Friday 20 May 2022

Covid-19 – Comparing Direct Case Measures and Waste-water Estimates (Alberta Canada)

Covid-19 – Comparing Direct Case Measures and Waste-water Estimates (Alberta Canada)

It is now fairly common throughout the world to estimate the “true” level of Covid in a community from analysis of waste-water in municipal sewage systems. People infected with the virus that causes Covid-09 will shed some virus particles in their feces, which can be detected by instrumentation at regular intervals.

The trend in those measures will then give an indication of how many infections are actually active in the community. Not every person who is infected ever gets tested, but they will shed virus particles into the sewage system, so in principle this can be considered a superior method for tracking the true infection rate.

I used the data from Edmonton, Alberta to see how the trend in these two measures correlated. Note that though this data is from a particular locality, the general conclusions of the analysis would likely hold true in most other locations, as well.

 Below is a graph of Covid Cases in Edmonton during the October 2021 to May 2022 period, the times for which waste-water analysis is available on the Alberta government website. The left y-axis shows Edmonton region Covid cases (in yellow), while the right y-axis gives Covid Cases per Millilitre of waste-water in the combined Edmonton and Fort Saskatchewan waste-water treatment plants, the two plants that handle the bulk of the metro area’s waste-water.


 

As you can see, there is a good correlation for much of the period (the two lines almost overlay each other), though that relationship breaks down in the latter half of the period.

The waste-water measures are not taken every day, The data for direct case measures have some jitter as well (case counts are affected by the cycle of weekdays and weekends). Therefore, I constructed another graph with this data, using a seven-day moving average for both measures and lagging direct case measures by two days, relative to waste-water measures (it takes some time to actually get tested after symptoms begin). Two days was the amount of time that had the highest correlation between case counts and waste-water measures.


 

As noted, the nice visual correlations between the two lines in the line graphs break down at about the mid-January period of 2022. This effect is perhaps even better seen in the third graph, a scatter-plot of waste-water measure vs Covid case counts.


 

That graph shows that the relationship between the waste-water measures and the case counts is very similar for the Delta (blue, Oct21 to mid-Dec22) and Omicron.BA1 (red, mid-Dec21 to mid-Feb 22) periods, but much different for the Omicron.BA2 period (yellow, mid-Feb22 to May22). During that latter period, the slope of the regression line is much shallower than in the other two periods. In other words, any given measure of Covid copies per millilitre of waste-water corresponds to far fewer Covid cases during the Omicron.BA2 period that it did in the earlier phases.

The regression results are given below, or can be read directly off the graph. I used the “force y-intercept to 0” option, as it makes sense that when the waste-water results are at or near 0, the covid cases will also be at or near 0. For various reasons (e.g. lag times, measurement error, dis-interest in getting tested) that won’t be exactly true, but it should be reasonably close.

Waste-water (X) vs Covid (Y)

Forcing y-intercept to 0 beta coeff R2 Ratio(Delta)

Delta (Oct 21 – mid-Dec 21) 2.43 0.94 1.00

OmicronBA1 (mid-Dec 21 – mid-Feb 22) 2.18 0.91 0.90

OmicronBA2 (mid-Feb 22 – May 22) 0.71 0.96 0.29

These figures can be interpreted as:

  • Beta coefficient – gives the number of cases expected for each increase in the Covid copies per millilitre of waste-water.

  • R2 – shows how close the fit is to a straight line, with numbers closer to 1 indicating a better fit.

  • Ratio – shows how the other periods in question compare to the Delta variant period. So, for example, the Omicron.BA2 period had less than one-third as many cases for a given waste-water measure than did Delta.

It seems reasonable to assume that the Delta period gives the best measure of the relationship between the waste-water results and Covid counts and thereby Covid infections (though counts would have under-represented infections even then). That’s because Covid counts were relatively low during that period, so the medical system would not have been swamped by high demand for testing. In other words, a fairly high proportion of people with Covid symptoms would have likely gone on to be tested, and thus have been included in official case counts.

