Latest Covid-19 Dataset
Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries. Data is disaggregated by country (and sometimes subregion). Coronavirus disease (COVID-19) is caused by the Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and has had a worldwide effect. On March 11 2020, the World Health Organization (WHO) declared it a pandemic, pointing to the over 118,000 cases of the coronavirus illness in over 110 countries and territories around the world at the time.
This dataset includes time series data tracking the number of people affected by COVID-19 worldwide, including:
- confirmed tested cases of Coronavirus infection
- the number of people who have reportedly died while sick with Coronavirus
- the number of people who have reportedly recovered from it
This is sample code by using you can get started working with this dataset.
git remote add origin [email protected]:laxmimerit/Covid-19-Preprocessed-Dataset.git
echo “running covid-19 data upload project”
# import # imports import plotly.express as px import plotly.graph_objects as go import plotly.figure_factory as ff from plotly.subplots import make_subplots import folium import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline import math import random from datetime import timedelta import warnings warnings.filterwarnings('ignore') #color pallette cnf = '#393e46' dth = '#ff2e63' rec = '#21bf73' act = '#fe9801'
# load the dataset df = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/covid_19_data_cleaned.csv', parse_dates=['Date']) country_daywise = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/country_daywise.csv', parse_dates=['Date']) countywise = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/countrywise.csv') daywise = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/daywise.csv', parse_dates=['Date']) # fill NA df['Province/State'] = df['Province/State'].fillna("") # grouping by date confirmed = df.groupby('Date').sum()['Confirmed'].reset_index() recovered = df.groupby('Date').sum()['Recovered'].reset_index() deaths = df.groupby('Date').sum()['Deaths'].reset_index() # See your first plot fig = go.Figure() fig.add_trace(go.Scatter(x = confirmed['Date'], y = confirmed['Confirmed'], mode = 'lines+markers', name = 'Confirmed', line = dict(color = "Orange", width = 2))) fig.add_trace(go.Scatter(x = recovered['Date'], y = recovered['Recovered'], mode = 'lines+markers', name = 'Recovered', line = dict(color = "Green", width = 2))) fig.add_trace(go.Scatter(x = deaths['Date'], y = deaths['Deaths'], mode = 'lines+markers', name = 'Deaths', line = dict(color = "Red", width = 2))) fig.update_layout(title = 'Worldwide Covid-19 Cases', xaxis_tickfont_size = 14, yaxis = dict(title = 'Number of Cases')) fig.show()
Data is in CSV format and updated daily. It is sourced from this upstream repository maintained by the amazing team at Johns Hopkins University Center for Systems Science and Engineering (CSSE) who have been doing a great public service from an early point by collating data from around the world.
The upstream dataset currently lists the following upstream datasources:
- World Health Organization (WHO): https://www.who.int/
- DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia
- BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
- National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
- China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
- Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
- Macau Government: https://www.ssm.gov.mo/portal/
- Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
- US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
- Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
- Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
- European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
- Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
- Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
Recently, some of the countries have stopped reporting Covid data and it is also found discrepancies in data if taken from multiple sources. Data reported till July 2020 is quite accurate so we will only explore from Jan 2020 to July 2020.
- Active Cases are calculated based on recoveries so this will not be also correct
- Anything that requires recoveries in the calculation isn’t 100% correct, like deaths/100recoveries, etc.
Here is some article to understand it
Most of these reports are based on the US but mostly it is true for the rest of the world.