Sunday, 27 February 2022

Terminal Lucidity – SF Short Fiction, Free on Amazon

 

Terminal Lucidity – SF Short Fiction, Free on Amazon

A lone agent is discovered orbiting a newly discovered exoplanet. Though his mind has been badly damaged, it is believed to hold vital secrets about a threatening alien species. But, can the medical staff uncover these secrets in time to prevent an interstellar war? Might one of them be a saboteur, working for the other side? And will their efforts, if successful, mean death for the agent and tragedy for his young daughter?


We have reason to believe that the last expedition had made contact with the aliens and had gained some vital intelligence about them. We know that, but that’s all we know. All other contact with the expedition was lost before we received any of the details.

Agent X was the only surviving member of that expedition. A rescue team found him on a small craft, orbiting Mendel. He couldn’t even speak and he soon fell into a coma, the state in which he was when you began your treatments. His mind was very diseased.”

And you need us to dig that information out of his diseased mind,” Louise said.

The General nodded.

Yes, the loss of our missions now seem to indicate that we are in a state of undeclared war with this species, a cold war if you will. But there are signs of hyper-space disruption in this system, all out of proportion to the limited activities of our spacecraft. We fear that the alien species may be mobilizing. A hot war may follow at any time. Earth may even be a target, as they could have calculated our originating location from the trail of our own spacecrafts’ hyper-space disruptions. If not from that, then perhaps from our electromagnetic signals, whether in hyper-space or normal space. We just don’t know.”

Thus, the urgency,” Charles said. “Thus, Terminal Lucidity.”

The story is about 13,000 words, or about 60-90 minutes at typical reading speeds.

It’s free this week (Feb 24 to 28, 2022) on Amazon, 99 cents otherwise.


Amazon U.S.: https://www.amazon.com/dp/B09SCV6284

Amazon U.K.: http://www.amazon.co.uk/gp/product/B09SCV6284

Amazon Germany: http://www.amazon.de/gp/product/B09SCV6284

Amazon Canada: http://www.amazon.ca/gp/product/B09SCV6284

Saturday, 26 February 2022

Covid-19 Cases and Deaths Comparison, Canada, U.S. and Mexico (Up to the end of February 2022)

Covid-19 Cases and Deaths Comparison, within North America (Up to the end of February 2022)

Recently, the governments of both Canada and the U.S. increased Covid-related restrictions for persons crossing their borders, either way. The reaction to this, from a large group of Canadians, was to organize protests in Ottawa, which then spread to protests and informal blockades at several border crossings. As these restrictions fell particularly heavily on truck-drivers, the protests became identified with that particular occupational group, though it was embraced by a much broader assortment of people than that. Eventually, this led to the Prime Minister instituting the Emergencies Act, something which no P.M. had done in the 33 year history of the act. This was followed by forceful removal of the protesters, resulting in some injuries, though thankfully no deaths.

The purported reason for the enhanced restrictions at the border was, of course, the fear of the spread of Covid-19, particularly in the form of the Omicron variant. This variant is now known to be highly transmissible, though causing severe disease and/or death in a much smaller proportion of infections than the earlier variants, such as Delta. The government was insisting that all drivers must be double vaccinated and quarantine after crossing the border, which they considered to be an undue and unnecessary burden, given that the Omicron variant was well established in Canada by then, and most spread was considered to be “community spread”.

This being the case, it seems like a good time to review the situation of Covid-19 within North America. I will look at the fundamental indicators, such as cases, ICU admissions and deaths, within the three North American countries of Canada, the United States and Mexico. This will include an overall trend, since the beginning of the pandemic, with a focus on the most recent time periods, since they are most relevant to the situation concerning the border restrictions and the ensuing protests and government response to those protests.

Looking at these facts should help provide context for assessing the reasonableness (or lack thereof) of the actions of the Canadian and U.S. governments, concerning this matter. Though Mexico is not directly involved in the Canada-U.S. border situation, Covid-19 statistics from that country should help round-out the picture in North America.

Note that if you want a quick summary, skip to the final section.

A Note on Data

As many people have noted throughout the pandemic period, there have always been some issues with regard to data reliability in the Covid-19 reporting. Some of those issues include:

  • How were cases determined, and what proportion of infections were actually reported as cases? Who was tested and how were the tests done? How did that vary throughout the world? Were rolling random samples tested, or were tests only initiated by medical visits? How about water-treatment plant tests?

