1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodology and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

2 Updates

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was b2c2a3a40ef290e0b3b476c396d76dfcac6bea5b.

The following major updates have been made:

  • On 29 May 2021 all plots and maps were updated to consistently not plot reproduction number estimates where the 95% confidence interval associated with that estimate is wider than 1.

3 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

4 Basic Exploration

Below cumulative case count is plotted on a log scale by continent.

Cumulative Reported Cases by Continent

Cumulative Reported Cases by Continent

Below a 7-day moving average of daily case count is plotted on a log scale by continent.

Daily Reported Cases by Continent (7-day moving average)

Daily Reported Cases by Continent (7-day moving average)

Below cumulative deaths by country is plotted on a log scale:

Cumulative Reported Deaths by Continent

Cumulative Reported Deaths by Continent

Below a 7-day moving average of daily deaths by country is plotted on a log scale:

Daily Deaths by Continent (7-day moving average)

Daily Deaths by Continent (7-day moving average)

5 Method & Assumptions

The methodology is described in detail here.

Countries with populations of less than 500 000 are excluded.

6 Results

6.1 World-wide

Estimated effecitve world-wide reproduction number
Estimated Type Daily Count (Last Week) Week Ending Reproduction Number [95% Confidence Interval]
cases 1,617,683 2022-03-09 1.07 [1.05 - 1.08]
deaths 7,021 2022-03-09 0.94 [0.91 - 0.96]

6.2 Current reproduction number estimates by country

Below current (last weekly) reproduction number estimates are plotted on a world map.

6.2.0.1 Cases

6.2.1 Deaths

6.3 Top 10 countries

Below we show various extremes of reproduction number estimates where the confidence interval does not exceed 1.

6.3.1 Lowest reproduction number estimates based on deaths

Lowest reproduction number estimates based on deaths
Country Estimated Type Daily Count (Last Week) Week Ending Reproduction Number [95% Confidence Interval]
South Africa deaths 28 2022-03-09 0.49 [0.41 - 0.58]
Peru deaths 57 2022-03-09 0.55 [0.48 - 0.62]
Israel deaths 12 2022-03-09 0.57 [0.45 - 0.70]
Sri Lanka deaths 13 2022-03-09 0.59 [0.47 - 0.72]
Colombia deaths 41 2022-03-09 0.61 [0.54 - 0.70]
Egypt deaths 18 2022-03-09 0.62 [0.50 - 0.74]
Paraguay deaths 13 2022-03-09 0.67 [0.53 - 0.81]
Serbia deaths 28 2022-03-09 0.73 [0.62 - 0.84]
Romania deaths 70 2022-03-09 0.73 [0.66 - 0.80]
Hungary deaths 59 2022-03-09 0.76 [0.68 - 0.83]

6.3.2 Lowest reproduction number estimates based on cases

Lowest reproduction number estimates based on cases
Country Estimated Type Daily Count (Last Week) Week Ending Reproduction Number [95% Confidence Interval]
Papua New Guinea cases 28 2022-03-09 0.37 [0.30 - 0.46]
Azerbaijan cases 359 2022-03-09 0.40 [0.35 - 0.45]
Georgia cases 1,967 2022-03-09 0.44 [0.39 - 0.49]
Armenia cases 156 2022-03-09 0.46 [0.40 - 0.53]
Palestine cases 675 2022-03-09 0.49 [0.47 - 0.53]
Kazakhstan cases 164 2022-03-09 0.50 [0.45 - 0.56]
Algeria cases 38 2022-03-09 0.52 [0.44 - 0.61]
Belarus cases 1,536 2022-03-09 0.53 [0.46 - 0.61]
Morocco cases 80 2022-03-09 0.53 [0.45 - 0.61]
Dominican Republic cases 152 2022-03-09 0.55 [0.50 - 0.59]

