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


  1. Hifast · April 20, 2020

    Reblogged this on HiFast News Feed.


  2. rw · April 25, 2020

    If only there was this much pressure to get things right on the general circulation models!

    (BTW, I have a question for you. I was reading the article in TAR about CO2->warming detection, and noticed a graph that showed control outputs of 3 GCMs for a ~1000 year period (I think; anyway, it was for several centuries at least). The striking thing was that there were no hints of long-term cycles. In other words, circa 2001 the GCMs didn’t produce anything more than short-term variability. I wondered if there had been any changes to the models since then. Or is this pretty much the situation even now?)


    • Ron Clutz · April 25, 2020

      Thanks for the question rw. Don’t have a detailed answer right now. AFAIK both CMIP5 and 6 hindcast back to 1850 and forward to 2100. The premise is that medieval warming did not happen (Mann hockey stick) and all the modern warming since beginning of industrial revolution is due to burning fossil fuels. So no real need to go back farther.


      • rw · April 25, 2020

        Okay. Sounds like nothing has changed.

        (I find it very hard to believe they couldn’t add solar effects that may give rise to the 1500-year cycle that Singer and Avery talk about in their book on the subject – if they wanted to. [I’d love to see what emails the Team has shared over this!])


      • Ron Clutz · May 1, 2020

        rw, maybe this is the issue you are raising. Apparently, they do attempt to validate the model’s CO2 temp sensitivity by using them to estimate paleoclimates. Here is a paper saying the latest batch CMIP6 produce unbelievably high past temps when tested in this way.


  3. Pingback: Militant Medicine Breeds Bad Pandemic Policies – Climate-

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