Fuel Moisture and the Dragon’s Breath

Fuel moisture content equilibrates with atmospheric humidity on a scale of hours to weeks depending whether it is dead or alive and how thick it is. There is widespread superstition that drier fuels from climate change can be blamed for the rash of fires recently. Here we explore the relationship between atmospheric temperature and humidity to show that temperatures have not increased enough to reduce fuel moisture more than 3%.

The United States Forest Service has been studying fires for a long time. A graphic from USFS research paper INT-359 below shows the relationship between atmospheric temperature and humidity in an Idaho forest.

It can be seen that the temperature changes about 25 degrees F and humidity changes about 25%. Santa Rosa, CA has a similar diurnal range. We can generalize that the relationship is symmetrical and inversely related at 1:1 degrees F to %humidity.

Santa Rosa and nearby Windsor have lost houses each of the last three years. How much has Santa Rosa’s climate changed? Below is a graphic from Jim Steele showing average high temperatures for Santa Rosa.

It can be seen that average maximum temperatures in Santa Rosa have declined since the 1930’s like most of California and the American West. Maximum temperatures control fuel moisture low points. Atmospheric humidity and fuel moisture must have increased from the 30’s, although they have been decreasing since 1980.

The average temperature of the entire state of California is a fatuous metric for fires, but even if we could come to believe the average somehow controls the many areas like Santa Rosa where temperature has decreased, the increase of 2.5 degrees F from 1930 would only yield a 2.5% decrease in fuel moisture. The dragon’s breath needs more than that.

Furthermore, NASA has come out with a global fire area assessment.

Seems the dragon just likes California.

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Will a Vaccine Work?

Last year’s seasonal flu vaccine was judged 45% effective. This is typical effectiveness for a seasonal flu vaccine after half a century of practice. Now let’s say 50% of the people will take the vaccine in line with recent studies. This is multiplicative so the population effectiveness is 22.5%. SARS 2 has an Ro of at least 3, so a vaccine would need to be effective for 70% of the population. 22.5% will help but will not end the pandemic. However, natural immunity from recovery has been building. Unlike vaccine immunity based only on who was given or will take the vaccine, natural immunity is based on the virus finding the most susceptible first. Natural group immunity may require only ~20% of the population to end the pandemic. The extent of natural immunity is currently unknown. The pandemic will end when either the natural or vaccine threshold of group immunity is achieved, or some combination thereof. What is clear is that a significant contribution from natural immunity will be necessary for a vaccine to work.

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Seasonal Darwinism

The time scale of viral evolution is hours. The infectious vectors called virions can each produce thousands of copies of themselves from a single infected cell. Within hours, natural selection will determine which virions will successfully infect another cell/organ/individual; and which will die.

In ecology, the opposing reproductive strategies of wide dispersal and rapid colonization vs. taking a stand and extracting maximal resources are mathematically modeled as r and k selection, respectively. R selection favors being a weed. K selection favors being a giant sequoia.

The hypothesis of seasonal Darwinism attempts to explain the mysterious seasonality of some viruses as near real time r selection, favoring dispersal and transmissivity, during the summer when human populations are better fed, more dispersed, more outside, and more vitamin D enriched; and k selection, favoring virulence, in the winter when these factors are reversed.

SARS 2 is a single stranded positive RNA virus. It must first create a negative copy for replication, making replication and translation at the same time more difficult. The virus must balance these two processes, and the balance struck determines how many copies of itself it makes–its virulence. Selection based on this balancing strategy, with virulence favored when it is easy to find new hosts, and transmission–producing fewer copies for a longer period of time–when finding new hosts becomes more difficult. 

One might argue that SARS 2 is not seasonal since a resurgence has taken place over much of the northern hemisphere summer, but this resurgence has clearly been less virulent, with a far lower case fatality ratio. The coming winter will tell.

Credit: These ideas are strongly influenced by Patrick Stewart on his blog, oldwivesandvirologists. He believes strongly in a temperature switch in the viral RNA secondary structure. Whether or not this proves to be the case, natural selection based on conditions of the host population–seasonal Darwinism–can explain much of viral seasonality.




