Surprise! Carbon Fuels are Plentiful, not Scarce.

Brentan Alexander writes at Forbes $40 Oil Will Return: This Isn’t The End Of Fossil Fuels. Excerpts in italics with my bolds and images.

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Yesterday, May futures for WTI crude, a benchmark often used for U.S.-sourced oil, crashed into negative territory for the first time ever. It was the last day to trade a May contract, and with storage space filling up as oil demand craters, contract holders with nowhere to put the oil they were obligated to physically accept were forced to pay to have somebody take contracts off their hands. This moment represents a stunning new chapter in the ongoing oil crisis that has seen record drops for oil consumption and prices globally. Spot prices in May will remain depressed, and the June market is likely to be painful as well. It may seem like the days of $40 oil are behind us, and that we’re witnessing the beginning of the end for oil as the lifeblood of the global economy. We aren’t:

Oil will one day return to $40 a barrel, but the last few weeks have demonstrated in hyperdrive how the oil endgame will play out.

It seems that oil isn’t the precious commodity it has been made out to be. Much ink has been spilled on the concept of peak oil, wherein dwindling reserves of oil cause rising prices as the marketplace becomes more and more supply-constrained. In the endgame scenario, supply shocks send prices soaring to levels that force global economies to find alternative fuels, renewable energy, or otherwise. A key issue with the peak oil theory is that ‘reserves’ are only counted if they’re known to exist and can be extracted with current technology.

As prices soared to upwards of $100 a barrel around 2008, many wondered if the high prices were here to stay, and if peak oil was coming to pass. Instead, high prices were just the motivation needed to unlock a bit of American ingenuity. Within 10 years, new technology unlocked vast fields of oil and gas throughout Texas, Pennsylvania, and the Dakotas. The ‘reserves’ in the United States multiplied, oil prices dropped, and the United States regained its status as the world’s leading producer of oil.

Peak oil, it turns out, is a story of peak demand.

As some economies of the world begin to face the realities of climate change, new renewable and net-zero (or negative!) technologies have emerged and will emerge to supplant fossil oil. At first, these technologies require higher fossil prices, government programs, or both, to compete in the market. But as they mature and grow, prices come down. Demand for fossil will drop accordingly. And at some point, so little demand will exist for crude oil that producers will have to pay somebody to take if off their hands or stop producing it altogether.

This market conversion has already begun. Tesla has proven electric vehicles can out-perform and out-sexy the incumbents. Biorefineries are being built to turn household trash in to jet fuel. Governments are taking action to incentivize cleaner fuels. Nevertheless, action thus far has been spotty at best and despite the current market, peak oil demand has not yet come to pass.

The unprecedented demand destruction caused by COVID-19 will eventually subside as the threat of the pandemic wanes. The public will fly again, drive again, and buy plastic again; oil demand will ratchet up again. Shuttered wells won’t restart, stored oil will be drawn down, OPEC will maintain supply controls to balance government budgets, and prices will rise to $40 or more again. But someday, hopefully in the not too distant future, oil will again find itself in decline when a different (and more permanent) source of demand destruction weans the global economy off of fossil carbon for good.

Comment:

This article makes a distinction between short and long term energy supply and demand.  Thus the price drop yesterday signifies a present glut of carbon fuels.  Climate activists can not count on the supply of fossil fuels falling as long as they are plentiful and inexpensive.  For example, others note coal reserves exceed 100 years at current rates of consumption, and will remain attractive for electrical power production.  In the absence of economical substitute energy sources, modern societies for years to come will depend on companies providing carbon-based fuels.

As Bjorn Lomborg has long maintained, this is the time to invest in advanced energy technologies, including nuclear, to engineer price-competitive energy alternatives and achieve an orderly transition for future generations.  It is not a time for short-term bad bets on immature wind and solar tech that do not scale to societies’ need for reliable affordable energy.

BTW, Bill Gates also shares and funds this perspective:

Financiers Failed Us: Focused on Fake Crisis

Terence Corcoran writes at Financial Post Why all the macroprudes failed on COVID-19. Excerpts in italics with my bolds

Global policy-makers shoved pandemic risk aside and spread climate alarm instead

One of the noble houses of global macroprudentialism, the International Monetary Fund, declared Tuesday that “The Great Lockdown” will plunge the global economy into the “worst recession since the Great Depression, surpassing that seen during the global financial crisis a decade ago.” Along with the rest of the world’s economic overseers and protectors of financial stability, the IMF seems to have been unprepared for — and overwhelmed by — the arrival of COVID-19.

That the IMF was blindsided is clear in the opening words of Tuesday’s World Economic Outlook. “The world has changed dramatically in the three months since our last World Economic Outlook update on the global economy. A pandemic scenario had been raised as a possibility in previous economic policy discussions, but none of us had a meaningful sense of what it would look like on the ground and what it would mean for the economy.”

That’s some statement: “None of us” had a sense of what such a pandemic might impose on the world economy.

It’s not clear who is included in the collective “us,” but it seems fair to assume the IMF is referring to the host of other members of the global fraternity of institutions that have assumed the role of guardians of the stability of the global financial system.

Among the institutions that should have been preparing for and assessing the risks of a global viral pandemic, in addition to the IMF, are the Financial Stability Board, the Bank for International Settlements, the G20 assembly of finance ministers, the World Bank and the European Central Bank.

In the wake of the 2008 financial crisis, which “none of us” had anticipated, these global entities and national authorities adopted “macroprudential policy” to prevent the next global financial meltdown and, if possible, prepare plans to deal with a new blow to global financial stability.

Wikipedia has an excellent and authoritative review of the origins of macroprudentialism, describing it as an “approach to financial regulation that aims to mitigate risk to the financial system as a whole.” In the aftermath of the 2008 financial crisis, policy-makers and economic researchers backed the need to reorient the global regulatory framework “towards a macroprudential perspective.”