So, it seems that Covid numbers were probably becoming more and more seriously under-counted as the first wave of Omicron.BA1 progressed. This seems to have worsened during 2022, such that the Covid case count was likely very far off the true figures.

Why might that be? Here are some hypotheses:

  • The rapid sequence of Omicron.BA1 followed by Omicron.BA2 may have overwhelmed the testing system, resulting in a smaller proportion of infected people actually getting tested as time went on. Thus, the relationship between the waste-water measures and case counts became shallower.

  • With the growing sense within the population that Covid isn’t as pathogenic as it first was, many people might have decided not to bother with seeing a doctor or going to a clinic, as they felt they could just let the disease run its course.

  • The Alberta government had provided free home-testing kits fairly early in the first Omicron wave. As time went on, these kits were used up, and people decided not to bother with the home-testing if they had to purchase the kits themselves. Thus, testing declined.

  • Omicron.BA2 may have some odd effect, whereby infected people shed virus particles into the sewage system at a much higher rate than the other variants. While this may be true, it seems unlikely, as there has been no indication of that published, at least of which I am aware.

So, essentially, the waste-water results indicate that the number of Covid cases is probably now being vastly under-counted. One problem with that is that it may be giving the impression that Omicron.BA2 is more pathogenic than it really is. That’s because deaths will still be fairly reliably counted, while case counts become a less and less measure of actual infections.

I will see what the data says about that in a future blog.

=====================================================



And, here’s are some travel books to peruse, now that the reduction in Covid cases and severity has opened up travel again (fingers crossed).

A Drive Across Newfoundland


U.S.: https://www.amazon.com/dp/B07NMR9WM8

U.K.: https://www.amazon.co.uk/dp/B07NMR9WM8

Germany: https://www.amazon.de/dp/B07NMR9WM8

Japan: https://www.amazon.co.jp/dp/B07NMR9WM8

Canada: https://www.amazon.ca/dp/B07NMR9WM8

Australia:https://www.amazon.com.au/dp/B07NMR9WM8

India: https://www.amazon.in/dp/B07NMR9WM8

Newfoundland, Canada’s most easterly province, is a region that is both fascinating in its unique culture and amazing in its vistas of stark beauty. The weather is often wild, with coastal regions known for steep cliffs and crashing waves (though tranquil beaches exist too). The inland areas are primarily Precambrian shield, dominated by forests, rivers, rock formations, and abundant wildlife. The province also features some of the Earth’s most remarkable geology, notably The Tablelands, where the mantle rocks of the Earth’s interior have been exposed at the surface, permitting one to explore an almost alien landscape, an opportunity available on only a few scattered regions of the planet.

The city of St. John’s is one of Canada’s most unique urban areas, with a population that maintains many old traditions and cultural aspects of the British Isles. That’s true of the rest of the province, as well, where the people are friendly and inclined to chat amiably with visitors. Plus, they talk with amusing accents and party hard, so what’s not to like?

This account focusses on a two-week road trip in October 2007, from St. John’s in the southeast, to L’Anse aux Meadows in the far northwest, the only known Viking settlement in North America. It also features a day hike visit to The Tablelands, a remarkable and majestic geological feature. Even those who don’t normally consider themselves very interested in geology will find themselves awe-struck by these other-worldly landscapes.


A Ride on the Kettle Valley Rail Trail: A Biking Journal Kindle Edition

by Dale Olausen (Author), Helena Puumala (Editor)


The Kettle Valley Rail Trail is one of the longest and most scenic biking and hiking trails in Canada. It covers a good stretch of the south-central interior of British Columbia, about 600 kilometers of scenic countryside. British Columbia is one of the most beautiful areas of Canada, which is itself a beautiful country, ideal for those who appreciate natural splendour and achievable adventure in the great outdoors.

