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 23afed4db62d9f1dec33612783ec9e10150e9e97.

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 2,946,256 2022-01-15 1.17 [1.13 - 1.22]
deaths 7,064 2022-01-15 1.11 [1.08 - 1.13]

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]
Netherlands deaths 11 2022-01-15 0.65 [0.50 - 0.81]
Czechia deaths 39 2022-01-15 0.74 [0.65 - 0.83]
Zimbabwe deaths 13 2022-01-15 0.74 [0.59 - 0.90]
Bolivia deaths 27 2022-01-15 0.74 [0.64 - 0.86]
Jordan deaths 17 2022-01-15 0.76 [0.63 - 0.91]
Austria deaths 10 2022-01-15 0.78 [0.61 - 0.98]
Thailand deaths 12 2022-01-15 0.80 [0.64 - 0.98]
South Korea deaths 39 2022-01-15 0.81 [0.71 - 0.91]
Lebanon deaths 14 2022-01-15 0.83 [0.67 - 1.00]
Sri Lanka deaths 12 2022-01-15 0.84 [0.68 - 1.04]

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]
Burundi cases 219 2022-01-15 0.27 [0.22 - 0.34]
Comoros cases 27 2022-01-15 0.28 [0.23 - 0.34]
Madagascar cases 148 2022-01-15 0.39 [0.32 - 0.46]
Cote d’Ivoire cases 337 2022-01-15 0.50 [0.45 - 0.57]
Burkina Faso cases 110 2022-01-15 0.52 [0.44 - 0.59]
Democratic Republic of Congo cases 304 2022-01-15 0.52 [0.42 - 0.65]
Sierra Leone cases 18 2022-01-15 0.53 [0.43 - 0.65]
Kenya cases 932 2022-01-15 0.54 [0.50 - 0.58]
Gambia cases 47 2022-01-15 0.54 [0.46 - 0.64]
Liberia cases 39 2022-01-15 0.54 [0.45 - 0.67]

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]
Australia deaths 44 2022-01-15 2.00 [1.61 - 2.45]
Oceania deaths 48 2022-01-15 1.98 [1.62 - 2.42]
Argentina deaths 75 2022-01-15 1.74 [1.54 - 1.93]
Ireland deaths 12 2022-01-15 1.71 [1.27 - 2.25]
Sweden deaths 19 2022-01-15 1.69 [1.37 - 2.07]
Colombia deaths 82 2022-01-15 1.67 [1.50 - 1.84]
Canada deaths 110 2022-01-15 1.64 [1.46 - 1.83]
South America deaths 510 2022-01-15 1.60 [1.47 - 1.71]
Brazil deaths 185 2022-01-15 1.56 [1.38 - 1.75]
Namibia deaths 16 2022-01-15 1.48 [1.20 - 1.79]

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]
Central African Republic cases 124 2022-01-15 2.79 [2.49 - 3.06]
Sudan cases 506 2022-01-15 2.43 [2.08 - 2.83]
Venezuela cases 858 2022-01-15 2.35 [1.86 - 2.84]
Mongolia cases 2,010 2022-01-15 2.34 [2.05 - 2.74]
El Salvador cases 192 2022-01-15 2.33 [1.86 - 2.85]
Tunisia cases 5,641 2022-01-15 2.28 [1.97 - 2.58]
Kosovo cases 427 2022-01-15 2.25 [1.89 - 2.67]
Bangladesh cases 2,897 2022-01-15 2.22 [1.89 - 2.60]
Pakistan cases 2,870 2022-01-15 2.07 [1.80 - 2.34]
Kyrgyzstan cases 448 2022-01-15 2.05 [1.66 - 2.56]

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 Thailand

6.5.2.40 Timor

6.5.2.41 Turkey

6.5.2.42 United Arab Emirates

6.5.2.43 Uzbekistan

6.5.2.44 Vietnam

6.5.2.45 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

6.5.6 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.