As the world sinks into lockdown and decline, one wonders why the whole macroprudential policy preparations, underway since the 2008 financial crisis and formally installed in 2016, so obviously failed to prepare for the financial stability shakeup brought on by the COVID-19 pandemic?

There are two explanations. One is that the whole financial stability-macroprudential effort is an international bureaucratic collection of agencies dedicated to the pursuit of meaningless bureaucratic interventions.

The second explanation is that the macroprudential apparatus, from the IMF through to the FSB and down, was hijacked by activists pushing climate change as the dominant systemic risk of our time.

In 2017, Mark Carney, then Bank of England governor and head of the FSB, reviewed the successes of macroprudential policy and highlighted new risks. The FSB, said Carney, is assessing “emerging vulnerabilities affecting the global financial system … within a macroprudential perspective.” Among the risks identified, he said, were “risks from FinTech, climate‐related financial risks and misconduct in financial institutions.”

Carney has been something of a poster boy for climate change. In a 2015 speech at Lloyd’s of London — titled “Breaking the tragedy of the horizon — climate change and financial stability,” Carney warned the insurance industry to prepare for big climate risks — including defaults, lawsuits, stranded assets and increased liabilities related to a changing climate.

The insurance execs picked up the macroprudential warnings. The replacement of pandemic risks with climate change as a threat to the global financial and economic system was highlighted this week by Roger Pielke Jr. at the University of Colorado. In 2008, the No. 1 risk cited by insurance executives was a pandemic, described as “a new highly infectious and fatal disease spreads through the human population.” In 2019, the top risk was identified as “global temperature change.” Pandemic was not even one of the top-10 insurance risks.

Over the past several years, but especially through 2019, the major efforts of the macroprudes has been to spread alarm about the financial stability risks allegedly building around climate change. Never mind pandemics and other more mundane but genuine financial risks, such a soaring government debt buildup and U.S. political schemes to dismantle Big Tech. Instead, banks and other financial institutions have been pressed to get out of fossil fuels and shift into ethical investing, sustainable financing, green financing, social financing, impact investing, ESG investment, responsible investing.

At the turn of the 2020 New Year, Carney appeared on BBC television calling for “action on financing” from banks against fossil investments. One day later, the Communist government in China informed the World Health Organization of pneumonia cases in Wuhan City, Hubei province, with unknown cause. Carney’s get-out-of oil call caused alarm within Canada’s fossil fuel industry. At the time, oil was trading at US$55 a barrel.

On Tuesday, thanks in part to the pandemic Carney and the macroprudes failed to plan for, West Texas crude continued to languish at just above US$20.

By promoting the risks of far-off climate change and ignoring the real financial and economic risks of a pandemic, the macroprudes got what they wanted by helping to usher in a global economic crisis they claimed to be attempting to prevent.

 

Covid19 Forensic Genetics

Fig. 1. Phylogenetic network of 160 SARS-CoV-2 genomes. Node A is the root cluster obtained with the bat (R. affinis) coronavirus isolate BatCoVRaTG13 from Yunnan Province. Circle areas are proportional to the number of taxa, and each notch on the links represents a mutated nucleotide position

A recent study employed modern genetic techniques to analyze the progressive mutations of the novel coronavirus as it spread across the world.  The paper is Phylogenetic network analysis of SARS-CoV-2 genomes.  Excerpts in italics with my bolds.

Significance
This is a phylogenetic network of SARS-CoV-2 genomes sampled from across the world. These genomes are closely related and under evolutionary selection in their human hosts, sometimes with parallel evolution events, that is, the same virus mutation emerges in two different human hosts. This makes character-based phylogenetic networks the method of choice for reconstructing their evolutionary paths and their ancestral genome in the human host. The network method has been used in around 10,000 phylogenetic studies of diverse organisms, and is mostly known for reconstructing the prehistoric population movements of humans and for ecological studies, but is less commonly employed in the field of virology.

Abstract
In a phylogenetic network analysis of 160 complete human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) genomes, we find three central variants distinguished by amino acid changes, which we have named A, B, and C, with A being the ancestral type according to the bat outgroup coronavirus. The A and C types are found in significant proportions outside East Asia, that is, in Europeans and Americans. In contrast, the B type is the most common type in East Asia, and its ancestral genome appears not to have spread outside East Asia without first mutating into derived B types, pointing to founder effects or immunological or environmental resistance against this type outside Asia. The network faithfully traces routes of infections for documented coronavirus disease 2019 (COVID-19) cases, indicating that phylogenetic networks can likewise be successfully used to help trace undocumented COVID-19 infection sources, which can then be quarantined to prevent recurrent spread of the disease worldwide.

In early March 2020, the GISAID database (https://www.gisaid.org/) contained a compilation of 253 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) complete and partial genomes contributed by clinicians and researchers from across the world since December 2019. To understand the evolution of this virus within humans, and to assist in tracing infection pathways and designing preventive strategies, we here present a phylogenetic network of 160 largely complete SARS-Cov-2 genomes (Fig. 1).

Although SARS-CoV-2 is an RNA virus, the deposited sequences, by convention, are in DNA format. Our initial alignment confirmed an earlier report by Zhou et al. (7) that the pangolin coronavirus sequences are poorly conserved with respect to the human SARS-CoV-2 virus, while the bat coronavirus yielded a sequence similarity of 96.2% in our analysis, in agreement with the 96.2% published by Zhou et al. We discarded partial sequences, and used only the most complete genomes that we aligned to the full reference genome by Wu et al. (8) comprising 29,903 nucleotides

Zhou et al. (7) recently reported a closely related bat coronavirus, with 96.2% sequence similarity to the human virus. We use this bat virus as an outgroup, resulting in the root of the network being placed in a cluster of lineages which we have labeled “A.” Overall, the network, as expected in an ongoing outbreak, shows ancestral viral genomes existing alongside their newly mutated daughter genomes.