The trail passes through a great variety of geographical and geological regions, from mountains to valleys, along scenic lakes and rivers, to dry near-desert condition grasslands. It often features towering canyons, spanned by a combination of high trestle bridges and long tunnels, as it passes through wild, unpopulated country. At other times, it remains quite low, in populated valleys, alongside spectacular water features such as beautiful Lake Okanagan, an area that is home to hundreds of vineyards, as well as other civilized comforts.

The trail is a nice test of one’s physical fitness, as well as one’s wits and adaptability, as much of it does travel through true wilderness. The views are spectacular, the wildlife is plentiful and the people are friendly. What more could one ask for?

What follows is a journal of two summers of adventure, biking most of the trail in the late 1990s. It is about 33,000 words in length (2 to 3 hours reading), and contains numerous photographs of the trail. There are also sections containing a brief history of the trail, geology, flora and fauna, and associated information.

After reading this account, you should have a good sense of whether the trail is right for you. If you do decide to ride the trail, it will be an experience you will never forget.

Amazon U.S.: https://www.amazon.com/dp/B01GBG8JE0

Amazon U.K.: https://www.amazon.co.uk/dp/B01GBG8JE0

Amazon Germany: https://www.amazon.de/dp/B01GBG8JE0

Amazon Canada: https://www.amazon.ca/dp/B01GBG8JE0

Amazon Australia: https://www.amazon.com.au/dp/B01GBG8JE0


On the Road with Bronco Billy

Spring is on us now, and that brings on thoughts of ROAD TRIP. Sure, it is still a bit early, but you can still start making plans for your next road trip with help of “On the Road with Bronco Billy”. Sit back and go on a ten day trucking trip in a big rig, through western North America, from Alberta to Texas, and back again. Explore the countryside, learn some trucking lingo, and observe the shifting cultural norms across this great continent. Then, come spring, try it out for yourself.

Amazon U.S.:http://www.amazon.com/gp/product/B00X2IRHSK

Amazon U.K.: http://www.amazon.co.uk/gp/product/B00X2IRHSK

Amazon Germany: http://www.amazon.de/gp/product/B00X2IRHSK

Amazon Canada: http://www.amazon.ca/gp/product/B00X2IRHSK













Wednesday 11 May 2022

The Sappers' War: 12th Field Company Royal Canadian Engineers, Oct 1943 to Sept 1945, free on Amazon(May11-15)

 The Sappers' War: 12th Field Company Royal Canadian Engineers, Oct 1943 to Sept 1945, free on Amazon(May11-15, 2022)

Are you a history buff, particularly interested in World War 2? Or, did you have a family member or other relative participate in the conflict and are therefore curious about their experiences? If so, you might want to read about the journey of a military engineering company, throughout their time in action during the war.

The book focuses on one particular company of soldier/sappers in the Canadian Army, but many of their experiences would be common to any of the Allied units in the European theatre. Some of the major battles in which they were involved included Ortona, Monte Casino, the Gothic Line, the battles for Ravenna and the Po Valley, the Liberation of Holland and final defeat of the Third Reich.

In addition, some content relates to the experiences of civilians in Britain during that time. Appendices also look at some of the details of military engineering (e.g. bridging, mines, storm boats, the M-test), casualties, the Aldershot Riots and other issues of post-war rehabilitation and return to civilian life.

Much of the material comes from company war diaries and related materials, though a brief sketch of the wider campaigns relevant to the experience of these men is included, as are some interesting side-bars (e.g. the unit served alongside the celebrated irregulars known as Popski’s Private Army during their time in Northern Italy). To get a more “micro” feel for the on-site experiences of the time, some of my own family’s stories are related (a soldier/sapper, a war bride/war worker, a P.O.W., and an Atlantic convoy merchant marine sailor, among others). The summations of the War Diaries also include much interesting information about day-to-day life, both military and non-military.

So, grab your Lee-Enfield rifle and your mine-detector, and check out the life of a war-time sapper.





