COVID-19 - ASEAN - 20-10-09

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 761106 114.0 8833 1.17% 1.14%
Active cases 139430 20.9 3447 2.53% 2.19%
Deaths 18671 2.8 217 1.18% 0.95%
Recovered 60300590.351690.86%0.63%
Deaths per infected 2.45%
Recovered per infected79.23%
Active per infected 18.32%
Deaths per recovered 3.1%
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 59d 9h 61d 1h
Time to reach a million cases 23d 9h24d 1h
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 116 58
Active na 106 59
Deaths na 102 37
Recovered na 109 70
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
Malaysia32.726147223863152107073.5313.891.290.4619d 23h53d 22h45.011.80.532.71.41:70.40.73
Indonesia266.91232465865314116772476671.360.320.761.3351d 10h91d 17h121.624.54.492.84.71:21.20.76
Thailand66.48436341305934450.24- 10h5.
Philippines108.4343347705331161522753070.753.381.00.0992d 18h69d 19h308.749.25.7253.92.21:44.80.82
Cambodia15.289283602770.3611.6700.07194d 3h1.
Burma68.67239061660256667386.095.025.414.1711d 17h13d 3h34.824.
ASEAN667.91761106139430186716030051.142.190.950.6361d 1h73d 1h114.020.92.890.33.11:32.30.79

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