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Coronavirus in Thailand

Affected Population

0.0032 %

This is the part of confirmed infection cases against the total 70 million of its population.

Affected 1 of every 31,363
Failed to recover 1 of every 2,677,908

Recovery Pie

The whole pie reflects the total number of confirmed cases of people infected by coronavirus in Thailand.

Raw Numbers on April 6th, 2020

2,220confirmed+51

793recovered0

26failed+3

1,401active+48

Daily Flow

The height of a single bar is the total number of people suffered from Coronavirus in Thailand and confirmed to be infected. It includes three parts: those who could or could not recover and those who are currently in the active phase of the disease.

New Confirmed Cases

This graph shows the number of new cases by day. The lightblue bars are the number of the new total confirmed cases appeared that day.

Daily Speed

This graph shows the speed of growth (in %) over time in Thailand. The main three parameters are the number of confirmed cases, the number of recoveries and failures. The orange line is the speed of changing of the number of active cases (i.e., of those, who are still ill).

Note. When the speed is positive, the number of cases grows every day. The line going down means that the speed decreases, and while there may be more cases the next day, the disease spread is slowing down. If the speed goes below zero, that means that fewer cases registered today than yesterday.

Mortality Level

The gray bars on this graph display the absolute number of deaths that happen in Thailand every month during the recent five years of available data. The red bars are the absolute numbers of people died due to the COVID-19 infection.

Note that the vertical axis is drawn in logarithmic scale by default.

Weekly Levels

This graph draws the number of deaths in Thailand connected to the COVID-19 infection aggregated by weeks of 2020.

Crude rates

Crude mortality rate is the number of people died in a country within a year per each 1000 of population.

Here, the crude rate for Thailand is shown for the last 50 years. The red bar against 2020 is the number of people died due to COVID-19 per each 1000 people. Thus, you can directly compare the two parameters.

Note that the vertical axis is drawn in logarithmic scale by default.