There are two subclusters of A which are distinguished by the synonymous mutation T29095C. In the T-allele subcluster, four Chinese individuals (from the southern coastal Chinese province of Guangdong) carry the ancestral genome, while three Japanese and two American patients differ from it by a number of mutations. These American patients are reported to have had a history of residence in the presumed source of the outbreak in Wuhan. The C-allele subcluster sports relatively long mutational branches and includes five individuals from Wuhan, two of which are represented in the ancestral node, and eight other East Asians from China and adjacent countries. It is noteworthy that nearly half (15/33) of the types in this subcluster, however, are found outside East Asia, mainly in the United States and Australia.

Two derived network nodes are striking in terms of the number of individuals included in the nodal type and in mutational branches radiating from these nodes. We have labeled these phylogenetic clusters B and C.

One practical application of the phylogenetic network is to reconstruct infection paths where they are unknown and pose a public health risk. The following cases where the infection history is well documented may serve as illustrations (SI Appendix). On 25 February 2020, the first Brazilian was reported to have been infected following a visit to Italy, and the network algorithm reflects this with a mutational link between an Italian and his Brazilian viral genome in cluster C (SI Appendix, Fig. S1). In another case, a man from Ontario had traveled from Wuhan in central China to Guangdong in southern China and then returned to Canada, where he fell ill and was conclusively diagnosed with coronavirus disease 2019 (COVID-19) on 27 January 2020. In the phylogenetic network (SI Appendix, Fig. S2), his virus genome branches from a reconstructed ancestral node, with derived virus variants in Foshan and Shenzhen (both in Guangdong province), in agreement with his travel history. His virus genome now coexists with those of other infected North Americans (one Canadian and two Californians) who evidently share a common viral genealogy. The case of the single Mexican viral genome in the network is a documented infection diagnosed on 28 February 2020 in a Mexican traveler to Italy. Not only does the network confirm the Italian origin of the Mexican virus (SI Appendix, Fig. S3), but it also implies that this Italian virus derives from the first documented German infection on 27 January 2020 in an employee working for the Webasto company in Munich, who, in turn, had contracted the infection from a Chinese colleague in Shanghai who had received a visit by her parents from Wuhan. This viral journey from Wuhan to Mexico, lasting a month, is documented by 10 mutations in the phylogenetic network.

 

 

The Virus Wars

The proverb is “Generals are always fighting the last war,” and its origin is uncertain. One possibility is a quote from Winston Churchill: “It is a joke in Britain to say that the War Office is always preparing for the last war.” 1948 Winston S. Churchill _The Second World War_ I (Boston: Houghton Mifflin, 1985) 426:

Konrad Lorenz demonstrated how imprinting works upon animal behavior, while military historians have reported how powerfully human social animals are influenced by the past and instilled lessons from others.

Austria – 20th century. Animal behaviourist Konrad Lorenz and mallard goslings

Which brings me to these reflections about the current WuHanFlu outbreak. The chart at the top summarizes our received epidemiological wisdom about the danger of viruses according to the dimensions of deadliness and contagiousness. As the diagram shows, extremely deadly viruses tend to kill their hosts too quickly to be transmitted widely. Conversely, a virus that spreads easily accomplishes that by slowly killing its hosts, perhaps even leaving them alive. The biggest threats are the germs that are lethal, but spread widely because the symptoms are slow to develop (longer incubation period).

Regarding the recent virus wars, consider these four (Source: Big Think. Excerpts in italics with my bolds)

SARS (started in Hong Kong in March 2003),
Swine flu (started in Mexico in March 2009),
Ebola (started in Western Africa in March 2014), and
MERS (started in South Korea in May 2015).

The video below explains the last two impactful wars were against SARS and Swine Flu (HINI).

For the sake of comparison, the graphs for each epidemic are aligned so they all start together on Day One of each outbreak.

At first, Ebola is the scary one. Not only had it infected the most people after just one day, it had killed two thirds of those.

By comparison, SARS killed its first victim only after three days (out of 38 people infected).

By Day 10, SARS had overtaken Ebola as the most infectious of the outbreaks (264 vs. 145 patients), but the latter was ten times more lethal (91 dead from Ebola vs. 9 from SARS). At this time, the coronavirus had infected 39 people, killed none, and was still playing in the same minor league as the swine flu and MERS.

Day 20, and SARS cases are skyrocketing: 1,550 people are ill, 55 have died. That’s a death rate of 3.5%. Ebola has affected only 203 people by now, but killed 61.6% of them, a total of 125. Meanwhile, the coronavirus has taken Ebola’s second place, but is still far behind SARS (284 infected). At this time, the coronavirus has claimed the lives of just five people.

But now the coronavirus cases are exploding; by Day 30, the new virus has infected 7,816 people, killing 204. That’s far more infected than any other virus (SARS comes a distant second with 2,710 patients), and significantly more killed (Ebola, though still just 242 people ill, has killed 147, due to its high fatality rate). Meanwhile, MERS is stuck in triple digits, and the swine flu in double digits.

The swine flu numbers keep growing exponentially: by Day 80, they’ve passed 362,000 cases (and 1,770 deaths), far surpassing any of the other diseases.

Day 100: swine flu cases are approaching 1 million, deaths have surpassed 5,000. That’s far more than all the other diseases combined—they have merged into a single line at the bottom of the graph.

By Day 150, swine flu hit 5.2 million patients, with 25,400 people killed. By the time it was declared over, a year later, the outbreak would eventually have infected more than 60 million people and claimed the lives of almost 300,000.

Swine flu was caused by the H1N1 virus, which also caused the Spanish flu. That outbreak, in 1918/19, infected about 500 million people, or 1 in 3 people alive at that time. It killed at least 50 million people. It was the combination of extreme infectiousness and high fatality that made the Spanish flu such a global, lethal pandemic.

