This is another analysis of the outbreak of Coronavirus / Covid-19 disease.
Many others have done excellent analysis, and my attempts are specifically trying to add flavour to the statistics in South Africa.
The data source is a repository maintained by Johns Hopkins University. The data is updated once per day.
Last updated at 2021-02-22 07:18:10
#> # A tibble: 10 x 5
#> # Groups: country [1]
#> country date lat long cases
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 South Africa 2021-02-12 -30.6 22.9 1487681
#> 2 South Africa 2021-02-13 -30.6 22.9 1490063
#> 3 South Africa 2021-02-14 -30.6 22.9 1491807
#> 4 South Africa 2021-02-15 -30.6 22.9 1492909
#> 5 South Africa 2021-02-16 -30.6 22.9 1494119
#> 6 South Africa 2021-02-17 -30.6 22.9 1496439
#> 7 South Africa 2021-02-18 -30.6 22.9 1498766
#> 8 South Africa 2021-02-19 -30.6 22.9 1500677
#> 9 South Africa 2021-02-20 -30.6 22.9 1502367
#> 10 South Africa 2021-02-21 -30.6 22.9 1503796
Using a linear scale it’s easiest to see the relative scale of the worst affected countries, but it’s hard to distinguish the countries that were affected later.
Using a logarithmic scale it’s easier to discern whether the pandemic is still in the exponential growth phase. On this scale, a straight line indicates exponential growth.
#> # A tibble: 10 x 3
#> # Groups: country [1]
#> country date deaths
#> <chr> <date> <dbl>
#> 1 South Africa 2021-02-12 47670
#> 2 South Africa 2021-02-13 47821
#> 3 South Africa 2021-02-14 47899
#> 4 South Africa 2021-02-15 48094
#> 5 South Africa 2021-02-16 48313
#> 6 South Africa 2021-02-17 48478
#> 7 South Africa 2021-02-18 48708
#> 8 South Africa 2021-02-19 48859
#> 9 South Africa 2021-02-20 48940
#> 10 South Africa 2021-02-21 49053
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