  • How were Covid-related hospitalizations counted? What proportion of these hospitalizations were primarily due to Covid-19, and how many were essentially incidental (for example, a person goes to the hospital with a broken leg, but is then tested positive for Covid).

  • How were Covid-related deaths counted? As with hospitalizations, how many Covid-related deaths were incidental, As an example, I knew one person who died of cancer in hospital during the early days of the pandemic, that was swabbed and tested within a half-hour of his cancer-related death. It turned out that he tested negative, but had he tested positive, by the protocols of the time he would have been called a Covid-related death.

The rise of the Omicron variant has brought more transparency to these issues. For example, in the December 30, 2021 issue of the Toronto Globe and Mail (a very mainstream source) we can read statements such as the following:

  • The scientific director of Ontario’s Covid-19 Science Advisory Table, Peter Juni, estimates that the province’s daily count is now capturing just one case out of every five to eight cases in the province.

  • The daily measurement has always had flaws. For one, it almost exclusively records cases that have been confirmed during lab-based polymerase chain reaction (or PCR) testing. In the past, when the demand for tests outstripped the capacity of labs, such as during the virus’s deadly first wave, many people who suspected they were infected couldn't get tested, causing government tallies to under-count the spread of the virus.

  • In Ontario, even before Omicron came along, case counts only captured about tow out of very five Covid-19 inflections according to Dr. Juni – a ratio verified by mortality data and serological testing.

Similar statements are coming out regarding incidental hospitalizations and deaths. In my opinion, it is still useful to look at trends, but the possibilities for over-counting and under-counting should be kept in mind.

Aggregate Cases and Deaths, up to end of Feb 2022

1 - Raw Numbers of Aggregate Cases and Deaths

The graphs of aggregate case counts and deaths are shown in the first two graphs below, for the three countries at different points in time over the period of the pandemic, thus far. Note that these graphs have not been normalized for population or any other relevant factors - they just show the raw totals for cases and deaths. Further on, graphs with these normalizations will be provided.

The graph of the case counts show that cases in the U.S. far out-stripped those in Canada and Mexico. Much of this is effect is due to the higher population in the U.S. than in Canada and Mexico (334 million vs 38 million and 131 million respectively).

The other striking feature of the graph is the huge jump in cases during the final couple of time intervals. U.S. case counts fairly leap up, while Mexico and Canada also show very noticeable increases. That gives an indication of the enhanced transmissibility of the Omicron variant.

As for Covid-related deaths, the U.S. is still highest by far, though the gap between Mexico and Canada has widened considerably. The slope of the lines also increased somewhat during the final two time intervals, but much less so than was the situation for cases. That is an indication that Omicron tends to be less severe for those infected.

Just looking at the two graphs already raises a question about differences in either data collection practices or health outcomes – why are Canada and Mexico so close in terms of cases but much further apart in terms of deaths?

Obviously, these comparisons, interesting as they are, need to be normalized for population, which I will do a little later on in this blog.

As always, it should be noted that there is a good deal of uncertainty in these numbers, due to differences in reporting standards and levels of economic development.

 

2 - Aggregate Cases and Deaths per Million Population

Case and death counts alone don’t tell the whole story. Regions with larger populations will experience higher case counts and deaths, all things being equal. When an adjustment for population is made (expressing the data as Cases per Million Population and Deaths per Million Population), things are quite different.



The U.S. still has the highest level of Cases per Million, at about 250,000, by the end of Feb 2022. That would imply that about one-quarter of Americans had Covid-19 at some point during the pandemic period.. In addition, about 65,000 of those 250,000 occurred in the two month period of Jan-Feb 2022, showing just how transmissible Omicron has been, especially in the U.S..

Canada has had a much lower Cases per Million count, reaching about 85,000 by the end of the period. This might indicate some problem with comparing case counts, as a difference of this magnitude between the U.S. and Canada seems rather high. The same could be said of Mexico, whose case count per million population, at about 50,000, is only about one-fifth of the U.S..

In all cases, the Cases per Million jumped up quite substantially during 2022, again demonstrating what we might call “the Omicron effect”.


As for Deaths per Million population, those were quite close between the U.S. and Mexico by Feb 2022, between about 2500 and 3000. Again, this probably indicates a problem with case count comparisons, as it implies a rather high case fatality rate for Mexico (that will be shown more directly later). Canada’s Death per Million rate was much lower than its North American neighbors, at about 1000.