None of the other infectious diseases comes close to that combination. The swine flu, although more infectious than other diseases, was less infectious than the Spanish flu, and also less deadly (0.5%). Unlike COVID-19 or its fellow coronaviruses SARS and MERS, Ebola is not spread via airborne particles, but via contact with infected blood. That makes it hard to spread. Ironically, it may also be too lethal (39.6%) to spread very far. And COVID-19 itself, while relatively lethal (2.4%), is well below the deadliness of the Spanish flu, and does not seem to spread with the same ease.

As that history lesson shows, our pandemic generals have likely been preoccupied with three previous enemies: Spanish Flu, Swine Flu, and SARS. The first one served as the catastrophic defeat to be avoided, H1N1 as the victory achieved by deploying vaccine, and SARS as the coronavirus prototype. Naming the Wuhan virus SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) predisposed tacticians and soldiers to fight against a viral pneumonia, and to expect air borne transmission as happened with SARS 1.

The battle plan was drawn up to protect the health care system against the deluge of victims coming to hospitals and ICUs. Flattening the curve of such cases was the strategy, and social distancing and personal immobility was imposed to that end. What has been the effect? For that there is an analysis from John Nolte What Terrible Coronavirus Models Tell Us About Global Warming Models H/T Joe D’Aleo Excerpts in italics with my bolds.

Let’s face it, the coronavirus models are terrible. Not just off, but way, way, way off in their predictions of a doomsday scenario that never arrived.

That’s not to say that over 20,000 dead Americans is not a heartbreaking reality. That’s not even to say that parts of the country should not have been shut down. But come on…

We shut the entire country down using the Institute for Health Metrics and Evaluation (IHME) models, and in doing so put 17 million (and counting) Americans out of work, shattered 17 million (and counting) lives, and… Well, take a look for yourself below.

That gigantic hump is the IHME’s April 1 prediction of coronavirus hospitalizations. The smaller humps way, way, waaaay below that are the IMHE’s predictions of coronavirus hospitalizations after they were revised just a few days later on April 5, 7, and 9.

The green line is the true number of hospitalizations, starting with the whole U.S., and into the states.

So why does this matter? And why are we looking at hospitalizations?

Well, remember, the whole reason for shutting down the economy was to ensure our healthcare system was not overloaded. And it should be noted that these expert models are based on full mitigation, based on what did indeed happen, which was basically a full shutdown of the economy by way of a lockdown. And these models are still horribly, terribly wrong.

Even if you believe the correct decision was made, that does not change how wildly wrong the coronavirus models were, are, and will almost certainly continue to be. That does not change the fact we shut down our entire economy based on incredibly flawed models.

Now I realize that the people who did the terribly flawed coronavirus models are not the same people who do the modeling for global cooling global warming climate change or whatever the hell these proven frauds are calling it today. But hear me out…

We’re still talking about “experts” our media and government grovel down to without question.

We’re still talking about models with the goal of destroying our way of life, our prosperity, our standard of living, and our individual freedoms to live our lives in whatever way we choose

We’re still talking about models with the goal of handing a tremendously scary amount of authority and power to a centralized government.

The coronavirus modeling was based on something real, on something happening at the time. The experts doing the coronavirus models had all kinds of data on which to make their assumptions. Not just reams and reams of scientific data based on previous pandemics, viruses, and human behavior; but also real-time data on the coronavirus itself from China, Italy, and other countries… And they still blew it. They still got it horribly wrong.

A health worker in protective gear waits to hand out self-testing kits in a parking lot of Rose Bowl Stadium in Pasadena, Calif., during the coronavirus outbreak, April 8, 2020. (Mario Anzuoni/Reuters)

What Went Wrong? California Provides a Clue

As the diagram at the top shows, WuHanFlu looked like an especially dangerous mix of deadly contagion. Thus California with its large population and extensive contact with China should be the US viral hot spot, and yet it isn’t. Maybe the contagion is real but the effects are milder than imagined.
Victor Davis Hanson writes at National Review Yes, California Remains Mysterious — Despite the Weaponization of the Debate. Excerpts in italics with my bolds.

How Many People Already Have COVID-19?

California is touchy, and yet still remains confused, about incomplete data showing that the 40-million-person state, as of Sunday, April 12, reportedly had 23,777 cases of residents who have tested posted for the COVID-19 illness. The number of infected by the 12th includes 674 deaths, resulting in a fatality rate of about 17 deaths per million of population. That is among the lowest rates of the larger American states (Texas has 10 deaths per million), and lower than almost all major European countries, (about half of Germany’s 36 deaths per million).

No doubt there are lots of questionable data in all such metrics. As a large state California has not been especially impressive in a per capita sense in testing its population (about 200,000 tests so far). Few of course believe that the denominator of cases based on test results represent the real number of those who have been or are infected.

There is the now another old debate over exactly how the U.S. defines death by the virus versus death because of the contributing factors of the virus to existing medical issues. Certainly, the methodology of coronavirus modeling is quite different from that of, say, the flu. The denominator of flu cases is almost always a modeled approximation, not a misleadingly precise number taken from only those who go to their doctors or emergency rooms and test positive for an influenza strain. And the numerator of deaths from the flu may be calibrated somewhat more conservatively than those currently listed as deaths from the coronavirus.

Nonetheless, the state’s population is fairly certain. And for now, the number of deaths by the virus is the least controversial of many of these data, suggesting that deaths per million of population might be a useful comparative number.

As I wrote in a recent NRO piece, the state on the eve of the epidemic seemed especially vulnerable given the large influx of visitors from China on direct flights to its major airports all fall and early winter until the January 31 ban (and sometime after). It ranks rather low in state comparisons of hospital beds, physicians, and nurses per capita. It suffers high rates of poverty, wide prevalence of state assistance, and medical challenges such as widespread diabetes.