6.3.3 Highest reproduction number estimates based on deaths

Highest reproduction number estimates based on deaths
Country Estimated Type Daily Count (Last Week) Week Ending Reproduction Number [95% Confidence Interval]
Hong Kong deaths 243 2022-03-09 1.64 [1.44 - 1.86]
South Korea deaths 179 2022-03-09 1.41 [1.29 - 1.55]
Norway deaths 13 2022-03-09 1.41 [1.13 - 1.71]
Africa deaths 230 2022-03-09 1.40 [1.22 - 1.57]
Thailand deaths 63 2022-03-09 1.33 [1.19 - 1.48]
Germany deaths 245 2022-03-09 1.24 [1.17 - 1.31]
Philippines deaths 97 2022-03-09 1.23 [1.11 - 1.38]
Canada deaths 62 2022-03-09 1.20 [1.08 - 1.32]
Denmark deaths 46 2022-03-09 1.19 [1.06 - 1.33]
Switzerland deaths 14 2022-03-09 1.17 [0.94 - 1.43]

6.3.4 Highest reproduction number estimates based on cases

Highest reproduction number estimates based on cases
Country Estimated Type Daily Count (Last Week) Week Ending Reproduction Number [95% Confidence Interval]
Tunisia cases 2,638 2022-03-09 2.12 [1.90 - 2.26]
Democratic Republic of Congo cases 33 2022-03-09 1.46 [1.26 - 1.67]
Vietnam cases 190,365 2022-03-09 1.46 [1.35 - 1.58]
Angola cases 16 2022-03-09 1.37 [1.11 - 1.67]
Switzerland cases 23,732 2022-03-09 1.36 [1.29 - 1.40]
South Korea cases 264,023 2022-03-09 1.35 [1.29 - 1.42]
Malta cases 116 2022-03-09 1.34 [1.23 - 1.45]
Finland cases 6,356 2022-03-09 1.34 [1.19 - 1.47]
Solomon Islands cases 176 2022-03-09 1.32 [1.19 - 1.52]
Germany cases 202,071 2022-03-09 1.31 [1.27 - 1.35]

6.4 Risk Quadrants

The plots below show weekly cases (or deaths) on the X-axis and the reproduction number on the Y-axis. By dividing this into 4 quadrants we can identify countries with high cases and high reproduction numbers, or high cases and low reproduction numbers etc.

Values where the reproduction number exceeds 3 are plotted at 3.

6.4.1 Cases

Risk Quadrants - Cases

6.4.2 Deaths

Risk Quadrants - Deaths

6.5 Country Plots by Continent

Below we plot results for each country/province in a list. Values larger than 3 are plotted at 3.