U.S.: https://www.amazon.com/dp/B09HSXN6Q2

U.K.: https://www.amazon.co.uk/dp/B09HSXN6Q2

Germany: https://www.amazon.de/dp/B09HSXN6Q2

France: https://www.amazon.fr/dp/B09HSXN6Q2

Spain: https://www.amazon.es/dp/B09HSXN6Q2

Italy: https://www.amazon.it/dp/B09HSXN6Q2

Netherlands: https://www.amazon.nl/dp/B09HSXN6Q2

Japan: https://www.amazon.co.jp/dp/B09HSXN6Q2

Brazil: https://www.amazon.com.br/dp/B09HSXN6Q2

Canada: https://www.amazon.ca/dp/B09HSXN6Q2

Mexico: https://www.amazon.com.mx/dp/B09HSXN6Q2

Australia: https://www.amazon.com.au/dp/B09HSXN6Q2

India: https://www.amazon.in/dp/B09HSXN6Q2

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The Sappers' War: 12th Field Company Royal Canadian Engineers, Oct 1943 to Sept 1945

What follows is a review of the history of the 12th Field Company, Royal Canadian Engineers, primarily relating to the time that the company was in the Italian and Northwestern European theatres during World War II. Though the book focuses on the experiences of a particular company of Canadian military engineers, it also discusses some of the wider issues of the second world war and how it affected the people who lived through the era, civilian and military. Among those are my father (a sapper or military engineer) and mother (a war worker in wartime Britain and ultimately a war bride).

Thus, this is meant to be an informal and unofficial history of the company, written by an interested party in an effort to understand what these men went through during this period, and how that experience affected them and other people who lived through the war. The military aspects of the company's history are there (e.g. fighting, building bridges, detecting mines, maintaining routes), as are the cultural factors that influenced them and their times (e.g. the movies that they watched, the drinking they did, the many diseases they faced, their interactions with the Italian, British and other civilians that they lived among, their worries for the future). Some focus on life on the British home front is also given, via the experiences of my mother and her family.

Since many people had family and relations that lived during this time, it is my hope that the account will be of general interest to them, and to any that have a particular interest in this critical interval in history. Also, though the text relates specifically to Canadian sappers, I believe that many of the experiences will be common to the soldiers and loved ones of other nations who lived through the war, especially Americans and those from Britain and the British Commonwealth.

The primary sources of this document are the 12th Field Company War Diaries and related orders, with some material from The History of the Corps of Royal Canadian Engineers, Volume 2 as well as various official histories by the Department of National Defence. Various other published sources are used as well, especially when discussing the wider issues of the war or the army experience (e.g. Churchill’s history of the war) , or conversely when relating very specific episodes of the war (e.g. Popski’s Private Army in late 1944). Personal accounts of my father’s or mother’s stories also augment the narrative. I have tried to fit those in during appropriate time periods, though some stories are more general and have therefore don’t necessarily relate to the time period being discussed. Nonetheless, they do help capture the essence of “being there” during the war years.

The War Diary is a day by day account of the primary activities of a given unit, as recorded by personnel in the headquarters staff of that unit, and signed off by the commander of the unit. As such, it is an official record, though the writers often brought a bit of their own character into the document. Naturally, as a relatively brief document it can’t hope to capture the complexity of the individual stories of 280 or so men, so the family lore generally has no corresponding entry in the War Diary, though there are sometimes tantalizing hints and near-verifications of these personal accounts.

There are a number of other sources for the book, from official histories to popular history books. I include quotations and references from these works (an eclectic mix), as I believe that they also shed light on different aspects of this period of time, and besides that, are just interesting accounts, in and of themselves.





Saturday 7 May 2022

Abortion Laws and Fertility Rates

 

Abortion Laws and Fertility Rates

I generally try to stay away from highly political subjects, but as a data scientist (who sometimes has to do a bit of demographic analysis) I can’t help observing patterns in data that might help to explain events in the world, to myself if to nobody else.