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Fun With Cut and Paste

Perusing the state new case curves on https://rt.live/ some patterns become apparent.

Some states with early exposure are on a clear downward trend.

Some southern states with later exposure are on an upward trend, California being an exception by early exposure.

Some  early starting midwestern states seem over the hump, DC being eastern with similar shape.

Some later starting states also seem over the hump.

Some northerly late starting states are climbing.

Some states are cruising.

Some show a second wave, and Wyoming which never lifted off.

  1. There is no consistent relationship between lifting house arrest and new cases.
  2.  There is no indication that seasonality is slowing the spread.
  3.  Below Rt=1 (faintly visible in background) Rt can rise and new cases can still fall. The inverse is also true.
  4.  Washington State has managed a modest decline with Rt>1.
  5.  Utah has had steady Rt>1 for the entire period. A month elapesd before there was appreciable increase in cases, and it experienced a striking rise without any increase in Rt.

The more you know, the more you know you don’t know Jack.


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Trouble with Model

We had been trying to do this ourselves but found this fun model: https://gabgoh.github.io/COVID/index.html


We can attest that the model parameters agree with our own incomplete efforts. It has lots of fun stuff to play with, and you can test the sensitivities of all the variables. We disagree with the commonly assumed R(initial) value ~2.5. The table of calculated values on the site has an average of 4.5. An excellent paper in review, Sanche et al at Los Alamos available on the CDC website finds Ri over 5. This model goes completely off the rails at these values so we set close to 3. We also disagree with a short infectious duration. Symptomatic spread alone is at least 5 days and there are at least 3 days of asymptomatic. Surprisingly, the model is insensitive to infectious duration, merely shifting the timing. Above we set the model to as close as possible to N=1 million so for the US you multiply by 327. The model seems excessively sensitive to initial infections. By limiting to 1 million and 1 initial behavior improved. Hospitalizations are a reasonable match, but deaths are too low. What we like about this is that contrary to the IHME model which predicts a decline that has not materialized, this shows we are going to be dealing with this for a while.

Above we tried to coax the model to replicate data we have for deaths and hospitalizations. We know from the Covid Tracking Project and Worldometers that daily hospitalization accelerated to 79,000 about April 15 and have declined only slightly since  ever since, defying the IHME model’s projected decline. Daily deaths reached a high of 2800 about April 20, and have likewise failed to decline according to IHME.

We were able to get a reasonably sensible approximation of actual US hospitalization and death only by removing intervention altogether. This makes no sense as intervention must surely be a factor. The model also takes way too long to reach appropriate values. We are seemingly only two months in.

We have developed some sympathy for the difficulty of modeling, but are forced to conclude that important variables are not yet included.



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The Possible Origins of 2019-nCoV Coronavirus

The possible origins of 2019-nCoV coronavirus
Botao Xiao1,2* and Lei Xiao3
1 Joint International Research Laboratory of Synthetic Biology and Medicine, School
of Biology and Biological Engineering, South China University of Technology,
Guangzhou 510006, China
2 School of Physics, Huazhong University of Science and Technology, Wuhan
430074, China
3 Tian You Hospital, Wuhan University of Science and Technology, Wuhan 430064,
* Corresponding author: xiaob@scut.edu.cn
Tel / Fax: 86-20-3938-0631

The 2019-nCoV coronavirus has caused an epidemic of 28,060 laboratory-confirmed
infections in human including 564 deaths in China by February 6, 2020. Two descriptions
of the virus published on Nature this week indicated that the genome sequences from
patients were 96% or 89% identical to the Bat CoV ZC45 coronavirus originally found in
Rhinolophus affinis (1,2). It was critical to study where the pathogen came from and how it
passed onto human.

