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Covid-19 (Coronovirus) analysis

Overview

On March 11, 2020, the World Health Organization (WHO) declared the Covid-19 (a.k.a. new coronavirus) a pandemic. Since January 22, 2020, the Johns Hopkins CSSE maintains a data repository to track the Covid-19 incidence worldwide. In order to understand a little bit how this disease will affect my country (Brazil), I performed some data analysis in this data.

For Portuguese speakers, I wrote a post in my blog about this analysis: O que os dados dizem sobre o Coronavírus?

Some plots and tables got during the analysis (updated on April 6, 2020)

Covid-19 worldwide (without China):

covid-19-wo-chinha

Deaths worldwide (without China):

deaths-wo-chinha

Top 10 infected countries

Country/Region Confirmed Deaths % Deaths % Population
US 337072 9619 2.85369 0.103027
Spain 131646 12641 9.60227 0.281754
Italy 128948 15887 12.3205 0.21338
Germany 100123 1584 1.58205 0.120735
France 93773 8093 8.63042 0.139986
China 82602 3333 4.03501 0.00593094
Iran 58226 3603 6.18796 0.0711807
United Kingdom 48436 4943 10.2052 0.0728482
Turkey 27069 574 2.12051 0.0328828
Switzerland 21100 715 3.38863 0.247753

Comparing confirmed cases around the world

comparing-countries

Early cases in Brazil

early-br

Comparing early cases around the world

early-compare

Running the code

The analysis was coded in Python using Jupyter Notebook. To install the requirement:

pip install requirements.txt

First, run the get_data.ipynb script to get the most updated data from the Johns Hopkins repository.

Next, run the analysis.ipynb code and have fun

Other analysis

Some people are also working on this data and providing some insightful analysis on Kaggle. You may want to check them as well:

If you find some bug or have any further question please let me know

covid-19's People

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