6.5.1 Africa

6.5.1.1 Algeria

6.5.1.2 Angola

6.5.1.3 Benin

6.5.1.4 Botswana

6.5.1.5 Burkina Faso

6.5.1.6 Burundi

6.5.1.7 Cameroon

6.5.1.8 Cape Verde

6.5.1.9 Central African Republic

6.5.1.10 Chad

6.5.1.11 Comoros

6.5.1.12 Congo

6.5.1.13 Cote d’Ivoire

6.5.1.14 Democratic Republic of Congo

6.5.1.15 Djibouti

6.5.1.16 Egypt

6.5.1.17 Equatorial Guinea

6.5.1.18 Eritrea

6.5.1.19 Eswatini

6.5.1.20 Ethiopia

6.5.1.21 Gabon

6.5.1.22 Gambia

6.5.1.23 Ghana

6.5.1.24 Guinea

6.5.1.25 Guinea-Bissau

6.5.1.26 Kenya

6.5.1.27 Lesotho

6.5.1.28 Liberia

6.5.1.29 Libya

6.5.1.30 Madagascar

6.5.1.31 Malawi

6.5.1.32 Mali

6.5.1.33 Mauritania

6.5.1.34 Mauritius

6.5.1.35 Morocco

6.5.1.36 Mozambique

6.5.1.37 Namibia

6.5.1.38 Niger

6.5.1.39 Nigeria

6.5.1.40 Rwanda

6.5.1.41 Senegal

6.5.1.42 Sierra Leone

6.5.1.43 Somalia

6.5.1.44 South Africa

6.5.1.45 South Sudan

6.5.1.46 Sudan

6.5.1.47 Tanzania

6.5.1.48 Togo

6.5.1.49 Tunisia

6.5.1.50 Uganda

6.5.1.51 Zambia

6.5.1.52 Zimbabwe

6.5.2 Asia

6.5.2.1 Afghanistan

6.5.2.2 Armenia

6.5.2.3 Azerbaijan

6.5.2.4 Bahrain

6.5.2.5 Bangladesh

6.5.2.6 Bhutan

6.5.2.7 Cambodia

6.5.2.8 China

6.5.2.9 Georgia

6.5.2.10 Hong Kong

6.5.2.11 India

6.5.2.12 Indonesia

6.5.2.13 Iran

6.5.2.14 Iraq

6.5.2.15 Israel

6.5.2.16 Japan

6.5.2.17 Jordan

6.5.2.18 Kazakhstan

6.5.2.19 Kuwait

6.5.2.20 Kyrgyzstan

6.5.2.21 Laos

6.5.2.22 Lebanon

6.5.2.23 Malaysia

6.5.2.24 Maldives

6.5.2.25 Mongolia

6.5.2.26 Myanmar

6.5.2.27 Nepal

6.5.2.28 Oman

6.5.2.29 Pakistan

6.5.2.30 Palestine

6.5.2.31 Philippines

6.5.2.32 Qatar

6.5.2.33 Saudi Arabia

6.5.2.34 Singapore

6.5.2.35 South Korea

6.5.2.36 Sri Lanka

6.5.2.37 Syria

6.5.2.38 Taiwan

6.5.2.39 Tajikistan

6.5.2.40 Thailand

6.5.2.41 Timor

6.5.2.42 Turkey

6.5.2.43 United Arab Emirates

6.5.2.44 Uzbekistan

6.5.2.45 Vietnam

6.5.2.46 Yemen

6.5.3 Europe

6.5.3.1 Albania

6.5.3.2 Austria

6.5.3.3 Belarus

6.5.3.4 Belgium

6.5.3.5 Bosnia and Herzegovina

6.5.3.6 Bulgaria

6.5.3.7 Croatia

6.5.3.8 Cyprus

6.5.3.9 Czechia

6.5.3.10 Denmark

6.5.3.11 Estonia

6.5.3.12 Finland

6.5.3.13 France

6.5.3.14 Germany

6.5.3.15 Greece

6.5.3.16 Hungary

6.5.3.17 Ireland

6.5.3.18 Italy

6.5.3.19 Kosovo

6.5.3.20 Latvia

6.5.3.21 Lithuania

6.5.3.22 Luxembourg

6.5.3.23 Malta

6.5.3.24 Moldova

6.5.3.25 Montenegro

6.5.3.26 Netherlands

6.5.3.27 North Macedonia

6.5.3.28 Norway

6.5.3.29 Poland

6.5.3.30 Portugal

6.5.3.31 Romania

6.5.3.32 Russia

6.5.3.33 Serbia

6.5.3.34 Slovakia

6.5.3.35 Slovenia

6.5.3.36 Spain

6.5.3.37 Sweden

6.5.3.38 Switzerland

6.5.3.39 Ukraine

6.5.3.40 United Kingdom

6.5.4 North America

6.5.4.1 Canada

6.5.4.2 Costa Rica

6.5.4.3 Cuba

6.5.4.4 Dominican Republic

6.5.4.5 El Salvador

6.5.4.6 Guatemala

6.5.4.7 Haiti

6.5.4.8 Honduras

6.5.4.9 Jamaica

6.5.4.10 Mexico

6.5.4.11 Nicaragua

6.5.4.12 Panama

6.5.4.13 Trinidad and Tobago

6.5.4.14 United States

6.5.5 Oceania

6.5.5.1 Australia

6.5.5.2 Fiji

6.5.5.3 New Zealand

6.5.5.4 Papua New Guinea

6.5.5.5 Solomon Islands

6.5.6 South America

6.5.6.1 Argentina

6.5.6.2 Bolivia

6.5.6.3 Brazil

6.5.6.4 Chile

6.5.6.5 Colombia

6.5.6.6 Ecuador

6.5.6.7 Guyana

6.5.6.8 Paraguay

6.5.6.9 Peru

6.5.6.10 Suriname

6.5.6.11 Uruguay

6.5.6.12 Venezuela

6.6 Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

7 Discussion

Limitation of this method to estimate the reproduction number are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

8 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1]
A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133.
[2]
A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013.
[3]
M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus pandemic (COVID-19),” Our World in Data, 2020.