This IHME projection is current as of April 14, at 12 p.m. ET, and will be updated periodically as the modelers input new data. The visualization shows the day each state may reach its peak between now and Aug. 4. The projected peak is when a state’s curve begins to show a consistent trend downward. To enlarge open image in new tab.  Source: NPR

Certainly, both then and more recently, there have been a number of anecdotal accounts, media stories, and small isolated studies suggesting that more people than once thought, both here and abroad, have been infected with the virus and developed immunity, that the virus may have reached the West and the U.S. earlier than once or currently admitted by Chinese researchers — so, inter alia, California in theory could weather the epidemic with much less death and illness than earlier models of an eventual 25.5 million infected had suggested. Since then, a number of models, including Governor Newsom’s projection of 25.5 million infected Californians over an eight-week period, have been questioned. Controversy exists over exactly why models are being recalibrated downward. One explanation is that the shelter-in-space orders have been more successful than expected; others point to various flawed modeling assumptions.

Front-line physicians who see sick patients do not necessarily agree with researchers in the lab. For example, a Los Angeles Times story was widely picked up by other news outlets that quoted Dr. Jeff Smith, the chief executive of Santa Clara County. Smith reportedly now believes that the virus arrived in California much earlier than often cited, at least in early 2020:

The severity of flu season made health care professionals think that patients were suffering from influenza given the similarity of some of the symptoms. In reality, however, a handful of sick Californians that were going to the doctor earlier this year may have been among the first to be carrying the coronavirus. “The virus was freewheeling in our community and probably has been here for quite some time,” Smith, a physician, told county leaders in a recent briefing. The failure of authorities to detect the virus earlier has allowed it to spread unchecked in California and across the nation. “This wasn’t recognized because we were having a severe flu season. . . . Symptoms are very much like the flu. If you got a mild case of COVID, you didn’t really notice. You didn’t even go to the doctor. . . . The doctor maybe didn’t even do it because they presumed it was the flu.”

Footnote:  See also Good Virus News from the Promised Land

New York Nukes Itself

This post is not about WuHanFlu, but about New York’s insane decision to close nuclear power plants in favor of wind farms.  Robert Bryce writes at Forbes New York Has 1,300 Reasons Not To Close Indian Point. Excerpts in italics with my bolds.

At the end of this month, the Unit 2 reactor at the Indian Point Energy Center in Buchanan, New York will be permanently shut down. Next April, the final reactor at the site, Unit 3, will also be shuttered.

TOMKINS COVE , NY – MAY 11: The Indian Point nuclear power plant is seen from Tomkins Cove, New York … [+] CORBIS VIA GETTY IMAGES

But the premature closure of the 2,069-megawatt nuclear plant is even worse land-use policy. Here’s why: replacing the 16 terawatt-hours of carbon-free electricity that is now being produced by the twin-reactor plant with wind turbines will require 1,300 times as much territory as what is now covered by Indian Point.

Here are the facts: Indian Point covers 239 acres, or about 1 square kilometer. To put Indian Point’s footprint into context, think of it this way: you could fit three Indian Points inside Central Park in Manhattan.

Based on projected output from offshore wind projects (which have higher capacity factors than onshore wind projects), producing that same amount of electricity as is now generated by Indian Point – about 16 terawatt-hours per year – would require installing about 4,000 megawatts of wind turbines. That estimate is based on the proposed South Fork offshore wind project, a 90-megawatt facility that is expected to produce 370 gigawatt-hours per year. (Note that these output figures are substantially higher than what can be expected from onshore wind capacity.) Using the numbers from South Fork, a bit of simple division shows that each megawatt of wind capacity will produce about 4.1 gigawatt-hours per year. Thus, matching the energy output of Indian Point will require about 4,000 megawatts of wind capacity.

That’s a lot of wind turbines. According to the American Wind Energy Association, existing wind-energy capacity in New York state now totals about 1,987 megawatts. That capacity will require enormous amounts of land. Numerous studies, including ones by the Department of Energy have found that the footprint, or capacity density, of wind energy projects is about 3 watts per square meter. Thus, 4,000 megawatts (four billion watts) divided by 3 watts per square meter = 1.33 billion square meters or 1,333 square kilometers. (Or roughly 515 square miles.)

UNITED STATES – AUGUST 20: Aerial view of New York City’s Central Park (Photo by Carol M. … [+] GETTY IMAGES

Those numbers are almost too big to imagine. Therefore, let’s look again at Central Park. Recall that three Indian Points could fit inside the confines of the famed park. Thus, replacing the energy production from Indian Point would require paving a land area equal to 400 Central Parks with forests of wind turbines.

Put another way, the 1,300 square kilometers of wind turbines needed to replace the electricity output of Indian Point is nearly equal to the size of Albany County. Would New York legislators who convene in the capitol in Albany consent to having the entire county covered in wind turbines? I can’t be sure, but I am guessing that they might oppose such plan. (See yellow area in Google Earth image  at top).

These basic calculations prove some undeniable facts. Among them: Indian Point represents the apogee of densification. The massive amount of energy being produced by the two reactors on such a small footprint provides a perfect illustration of what may be nuclear energy’s single greatest virtue: its unsurpassed power density. (Power density is a measure of energy flow from a given area, volume, or mass.) High power density sources, like nuclear, allow us to spare land for nature. Density is green.

Alas, the environmental groups that are influencing policymakers in New York and in other states are strident in their belief that nuclear energy is bad and that renewables are good. But that theology ignores the greenness of density and the essential role that nuclear energy must play if we are to have any hope of making significant reductions in carbon-dioxide emissions.

In short, the premature closure of Indian Point – and the raging land-use battles over renewable energy siting in New York – should lead environmental groups to rethink their definition of what qualifies as “green.” Just because wind and solar are renewable doesn’t mean they are green. In fact, the land-use problems with renewables show the exact opposite.

Good Virus News from the Promised Land

This report comes by way of Times of Israel, Top Israeli prof claims simple stats show virus plays itself out after 70 days.  Excerpts in italics, along with images from his article in Hebrew (here)

Isaac Ben-Israel, who is not a medical expert, says analysis worldwide shows new cases peaking after about 40 days, slams economic closures; leading doctor dismisses his claims

A prominent Israeli mathematician, analyst and former general claims simple statistical analysis demonstrates that the spread of COVID-19 peaks after about 40 days and declines to almost zero after 70 days — no matter where it strikes, and no matter what measures governments impose to try to thwart it.