Though the Deaths per Million rate did accelerate during the final two periods of the data for all three countries, the rise was much less steep than was seen for Cases per Million. This demonstrates the other main feature of the “Omicron Effect”, namely that the outcomes of cases tended to be much less severe than it was in earlier phases of the pandemic (and thus earlier variants).



Case Count and Deaths by Time Periods up to End of 2021

3 - Raw Numbers per Time Interval

Here are a couple of graphs, looking at raw numbers of case counts and deaths during particular intervals. The intervals are mostly months, though in the earlier period they are sometimes longer and the last period is relatively short (in an effort to get more detail about Omicron).


For the Cases per Time Interval graph, the U.S. data dominates, so much so that it is difficult to see much detail in the lines for the other two countries. That said, one can see three main waves in the data: late 2020-early 2021, late summer-autumn 2021 (Delta) and late 2021-early 2022 (Omicron). As with the aggregate data, Omicron shows much higher case rates than the other waves. I should note that more fine-grained data would make some of the other waves that were known to occur more obvious.


More detailed country comparisons can be seen in the Deaths per Interval graph. This shows the correspondence in time of several waves. Again, the count of deaths in the U.S. and Mexico are relatively high, compared to Canada. This is mostly due to the populations differences between the countries, though some differences between the three countries health care systems (and thus case fatality rates) likely play a role.

Deaths were especially high during the late 2020-early 2021 wave. Deaths in the U.S. remained relatively high for much of late 2021-early 2021, though they dropped off substantially during the final period.

Deaths were also quite high during the first waves, in early 2020, something not so evident in the Cases per Interval graph. It may be that the virus was more pathogenic at that time or it may simply represent difficulties in testing and recognizing cases then, relative to later time periods.

4 - Case Counts and Deaths per Day per Million Population per Time Interval

As with the aggregate counts and deaths, normalizing the data for population and number of days during each time interval helps with inter-country comparisons. Since the time periods in the graph were not equal in duration, the data has been converted to a “per day” basis. Similarly, since the three counties are not equal in population, they have been normalized to a “per million population” basis. Combining the two normalizations gives a “per day, per million population” figure. For purposes of comparison, this is the preferred graph. That said, the usual caveats apply regarding the uncertainties about consistency in such matters as differences in defining and collecting data on cases and deaths in different jurisdictions.


The graph of Cases per Day per Million Population (note that it could also be described as Cases per Million Population per Day) continues to show the U.S. higher than the other two countries, though the differences are now reduced. Three main waves are once more visible, and at times the lines for the three countries actually cross. Nonetheless, the pandemic in the U.S. appears to be more severe than in Canada and Mexico, when looking at this data.



Looking at the Deaths per Day per Million Population gives a substantially different view of the pandemic, as experienced by these three countries. In this view, Mexico and the U.S. show rather similar profiles, during much of the pandemic. Both peak at about the same level in late 202-early 2021 and in late summer-autumn 2021, though the U.S. peak is much higher during the Omicron wave. Canada was generally quite far below its North American neighbours, using this measure.


5 - Case Fatality Rates, Aggregate Trend and by Time Periods

Of particular interest is the trajectory of the Case Fatality Rates (CFR) for the three North American countries. I have defined this as (Deaths/Cases). Deaths generally lag cases by about 2 weeks, as it takes some time for the disease to progress to the end-point of death; however at this scale it generally seems reasonable to ignore the time lag.

That may differ for the final period, in mid to late February as that was a fairly short interval, when cases were falling dramatically, while deaths remained relatively high (as many of these were from cases diagnosed during the previous time period). Thus, that could artificially inflate the calculated Case Fatality Rate for that final period.

The data is shown in the two associated graphs. The first graph shows the trend in the aggregated Case Fatality Rates for the three North American countries over the two years of the pandemic. In other words, at each time step all of the cases and all of the deaths up to that point in time are used for the calculation. The second graph shows the trend in the Case Fatality Rates for each time interval for these countries over the two years of the pandemic. In other words, at each time step all of the cases and all of the deaths that occurred during that time interval are used for the calculation.

Several observations stand out for the Aggregate Case Fatality Rate:

  • Perhaps the most striking observation about the graph is simply that the aggregate Case Fatality Rate falls over time for all three countries.

  • For the U.S. and Canada, the rate at the beginning of the pandemic was over 5%, while by the end of the pandemic it was under 1%. So, the Case Fatality rate fell by at least four-fifths, perhaps a bit more.