Raw Daily Numbers

Download as CSV | XLS

Date Confirmed
cases
Daily
growth, %
Recovered
cases
Died
cases
Active
cases
Recovery
rate, %
Mortality
rate, %
Affected
population, %
Confirmed
per million
Died
per million
Apr 6th 2,220 2.4 % 793 26 1,401 35.7 % 1.2 % 0.0032 % 31.88 0.37
Apr 5th 2,169 4.9 % 793 23 1,353 36.6 % 1.1 % 0.0031 % 31.15 0.33
Apr 4th 2,067 4.5 % 674 20 1,373 32.6 % 1.0 % 0.003 % 29.69 0.29
Apr 3rd 1,978 5.5 % 612 19 1,347 30.9 % 1.0 % 0.0028 % 28.41 0.27
Apr 2nd 1,875 5.9 % 505 15 1,355 26.9 % 0.8 % 0.0027 % 26.93 0.22
Apr 1st 1,771 7.3 % 505 12 1,254 28.5 % 0.7 % 0.0025 % 25.44 0.17
Mar 31st 1,651 8.3 % 342 10 1,299 20.7 % 0.6 % 0.0024 % 23.71 0.14
Mar 30th 1,524 9.8 % 229 9 1,286 15.0 % 0.6 % 0.0022 % 21.89 0.13
Mar 29th 1,388 11.5 % 97 7 1,284 7.0 % 0.5 % 0.002 % 19.94 0.10
Mar 28th 1,245 9.6 % 97 6 1,142 7.8 % 0.5 % 0.0018 % 17.88 0.09
Mar 27th 1,136 8.7 % 97 5 1,034 8.5 % 0.4 % 0.0016 % 16.32 0.07
Mar 26th 1,045 11.9 % 88 4 953 8.4 % 0.4 % 0.0015 % 15.01 0.06
Mar 25th 934 12.9 % 70 4 860 7.5 % 0.4 % 0.0013 % 13.41 0.06
Mar 24th 827 14.7 % 52 4 771 6.3 % 0.5 % 0.0012 % 11.88 0.06
Mar 23rd 721 20.4 % 52 1 668 7.2 % 0.1 % 0.001 % 10.36 0.01
Mar 22nd 599 45.7 % 44 1 554 7.3 % 0.2 % < 0.001 % 8.60 0.01
Mar 21st 411 27.6 % 42 1 368 10.2 % 0.2 % < 0.001 % 5.90 0.01
Mar 20th 322 18.4 % 42 1 279 13.0 % 0.3 % < 0.001 % 4.62 0.01
Mar 19th 272 28.3 % 42 1 229 15.4 % 0.4 % < 0.001 % 3.91 0.01
Mar 18th 212 19.8 % 42 1 169 19.8 % 0.5 % < 0.001 % 3.04 0.01
Mar 17th 177 20.4 % 41 1 135 23.2 % 0.6 % < 0.001 % 2.54 0.01
Mar 16th 147 28.9 % 35 1 111 23.8 % 0.7 % < 0.001 % 2.11 0.01
Mar 15th 114 39.0 % 35 1 78 30.7 % 0.9 % < 0.001 % 1.64 0.01
Mar 14th 82 9.3 % 35 1 46 42.7 % 1.2 % < 0.001 % 1.18 0.01
Mar 13th 75 7.1 % 35 1 39 46.7 % 1.3 % < 0.001 % 1.08 0.01
Mar 12th 70 18.6 % 34 1 35 48.6 % 1.4 % < 0.001 % 1.01 0.01
Mar 11th 59 11.3 % 34 1 24 57.6 % 1.7 % < 0.001 % 0.85 0.01
Mar 10th 53 6.0 % 33 1 19 62.3 % 1.9 % < 0.001 % 0.76 0.01
Mar 9th 50 0.0 % 31 1 18 62.0 % 2.0 % < 0.001 % 0.72 0.01
Mar 8th 50 0.0 % 31 1 18 62.0 % 2.0 % < 0.001 % 0.72 0.01
Mar 7th 50 4.2 % 31 1 18 62.0 % 2.0 % < 0.001 % 0.72 0.01
Mar 6th 48 2.1 % 31 1 16 64.6 % 2.1 % < 0.001 % 0.69 0.01
Mar 5th 47 9.3 % 31 1 15 66.0 % 2.1 % < 0.001 % 0.68 0.01
Mar 4th 43 0.0 % 31 1 11 72.1 % 2.3 % < 0.001 % 0.62 0.01
Mar 3rd 43 0.0 % 31 1 11 72.1 % 2.3 % < 0.001 % 0.62 0.01
Mar 2nd 43 2.4 % 31 1 11 72.1 % 2.3 % < 0.001 % 0.62 0.01
Mar 1st 42 0.0 % 28 1 13 66.7 % 2.4 % < 0.001 % 0.60 0.01
Feb 29th 42 2.4 % 28 0 14 66.7 % 0.0 % < 0.001 % 0.60 0.00
Feb 28th 41 2.5 % 28 0 13 68.3 % 0.0 % < 0.001 % 0.59 0.00
Feb 27th 40 0.0 % 22 0 18 55.0 % 0.0 % < 0.001 % 0.57 0.00
Feb 26th 40 8.1 % 22 0 18 55.0 % 0.0 % < 0.001 % 0.57 0.00
Feb 25th 37 5.7 % 22 0 15 59.5 % 0.0 % < 0.001 % 0.53 0.00
Feb 24th 35 0.0 % 21 0 14 60.0 % 0.0 % < 0.001 % 0.50 0.00
Feb 23rd 35 0.0 % 21 0 14 60.0 % 0.0 % < 0.001 % 0.50 0.00
Feb 22nd 35 0.0 % 17 0 18 48.6 % 0.0 % < 0.001 % 0.50 0.00
Feb 21st 35 0.0 % 17 0 18 48.6 % 0.0 % < 0.001 % 0.50 0.00
Feb 20th 35 0.0 % 15 0 20 42.9 % 0.0 % < 0.001 % 0.50 0.00
Feb 19th 35 0.0 % 15 0 20 42.9 % 0.0 % < 0.001 % 0.50 0.00
Feb 18th 35 0.0 % 15 0 20 42.9 % 0.0 % < 0.001 % 0.50 0.00
Feb 17th 35 2.9 % 15 0 20 42.9 % 0.0 % < 0.001 % 0.50 0.00
Feb 16th 34 3.0 % 14 0 20 41.2 % 0.0 % < 0.001 % 0.49 0.00
Feb 15th 33 0.0 % 12 0 21 36.4 % 0.0 % < 0.001 % 0.47 0.00
Feb 14th 33 0.0 % 12 0 21 36.4 % 0.0 % < 0.001 % 0.47 0.00
Feb 13th 33 0.0 % 12 0 21 36.4 % 0.0 % < 0.001 % 0.47 0.00
Feb 12th 33 0.0 % 10 0 23 30.3 % 0.0 % < 0.001 % 0.47 0.00
Feb 11th 33 3.1 % 10 0 23 30.3 % 0.0 % < 0.001 % 0.47 0.00
Feb 10th 32 0.0 % 10 0 22 31.3 % 0.0 % < 0.001 % 0.46 0.00
Feb 9th 32 0.0 % 10 0 22 31.3 % 0.0 % < 0.001 % 0.46 0.00
Feb 8th 32 28.0 % 10 0 22 31.3 % 0.0 % < 0.001 % 0.46 0.00
Feb 7th 25 0.0 % 5 0 20 20.0 % 0.0 % < 0.001 % 0.36 0.00
Feb 6th 25 0.0 % 5 0 20 20.0 % 0.0 % < 0.001 % 0.36 0.00
Feb 5th 25 0.0 % 5 0 20 20.0 % 0.0 % < 0.001 % 0.36 0.00
Feb 4th 25 31.6 % 5 0 20 20.0 % 0.0 % < 0.001 % 0.36 0.00
Feb 3rd 19 0.0 % 5 0 14 26.3 % 0.0 % < 0.001 % 0.27 0.00
Feb 2nd 19 0.0 % 5 0 14 26.3 % 0.0 % < 0.001 % 0.27 0.00
Feb 1st 19 0.0 % 5 0 14 26.3 % 0.0 % < 0.001 % 0.27 0.00
Jan 31st 19 35.7 % 5 0 14 26.3 % 0.0 % < 0.001 % 0.27 0.00
Jan 30th 14 0.0 % 5 0 9 35.7 % 0.0 % < 0.001 % 0.20 0.00
Jan 29th 14 0.0 % 5 0 9 35.7 % 0.0 % < 0.001 % 0.20 0.00
Jan 28th 14 75.0 % 5 0 9 35.7 % 0.0 % < 0.001 % 0.20 0.00
Jan 27th 8 0.0 % 2 0 6 25.0 % 0.0 % < 0.001 % 0.11 0.00
Jan 26th 8 14.3 % 2 0 6 25.0 % 0.0 % < 0.001 % 0.11 0.00
Jan 25th 7 40.0 % 0 0 7 0.0 % 0.0 % < 0.001 % 0.10 0.00
Jan 24th 5 66.7 % 0 0 5 0.0 % 0.0 % < 0.001 % 0.07 0.00
Jan 23rd 3 50.0 % 0 0 3 0.0 % 0.0 % < 0.001 % 0.04 0.00
Jan 22nd 2 0 0 2 0.0 % 0.0 % < 0.001 % 0.03 0.00

