[UPDATES]
- https://www.nature.com/articles/d41586-020-00154-w?utm_source=facebook&utm_medium=social&utm_content=organic&utm_campaign=NGMT_USG_JC01_GL_Nature&fbclid=IwAR3I1vxjD05wwXbGYzqt9jnXVPE6pUiQzbTjISHT6W-niFs5MistDkL2l80
- https://public.tableau.com/profile/ankit.grover4668#!/vizhome/COVID-19_15854929469350/Story1
2019-nCoV Global Cases by Johns Hopkins
https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
Can we build an original, comprehensive solution to help handle the crisis?
We are looking to predict, visualize and act to contain the 2019 n-Coron Virus, through the power of data science.
With the objective of understanding and minimizing epidemic spread, we devloped an method to accuractely predict and visualize the necessary features relating to n-Corona virus using Time Series Analysis and EDA.
The following dataset has been taken from Novel Corona Virus 2019 Dataset: along with custom feature engineering by extracting data from web.
- Mathematical Simulation of nCOV Transmission Model.
- Feature engineeing and analysis of their effects in predicting extent of the nCOV Virus.
- Prediction model for expected new cases in the Mainland China region.
- Prediction model for expected new cases in the Mainland China region.
Following trend indicates daily,weekly,monthly trend in confirmed death rates in China .It was obtained using Prophet an open source tool for Time Series analysis.
*Mainland China includes SAR provinces and Hong Kong.
Features include StockPrice,Lowest/Highest Daily Temperature, Humidity(%), Currency, related search terms such as cold,etc.
We explore the relationship of various features with the spread of the disease by plotting graphs.
- Stock Price Relationship
- Humidity Relationship
- Flights Search Relation
- Lowest Temperature Relation
Data Sources:
- 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
[1] https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset
[2] https://www.cdc.gov/coronavirus/2019-ncov/index.html
[3]https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
[4] https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance
[6]https://facebook.github.io/prophet/
[7]https://ai.googleblog.com/2017/07/facets-open-source-visualization-tool.html
[8]https://scikit-learn.org/stable/
[9]https://seaborn.pydata.org/