COVID-19 - Middle Africa - 20-10-09

Data from JHU, ca. 9:00 CET

Middle Africa is a Group with the following members:
Angola - Cameroon - Central African Republic - Chad - Congo (Brazzaville) - Congo (Kinshasa) - Equatorial Guinea - Gabon - Sao Tome and Principe

Go to the data tables

absolutecases per 100.000 abs. development
1 day
rel. development
1 day
rel. development
5 days/average
Total infections 64134 35.7 149 0.23% 0.36%
Active cases 8909 5.0 64 0.72% 0.98%
Deaths 1305 0.7 6 0.46% 0.33%
Recovered 5392030.0790.15%0.21%
Deaths per infected 2.03%
Recovered per infected84.07%
Active per infected 13.89%
Deaths per recovered 2.42%
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 298d 0h 192d 16h
Time to reach a million cases stablestable
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 145 117
Active na 138 74
Deaths na 142 84
Recovered na 141 124
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: Middle Africa

Angola - Cameroon - Central African Republic - Chad - Congo (Brazzaville) - Congo (Kinshasa) - Equatorial Guinea - Gabon - Sao Tome and Principe

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
Congo (Kinshasa)91.93110835317276102420.>1yr>1yr11.80.30.311.12.71:37.20.95
Cameroon26.54621203663423201170.27- 6h>1yr79.92.51.675.82.11:47.60.95
Equatorial Guinea1.3585063868348940.070.740.00.06stable372.76.36.1360.31.71:58.80.97
Congo (Brazzaville)5.244511811419038870.>1yr97.621.81.774.12.31:43.10.76
Central African Republic5.496485328776219140.
Chad15.6931274829011020.926.250.690.4375d 16h100d 10h8.
Angola31.136031313421226852.243.491.330.4531d 7h52d 9h19.410.
Sao Tome and Principe0.292217158900. 13h461.08.57.5445.01.71:59.20.97
Middle Africa179.776413489091305539200.360.980.330.21192d 16h212d 2h35.75.00.730.02.41:41.30.84

More Data by Country

Click top row to sort:±±±±±±±±±±±±±±±
CountryPop (mio)InfectedActiveDeathsRecoveredDeaths per RecoveredRecovered per
Deaths per infected ... days agoPossible
Total %
absolute values% / ratio0d5d7d10d14d
Congo (Kinshasa)91.93110835317276102422.71:37.20.950. (Kinshasa)
Equatorial Guinea1.3585063868348941.71:58.80.970. Guinea
Congo (Brazzaville)5.244511811419038872.31:43.10.760. (Brazzaville)
Central African Republic5.496485328776219143.21:30.90.390. African Republic
Sao Tome and Principe0.292217158901.71:59.20.970. Tome and Principe
Middle Africa179.776413489091305539202.41:41.30.840. 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