  • Mexico’s drop in the CFR was less dramatic, but it still fell from over 10% to somewhat above 5%. That was still a drop of over one-half.

  • Also striking is the convergence between Canada and the U.S. in this measure. By the mid-point of the pandemic, the CFR of the two countries was essentially equal and it remained like that right up until the end of the available data.

  • The difference between Mexico and the other two countries remained high. This may reflect differences in the underlying pandemics or it may be primarily an effect of reporting practices. It seems likely that it is a combination of the two phenomenon.



The second graph, showing Case Fatality Rates specific to the various time intervals shows a lot more variation, but some trends are clear:

  • The Case Fatality Rates were high in all three countries during the early stages of the pandemic, at the beginning of 2020.

  • Rates fell quite substantially after that time, in the U.S. and Canada, though less so in Mexico.

  • Canada and the U.S. show increases in the Case Fatality Rates during at least 4 (possibly 5) later waves.

  • These latter waves generally track between about 1% and 2.5%, from trough to peak.

  • There is considerable correlation in time between Canada and the U.S. during these peaks and troughs.

  • The data for Mexico is much less closely aligned with these Canada-U.S. trends, though Mexico does follow roughly similar waves.

  • There is an uptick in Case Fatality Rates at the end of the series, though as noted previously that is probably due to the lag time between a person being infected and dying of the disease.

  • Given the similarity in Case Fatality Rates between the U.S. and Canada, but the much higher Cases and Deaths per Million Population in the U.S., it does seem reasonable to conclude that case numbers really were much higher in the U.S. than Canada (i.e. it is not just some reporting artifact).

Summary Comments about the Emergency Act

Getting back to the implementation of the Emergency Act in Canada, there are a few salient points that we can make from the data. I will add a few points that are my own opinions, but I think they are supported by the facts as displayed in the graphs:

Case Counts


  • Though Cases per Million Population were higher in the U.S. (about 1750 cases per million population per day) than in Canada at the time that the Emergency was declared (mid-February), Canada was already in the midst of the Omicron wave (with a rolling average of about 700 cases per million people per day). So, heightening restrictions at the border at that time was essentially a case of closing the barn door after the horse has left the barn.

  • From that point of view, it is hardly surprising that truck drivers felt that they were being singled out for no good reason. Thus, the demonstrations and protests in Ottawa and other areas (“the Honkening”).

  • Some provinces had recently allowed unvaccinated nurses and other hospital staff back to work, even to the point of having them work with Covid patients. Cracking down on truck drivers at this time seemed misguided, even gratuitous.

  • The fact that both hospital workers and truck drivers had been lauded in earlier stages of the pandemic for staying on the job, only heightened the contradiction and likely led to a sense of alienation by these typically working class truck drivers and their supporters.

Case Fatality Rates

 

  • Though the rise in cases was causing a rise in deaths, the Case Fatality Rate was at its lowest level ever by the time that the Emergency Act was implemented (well under 1%).

  • So, this latest Covid-19 variant was actually turning out to be much less deadly than earlier variants had been.

  • The rise in infections (thought to be up to 10 times the rate of actually diagnosed cases) was producing an increase in natural immunity.

  • This, in association with earlier vaccination programs, seemed to really be putting Canada well on the road to the long hoped-for “herd immunity”. So, it was quite reasonable to think that further restrictions were not only pointless, but they may have also been counter-productive.

  • Many countries and provinces within countries were relaxing restrictions (or giving up on them altogether) by that time. The inconsistency of the heightened border restrictions with those facts only further alienated people, leading to the demonstrations and protests.

  • The fact that the federal government revoked the Emergency Act only two days after insisting on its necessity during the House of Commons debate only furthers the sense that the policy was misguided and needlessly provocative.

Sources:

The Globe and Mail

https://www.worldometers.info/coronavirus/#countries

https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations

Some earlier Covid-19 blogs:

https://dodecahedronbooks.blogspot.com/2021/07/covid-19-cases-and-deaths-by-continent.html

https://dodecahedronbooks.blogspot.com/2021/07/covid-19-cases-by-continent-jan-2000-to.html

https://dodecahedronbooks.blogspot.com/2021/03/covid-19-vaccines-how-successfully-are.html

https://dodecahedronbooks.blogspot.com/2020/12/covid-19-vaccines-comparison-of.html

https://dodecahedronbooks.blogspot.com/2020/09/covid-19-continues-to-travel-around.html

https://dodecahedronbooks.blogspot.com/2020/07/has-covid-19-become-less-deadly.html

https://dodecahedronbooks.blogspot.com/2020/07/july-2020-update-covid-19-death-rates.html

https://dodecahedronbooks.blogspot.com/2020/05/covid-19-death-rates-correlate-highly.html

https://dodecahedronbooks.blogspot.com/2020/06/covid-19-impact-on-employment-no-impact.html

https://dodecahedronbooks.blogspot.com/2020/04/is-there-model-that-can-predict-when-to.html

https://dodecahedronbooks.blogspot.com/2020/03/estimating-fatality-rate-of-coronavirus.html