Here’s a pattern that I think helps explain why the Roe vs Wade (abortion rights) issue has re-emerged in the United States this. I say re-emerge, though obviously this issue has never been far from the forefront of the political battlefield. However, the Supreme Court has taken up the issue again, after being mostly unwilling to touch it, after 50 years. Why is that?

Well obviously, there have been changes in the makeup of the Supreme Court recently which have shifted the balance of the membership to a more restrictive stance (or so it is assumed). But, the membership of the court has always been in flux and has had right-leaning majorities at other times in the past five decades. So, what else has changed?

Here is where the graph shown above comes into the picture. It takes a global view of the relationship between abortion laws and fertility rates, at a country-by-country level. 

Abortion laws have been rated by the degree to which they restrict rights to abortion. Completed fertility rates indicate the average number of children a woman is expected to have over her lifetime, given current trends in any given country. (Detailed data and sources for the data are given at the end of the blog.)

The graph shows that there is a clear association between abortion rights and completed fertility rates. Countries with more restrictive laws tend to have higher fertility rates, while those with less restrictive laws have lower fertility rates.

That’s not to say that this is a cause and effect relationship – it is more likely correlational. It isn’t necessarily that more liberal abortion laws lead to more abortions, which lead to a lower birth rate. That is probably far too simplistic. (In fact there has been some research showing that the reverse is often true, as women who have an abortion often stop at one child).

It is more a matter of what economists refer to as “nudges” or what political scientists would likely call signalling from the wider public culture. The relative strictness of abortion laws within a society sends a signal about the value that is placed on family formation and reproduction by that society. That signal is, of course, tangled up within a mesh of other cultural aspects, such as religion, economics and cultural traditions and standards. The same is true of matters such as public support for daycare and other efforts to make life-work balance more amenable to child-rearing in general.


Why would this be happening now? The second graph helps to explain that. As you can see nearly two-thirds of the world’s population are now living in relatively low fertility countries, with nearly four-tenths living in below replacement fertility countries (replacement fertility is conventionally estimated to be about 2.1). So, at the global level, the ratio of population sources to population sinks is shifting. That means that using immigration to drive population policies will become less and less tenable.

Along with that, other phenomena are showing the limits of globalization theory. For example:

  • Pandemics are more likely when travel and trade is relatively unrestricted.

  • The fragility of complex world-wide supply-chains is being revealed, partly from the Covid pandemic and partly from geopolitical conflict, such as the situation in Ukraine.

  • The impact of population policies on global warming will become more and more urgent.

  • Countries may be less and less willing to allow emigration, especially of their brightest and best educated. There may come an expectation that they should stay home and help their native land to develop. “Poaching” these highly skilled people may even become seen to be immoral, a new form of human resource colonialism.

I am inclined to believe that demographic issues will become more and more contested in the near future, especially in the lower fertility countries. The re-opening of Roe vs Wade is likely just an early phase in this development. Sometimes efforts to address these matters will be cast in what are conventionally considered conservative terms (e.g. stricter abortion laws), sometimes in what are conventionally considered liberal terms (e.g. more public funding for early childcare). Often the demographic realities underlying the policies will be downplayed or ignored, as there is a sort of taboo about these issues, though I think those taboos will dissipate in the near future. Demographic realities pretty well demand that.

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There are two sources for the graph data, which are given below.

The x-axis is a categorization of countries by how restrictive their abortion laws currently are. The data is from the group “Center for Reproductive Rights”. The categorizations are given below.

Category I. Prohibited Altogether (24 countries)

Category II. To Save the Woman’s Life (43 countries)

Category III. To Preserve Health (53 countries)

Category IV. Socioeconomic Grounds (13 countries)

Category V. On Request (Gestational Limits Vary) (75 countries)

website: https://reproductiverights.org/maps/worlds-abortion-laws/

The y-axis is from the website of the organization World Population Review, which has statistics on both population and completed fertility rates.

https://worldpopulationreview.com/country-rankings/total-fertility-rate

For completeness here are graphs using unweighted means and medians for the fertility measures.



