', 'speed_error','max-day_error','total-infected_error']\n",
"\n",
"fit_time_serie_df.index = [datetime.strptime( f\"2020 {int(k)}\" , '%Y %j') for k in fit_time_serie_df.index]\n",
"\n",
"fit_time_serie_df"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x648 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fit_time_serie_df[['speed','max-day','total-infected']].plot(subplots=True, figsize=[20,9],lw=4);"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {},
"outputs": [
{
"data": {
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00:00:00\",\"2020-02-27 00:00:00\",\"2020-02-28 00:00:00\",\"2020-02-29 00:00:00\",\"2020-03-01 00:00:00\",\"2020-03-02 00:00:00\",\"2020-03-03 00:00:00\",\"2020-03-04 00:00:00\",\"2020-03-05 00:00:00\",\"2020-03-06 00:00:00\",\"2020-03-07 00:00:00\",\"2020-03-08 00:00:00\",\"2020-03-09 00:00:00\",\"2020-03-10 00:00:00\",\"2020-03-11 00:00:00\"],\"value\":{\"__ndarray__\":\"PjAbbs/YS0B87j5RgtpMQFDIvdKUdE1A86DLGjehTUCF5YMOyjNQQJAoeuE8KU9Aez5XNTkkT0ClfiYvXopPQFYaAQLoJlBAfOWIGPtmUEA7D+VuUg1RQL0HZwKt01FAgD13Z8CBUkDulz+Er7NRQEVvu5R2AFJA\",\"dtype\":\"float64\",\"shape\":[15]}},\"selected\":{\"id\":\"26425\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"26509\",\"type\":\"UnionRenderers\"}},\"id\":\"26424\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"max_interval\":1800000.0,\"min_interval\":1000.0,\"num_minor_ticks\":0},\"id\":\"26512\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"text\":\"Variable: max-day\",\"text_color\":{\"value\":\"black\"},\"text_font_size\":{\"value\":\"12pt\"}},\"id\":\"26394\",\"type\":\"Title\"},{\"attributes\":{\"grid_line_color\":null,\"ticker\":{\"id\":\"26449\",\"type\":\"DatetimeTicker\"}},\"id\":\"26452\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"#1f77b3\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"26383\",\"type\":\"Line\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"26513\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"max_interval\":43200000.0,\"min_interval\":3600000.0,\"num_minor_ticks\":0},\"id\":\"26534\",\"type\":\"AdaptiveTicker\"},{\"attributes\":{},\"id\":\"26388\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]},\"id\":\"26535\",\"type\":\"DaysTicker\"},{\"attributes\":{\"axis_label\":\"\",\"bounds\":\"auto\",\"formatter\":{\"id\":\"26434\",\"type\":\"BasicTickFormatter\"},\"major_label_orientation\":\"horizontal\",\"ticker\":{\"id\":\"26408\",\"type\":\"BasicTicker\"}},\"id\":\"26407\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"26398\",\"type\":\"LinearScale\"}],\"root_ids\":[\"26548\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
" var render_items = [{\"docid\":\"9a2fcf7d-369c-4faa-9e76-ea9e28128964\",\"roots\":{\"26548\":\"2c12bcef-6885-4ff3-95ef-47654ffc3754\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" clearInterval(timer);\n",
" embed_document(root);\n",
" } else {\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" clearInterval(timer);\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" }\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);</script>"
],
"text/plain": [
":NdLayout [Variable]\n",
" :Curve [index] (value)"
]
},
"execution_count": 263,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "26548"
}
},
"output_type": "execute_result"
}
],
"source": [
"%%opts Curve {+axiswise}\n",
"%%opts Curve [height=200, width=1200]\n",
"\n",
"fit_time_serie_df[['speed','max-day','total-infected']].hvplot(subplots=True).cols(1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "mascs_analysis (python_3.6.8)",
"language": "python",
"name": "mascs_analysis_python_3.6.8"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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