Beast code in Giters

【已发布弹幕版本，大家不要搞坏了！新版大屏beta 版本，全实时数据，交互式大屏，精确到城市级，弹幕来袭】关注2019新型冠状病毒（2019-nCoV），数据可视化感染人群热点图、迁徙扩散轨迹，以提供帮助分析疫情。愿世界安好。Focus on Wuhan 2019-nCoV, data visualization, to help analyze the epidemic situation. May the world be well.

MIT000

Novel-Corona

Context From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people. So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community. Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here. Edited: Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community. Content 2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number. The data is available from 22 Jan, 2020. Column Description Main file in this dataset is covid_19_data.csv and the detailed descriptions are below. covid_19_data.csv Sno - Serial number ObservationDate - Date of the observation in MM/DD/YYYY Province/State - Province or state of the observation (Could be empty when missing) Country/Region - Country of observation Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it) Confirmed - Cumulative number of confirmed cases till that date Deaths - Cumulative number of of deaths till that date Recovered - Cumulative number of recovered cases till that date 2019_ncov_data.csv This is older file and is not being updated now. Please use the covid_19_data.csv file Added two new files with individual level information COVID_open_line_list_data.csv This file is obtained from this link COVID19_line_list_data.csv This files is obtained from this link Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: India - https://www.kaggle.com/sudalairajkumar/covid19-in-india South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases Acknowledgements Johns Hopkins University for making the data available for educational and academic research purposes MoBS lab - https://www.mobs-lab.org/2019ncov.html 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 Picture courtesy : Johns Hopkins University dashboard Inspiration Some insights could be Changes in number of affected cases over time Change in cases over time at country level Latest number of affected cases

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cxfancy

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2019-nCoV

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