COVID-19 - Eastern Africa - 20-11-27

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 297979 73.0 2993 1.01% 0.69%
Active cases 81842 20.0 1538 1.92% 0.22%
Deaths 4841 1.2 35 0.73% 0.43%
Recovered 21129651.814200.68%0.59%
Deaths per infected 1.62%
Recovered per infected70.91%
Active per infected 27.47%
Deaths per recovered 2.29%
Projection:
Total Case Percentage
0.32%

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 68d 15h 101d 4h
Time to reach a million cases 119d 22h176d 18h
Time to saturate the population>1yr 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: 192Population na
Total casesCases per 100.000Average grow rate (5 days)
Infected na 140 92
Active na 115 107
Deaths na 133 78
Recovered na 139 74
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!

Diagrams

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
infected
absolute valuesPercentage growthrate 5dTime to doubleCases per 100.000% / ratio
Rwanda12.37458724284753970.720.490.00.6996d 11h47.53.50.443.60.91:114.90.92
Ethiopia98.665108438392461686675060.50.280.30.4139d 21h229d 20h109.939.81.768.42.51:40.00.62
Kenya47.56481656260901441541251.080.540.670.7764d 5h103d 8h171.754.93.0113.82.71:37.60.66
Mauritius1.26650148104430.28-1.140.00.32245d 23h39.63.80.835.02.31:44.20.88
Tanzania55.891509305211830.00.00.00.00.90.50.00.311.51:8.70.36
Zambia17.38117569367357168450.172.70.00.09>1yr101.12.12.196.92.11:47.20.96
Somalia15.893445192111334170.030.020.00.03stable28.05.80.721.53.31:30.20.77
Djibouti1.0785676396155760.03-4.620.00.08stable526.33.65.7517.11.11:91.70.98
Madagascar25.6817341433251166570.00.00.00.067.51.71.064.91.51:66.20.96
Zimbabwe15.16971496927584701.056.340.450.3566d 9h155d 14h64.16.41.855.93.21:30.80.87
Eritrea3.55668904770.542.7400.17128d 12h16.22.50.013.60.0na0.84
Mozambique30.07155061747128136310.620.680.640.56112d 18h108d 20h51.65.80.445.30.91:106.40.88
Uganda40.3195881055119788401.741.892.140.5340d 2h32d 18h48.626.20.521.92.21:44.80.45
Burundi11.4768110515750.573.340.00.0122d 6h5.90.90.05.00.21:588.20.84
Comoros0.856101875850.471.850.00.45148d 11h71.82.10.868.81.21:83.30.96
Malawi18.14602438618554530.070.420.00.03stable33.22.11.030.13.41:29.50.91
South Sudan12.923104896129540.37-16.970.3325.63186d 16h208d 6h24.00.70.522.92.11:48.50.95
Eastern Africa408.32979798184248412112960.690.220.430.59101d 4h161d 10h73.020.01.251.82.31:43.70.71

More Data by Country

Click top row to sort:±±±±±±±±±±±±±±±
CountryPop (mio)InfectedActiveDeathsRecoveredDeaths per RecoveredRecovered per
infected
Deaths per infected ... days agoPossible
Total %
Country
absolute values% / ratio0d5d7d10d14d
Rwanda12.37458724284753970.91:114.90.920.010.010.010.010.010.1Rwanda
Ethiopia98.665108438392461686675062.51:40.00.620.020.020.020.020.020.46Ethiopia
Kenya47.56481656260901441541252.71:37.60.660.020.020.020.020.020.82Kenya
Mauritius1.26650148104432.31:44.20.880.020.020.020.020.020.21Mauritius
Tanzania55.8915093052118311.51:8.70.360.040.040.040.040.040.01Tanzania
Zambia17.38117569367357168452.11:47.20.960.020.020.020.020.020.56Zambia
Somalia15.893445192111334173.31:30.20.770.030.030.030.030.030.19Somalia
Djibouti1.0785676396155761.11:91.70.980.010.010.010.010.011.53Djibouti
Madagascar25.6817341433251166571.51:66.20.960.010.010.010.010.010.26Madagascar
Zimbabwe15.16971496927584703.21:30.80.870.030.030.030.030.030.49Zimbabwe
Eritrea3.55668904770.0na0.840.00.00.00.00.00.0Eritrea
Mozambique30.07155061747128136310.91:106.40.880.010.010.010.010.010.12Mozambique
Uganda40.3195881055119788402.21:44.80.450.010.010.010.010.010.13Uganda
Burundi11.4768110515750.21:588.20.840.00.00.00.00.00.0Burundi
Comoros0.856101875851.21:83.30.960.010.010.010.010.010.22Comoros
Malawi18.14602438618554533.41:29.50.910.030.030.030.030.030.28Malawi
South Sudan12.923104896129542.11:48.50.950.020.020.020.020.020.13South Sudan
Eastern Africa408.32979798184248412112962.31:43.70.710.020.020.020.020.020.32Eastern 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