An article published on The Lancet reported that 41 people in Wuhan were found to
have the acute respiratory syndrome and 27 of them had contact with Huanan Seafood
Market 3. The 2019-nCoV was found in 33 out of 585 samples collected in the market after
the outbreak. The market was suspicious to be the origin of the epidemic, and was shut
down according to the rule of quarantine the source during an epidemic.
The bats carrying CoV ZC45 were originally found in Yunnan or Zhejiang province,
both of which were more than 900 kilometers away from the seafood market. Bats were
normally found to live in caves and trees. But the seafood market is in a densely-populated
district of Wuhan, a metropolitan of ~15 million people. The probability was very low for the bats to fly to the market. According to municipal reports and the testimonies of 31 residents and 28 visitors, the bat was never a food source in the city, and no bat was traded in the market. There was possible natural recombination or intermediate host of the coronavirus, yet little proof has been reported.
Was there any other possible pathway? We screened the area around the seafood
market and identified two laboratories conducting research on bat coronavirus. Within ~280 meters from the market, there was the Wuhan Center for Disease Control & Prevention (WHCDC) (Figure 1, from Baidu and Google maps). WHCDC hosted animals in laboratories for research purpose, one of which was specialized in pathogens collection and identification (4-6).

In one of their studies, 155 bats including Rhinolophus affinis were captured in Hubei
province, and other 450 bats were captured in Zhejiang province (4). The expert in collection was noted in the Author Contributions (JHT). Moreover, he was broadcasted for collecting viruses on nation-wide newspapers and websites in 2017 and 2019 (7,8). He described that he was once by attacked by bats and the blood of a bat shot on his skin. He knew the extreme danger of the infection so he quarantined himself for 14 days (7).

In another accident, he quarantined himself again because bats peed on him. He was once thrilled for capturing a bat carrying a live tick (8). Surgery was performed on the caged animals and the tissue samples were collected for DNA and RNA extraction and sequencing (4, 5). The tissue samples and contaminated trashes were source of pathogens. They were only ~280 meters from the seafood market. The WHCDC was also adjacent to the Union Hospital (Figure 1, bottom) where the first group of doctors were infected during this epidemic. It is plausible that the virus leaked around and some of them contaminated the initial patients in this epidemic, though solid proofs are needed in future study.
The second laboratory was ~12 kilometers from the seafood market and belonged to
Wuhan Institute of Virology, Chinese Academy of Sciences (1, 9, 10). This laboratory
reported that the Chinese horseshoe bats were natural reservoirs for the severe acute
respiratory syndrome coronavirus (SARS-CoV) which caused the 2002-3 pandemic (9).
The principle investigator participated in a project which generated a chimeric virus using
the SARS-CoV reverse genetics system, and reported the potential for human emergence (10). A direct speculation was that SARS-CoV or its derivative might leak from
the laboratory.
In summary, somebody was entangled with the evolution of 2019-nCoV coronavirus.
In addition to origins of natural recombination and intermediate host, the killer coronavirus probably originated from a laboratory in Wuhan. Safety level may need to be reinforced in high risk biohazardous laboratories. Regulations may be taken to relocate these laboratories far away from city center and other densely populated places.