Prof Isaac Ben-Israel, head of the Security Studies program in Tel Aviv University and the chairman of the National Council for Research and Development, told Israel’s Channel 12 (Hebrew) Monday night that research he conducted with a fellow professor, analyzing the growth and decline of new cases in countries around the world, showed repeatedly that “there’s a set pattern” and “the numbers speak for themselves.”

Ben-Israel’s Analysis

Addition of the number of patients per day in Israel

From the graph above you can see that the increment of patients per day peaked (around the 41st day) to about 700 additional patients a day, beginning to fade since.

From a graphical point of view, this phenomenon is remarkably exemplary in almost every country where there is data. For comparison, let’s see what happens in the US. The numbers are bigger) The US has about 330 million people! The drop phenomenon is clear:

Red line is exponential projection, blue line is pattern of observations.

The infection behavior is not only unique to Israel or the United States and is a global phenomenon, as reflected in the following sketch.  The daily supplement is added worldwide (or more precisely in countries that publish data). This is actually a worldwide phenomenon, as can be clearly seen from the drawing that brings together the data of all countries.

In Israel, the absolute numbers are smaller but the phenomenon is similar:

The pattern of onset and decline in the number of patients after a few weeks is also shared by completely different countries (as illustrated in the following two drawings). This is true regardless of their behavior during the crisis.  For example, Italy imposed a total closure, including the paralysis of the economy while Sweden has not yet taken these steps.

Summary from Ben-Israel’s document:

Let’s return to the question in the headline: Is the Corona Expansion Exponential?  The answer to the numbers is simple: No. Expansion begins exponentially but moderately and fades quickly after about 8 weeks of its breakout.

Continuation of Times of Israel article:

While he said he supports social distancing, the widespread shuttering of economies worldwide constitutes a demonstrable error in light of those statistics. In Israel’s case, he noted, about 140 people normally die every day. To have shuttered much of the economy because of a virus that is killing one or two a day is a radical error that is unnecessarily costing Israel 20% of its GDP, he charged.

Prof. Gabi Barbash, a hospital director and the former Health Ministry director general, insisted in a bitter TV exchange that Ben-Israel is mistaken, and that the death tolls would have been far higher if Israel and other countries had not taken the steps they did.

But Ben-Israel said the figures — notably from countries, such as Singapore, Taiwan, and Sweden, which did not take such radical measures to shutter their economies — proved his point. (He also posted a Hebrew paper to this effect on Facebook, with graphs showing the trajectories.)

When Barbash cited New York as ostensible proof that Ben-Israel was mistaken, Ben-Israel noted the latest indications from New York were precisely in line with his statistics that indicate daily new cases figures peaking and starting to fall after about 40 days.

Asked to explain the phenomenon, Ben-Israel, who also heads Israel’s Space Agency, later said: “I have no explanation. There are all kinds of speculations. Maybe it’s related to climate, or the virus has a life-span of its own.”

Asked to explain why the virus had caused such a high death toll in countries such as Italy, he said the Italian health service was already overwhelmed. “It collapsed in 2017 because of the flu,” he said.

He said the policy of lockdowns and closures was a case of “mass hysteria.” Simple social distancing would be sufficient, he said.

If the lockdowns instituted in Israel and elsewhere were not causing such immense economic havoc, there wouldn’t be a problem with them, he said. “But you shouldn’t be closing down the entire country when most of the population is not at high risk.”

See Also:  Canada Bends the Curve April 15 Update

Footnote:  The Anatomy of a Viral Outbreak every year in Canada

Red line is Flu A and blue line in Flu B. To enlarge open image in new tab.

 

 

On Covid Statistics

As reported previously, and increasingly confirmed by physicians around the world, the pattern of mortality is the same this year compared to previous history. For example, in Italy, the median age of those dying with the virus is 84, and more often males than females, with deaths rarely in younger age groups.

The virus is there along with one or more of the usual comorbidities: cancer, heart disease, arteries, chronic lung disease, and so on. Some Italian doctors have lamented that some patients who normally would have gone into palliative care in their nursing homes have instead died on ventilators in an ICU.

Andrew McCarthy writes at National Review The Problem with New York City’s COVID-19 Death-Rate Estimates

Still, quantifying fatalities and the mortality rate remains elusive. Case in point: New York City. As the New York Times reported yesterday, Gotham’s Health Department abruptly added 3,700 victims to the COVID-19 death toll even though these decedents were not tested.

Despite the lack of coronavirus diagnoses in these cases, the inference that it was a factor in death (or, as the city insists, the proximate cause) is not irrational. The Health Department says that 3,000 more people died in the last month than would ordinarily have been expected in the City this time of year. The City has been vexed by the sparse availability of testing. By counting only people who had tested positive, it was surely undercounting COVID-19 deaths to some degree.

But to what degree? We really don’t know. In truth, we will never know beyond educated supposition.

City health officials deduce that some of the spike in “excess deaths” is only indirectly attributable to the coronavirus. On this theory, COVID-19 infections so overwhelmed the health-care system that some non-infected people are assumed to have died of conditions that would otherwise have been treatable.

Meantime, health officials have been tracking deaths they’ve hypothesized could have been related to the virus, based on symptoms and medical history. But what does that mean? Was the coronavirus present in the decedents (unconfirmable because they weren’t tested)? Are health officials saying COVID-19 was actually the proximate cause of death? That it may have exacerbated underlying health problems? That such comorbidities would not have killed the decedents but for the (unconfirmed) presence of COVID-19?

New York City is dysfunctional, but this is not a New York issue. The guesstimating is being done at the express invitation of the federal Centers for Disease Control.