Statistics per Continent

Spread over the continents timeline

Whole world

Africa

Asia

Europe

North America

Oceania

South America

Statistics per Country

The whole world

World excluding China

More statistics on countries

Countries vs China

Whole world

Afghanistan

Albania

Algeria

Andorra

Angola

Anguilla

Antigua and Barbuda

Argentina

Armenia

Aruba

Australia

Austria

Azerbaijan

Bahamas

Bahrain

Bangladesh

Barbados

Belarus

Belgium

Belize

Benin

Bermuda

Bhutan

Bolivia

Bonaire, St. Eustatius & Saba

Bosnia and Herzegovina

Botswana

Brazil

British Virgin Islands

Brunei Darussalam

Bulgaria

Burkina Faso

Burundi

Cabo Verde

Cambodia

Cameroon

Canada

Cayman Islands

Central African Republic

Chad

Chile

China

China, Hong Kong SAR

China, Macao SAR

Colombia

Congo

Costa Rica

Croatia

Cuba

Curaçao

Cyprus

Czechia

Dem. Rep. of the Congo

Denmark

Djibouti

Dominica

Dominican Republic

Ecuador

Egypt

El Salvador

Equatorial Guinea

Eritrea

Estonia

Eswatini

Ethiopia

Falkland Islands

Faroe Islands

Fiji

Finland

France

French Guiana

French Polynesia

Gabon

Gambia

Georgia

Germany

Ghana

Gibraltar

Greece

Greenland

Grenada

Guadeloupe

Guam

Guatemala

Guinea

Guinea-Bissau

Guyana

Haiti

Holy See

Honduras

Hungary

Iceland

India

Indonesia

Iran

Iraq

Ireland

Isle of Man

Israel

Italy

Jamaica

Japan

Jordan

Kazakhstan

Kenya

Kosovo

Kuwait

Kyrgyzstan

Lao People's Dem. Rep.

Latvia

Lebanon

Liberia

Libya

Liechtenstein

Lithuania

Luxembourg

Madagascar

Malawi

Malaysia

Maldives

Mali

Malta

Martinique

Mauritania

Mauritius

Mayotte

Mexico

Moldova

Monaco

Mongolia

Montenegro

Montserrat

Morocco

Mozambique

Myanmar

Namibia

Nepal

Netherlands

New Caledonia

New Zealand

Nicaragua

Niger

Nigeria

North Macedonia

Norway

Oman

Pakistan

Panama

Papua New Guinea

Paraguay

Peru

Philippines

Poland

Portugal

Puerto Rico

Qatar

Réunion

Romania

Russian Federation

Rwanda

Saint Barthélemy

Saint Kitts and Nevis

Saint Lucia

Saint Pierre and Miquelon

San Marino

Sao Tome and Principe

Saudi Arabia

Senegal

Serbia

Seychelles

Sierra Leone

Singapore

Sint Maarten

Slovakia

Slovenia

Somalia

South Africa

South Korea

South Sudan

Spain

Sri Lanka

State of Palestine

Sudan

Suriname

Sweden

Switzerland

Syrian Arab Republic

Taiwan

Tanzania

Thailand

Timor-Leste

Togo

Trinidad and Tobago

Tunisia

Turkey

Turks and Caicos Islands

Uganda

Ukraine

United Arab Emirates

United Kingdom

United States of America

Uruguay

Uzbekistan

Venezuela

Viet Nam

Western Sahara

Zambia

Zimbabwe

The green and red arrows display the change of the number of new confirmed cases for the last two days.

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Based on data collected by the Johns Hopkins University Center for Systems Science and Engineering.

This website presents the very same data as the JHU’s original dashboard but from a less-panic perspective. Updated daily around 8 a.m. European time.

Read the Technology blog. Look at the source code: GitHub. Powered by Raku.

Created by Andrew Shitov. Twitter: @andrewshitov. Contact by e-mail.