{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# COVID-19 Insights for India\n", "\n", "This notebook provides a catalog of open datasets for deriving insights related to COVID-19 and helping open source and open data community to collaborate in fighting this global threat. The notebook provides (a) reusable API to speed up open data analytics related to COVID-19, customized for India however can be adopted for other countries, (b) sample usage of the API, (c) documentation of insights, and (d) catalog of open datasets referenced.\n", "\n", "The notebook is created by aggregating content from hundreds of global contributors, whome we have tried our best to acknowledge, if you note any missed ones, please inform us by creating an issue on this Github repository. The code, links, and datasets are provided on AS-IS basis under open source. This is the work of the individual author and contributors to this repository with no endorsements from any organizations including their own." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import covid as cv" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating stats for today...\n", "Stats file for today saved: 2020-03-24-covid-india-stats.csv\n" ] } ], "source": [ "df = cv.get_today_stats(force=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Latest
Indian476
Foreign43
Discharged40
Death10
Confirmed519
Active469
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State Indian Foreign Discharged Death Confirmed Active
8Kerala878409591
10Maharashtra863028987
17Telengana2510103534
7Karnataka370313733
4Gujarat321013332
15Rajasthan302303229
14Punjab290012928
21Uttar Pradesh3211103322
3Delhi291623022
5Haryana14141102817
16Tamil Nadu132101514
20Ladakh130001313
0Andhra Pradesh800088
23West Bengal900198
9Madhya Pradesh700077
18Chandigarh700077
19Jammu and Kashmir400044
22Uttarakhand310044
1Bihar300132
6Himachal Pradesh300132
12Odisha200022
13Puducherry100011
11Manipur100011
2Chhattisgarh100011
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cv.linear_regression(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### COVID-19 Open Datasets, Dashboards, and Apps\n", "\n", "\n", "#### India Stats\n", "\n", "1. [Ministry of Health and Family Welfare - MOHFW](https://www.mohfw.gov.in/) publishes COVID India stats. This notebook pulls the stats from HTML table on site.\n", "\n", "2. [India Affected People Dataset](http://portal.covid19india.org/) by covid19india.org\n", "\n", "3. [Patient Travel History](https://api.covid19india.org/travel_history.json) by covid19india.org \n", "\n", "\n", "#### India Dashboards\n", "\n", "1. Kiprosh [covidout.in dashboard](https://covidout.in/) provides MOHFW stats, daily and cummulative trends.\n", "\n", "\n", "#### India Apps\n", "\n", "1. [COVID-19 India Cluster Graph Visualization](https://cluster.covid19india.org/) by covid19india.org\n", "\n", "\n", "#### India Hospitals, Testing Labs\n", "\n", "1. [ICMR](https://icmr.nic.in/what-s-new) List of Government [Laboratories](https://icmr.nic.in/sites/default/files/upload_documents/Govt_Lab_COVID_19_Testing_V2.pdf) for COVID-19 Testing\n", "\n", "2. Statewise Hospital Beds from [PIB](https://pib.gov.in/PressReleasePage.aspx?PRID=1539877) extracted to [CSV dataset](https://www.kaggle.com/sudalairajkumar/covid19-in-india#HospitalBedsIndia.csv) on Kaggle.\n", "\n", "\n", "#### Census, Demographics\n", "\n", "1. India rural, urban population and area by states on [Wikipedia](https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population) extracted to [CSV dataset](https://www.kaggle.com/sudalairajkumar/covid19-in-india#population_india_census2011.csv) on Kaggle.\n", "\n", "2. [World Bank Indicators](https://data.humdata.org/dataset/world-bank-indicators-of-interest-to-the-covid-19-outbreak) of Interest to the COVID-19 Outbreak.\n", "\n", "\n", "#### Global Stats\n", "\n", "1. [Geographic distribution of COVID-19 cases worldwide](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide) from European Centre for Disease Prevention and Control available as daily [Excel dataset](https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-03-22.xlsx) (2020-03-22). Replace yyyy-mm-dd suffix on file to get historical/current data.\n", "\n", "2. Johns Hopkins University [Global Dashboard](https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) and GitHub [datasets](https://github.com/CSSEGISandData/COVID-19).\n", "\n", "3. Situational Awareness Dashboard from [World Health Organization](https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd).\n", "\n", "\n", "#### Research\n", "\n", "1. COVID-19 [Open Research Dataset](https://pages.semanticscholar.org/coronavirus-research) (CORD-19) from Allen Institute for AI. Contains over 44,000 scholarly articles, including over 29,000 with full text, about COVID-19 and the coronavirus family of viruses for use by the global research community.\n", "\n", "2. NCBI [SARS-CoV-2 Genetic Sequences](https://www.ncbi.nlm.nih.gov/genbank/sars-cov-2-seqs/)\n", "\n", "3. Nextstrain [Genomic epidemiology of novel coronavirus](https://nextstrain.org/ncov)\n", "\n", "4. GISAID App for [Genomic epidemiology of hCoV-19](https://www.gisaid.org/epiflu-applications/next-hcov-19-app/)\n", "\n", "\n", "#### News Analysis\n", "\n", "1. ACAPS COVID-19: [Government Measures Dataset](https://data.humdata.org/dataset/acaps-covid19-government-measures-dataset)\n", "\n", "#### Notebooks\n", "\n", "1. Notebook from Parul Pandey on [Tracking India's Coronavirus Spread](https://www.kaggle.com/parulpandey/tracking-india-s-coronavirus-spread-wip/notebook) compares trends across India, Italy, Korea.\n", "\n", "2. [COVID-19 Literature Clustering](https://www.kaggle.com/maksimeren/covid-19-literature-clustering) visualizes CORD-19 dataset of over 44,000 scholarly articles.\n", "\n", "3. [Coronavirus (COVID-19) Visualization & Prediction](https://www.kaggle.com/therealcyberlord/coronavirus-covid-19-visualization-prediction) does timeseries predictive analysis of virus spread based on Johns Hopkins dataset.\n", "\n", "\n", "#### Meta Dataset Sources\n", "\n", "1. [Registry of Open Data on AWS](https://registry.opendata.aws/)\n", "\n", "2. [MyGov COVID-19 Solution Challenge / Resources](https://innovate.mygov.in/covid19/#tab6)\n", "\n", "3. [Covidout Data Sources](https://covidout.in/sources)\n", "\n", "4. [Kaggle COVID datasets](https://www.kaggle.com/search?q=covid+coronavirus+in%3Adatasets)\n", "\n", "5. [HDX Datasets on COVID-19 Outbreak](https://data.humdata.org/event/covid-19)\n", "\n", "6. [api.rootnet.in](https://api.rootnet.in/) multiple official and unofficial India specific datasets as JSON files\n" ] } ], "metadata": { "kernelspec": { "display_name": "conda_python3", "language": "python", "name": "conda_python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 4 }