And here is a detailed list of countries used for the graphs, with Abortion Law categorizations, fertility rates and populations by country.

LawCateg

CountryLaw

Median - fertilityRate

Average - fertilityRate

Sum - Population

1

Andorra

1.30

1.30

77,463


Aruba



107,609


Congo (Brazzaville)

4.40

4.40

5,797,805


Curaçao



165,529


Dominican Republic

2.30

2.30

11,056,370


Egypt

3.30

3.30

106,156,692


El Salvador

2.00

2.00

6,550,389


Haiti

2.90

2.90

11,680,283


Honduras

2.50

2.50

10,221,247


Iraq

3.70

3.70

42,164,965


Jamaica

2.00

2.00

2,985,094


Laos

2.70

2.70

7,481,023


Madagascar

4.10

4.10

29,178,077


Malta

1.20

1.20

444,033


Mauritania

4.60

4.60

4,901,981


Nicaragua

1.70

1.70

6,779,100


Palau: ?

2.20

2.20

18,233


Philippines

2.60

2.60

112,508,994


San Marino



34,085


Senegal

4.60

4.60

17,653,671


Sierra Leone

4.30

4.30

8,306,436


Suriname

2.40

2.40

596,831


Tonga

3.60

3.60

107,749


West Bank & Gaza Strip



626,161

2

Afghanistan

4.50

4.50

40,754,388


Antigua & Barbuda

2.00

2.00

99,509


Bahrain

2.00

2.00

1,783,983


Bangladesh

2.00

2.00

167,885,689


Bhutan: R, I, +

2.00

2.00

787,941


Brazil: R, +

1.70

1.70

215,353,593


Brunei Darussalam

1.80

1.80

445,431


Chile: R, F

1.60

1.60

19,250,195


Côte d’Ivoire: R

4.60

4.60

27,742,298


Dominica

1.90

1.90

72,344


Gabon: R, I, F, +

4.00

4.00

2,331,533


Gambia: F

5.20

5.20

2,558,482


Guatemala

2.90

2.90

18,584,039


Indonesia: R, F, SA

2.30

2.30

279,134,505


Iran: F

2.10

2.10

86,022,837


Kiribati

3.60

3.60

123,419


Lebanon

2.10

2.10

6,684,849


Libya

2.20

2.20

7,040,745


Malawi

4.20

4.20

20,180,839


Mali: R, I

5.90

5.90

21,473,764


Marshall Islands: ?