BX designed the comment and performed literature search. All authors performed data
acquisition and analysis, collected documents, draw the figure, and wrote the papers.
This work is supported by the National Natural Science Foundation of China (11772133,
Declaration of interests
All authors declare no competing interests.
1. Zhou P, Yang X-L, Wang X-G, et al. A pneumonia outbreak associated with a new
coronavirus of probable bat origin. Nature 2020. https://doi.org/10.1038/s41586-020-2012-7.
2. Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease
in China. Nature 2020. https://doi.org/10.1038/s41586-020-2008-3.
3. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel
coronavirus in Wuhan, China. The Lancet 2019. https://doi.org/10.1016/S0140-
4. Guo WP, Lin XD, Wang W, et al. Phylogeny and origins of hantaviruses harbored by bats,
insectivores, and rodents. PLoS pathogens 2013; 9(2): e1003159.
5. Lu M, Tian JH, Yu B, Guo WP, Holmes EC, Zhang YZ. Extensive diversity of rickettsiales
bacteria in ticks from Wuhan, China. Ticks and tick-borne diseases 2017; 8(4): 574-80.
6. Shi M, Lin XD, Chen X, et al. The evolutionary history of vertebrate RNA viruses. Nature
2018; 556(7700): 197-202.
7. Tao P. Expert in Wuhan collected ten thousands animals: capture bats in mountain at night.
Changjiang Times 2017.
8. Li QX, Zhanyao. Playing with elephant dung, fishing for sea bottom mud: the work that will
change China’s future. thepaper 2019.
9. Ge XY, Li JL, Yang XL, et al. Isolation and characterization of a bat SARS-like coronavirus
that uses the ACE2 receptor. Nature 2013; 503(7477): 535-8.
10. Menachery VD, Yount BL, Jr., Debbink K, et al. A SARS-like cluster of circulating bat
coronaviruses shows potential for human emergence. Nature medicine 2015; 21(12): 1508-13.

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Pandemic Persistence

We labor under a serious misconception that Corona can be eliminated, like polio. The history of pandemics tells us otherwise. The H2N2 virus likely caused the pandemic of 1890. It became the seasonal flu until it was supplanted by H3N2 during the pandemic of 1900. H3N2 was in turn supplanted by the H1N1 virus that caused the 1918 pandemic,which became the seasonal flu until the pandemic of 1957, when it was supplanted by an evolved H2N2. Of course, this became the seasonal flu until 1968, when it was supplanted by a new and improved H3N2 during the pandemic of that year. Vaccination became routine in the U.S. in the 1990’s, but none of these viruses has been eliminated.

The graphic above by Ed Rybiki illustrates this game of virus leap frog. An H1N1 genetically identical to laboratory samples from 1918 caused an epidemic in 1977, but many Americans had immunity. There can be little doubt the critter escaped. Another H1N1 variant caused an epidemic in 2009 that caused fewer U.S. deaths because older Americans still had immunity.

Corona is the new lead dog pulling this sled. It may abate as the weather warms, but it is not going away. We will get antibodies from exposure or vaccine, or succumb. Let’s hope seasonality drums it down and gives us time to develop a vaccine.

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Pandemic Seasonality

We do not yet understand seasonality in viruses. There are ideas about UV from sunlight zapping them. There are ideas about elevated vitamin D in humans during the warm season. There are ideas that warm weather just dries them out, that they can’t handle the osmotic pressure of higher humidity, and that melatonin based human immune seasonality and virus seasonality co-evolved.

In general, coated viruses are seasonal. Covid-19 is coated. Three human coated corona viruses that commonly circulate as colds are strongly seasonal. We show below  the seasonality of past U.S. Pandemics.

These deaths are not adjusted per capita, as that has no effect on seasonality and separates the curves. Clearly, prior pandemic deaths are strongly seasonal. The 1967-68 year looks like it may have been pretty bad. Unfortunately, CDC has no monthly data except for 68-69.

Corona got off to a very late start in the U.S. Will it go “away in May”? Nobody will write a guarantee as this virus has been breaking rules. If it does, it may be back very strong next fall. We should have a vaccine by then.

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The Shapes of Pandemic Curves

Getting monthly data for even 1957 and 1968 is tough sledding. We finally found in the CDC Vital Statistics Archives monthly data for these pandemics. Monthly data for surrounding years is available for 1968, but only 1957 and 58 for that period. Influenza is a separate line immediately above pneumonia. Our interest was the shapes of the curves, so rather than get into the modern morass of adding flu and pneumonic deaths and looking for “excess”, we report here only deaths ascribed solely to flu.

Corona deaths are the crude totals from Worldometers and there has only been one complete monthly bin so far. CDC will eventually parse these crude deaths into flu and pneumonic, likely reducing the trend. Germany recently reduced its corona deaths nearly 50%. It appears corona will be worse than ’57 and ’68, but nowhere near as bad as 1918.

