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. This data is extracted from the same link and made available in csv format.

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.

The data is available from 22 Jan 2020.

Acknowledgements

Johns Hopkins university has made the data available in google sheets format here. Sincere thanks to them.

Thanks to WHO, CDC, NHC and DXY for making the data available in first place.

Picture courtesy : Johns Hopkins University dashboard

Inspiration

Some insights could be

1. Changes in number of affected cases over time
2. Change in cases over time at country level
3. Latest number of affected cases

This image reports a Maximum Entropy model that estimates suitable locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.

The model was trained just on locations in Italy that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:

• Average Annual Surface Air Temperature in 2018 (NASA)
• Average Annual Precipitation in 2018 (NASA)
• CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)
• Elevation (NOAA ETOPO2)
• Population per 0.5° cell (NASA Gridded Population of the World)

A higher resolution map, the model file (in ASC format) and all parameters used are also attached.

The model indicates highest correlation with infection rate for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation and Population density are poorly correlated with infection rate.

One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location, and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.

Evaluation:

A risk score was calculated for each country/region reported by the JHU monitoring system (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.

We assessed the reliability of this score, by selecting the country/regions that reported the highest rates of infection. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.

The agreement between the two maps (covid_high_rate_vs_high_risk.png, where violet dots indicate high infection rates and countries' colours indicate estimated high risk score) is the following:

Accuracy (overall percentage of correctly predicted high-rate zones): 77.25%
Kappa (agreement between the two maps): 0.46 (Good, according to Fleiss' intepretation of the score)

This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.

Analysis of ‘COVID-19 Coronavirus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vignesh1694/covid19-coronavirus on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

A SARS-like virus outbreak originating in Wuhan, China, is spreading into neighboring Asian countries, and as far afield as Australia, the US a and Europe.

On 31 December 2019, the Chinese authorities reported a case of pneumonia with an unknown cause in Wuhan, Hubei province, to the World Health Organisation (WHO)’s China Office. As more and more cases emerged, totaling 44 by 3 January, the country’s National Health Commission isolated the virus causing fever and flu-like symptoms and identified it as a novel coronavirus, now known to the WHO as 2019-nCoV.

The following dataset shows the numbers of spreading coronavirus across the globe.

Content

Sno - Serial number Date - Date of the observation Province / State - Province or state of the observation Country - Country of observation Last Update - Recent update (not accurate in terms of time) Confirmed - Number of confirmed cases Deaths - Number of death cases Recovered - Number of recovered cases

Acknowledgements

Thanks to John Hopkins CSSE for the live updates on Coronavirus and data streaming. Source: https://github.com/CSSEGISandData/COVID-19 Dashboard: https://public.tableau.com/profile/vignesh.coumarane#!/vizhome/DashboardToupload/Dashboard12

Inspiration

Inspired by the following work: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

--- Original source retains full ownership of the source dataset ---

Analysis of ‘COVID-19 in Turkey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gkhan496/covid19-in-turkey on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

COVID-19 data in Turkey. Daily Covid-19 data published by our health ministry.

Content

time_series_covid_19_confirmed_tr
time_series_covid_19_recovered_tr
time_series_covid_19_deaths_tr
time_series_covid_19_intubated_tr
time_series_covid_19_intensive_care_tr.csv 
time_series_covid_19_tested_tr.csv 
test_numbers : Number of test (daily)

Total data

covid_19_data_tr

Github

Github repo : https://github.com/gkhan496/Covid19-in-Turkey/

Acknowledgements

We would like to thank our health ministry and all health workers.

Country level datasets

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases France - https://www.kaggle.com/lperez/coronavirus-france-dataset Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan 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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2311214%2Feaf61a1cf97850b64aefd52d3de5890b%2FXMhaJ.png?generation=1586182028591623&alt=media" alt="">

Source : https://fastlifehacks.com/n95-vs-ffp/

https://covid19.saglik.gov.tr https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html?fbclid=IwAR0k49fzqTxI4HBBZF7n4hLX4Zj0Q2KII_WOEo7agklC20KODB3TOeF8RrU#/bda7594740fd40299423467b48e9ecf6 http://who.int/ --situation reports https://evrimagaci.org/covid19#turkey-statistics

--- Original source retains full ownership of the source dataset ---

Context

Coronaviruses are a large family of viruses found in both animals and humans. Some infect people and are known to cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS).