4.00

4.00

60,057


Mexico: R, F,

2.10

2.10

131,562,772


Micronesia: ?,

3.10

3.10

117,489


Myanmar

2.20

2.20

55,227,143


Nigeria

5.40

5.40

216,746,934


Oman

2.90

2.90

5,323,993


PA



0


Panama: R, F, PA

2.50

2.50

4,446,964


Papua New Guinea

3.60

3.60

9,292,169


Paraguay

2.40

2.40

7,305,843


Solomon Islands

4.40

4.40

721,159


Somalia



16,841,795


South Sudan

4.70

4.70

11,618,511


Sri Lanka

2.20

2.20

21,575,842


Sudan: R

4.40

4.40

45,992,020


Syria: SA, PA

2.80

2.80

19,364,809


Tanzania

4.90

4.90

63,298,550


Timor-Leste: PA

4.00

4.00

1,369,429


Tuvalu



12,066


Uganda

5.00

5.00

48,432,863


United Arab Emirates (UAE): F, SA,

1.40

1.40

10,081,785


Venezuela

2.30

2.30

29,266,991


Yemen: SA

3.80

3.80

31,154,867

3

Algeria

3.00

3.00

45,350,148


Angola: R, I, F, PA

5.50

5.50

35,027,343


Bahamas

1.80

1.80

400,516


Benin: R, I, F

4.80

4.80

12,784,726


Bolivia: R, I

2.70

2.70

11,992,656


Botswana: R, I, F

2.90

2.90

2,441,162


Burkina Faso: R, I, F

5.20

5.20

22,102,838


Burundi

5.40

5.40

12,624,840


Cameroon: R

4.60

4.60

27,911,548


Central African Rep.: R, I, F, +

4.70

4.70

5,016,678


Chad: R, I, F

5.70

5.70

17,413,580


Colombia: R, I, F

1.80

1.80

51,512,762


Comoros

4.20

4.20

907,419


Costa Rica

1.80

1.80

5,182,354


Dem. Rep. of Congo: R, I, F

5.90

5.90

95,240,792


Djibouti

2.70

2.70

1,016,097


Ecuador: +

2.40

2.40

18,113,361


Equatorial Guinea: SA, PA

4.50

4.50

1,496,662


Eritrea: R, I, +

4.10

4.10

3,662,244


Eswatini (formerly Swaziland):

3.00

3.00

1,184,817


Ghana: R, I, F, +

3.90

3.90

32,395,450


Grenada

2.10

2.10

113,475


Guinea: R, I, F

4.70

4.70

13,865,691


Israel: R, I, F, +

3.10

3.10

8,922,892


Jordan

2.80

2.80

10,300,869


Kenya

3.50

3.50

56,215,221


Kuwait: F, SA, PA

2.10

2.10

4,380,326


Lesotho: R, I, F

3.10

3.10

2,175,699


Liberia: R, I, F

4.30

4.30

5,305,117


Liechtenstein: R, PA, +

1.60

1.60

38,387


Malaysia

2.00

2.00

33,181,072


Mauritius: R, I, F, PA

1.40

1.40

1,274,727


Monaco: R, I, F, 



39,783


Morocco: SA

2.40

2.40

37,772,756


Namibia: R, I, F

3.40

3.40

2,633,874


Nauru: R, I, F, +



10,903


Niger: F

6.90

6.90

26,083,660


Pakistan

3.50

3.50

229,488,994


Peru

2.30

2.30

33,684,208


Poland: R, I, PA

1.50

1.50

37,739,785


Qatar: F

1.90

1.90

2,979,915


R, I, F



0


Rep. of Korea: R, I, SA, +

1.00

1.00

51,329,899


Saint Kitts & Nevis: †

2.10

2.10

53,871


Saint Lucia: R, I

1.40

1.40

185,113


Samoa

3.90

3.90

202,239


Saudi Arabia: SA, PA

2.30

2.30

35,844,909


Seychelles: R, I, F, +

2.40

2.40

99,426


Togo: R, I, F

4.30

4.30

8,680,837


Trinidad & Tobago: †

1.70

1.70

1,406,585


Vanuatu

3.80

3.80

321,832


Zimbabwe: R, I, F,

3.60

3.60

15,331,428

4

Barbados: R, I, F, PA

1.60

1.60

288,023


Belize: F

2.30

2.30

412,190


Ethiopia: R, I, F, +

4.20

4.20

120,812,698


Fiji: R, I, F, PA

2.80

2.80

909,466


Finland: R, F, +

1.40

1.40

5,554,960


Great Britain: F

1.70

1.70

68,497,907


Hong Kong: R, I, F

1.10

1.10

7,604,299


India: R, F, PA

2.20

2.20

1,406,631,776


Japan: R, SA

1.40

1.40

125,584,838


Rwanda: R, I, F, +

4.00

4.00

13,600,464


Saint Vincent & Grenadines: R, I, F

1.90

1.90

39,730


Taiwan: R, I, F, SA, PA



23,888,595


Zambia: F

4.60

4.60

19,470,234

5

Albania: PA

1.60

1.60

2,866,374


Argentina W14

2.30

2.30

46,010,234


Armenia: PA

1.80

1.80

2,971,966


Australia: 

