Covid GUI

版本 3.1.0 (56.7 KB) 作者: Peter Seibold
This graphic interface displays the covid time series (cases, deaths, vaccinations and much more) in many selectable manners.
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更新时间 2021/11/14

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This graphic interface displays the covid time series in many selectable manners.
The data from github are retrieved automatically from the web,
URL of main data page: https://github.com/owid/covid-19-data
URL of data csv:
https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv
URL of description csv:
https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-codebook.csv
Features:
• Automatic download of data.
• Display cases, deaths, vaccinations and much more.
• Display several countries at once
• Display data per million population
• Display data tip by mouse click or arrow keys
• Sortable table with data of last (current) day.
Manual:
Select several countries in the table on the left side
Click on curve to get data tips, hold down mouse button and move mouse,
or use arrow keys.
If download fails, get the csv data with your browser and place the files into the covid folder, see URLs above.

引用格式

Peter Seibold (2021). Covid GUI (https://www.mathworks.com/matlabcentral/fileexchange). Use at your own risk.

MATLAB 版本兼容性
创建方式 R2016a
与 R2020a 及更高版本兼容
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版本 已发布 发行说明
3.1.0

Display also booster vaccinations

3.0.3

Improved store of user settings

3.0.2

Other data source, some minor changes

3.0.1

Changed file separator from \ to /, now compatible with Linux.

3.0.0

Data source from Github.
Display in addition vaccination and much more

2.3.4

Added button for 7 days average of infected per day

2.3.3

Solved problem regarding country/Matlab-version for calendar dates

2.3.2

Improved data tip

2.3.1

Changed image

2.3.0

Included data tip by mouse click

2.2.1

Replaced image

2.2.0

Sortable table for cuurent data included

2.1.0

Display of top 10 for infected/day, recovered and mortality.
Log/Linear y-scale.
GUI resizeable

2.0.1

Removed some of the spelling errors.

2.0.0

Implemented prediction of infected, recovered and mortality. Approximation with error function (erf.m).

1.0.0