COVID-19 - ASEAN - 20-09-22

Data from JHU, ca. 9:00 CET

ASEAN is a Group with the following members:
Brunei - Burma - Cambodia - Indonesia - Laos - Malaysia - Philippines - Singapore - Thailand - Vietnam

Go to the data tables

absolutecases per 100.000 abs. development
1 day
rel. development
1 day
rel. development
5 days/average
Total infections 624681 93.5 6264 1.01% 1.29%
Active cases 120968 18.1 1420 1.19% -0.68%
Deaths 15256 2.3 226 1.5% 0.98%
Recovered 48845773.146180.95%1.62%
Deaths per infected 2.44%
Recovered per infected78.19%
Active per infected 19.36%
Deaths per recovered 3.12%
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 68d 18h 53d 22h
Time to reach a million cases 46d 16h36d 14h
Time to saturate the population>1yr >1yr

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 118 43
Active na 109 135
Deaths na 106 34
Recovered na 109 29
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: ASEAN

Brunei - Burma - Cambodia - Indonesia - Laos - Malaysia - Philippines - Singapore - Thailand - Vietnam

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
Malaysia32.7261035866513095630.62.320.310.31115d 12h223d 0h31.72.00.429.21.41:73.50.92
Indonesia266.9122529235878898371842981.690.580.971.6441d 10h71d 21h94.822.03.769.05.31:18.70.73
Thailand66.48435141105933450.1-0.350.340.09>1yr201d 8h5.
Philippines108.4342917895609750492306431.1-2.080.882.1163d 11h78d 23h269.151.74.7212.72.21:45.70.79
Burma68.6769594892116195110.148.5510.689.127d 4h6d 19h10.
ASEAN667.91624681120968152564884571.29-0.680.981.6253d 22h71d 10h93.518.12.373.13.11:32.10.78

More Data by Country

Click top row to sort:±±±±±±±±±±±±±±±
CountryPop (mio)InfectedActiveDeathsRecoveredDeaths per RecoveredRecovered per
Deaths per infected ... days agoPossible
Total %
absolute values% / ratio0d5d7d10d14d

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