# Proof that COVID-19 Data Accuracy depends on countries' development levels

April 27, 2020

If COVID-19 data accuracy did not depend on a country's development level, then there would be no correlation between the number of confirmed coronavirus cases per 100,000 people and GDP per capita, the Human Freedom Index, or the Corruption Perceptions Index.

Let's check:

A correlation coefficient values range from -1 to 1 and indicate relationships between two variables, where -1 is the absolute negative correlation, 0 is the absence of correlation, and 1 is the absolute correlation.

There is a strong statistically significant correlation between countries' confirmed COVID-19 cases and GDP per capita. The correlation coefficient is 0.8 (95% CI: 0.74 - 0.85), which wouldn't have existed if the data accuracy was the same in every country.

The chart includes only countries with a population of more than 300,000 people. Otherwise small countries like the Vatican would distort the values of confirmed cases per 100,000 people.

But even for the sample of all 197 countries, the correlation coefficient is 0.49 (95% CI: 0.38 - 0.59) that is still a strong statistically significant correlation.

Confirmed COVID-19 cases per 100,000 people also correlate with the Human Freedom Index (by Cato Institute) and the Corruption Perceptions Index (by Transparency International).

For comparison, here's how the graph of coronavirus cases per 100K by total population looks like:

The correlation coefficient is -0.04 (95% CI: -0.19 - +0.11) and the regression line is almost parallel to the abscissa axis, showing no correlation, as expected.

173 countries with a population more than 300,000 ranked by confirmed COVID-19 cases:

RankCountryPopulationConfirmedGDP per capitaHuman Freedom IndexCorruption Perceptions Index
1United States
325.1M
985,975
\$63k
8.39
71
2Spain
46.5M
226,629
\$30k
8.22
58
3Italy
60.6M
197,675
\$34k
8.01
52
4France
66.6M
162,100
\$41k
8.25
72
5Germany
83M
157,770
\$48k
8.42
80
6United Kingdom
65.1M
152,840
\$43k
8.55
80
7Turkey
82M
110,130
\$9.4k
6.96
41
8Iran
77.4M
90,481
\$5.6k
5.29
28
9Russia
146.8M
87,147
\$11k
6.49
28
10China
1.4B
82,830
\$9.8k
6.11
39
11Brazil
208.5M
63,100
\$8.9k
7.05
35
35.7M
46,895
\$46k
8.69
81
13Belgium
11.2M
46,687
\$48k
8.28
75
14Netherlands
17.2M
37,845
\$53k
8.49
82
15Switzerland
8.2M
29,061
\$83k
8.83
85
16India
1.3B
27,977
\$2k
6.8
41
17Peru
30.4M
27,517
\$6.9k
7.46
35
18Portugal
10.6M
23,864
\$23k
8.11
64
15.7M
22,719
\$6.3k
6.7
34
20Ireland
4.8M
19,262
\$79k
8.48
73
21Sweden
10.3M
18,640
\$55k
8.48
85
22Saudi Arabia
33M
17,522
\$23k
5.66
49
23Israel
8.9M
15,466
\$42k
7.41
61
24Austria
8.8M
15,274
\$51k
8.42
76
25Mexico
130.5M
14,677
\$9.7k
6.94
28
26Singapore
5.9M
14,423
\$65k
8.1
85
27Japan
127.1M
13,441
\$39k
8.31
73
28Chile
18.1M
13,331
\$16k
8.11
67
29Pakistan
197M
13,328
\$1.5k
5.44
33
30Poland
38.5M
11,761
\$15k
8.02
60
31Romania
19.6M
11,339
\$12k
8
47
32Belarus
9.5M
11,289
\$6.3k
6.16
44
33South Korea
51.1M
10,738
\$31k
8.15
57
34United Arab Emirates
9.3M
10,349
\$43k
6.56
70
35Qatar
2.2M
10,287
\$69k
6.22
62
36Indonesia
264M
9,096
\$3.9k
6.81
38
37Ukraine
42.6M
9,009
\$3.1k
6.81
32
38Denmark
5.8M
8,698
\$61k
8.73
88
39Serbia
7M
8,042
\$7.2k
7.19
39
40Philippines
101M
7,777
\$3.1k
6.92
36
41Norway
5.3M
7,527
\$82k
8.61
84
42Czech Republic
10.6M
7,408
\$23k
8.3
59
43Australia
23.4M
6,720
\$57k
8.73
77
44Dominican Republic
10.4M
6,135
\$8.1k
6.6
30
156.6M
5,913
\$1.7k
5.8
26
46Malaysia
31.6M
5,820
\$11k
6.38
47
47Panama
3.9M
5,779
\$16k
7.53
37
48Colombia
49.1M
5,379
\$6.7k
6.78
36
49Finland
5.5M
4,695
\$50k
8.7
85
50South Africa
57.7M
4,546
\$6.4k
6.78
43
51Egypt
94.8M
4,534
\$2.5k
5.82
35
52Morocco
36M
4,115
\$3.2k
6.24
43
53Argentina
44.9M
3,892
\$12k
7.1
40
54Luxembourg
614k
3,723
\$117k
8.46
81
55Moldova
2.6M
3,408
\$3.2k
7.17
33
56Algeria
41.3M
3,382
\$4.1k
5.29
35
57Kuwait
4.7M
3,075
\$34k
6.6
41
58Thailand
65.9M
2,931
\$7.3k
7.11
36
59Kazakhstan
18M
2,791
\$9.8k
6.91
31
60Bahrain
1.3M
2,647
\$24k
7.15
36
61Hungary
10M
2,583
\$16k
8.11
46
62Greece
10.8M
2,517
\$20k
7.77
45
63Oman
3.6M
2,049
\$16k
6.3
52
64Croatia
4.1M
2,030
\$15k
7.46
48
65Uzbekistan
32.4M
1,887
\$1.5k
-
23
66Iraq
33.4M
1,820
\$5.8k
4.28
18
67Armenia
2.9M
1,808
\$4.2k
7.42
35
68Iceland
357k
1,792
\$73k
8.22
76
69Afghanistan
34.9M
1,703
\$521
-
16
70Estonia
1.3M
1,647
\$23k
8.44
73
71Azerbaijan
10M
1,645
\$4.7k
6.44
25
72Cameroon
24.1M
1,621
\$1.5k
5.7
25
73Ghana
28.3M
1,550
\$2.2k
7.21
41
74Bosnia and Herzegovina
3.5M
1,516
\$6.1k
7.27
38
75New Zealand
4.9M
1,469
\$42k
8.94
87
76Lithuania
2.8M
1,449
\$19k
7.94
59
77Slovenia
2.1M
1,402
\$26k
7.88
60
78North Macedonia
2.1M
1,386
\$6.1k
7.55
37
79Slovakia
5.4M
1,381
\$19k
8.29
50
80Cuba
11.3M
1,369
\$8.8k
-
47
81Bulgaria
7.3M
1,348
\$9.3k
7.78
42
82Nigeria
190.9M
1,273
\$2k
6.1
27
83Ivory Coast
20.3M
1,150
\$1.7k
5.95
35
84Djibouti
850k
1,023
\$3.1k
-
31
85Guinea
11.6M
996
\$879
5.68
28
86Bolivia
11.1M
950
\$3.5k
6.75
29
87Tunisia
11.6M
949
\$3.4k
6.1
43
88Latvia
2M
818
\$18k
8.16
58
89Cyprus
1.1M
817
\$28k
8.39
59
90Albania
3M
726
\$5.3k
7.68
