COVID-19 - Eastern Africa - 20-10-09

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 207670 50.9 1630 0.79% 0.68%
Active cases 65493 16.0 254 0.39% -2.1%
Deaths 3446 0.8 20 0.58% 0.45%
Recovered 13873134.013560.99%1.96%
Deaths per infected 1.66%
Recovered per infected66.8%
Active per infected 31.54%
Deaths per recovered 2.48%
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 87d 23h 101d 22h
Time to reach a million cases 199d 11h231d 2h
Time to saturate the populationstable stable

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 139 85
Active na 111 153
Deaths na 137 71
Recovered na 138 22
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
Ethiopia98.66582662442891271371020.96-0.20.651.9772d 18h107d 10h83.844.91.337.63.41:29.20.45
Kenya47.564406207989755318760.6-8.760.544.49116d 3h128d 5h85.416.81.667.02.41:42.20.78
Mauritius1.26639527103580.529.510.00.06134d 7h31.22.10.828.32.81:35.80.91
Zambia17.38115339564336144390.383.060.120.25183d 10h>1yr88.33.21.983.12.31:42.90.94
Madagascar25.6816676464237159750.14-9.410.260.54>1yr269d 12h64.91.80.962.21.51:67.60.96
Zimbabwe15.167994129122964740.27-0.370.090.27259d 11hstable52.78.51.542.73.51:28.20.81
Eritrea3.54054103640.350.500.34197d 9h11.
Mozambique30.07974225026971711.16-3.970.63.4160d 1h115d 13h32.
Uganda40.3953835958658571.61-2.120.974.1143d 12h71d 14h23.
Comoros0.854951374750.33-0.890.00.38212d 4h58.21.50.855.91.51:68.00.96
South Sudan12.92276114175412900. 10h58d 2h21.411.00.410.04.21:23.90.47
Eastern Africa408.32076706549334461387310.68-2.10.451.96101d 22h154d 9h50.916.00.834.02.51:40.30.67

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 Sudan12.92276114175412904.21:23.90.470. Sudan
Eastern Africa408.32076706549334461387312.51:40.30.670. 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