Corona Prophet Prediction

[UPDATES]

2019-nCoV Global Cases by Johns Hopkins

https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Visual Dashboard (desktop):
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Visual Dashboard (mobile):
http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61

Lancet Article:
An interactive web-based dashboard to track COVID-19 in real time

Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE):
https://systems.jhu.edu/

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

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov/

Contact Us:

Email: jhusystems@gmail.com

Terms of Use:

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Coronavirus 2019-nCoV Global Cases by Johns Hopkins CSSE

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.

Kernels

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.

Mainland China* Death Trends

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.

Uncertainty in Prediction

Prediction with uncertainty and changepoint

Long-term forecast and trends

Correlation Matrix

Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.

Features include StockPrice,Lowest/Highest Daily Temperature,Humidity(%),StockPrice,Currency,search terms such as cold,etc.