36
91Lebanon
4.5M
707
\$8.3k
6.72
28
92Niger
17.1M
696
\$414
6
34
93Costa Rica
4.9M
695
\$12k
7.81
56
94Kyrgyzstan
6.2M
695
\$1.3k
6.65
29
95Senegal
14.1M
671
\$1.5k
6.29
45
96Honduras
8.1M
661
\$2.5k
7.13
29
97Burkina Faso
19.2M
632
\$715
6.84
41
98Uruguay
3.4M
606
\$17k
7.93
70
99Sri Lanka
21.4M
557
\$4.1k
5.69
38
100Guatemala
15.5M
500
\$4.5k
7.21
27
101Georgia
3.7M
496
\$4.7k
7.48
58
102Palestine
4.7M
495
\$3.2k
-
-
103DR Congo
86.8M
459
\$562
5.27
20
104Malta
423k
448
\$30k
8.42
54
105Jordan
10.4M
447
\$4.2k
6.59
49
106Somalia
11M
436
\$315
-
10
107Taiwan
23.6M
429
\$25k
7.79
63
108Mali
18.5M
389
\$900
6.07
32
109Kenya
48.5M
363
\$1.7k
6.61
27
110Jamaica
2.9M
350
\$5.4k
7.16
44
111Mauritius
1.3M
332
\$11k
8.18
51
112Venezuela
28.5M
325
\$16k
5.27
18
6.3M
323
\$4.1k
7.4
35
114Montenegro
622k
321
\$8.8k
7.67
45
115Tanzania
57.3M
299
\$1.1k
6.37
36
116Vietnam
94.7M
270
\$2.6k
6.31
33
117Equatorial Guinea
1.2M
258
\$10k
-
16
118Sudan
40.5M
237
\$977
4.44
16
119Paraguay
6.8M
228
\$5.8k
7.02
29
120Maldives
345k
214
\$10k
-
31
121Congo
5.3M
200
\$2.1k
5.55
19
122Rwanda
12.2M
191
\$773
7
56
123Gabon
1.7M
176
\$8k
5.79
31
124Myanmar
53.3M
146
\$1.3k
4.81
29
125Brunei
418k
138
\$32k
6.66
63
126Ethiopia
105M
124
\$772
5.33
34
127Liberia
4.7M
124
\$677
6.6
32
22.9M
124
\$528
6.69
25
129Cambodia
15.1M
122
\$1.5k
7.22
20
1.3M
116
\$17k
6.81
41
131Cape Verde
546k
106
\$3.6k
7.4
57
132Togo
6.3M
98
\$679
5.67
30
133Sierra Leone
7.6M
93
\$534
6.24
30
134Zambia
17.1M
88
\$1.5k
6.67
35
135Bahamas
377k
80
\$32k
7.97
65
136Uganda
42.9M
79
\$643
6.4
26
137Mozambique
25.8M
76
\$499
6.14
23
138Guyana
800k
74
\$5k
6.68
37
139Haiti
10.3M
74
\$868
7.04
20
140Benin
10M
64
\$902
6.85
40
141Libya
5.7M
61
\$7.2k
4.98
17
142Eswatini
1.4M
59
\$4.1k
6.11
38
143Guinea-Bissau
1.5M
53
\$778
5.97
16
144Nepal
29.4M
52
\$1k
6.83
31
11M
46
\$728
5.23
19
146Syria
19.8M
43
\$2k
5.34
13
147Eritrea
3.5M
39
\$811
-
24
148Mongolia
3.1M
38
\$4.1k
7.17
37
149Malawi
18.6M
34
\$389
6.68
32
150Zimbabwe
14.1M
31
\$2.1k
4.96
22
151Angola
29.8M
26
\$3.4k
5.02
19
152East Timor
1.2M
24
\$2k
6.41
35
153Botswana
2M
22
\$8.3k
6.81
61
154Laos
6.8M
19
\$2.5k
6.2
29
155Central African Republic
5M
19
\$476
5.58
26
156Belize
375k
18
\$4.9k
7.37
-
157Fiji
881k
18
\$6.3k
7.46
-
158Namibia
2.3M
16
\$5.9k
6.97
53
159Nicaragua
6.2M
13
\$2k
7.22
25
160Burundi
10.9M
11
\$272
5.7
17
161Suriname
539k
10
\$6.2k
7.19
43
162Gambia
1.9M
10
\$716
6.52
37
163Papua New Guinea
7.3M
8
\$2.7k
6.99
28
164Bhutan
754k
7
\$3.2k
6.85
68
165Mauritania
3.5M
7
\$1.2k
5.52
27
166South Sudan
12.6M
6
\$1.1k
-
13
167Yemen
26.2M
1
\$944
5.4
14
168North Korea
24.9M
0
\$1.7k
-
14
169Solomon Islands
561k
0
\$2.1k
-
44
170Tajikistan
8.9M
0
\$827
6.64
25
171Turkmenistan
5.1M
0
\$7k
-
20
172Comoros
735k
0
\$1.4k
-
27
173Lesotho
2.2M
0
\$1.3k
6.16
41

Okay, but what does it all mean?

It shows that the number of confirmed coronavirus cases in each country is literally what the country has managed to detect.

But little it says about the actual numbers, especially in developing countries, as testing policies and capabilities vary greatly.

So:

1. It would make little sense to blame countries solely for being at the top of the infected list, as it's like blaming for being transparent and capable.
2. Keep in mind while comparing that accuracy of the data depends on the level of a country's development.