A novel coronavirus (CoV) is a new strain of coronavirus that has not been previously identified in humans. The new, or “novel” coronavirus, now called 2019-nCoV, had not previously detected before the outbreak was reported in Wuhan, China in December 2019.

Short story: On December 31, 2019, the WHO was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents. As of January 23, there are over 800 cases of 2019-nCoV confirmed globally, including cases in at least 20 regions in China and nine countries/territories. The first reported infected individuals, some of whom showed symptoms as early as December 8, were discovered to be among stallholders from the Wuhan South China Seafood Market. Subsequently, the wet market was closed on Jan 1. The virus causing the outbreak was quickly determined to be a novel coronavirus. On January 10, gene sequencing further determined it to be the new Wuhan coronavirus, namely 2019-nCoV, a betacoronavirus, related to the Middle Eastern Respiratory Syndrome virus (MERS-CoV) and the Severe Acute Respiratory Syndrome virus (SARS-CoV). However, the mortality and transmissibility of 2019-nCoV are still unknown, and likely to vary from those of the prior referenced coronaviruses.

See more information on the webpage of World Health Organization

Content

John Hopkins University Google Sheet of time series confirmed|recovered|death case numbers converted to CSV format.

The data operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE).

See also GitHub.

Track virus here.

Acknowledgements

Thanks to
- JHU CCSE
- WHO
- Centers for Disease Control and Prevention (CDC)
- European Centre for Disease Prevention and Control (ECDC)
- DXY
- National Health Commission of the People's Republic of China (NHC)

Forsøg på at klassificere antallet af bekræftede SARS-CoV-2-tilfælde i fransk område efter region.

Dataene kommer fra statens medier og sundhedswebsteder. Formålet med dokumentet er at være så præcist som muligt.

Kilder: — https://solidarites-sante.gouv.fr/soins-et-maladies/maladies/maladies-infectieuses/coronavirus/article/points-de-situation-coronavirus-covid-19 — https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde — https://france3-regions.francetvinfo.fr — https://www.ars.sante.fr/ — https://www.facebook.com/MinSoliSante/ — https://geodes.santepubliquefrance.fr/#c=home

Andre kilder på data.gouv.fr interessant:

— https://www.data.gouv.fr/fr/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/ — https://www.data.gouv.fr/fr/reuses/visualisation-et-analyse-covid-19-monde-france-regions-francaises/ — https://www.data.gouv.fr/fr/reuses/tableau-de-bord-de-suivi-de-lepidemie-de-covid19/

Andre varer:

— https://www.arcgis.com/apps/opsdashboard/index.html#/3a278da2d7ab4a8a8e1b4ea8bea7121b — https://www.esrifrance.fr/coronavirus-ressources.aspx — https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 — https://nextstrain.org/ncov

Versuche, die Zahl der bestätigten SARS-CoV-2-Fälle auf französischem Territorium nach Regionen einzuordnen.

Die Daten stammen aus den Medien und Gesundheitsseiten des Staates. Das Ziel des Dokuments ist, so genau wie möglich zu sein.

Quelle: — https://solidarites-sante.gouv.fr/soins-et-maladies/maladies/maladies-infectieuses/coronavirus/article/points-de-situation-coronavirus-covid-19 — https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde — https://france3-regions.francetvinfo.fr — https://www.ars.sante.fr/ — https://www.facebook.com/MinSoliSante/ — https://geodes.santepubliquefrance.fr/#c=home

Weitere interessante Quellen auf data.gouv.fr:

— https://www.data.gouv.fr/fr/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/ — https://www.data.gouv.fr/fr/reuses/visualisation-et-analyse-covid-19-monde-france-regions-francaises/ — https://www.data.gouv.fr/fr/reuses/tableau-de-bord-de-suivi-de-lepidemie-de-covid19/

Sonstiges:

— https://www.arcgis.com/apps/opsdashboard/index.html#/3a278da2d7ab4a8a8e1b4ea8bea7121b — https://www.esrifrance.fr/coronavirus-ressources.aspx — https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 — https://nextstrain.org/ncov