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Here's a book for you , if you want to know a bit more about trucking:


It's Time for a Road Trip – On the Road with Bronco Billy

It's late February, and the sun is beginning to come on noticeably stronger in the more temperate regions. Spring is around the corner 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


Here’s the summary:

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

What follows is an account of a ten day journey through western North America during a working trip, delivering lumber from Edmonton Alberta to Dallas Texas, and returning with oilfield equipment. The writer had the opportunity to accompany a friend who is a professional truck driver, which he eagerly accepted. He works as a statistician for the University of Alberta, and is therefore is generally confined to desk, chair, and computer. The chance to see the world from the cab of a truck, and be immersed in the truck driving culture was intriguing. In early May 1997 they hit the road.

Some time has passed since this journal was written and many things have changed since the late 1990’s. That renders the journey as not just a geographical one, but also a historical account, which I think only increases its interest.

We were fortunate to have an eventful trip - a mechanical breakdown, a near miss from a tornado, and a large-scale flood were among these events. But even without these turns of fate, the drama of the landscape, the close-up view of the trucking lifestyle, and the opportunity to observe the cultural habits of a wide swath of western North America would have been sufficient to fill up an interesting journal.

The travelogue is about 20,000 words, about 60 to 90 minutes of reading, at typical reading speeds.

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

 

 

 

And, here’s a book about a struggle against a different wily opponent of history.

The Sapper’s War

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

 

Wednesday, 2 February 2022

Running a dummy variable regression in Excel

 

How do you run dummy variable regression in Excel?


I don’t believe that Excel has an automatic way to do this, unlike some stats packages. So, you have to do a bit of data preparation yourself.

  • If the variable that interests you has N categories (say, 3 regions for example), then you need N-1 categories (for the example, that would be 2).

  • Choose a reference category, which will be the category that doesn’t actually get a dummy-coded variable. Which category you choose might depend on your research/business question. If there is no compelling reason to do otherwise, pick the category with the most observations.

  • In some part of your worksheet, copy over your original data, but insert new columns for the dummy variables. So, in the example case, set up two new variables. You can drop the original variable from this copy dataset, once you have assigned the dummy variables, as you won’t use it in the regression.

  • Code those new variables (in this case 2 new variables) as 1 or 0, depending on whether the case satisfies the condition for the new variable. For example if your old variable was region (East, Central, West), code the dummy variable for East as 1 if the observation is from the eastern region.

  • A useful convention is to start your dummy variable names with the letter d, and something descriptive about the variable. So, assuming “East” had the most observations in the original categorical variable, you would have two new variables D_Central and D_West. There would be no D_East, as that is your reference case variable.

  • A common way to recode the dummy variables is with an “if” function. So, in D_West code if(Region=”West”, 1,0). “Region” would refer to the cell which had the value for Region in your original dataset.

  • Once you have set that up, either use the regression functions in Excel or use the regression options in the Data Analysis package in Excel. If you don’t see that, load it from the “add-ins” menu.

  • To interpret the output, the coefficient for the dummy variables gives the strength of that variable compared to the reference variable, on the value of your output variable.

  • If you computed a point estimate with you regression, when both dummy variables are 0, that gives the point estimate for a case from the East region. If you assign the value 1 to one of the dummy variables, then that gives the value for a case from that region. You can only assign the value 1 to one of the dummy variables, the other has to be 0 (you can’t be in two regions at the same time).

You can use these dummy variables to include interaction effects, by including some more data transformation steps. If your dataset is fairly large, or as your model starts to get pretty complicated, you might want to use a stats program like SPSS or R. The later is a little tricky to learn, but it is open source and thus free. These more purely statistically-oriented software products will sometimes have options or packages that automate a lot of the data transformations (though doing it yourself really helps you to understand the underlying ideas).

The usual rules about evaluating models apply. The XKCD comic has a amusing example of model evaluation.