The CDC instructs officials to report deaths as COVID-19 deaths whenever the patient has either tested positive or, despite the absence of a test, presents circumstances from which presence of the infection can be inferred “within a reasonable degree of certainty” — such that its contribution to death is “probable” or may be “presumed.” This is drawn from CDC guidance, which directs that COVID-19 be specified in death certificates whenever “COVID-19 played a role in the death.”

Who knows? The fact is, they are just making estimates. But, as the Times computes it, this estimate has suddenly increased the overall U.S. death count from COVID-19 by a whopping 17 percent. And if the Big Apple is going to cook the books this way, what is to stop Newark, New Orleans, Philadelphia, Boston, Chicago, Detroit, Los Angeles, and the rest?

Canada Bends the Curve April 15 Update

Kung Flu in Canada is reported at Coronavirus disease (COVID-19): Outbreak update

The image presents coronavirus data as of the latest week 14 statistics complete April 14, 2020. From the underlying data we can see that this covid 19 outbreak started toward the end of the annual flu season. Here are the daily reported tests, cases, and deaths smoothed with 7 day averaging.
The graph shows that the number of cases has flattened, averaging 1287 the last 3 days after peaking at 1380 on April 8.  ( All daily figures are averages of the 7 day period ending with the stated date.) Tests also peaked at 17,800 on April 4, and are averaging 14,500 presently.

The cumulative graph shows how the proportions held during this period.
Out of a total 454,983 tests, 27,046 (6%) cases were detected, and 903 died (3% of cases).

Background from Previous Post

With coronavirus sucking all the air out of room globally, I got interested in looking at how the Canadian national flu seasons compare with the new Wuhan virus. The analysis is important since there are many nations at higher latitudes that are in equilibrium relative to infectious diseases, but vulnerable to outbreaks of new viruses. Where I live in Canada, we have winter outbreaks every year, but are protected by a combination of sanitary practices, health care system and annual vaccines, contributing to herd immunity.

For example, 2018-19 was slightly higher than a typical year, with this pattern:
The various flu types are noted, all together making a total of 48,818 influenza detections during the 2018-19 season. A total of 946 hospitalizations were reported by CIRN-SOS sentinels that season (age = or >16).  Source:  Annual Influenza Reports

A total of 137 (14%) ICU admissions and 65 (7%) deaths were reported.  The seasonality is obvious, as is the social resilience, when we have the antibodies in place.

For further background, look at the latest Respiratory Virus Report for week 13 ending March 28, 2020. [In this Respiratory Virus Report, the number of detections of coronavirus reflects only seasonal human coronaviruses, not the novel pandemic coronavirus (SARS-CoV2) that causes COVID-19. Kung Flu statistics are above at the beginning.]

For the period shown in the graph, 320560 flu tests were done, resulting in 32751 type A positives and 22683 type B positives. That is a ratio of 17% of tests confirming conventional flu infection cases. Public Health Canada went on to say in reporting March 22 to 28, 2020 (week 13):
The percentage of tests positive for influenza fell below 5% this week. This suggests that Canada is nearing the end of the 2019-2020 influenza season at the national level. [Keep that 5% in mind]

Summary:  It’s true that total cases and deaths are still rising, and everyone should practice sanitary behaviors and social distancing.  But it appears that we are weathering this storm and have the resources to beat it.  Let us hope for reasonable governance, Spring weather and a return to economic normalcy.

Postscript:  Good News from Calgary and Hamilton

The Calgary Herald reports U of C researchers to begin hydroxychloroquine trial on COVID-19 patients.  Excerpts in italics with my bold.

A provincewide clinical trial led by the University of Calgary will test the effectiveness of the anti-malarial drug hydroxychloroquine on COVID-19 patients, with the goal of reducing pressure on hospitals and preventing further infections.

“There is minimal evidence for use of hydroxychloroquine to use it, but there is enough (evidence) to study it,” said Metz, the acting facility medical director at Foothills Medical Centre and a professor in the department of clinical neurosciences at the Cumming School of Medicine.

“It just has to be done. If this drug does, indeed, reduce the severity and help people get better faster, it can help us in flattening the curve.”

The “HOPE” trial, to begin April 15, will target 1,600 Alberta outpatients who have tested positive for COVID-19 and are at risk of developing severe symptoms. The study will determine if hydroxychloroquine can prevent hospitalization for those at highest risk of developing a severe illness.

Participants will give their permission to Alberta Health Services after testing positive for COVID-19 and provide their contact information to U of C researchers. They’ll then be screened for safety and eligibility through a telephone interview and review of their electronic health record.

Those patients accepted will be sent hydroxychloroquine to their homes and will be required to take the drug over a five-day period. Researchers will follow up with participants seven and 30 days after starting the treatment.

Metz said timing of the trial is crucial and must begin within 96 hours of confirmation of a positive COVID-19 result, and within 12 days of symptom onset.

Should the drug prove effective, it may reduce the pressures that COVID-19 is expected to put on the health-care system.

“If we can keep more people out of hospital, then we’re not going to have that huge rise in hospitalizations and more people can get better at home,” she said. “Our system will get back to normal life or whatever we choose to move to in the future.

“We’ll be able to get there if we find this works.”

Hamilton Health Services starting an Anti-Covid19 study this month

Title: Anti-Coronavirus Therapies to Prevent Progression of COVID-19, a Randomized Trial

The ACT COVID-19 program consists of two parallel trials evaluating azithromycin and chloroquine therapy (ACT) versus usual care in outpatients and inpatients who have tested positive for COVID-19. The trial is an open-label, parallel group, randomized controlled trial with an adaptive design. Adaptive design features include adaptive intervention arms and adaptive sample size based on new and emerging data.

Experimental: Azithromycin and Chloroquine Therapy (ACT)
Chloroquine (Adults with a bodyweight ≥ 50 kg: 500 mg twice daily for 7 days; Adults with a bodyweight < 50 kg: 500 mg twice daily on days 1 and 2, followed by 500 mg once daily for days 3-7), plus Azithromycin (500 mg on day 1 followed by 250 mg daily for 4 days)  Source:  ClinicalTrials.gov

Some coronavirus humor.

