{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[COVID-surge](https://github.com/dpploy/covid-surge) [https://github.com/dpploy/covid-surge] : V. F. de Almeida **14May20**\n",
"\n",
"## US States Counties COVID-19 Surge Period Analysis\n",
"\n",
"$\n",
" \\newcommand{\\Amtrx}{\\boldsymbol{\\mathsf{A}}}\n",
" \\newcommand{\\Bmtrx}{\\boldsymbol{\\mathsf{B}}}\n",
" \\newcommand{\\Cmtrx}{\\boldsymbol{\\mathsf{C}}}\n",
" \\newcommand{\\Mmtrx}{\\boldsymbol{\\mathsf{M}}}\n",
" \\newcommand{\\Imtrx}{\\boldsymbol{\\mathsf{I}}}\n",
" \\newcommand{\\Pmtrx}{\\boldsymbol{\\mathsf{P}}}\n",
" \\newcommand{\\Qmtrx}{\\boldsymbol{\\mathsf{Q}}}\n",
" \\newcommand{\\Lmtrx}{\\boldsymbol{\\mathsf{L}}}\n",
" \\newcommand{\\Umtrx}{\\boldsymbol{\\mathsf{U}}}\n",
" \\newcommand{\\xvec}{\\boldsymbol{\\mathsf{x}}}\n",
" \\newcommand{\\yvec}{\\boldsymbol{\\mathsf{y}}}\n",
" \\newcommand{\\zvec}{\\boldsymbol{\\mathsf{z}}}\n",
" \\newcommand{\\avec}{\\boldsymbol{\\mathsf{a}}}\n",
" \\newcommand{\\bvec}{\\boldsymbol{\\mathsf{b}}}\n",
" \\newcommand{\\cvec}{\\boldsymbol{\\mathsf{c}}}\n",
" \\newcommand{\\rvec}{\\boldsymbol{\\mathsf{r}}}\n",
" \\newcommand{\\norm}[1]{\\bigl\\lVert{#1}\\bigr\\rVert}\n",
" \\DeclareMathOperator{\\rank}{rank}\n",
" \\DeclareMathOperator{\\abs}{abs}\n",
"$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"### Table of Contents\n",
" - [1) Introduction.](#intro)\n",
" - [2) Find states with fully evolved surges](#data)\n",
" - [3) Loop over: configure run, fit model to data, analysis.](#loop)\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Introduction](#toc)\n",
"\n",
"On-line COVID-19 data is used in this notebook, and a fit to the sigmoid function \n",
" \n",
"\\begin{equation*}\n",
"\\boxed{ f(t) = \\frac{\\alpha_0}{1 + \\alpha_1\\, e^{\\alpha_2\\,t} } }\n",
"\\end{equation*}\n",
" \n",
"is systematically made. The time between points where the function has maximum and minimum curvature is computed and reported as the **surge period**. This period is relevant to public health officials to decide how long measures to control the epidemic should be in place. In addition, the surge period provides insight in comparing how different communities react to the epidemic."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"'''Load the covid-surge package'''\n",
"#!pip install --upgrade --quiet covid-surge\n",
"\n",
"from covid_surge import Surge\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Import Data](#toc)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# of states/districts: 56\n",
"# of days: 121\n"
]
}
],
"source": [
"'''Import data'''\n",
"\n",
"# Get complete US surge data\n",
"us_surge = Surge()\n",
"\n",
"print('# of states/districts: ',len(us_surge.names))\n",
"print('# of days: ',us_surge.dates.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"'''Set parameters'''\n",
"\n",
"us_surge.end_date = '4/20/20' # set end date wanted\n",
"us_surge.end_date = None # get all the data available\n",
"us_surge.ignore_last_n_days = 2 # allow for data repo to be corrected/updated\n",
"us_surge.min_n_cases_abs = 500 # min # of absolute cases for analysis\n",
"us_surge.deaths_100k_minimum = 41 # US death per 100,000 for Chronic Lower Respiratory Diseases per year: 41 (2019)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" 0) Virginia: surge period 18.80 [day]\n",
" 1) New York: surge period 19.41 [day]\n",
" 2) Massachusetts: surge period 20.97 [day]\n",
" 3) Connecticut: surge period 21.00 [day]\n",
" 4) Michigan: surge period 21.91 [day]\n",
" 5) Minnesota: surge period 23.07 [day]\n",
" 6) New Jersey: surge period 23.32 [day]\n",
" 7) Maryland: surge period 23.57 [day]\n",
" 8) Pennsylvania: surge period 23.72 [day]\n",
" 9) Louisiana: surge period 23.99 [day]\n",
"10) North Carolina: surge period 24.88 [day]\n",
"11) Indiana: surge period 25.90 [day]\n",
"12) Georgia: surge period 26.48 [day]\n",
"13) Florida: surge period 26.79 [day]\n",
"14) Ohio: surge period 26.80 [day]\n",
"15) Colorado: surge period 26.98 [day]\n",
"16) California: surge period 27.40 [day]\n",
"17) Illinois: surge period 28.34 [day]\n",
"18) Missouri: surge period 28.54 [day]\n",
"19) Washington: surge period 28.91 [day]\n"
]
}
],
"source": [
"'''Fit data to model function'''\n",
"\n",
"# Fit data to all states\n",
"fit_data = us_surge.multi_fit_data()\n",
"\n",
"states = list()\n",
"\n",
"print('')\n",
"for (i,(sort_key,data)) in enumerate(fit_data):\n",
" name = data[0]\n",
" states.append(name)\n",
" print('%2i) %15s: surge period %1.2f [day]'%(i,name,sort_key))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Loop over: Configure Run, Fit Model to Data, Analysis.](#toc)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"***************************************************************\n",
" Virginia\n",
"***************************************************************\n",
"# of counties: 133\n",
"# of fittings done = 1\n",
"\n",
" Fairfax: surge period 17.58 [day]\n",
"\n",
"----------------------------------------------------------------\n",
" Bins \n",
"----------------------------------------------------------------\n",
" Bin 0 [17.0, 18.0]\n",
"\n",
"----------------------------------------------------------------\n",
" County Groups \n",
"----------------------------------------------------------------\n",
" Group 0 ['Fairfax']\n"
]
},
{
"data": {
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\n",
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