COVID-19 - Eastern Africa - 20-06-05

Data from JHU, ca. 9:00 CET

Eastern Africa is a Group with the following members:
Burundi - Comoros - Djibouti - Eritrea - Ethiopia - Kenya - Madagascar - Malawi - Mauritius - Mayotte - Mozambique - Reunion - Rwanda - Seychelles - Somalia - South Sudan - Tanzania - Uganda - Zambia - Zimbabwe

Go to the data tables

absolutecases per 100.000 abs. development
1 day
rel. development
1 day
rel. development
5 days/average
Total infections 16760 4.1 483 2.97% 3.89%
Active cases 11122 2.7 380 3.54% 3.59%
Deaths 273 0.1 2 0.74% 2.04%
Recovered 53651.31011.92%2.76%
Deaths per infected 1.63%
Recovered per infected32.01%
Active per infected 66.36%
Deaths per recovered 5.09%
Total Case Percentage

Gives the time necessary to double the existing infections - to reach a million from the existing status, or to reach the population limit of the given group (which can well be below a million). This assumes the exponential growrate of the last day or average of the last five days continues - which it does not, as there will be saturation towards the maximum. Please take these calulations with two grains of salt. As long as there is turning point in sight the data may well hold, though.
Last days rate Last average rate
Time to double cases 23d 16h 18d 4h
Time to reach a million cases 139d 19h107d 5h
Time to saturate the population345d 10h 264d 22h

A ranking may taste a bit fishy, as this is not a competition between countries, but to compare the relative efficiency of measures taken, or for detecting which countries are likely to get critical next, this ranking imho deserves a place in this overview.
Total: 189Population na
Total casesCases per 100.000Average grow rate (5 days)
Infected na 156 40
Active na 126 41
Deaths na 146 45
Recovered na 152 53
The ranking is made over all groups and countries, including small or recent that are ommitted in the table on the front page - which may lead to minor discrepancies between both!


Members of Group: Eastern Africa

Burundi - Comoros - Djibouti - Eritrea - Ethiopia - Kenya - Madagascar - Malawi - Mauritius - Mayotte - Mozambique - Reunion - Rwanda - Seychelles - Somalia - South Sudan - Tanzania - Uganda - Zambia - Zimbabwe

Data by Country

Click on the header to sort - Click on the name for the countries dashboard! - Only countries with more then 100 infections and more then 100.000 citizens are listed!
Click top row to sort:±±±±±±±±±±±±±±±±±±
CountryPop (mio)InfectedActiveDeathsRecoveredInfectedActiveDeathsRecoveredInfectedDeathsInfectedActiveDeathsRecoveredDeaths per RecoveredRecovered per
absolute valuesPercentage growthrate 5dTime to doubleCases per 100.000% / ratio
Rwanda12.37442013622822.572.5920.01.8227d 7h3d 19h3.
Ethiopia98.66518051524192629.037.6110.613.688d 0h6d 20h1.
Kenya47.56424741752796434.753.314.074.4514d 22h17d 9h5.
Zambia17.381108917079120.61-7.450.03.41114d 19h6.
Somalia15.89322041707794182.211.960.263.8331d 15h270d 16h13.910.70.52.618.91:5.30.19
Djibouti1.078412323902617074.235.171.632.3216d 17h42d 18h382.3221.62.4158.31.51:65.80.41
Madagascar25.6897576772014.834.733.333.5714d 16h21d 3h3.
Zimbabwe15.162652284338.377.030.01.388d 14h1.
Mozambique30.0735423321197. 0h1.
Uganda40.35574750825.995.0502.7811d 22h1.
Comoros0.85132752554.910.970.021.5114d 11h15.
Malawi18.144093504557.77.540.04.299d 8h2.
South Sudan12.929949781060.
Eastern Africa408.3167601112227353653.893.592.042.7618d 4h34d 6h4.

More Data by Country

Click top row to sort:±±±±±±±±±±±±±±±
CountryPop (mio)InfectedActiveDeathsRecoveredDeaths per RecoveredRecovered per
Deaths per infected ... days agoPossible
Total %
absolute values% / ratio0d5d7d10d14d
South Sudan12.92994978106166.71.7: Sudan
Eastern Africa408.3167601112227353655.11:19.60.320. Africa

Heinsberg study

A study of the University of Bonn on the spread of Covid-19 at Heinsberg - the most affected county in Germany - showed a rate of deaths to infected of 0.37%. As this is the first reliable data on this ratio I use it as a projection from the number of reported deaths to the spread of the disease in the whole population, given as percentage. Note that locally other factors, like a developing spread, unreported deaths or a situation where people are more prone to die due to an overstretch of the health system will affect this number. Countries with a different population structure (eg. more young vs. old) may also get other ratios, so take this with a grain of salt - I will keep an eye on the developing scientific results.
Links (German):
Preliminary results - Press coverage: Die Welt