1.70

1.70

26,068,792


Austria D90

1.50

1.50

9,066,710


Azerbaijan

1.70

1.70

10,300,205


Belarus

1.40

1.40

9,432,800


Belgium W14

1.60

1.60

11,668,278


Bosnia-Herzegovina: PA

1.30

1.30

3,249,317


Bulgaria

1.60

1.60

6,844,597


Cambodia W14 : PA

2.50

2.50

17,168,639


Canada°

1.50

1.50

38,388,419


Cape Verde



567,678


China°: SX

1.70

1.70

1,448,471,400


CroatiaW10 : PA

1.50

1.50

4,059,286


Cuba: PA

1.60

1.60

11,305,652


Cyprus

1.30

1.30

1,223,387


Czech Rep.: PA

1.70

1.70

10,736,784


Dem. People’s Rep. of Korea°

1.90

1.90

25,990,679


Denmark: PA

1.70

1.70

5,834,950


Estonia

1.70

1.70

1,321,910


FranceW14

1.90

1.90

65,584,518


French Guiana



314,169


Georgia: PA

2.10

2.10

3,968,738


Germany

1.60

1.60

83,883,596


Greece: PA

1.40

1.40

10,316,637


Guinea-Bissauº

4.50

4.50

2,063,367


GuyanaW8

2.50

2.50

794,045


Hungary

1.60

1.60

9,606,259


Iceland W22

1.70

1.70

345,393


Ireland

1.80

1.80

5,020,199


ItalyD90

1.30

1.30

60,262,770


Kazakhstan

2.80

2.80

19,205,043


Kosovo W10 : PA, SX



0


Kyrgyzstan

3.30

3.30

6,728,271


Latvia: PA

1.60

1.60

1,848,837


Lithuania: PA

1.60

1.60

2,661,708


Luxembourg W14

1.40

1.40

642,371


Macedonia (formerly

1.50

1.50

2,081,304


Macedonia): PA



0


MaldivesD120

1.90

1.90

540,985


Moldova: PA

1.30

1.30

4,013,171


Mongolia D90

2.90

2.90

3,378,078


Montenegro: PA, SX

1.70

1.70

627,950


Mozambique

4.90

4.90

33,089,461


Nepal: SX

1.90

1.90

30,225,582


Netherlands∞

1.60

1.60

17,211,447


New Caledonia



290,915


New ZealandW20

2.00

2.00

4,898,203


Northern Ireland



0


Norway: PA

1.60

1.60

5,511,370


Portugal

1.40

1.40

10,140,570


Puerto Rico∞



2,829,812


Republic of North



0


RomaniaW14

1.80

1.80

19,031,335


Russian Fed.

1.60

1.60

145,805,947


Sao Tome & Principe

4.30

4.30

227,679


Serbia: PA

1.50

1.50

8,653,016


SingaporeW24

1.10

1.10

5,943,546


Slovak Rep.: PA

1.50

1.50

5,460,193


Slovenia: PA

1.60

1.60

2,078,034


South Africa

2.40

2.40

60,756,135


SpainW14 : PA

1.30

1.30

46,719,142


SwedenW18

1.80

1.80

10,218,971


Switzerland

1.50

1.50

8,773,637


Tajikistan

3.60

3.60

9,957,464


Thailand

1.50

1.50

70,078,203


TunisiaD90

2.20

2.20

12,046,656


Turkey W10 : SA, PA

2.10

2.10

85,561,976


Turkmenistan

2.80

2.80

6,201,943


Ukraine

1.30

1.30

43,192,122


United States∞: PA, 

1.70

1.70

334,805,269


Uruguay: PA

2.00

2.00

3,496,016


Uzbekistan

2.40

2.40

34,382,084


Vietna

2.00

2.00

98,953,541

Total Result


2.20

2.68

7,944,436,655