We show here San Francisco data as a proxy for the entire US based on the following:

We got monthly data by tracing in Autocad according to the monthly grid and apportioning the areas under the curve. We originally were going to use Boston as more representative, but population data proved difficult and SF had a nice round 500k population in 1918.

The graph above is for all causes of deaths. We had previously determined that the shapes of the curves for all causes in ’57 and ’68 closely matched the influenza only curves. On this basis, we apportioned our SF data to match 670k US flu and pneumonia deaths and multiplied it by the ratio of flu only to pneumonia deaths in 1968 (.074) to get flu only deaths in 1918. We then subtracted 1000 to roughly align the start points.

Obviously this is not ideal, but it is at least a rational basis for comparing the shapes.

There is a notable similarity of the shapes, reaching initial peaks in about a month and a half. It is also notable that the fall from initial highs seems much faster than IHME projects.

We have shown above only the second and third peaks of the widely circulated triple whammy from England shown above. US data for the first wave is dismal. A handful of deaths at an army base and nearby towns. The CDC offers this curve as anecdotal without axis data.There is an interesting paper (Olsen et al 2005) arguing for a US beginning in New York as early as 1916.

Sobering image of a ravaged lung from a soldier who died from the 1918 flu from Smithsonian Magazine:

We now feel trying to force everything to actual deaths for influenza only was an error that distorts the true historical perspective, so we make this addendum April 12. Below we have converted everything to deaths per capita and added the IHME model in monthly bins.


From this perspective, it appears very unlikely that Corona will ever make the big time in the US. be about the same as 68-69. Current data suggests that US cases and deaths are at or very near their peaks, somewhat earlier than IHME prediction.

A separate axis is required for 1918. It is a completely different animal. US population was 1/3 current, and deaths were six times higher. Let’s not go there, ever.

Note* We made a spreadsheet error from prior dithering with virus only that incorrectly reduced the IHME model. The graphic and text were modified 4-16-20.

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Unified Geological Map of the Grand Canyon

We were puzzled about the Supergroup so we bought a pdf map from the Geological Society of America that covered the eastern part. We found that a forest of inconsequential faults, strikes and dips, and other notations was distracting us from the trees. We converted it to Autocad with the notion of moving these nuisances to a different layer. This tediously done, we still did not like the hatches so we set about creating our own with better contrast.

This proved to be astonishingly difficult as the unit boundaries proved to be multiple crudely overlapping splines and polylines that confounded our new hatches. We wound up erasing duplicates and retracing the boundaries. Lots of Supergroup exposures lie west of our GSA map, so we used a similar approach with scaled screen captures from the USGS “Mapview”. These we positioned in

Arcmap to create the previously posted image below.

Arcmap allows crude two point geolocation of Autocad linework that is good enough for views from the stratosphere as above, but when we dug into the schists we really wanted more. We had been unable to accurately position the GSA map in Autocad. Autocad warps the underlying Bing image to match the linework from a single geolocation point, and none of their projections matched. 

During the schist work we realized that the Autocad imagery was good enough that we could SEE the layers and began drawing directly on the imagery. 

Thus the Unified Map of the Grand Canyon was born.

It was a lot of work. It is a completely different kind of geological map based on the river runner’s encounter with the TOP of each layer. Top means when you can’t see it any more because it is buried by the layer above, not when there is air above it. If there is air above it, you are somewhere below the top and above the bottom; which here is the top of the layer below. Using only the top forces constant mindfulness of the stratigraphy. 

We use no regions or hatches. The imagery itself becomes the hatch, true to the nature of the layer. We ignore land slides and faults not critical for understanding. Recent lava is a distraction. It is plainly visible. We die into it when it is thick, but often find a line that telegraphs through.

The map is unified in that the USGS Mapview uses many different maps compiled over half a century with different groupings and different names. Our interpretation groups Esplanade with Supai, Surprise Canyon with Redwall, and Temple Butte with Muav. The latter two are discontinuous, and well, we didn’t need any more lines.

We will get further into the features of this map in the next post. 








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