 

Are You Afraid Yet?

David Warren writes at his blog Essays in Idleness on the scourge of modern media in the coronavirus age. Be afraid, be very afraid. Excerpts in italics with my bolds.

Be afraid, be very afraid

Gentle reader may be wondering how many people are in hospital with the Batflu. Let us take North America’s “epicentre” of New York City, for our spot check. The experts guessed, with all the confidence of settled science, that 140,000 beds would be needed at this point in the pandemic, give or take a few thousand. By the end of last week, 8,500 were occupied. That’s about one bed for every sixteen they anticipated.

(A hotelier who trusted their calculations would be out of business by summer.)

Those glued to the news may have heard about this shortfall. Their horror is now, that if we go back to work, and try to put our lives back together, some of those beds may be refilled.

Horrible things do happen on this planet, and from the accounts I’ve read, the Chinese Communist Batflu is among them. If you get it, you could indeed become quite ill. But this is unlikely, unless you are already severely immuno-compromised. In a world that could think straight, the old and weak would be the very people we were racing to shelter: not de-prioritizing because they’re going to die soon anyway.

Your chances of getting the Xi Jinping Batflu are slight, and falling, but not actually zero. Of course, it is in the interest of the media of entertainment (which is to say, all the Western media today, with their heavy Chinese investments) to sensationalize; and thus produce a sensation that every political operative may apply to his self-interested, political ends.

It is hard for people, especially while scared, to consider anything in proportion. And it is difficult to find contextual information: for we cannot expect the media of entertainment to tell us anything that might ruin the show, while they’re in the theatre business.

The “beauty” of computer projections, working from speculative data by theory, is that it won’t be off by double, or half. It will be off by orders of magnitude. This will even help the researchers wet themselves. Whereas, mere common sense will fail every time.

Leafing through an old Idler magazine, during my own compulsory isolation, I was reminded of the scary age of Reagan. If my reader is old enough, he will remember nuclear annihilation. Did I know, I was then told, that the superpowers had enough A-bombs to vaporize everyone on the planet ten times over? — provided they were efficiently deployed, and we all held still.

But as I argued then, there were other terrifying threats to human life.

“There is, for instance, enough water in the planet to drown everyone four thousand times; there are enough matches to set fire to every wooden building; enough kitchen knives to murder all the husbands of the world; enough hairspray (if drunk) to poison all their wives; enough pillows to smother the entire population of Asia; enough pencils to put out everyone’s eyes; enough fishbones to choke the combined population of France and Italy; enough ties, belts, suspenders, and pyjama draw strings to hang everyone over the age of forty; enough cigarettes (if eaten) to make everyone in Africa south of the Sahara throw up; enough stairs for all the toddlers in the world to fall down; enough statues to crush the inhabitants of the fourteen largest cities in the American Midwest; enough piano wire to garrot three-quarters of the population of Roumania; enough frozen lamb chops to club to death the entire Scottish aristocracy.”

Granted, the weight of human suffering. Granted, that we all progress to biological death, after a brief illusion of invincibility. But would it be entirely irresponsible, to dance our way through the interim? Even while the vultures are circling in the sky?

Another Way Carbon Makes Life Better

 

With wall thicknesses of about 160 nanometers, a closed-cell, plate-based nanolattice structure designed by researchers at UCI and other institutions is the first experimental verification that such arrangements reach the theorized limits of strength and stiffness in porous materials. Credit: Cameron Crook and Jens Bauer / UCI

Brian Bell, University of California, Irvine, writes at phys.org announcing a new way that carbon will serve humanity in years to come.  It’s another example of scientific progress making human life better. The article is Team designs carbon nanostructure stronger than diamonds. Excerpts in italics with my bolds.

Researchers at the University of California, Irvine and other institutions have architecturally designed plate-nanolattices—nanometer-sized carbon structures—that are stronger than diamonds as a ratio of strength to density.

In a recent study in Nature Communications, the scientists report success in conceptualizing and fabricating the material, which consists of closely connected, closed-cell plates instead of the cylindrical trusses common in such structures over the past few decades.

“Previous beam-based designs, while of great interest, had not been so efficient in terms of mechanical properties,” said corresponding author Jens Bauer, a UCI researcher in mechanical & aerospace engineering. “This new class of plate-nanolattices that we’ve created is dramatically stronger and stiffer than the best beam-nanolattices.”

According to the paper, the team’s design has been shown to improve on the average performance of cylindrical beam-based architectures by up to 639 percent in strength and 522 percent in rigidity.

Members of the architected materials laboratory of Lorenzo Valdevit, UCI professor of materials science & engineering as well as mechanical & aerospace engineering, verified their findings using a scanning electron microscope and other technologies provided by the Irvine Materials Research Institute.

Bauer said the team’s achievement rests on a complex 3-D laser printing process called two-photon lithography direct laser writing. As an ultraviolet-light-sensitive resin is added layer by layer, the material becomes a solid polymer at points where two photons meet. The technique is able to render repeating cells that become plates with faces as thin as 160 nanometers.

One of the group’s innovations was to include tiny holes in the plates that could be used to remove excess resin from the finished material. As a final step, the lattices go through pyrolysis, in which they’re heated to 900 degrees Celsius in a vacuum for one hour. According to Bauer, the end result is a cube-shaped lattice of glassy carbon that has the highest strength scientists ever thought possible for such a porous material.

Nanolattices hold great promise for structural engineers, particularly in aerospace, because it’s hoped that their combination of strength and low mass density will greatly enhance aircraft and spacecraft performance.

Other co-authors on the study were Anna Guell Izard, a UCI graduate student in mechanical & aerospace engineering, and researchers from UC Santa Barbara and Germany’s Martin Luther University of Halle-Wittenberg. The project was funded by the Office of Naval Research and the German Research Foundation.

Footnote: This material adds to the many ways our lives are already enriched by carbon-based materials.