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Coronavirus in Philippines

Affected Population

0.0034 %

This is the part of confirmed infection cases against the total 108 million of its population.

Affected 1 of every 29,540
Failed to recover 1 of every 663,292

Recovery Pie

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

Raw Numbers on April 6th, 2020

3,660confirmed+414

73recovered+9

163failed+11

3,424active+394

Daily Flow

The height of a single bar is the total number of people suffered from Coronavirus in Philippines 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 Philippines. 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 Philippines 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 Philippines 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 Philippines 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 3,660 12.8 % 73 163 3,424 2.0 % 4.5 % 0.0034 % 33.85 1.51
Apr 5th 3,246 4.9 % 64 152 3,030 2.0 % 4.7 % 0.003 % 30.02 1.41
Apr 4th 3,094 2.5 % 57 144 2,893 1.8 % 4.7 % 0.0029 % 28.62 1.33
Apr 3rd 3,018 14.6 % 52 136 2,830 1.7 % 4.5 % 0.0028 % 27.91 1.26
Apr 2nd 2,633 13.9 % 51 107 2,475 1.9 % 4.1 % 0.0024 % 24.35 0.99
Apr 1st 2,311 10.9 % 50 96 2,165 2.2 % 4.2 % 0.0021 % 21.38 0.89
Mar 31st 2,084 34.8 % 49 88 1,947 2.4 % 4.2 % 0.0019 % 19.28 0.81
Mar 30th 1,546 9.0 % 42 78 1,426 2.7 % 5.0 % 0.0014 % 14.30 0.72
Mar 29th 1,418 31.9 % 42 71 1,305 3.0 % 5.0 % 0.0013 % 13.12 0.66
Mar 28th 1,075 33.9 % 35 68 972 3.3 % 6.3 % < 0.001 % 9.94 0.63
Mar 27th 803 13.6 % 31 54 718 3.9 % 6.7 % < 0.001 % 7.43 0.50
Mar 26th 707 11.2 % 28 45 634 4.0 % 6.4 % < 0.001 % 6.54 0.42
Mar 25th 636 15.2 % 26 38 572 4.1 % 6.0 % < 0.001 % 5.88 0.35
Mar 24th 552 19.5 % 20 35 497 3.6 % 6.3 % < 0.001 % 5.11 0.32
Mar 23rd 462 21.6 % 18 33 411 3.9 % 7.1 % < 0.001 % 4.27 0.31
Mar 22nd 380 23.8 % 15 25 340 3.9 % 6.6 % < 0.001 % 3.51 0.23
Mar 21st 307 33.5 % 13 19 275 4.2 % 6.2 % < 0.001 % 2.84 0.18
Mar 20th 230 6.0 % 8 18 204 3.5 % 7.8 % < 0.001 % 2.13 0.17
Mar 19th 217 7.4 % 8 17 192 3.7 % 7.8 % < 0.001 % 2.01 0.16
Mar 18th 202 8.0 % 5 19 178 2.5 % 9.4 % < 0.001 % 1.87 0.18
Mar 17th 187 31.7 % 5 12 170 2.7 % 6.4 % < 0.001 % 1.73 0.11
Mar 16th 142 1.4 % 2 12 128 1.4 % 8.5 % < 0.001 % 1.31 0.11
Mar 15th 140 26.1 % 2 11 127 1.4 % 7.9 % < 0.001 % 1.29 0.10
Mar 14th 111 73.4 % 2 8 101 1.8 % 7.2 % < 0.001 % 1.03 0.07
Mar 13th 64 23.1 % 2 5 57 3.1 % 7.8 % < 0.001 % 0.59 0.05
Mar 12th 52 6.1 % 2 2 48 3.8 % 3.8 % < 0.001 % 0.48 0.02
Mar 11th 49 48.5 % 2 1 46 4.1 % 2.0 % < 0.001 % 0.45 0.01
Mar 10th 33 65.0 % 2 1 30 6.1 % 3.0 % < 0.001 % 0.31 0.01
Mar 9th 20 100.0 % 1 1 18 5.0 % 5.0 % < 0.001 % 0.18 0.01
Mar 8th 10 66.7 % 1 1 8 10.0 % 10.0 % < 0.001 % 0.09 0.01
Mar 7th 6 20.0 % 1 1 4 16.7 % 16.7 % < 0.001 % 0.06 0.01
Mar 6th 5 66.7 % 1 1 3 20.0 % 20.0 % < 0.001 % 0.05 0.01
Mar 5th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Mar 4th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Mar 3rd 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Mar 2nd 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Mar 1st 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 29th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 28th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 27th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 26th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 25th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 24th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 23rd 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 22nd 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 21st 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 20th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 19th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 18th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 17th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 16th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 15th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 14th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 13th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 12th 3 0.0 % 1 1 1 33.3 % 33.3 % < 0.001 % 0.03 0.01
Feb 11th 3 0.0 % 0 1 2 0.0 % 33.3 % < 0.001 % 0.03 0.01
Feb 10th 3 0.0 % 0 1 2 0.0 % 33.3 % < 0.001 % 0.03 0.01
Feb 9th 3 0.0 % 0 1 2 0.0 % 33.3 % < 0.001 % 0.03 0.01
Feb 8th 3 0.0 % 0 1 2 0.0 % 33.3 % < 0.001 % 0.03 0.01
Feb 7th 3 50.0 % 0 1 2 0.0 % 33.3 % < 0.001 % 0.03 0.01
Feb 6th 2 0.0 % 0 1 1 0.0 % 50.0 % < 0.001 % 0.02 0.01
Feb 5th 2 0.0 % 0 1 1 0.0 % 50.0 % < 0.001 % 0.02 0.01
Feb 4th 2 0.0 % 0 1 1 0.0 % 50.0 % < 0.001 % 0.02 0.01
Feb 3rd 2 0.0 % 0 1 1 0.0 % 50.0 % < 0.001 % 0.02 0.01
Feb 2nd 2 100.0 % 0 1 1 0.0 % 50.0 % < 0.001 % 0.02 0.01
Feb 1st 1 0.0 % 0 0 1 0.0 % 0.0 % < 0.001 % 0.01 0.00
Jan 31st 1 0.0 % 0 0 1 0.0 % 0.0 % < 0.001 % 0.01 0.00
Jan 30th 1 0 0 1 0.0 % 0.0 % < 0.001 % 0.01 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.