Friday, April 24, 2020

COVID-19 Prophylaxes: Face Mask Material, Breathability

In my first post of this mini-series within a series, I explained that the Prophylactic Index for widespread use of face masks (for those of us north of the tropics) is 12.7. I recommended that reasonable face mask requirements be part of any plan to relax social distancing rules. I said nothing about the efficacy of various types of face mask materials. I corrected that shortcoming, and then some, in my previous post.

While that previous post provided you with filtration information on nearly three dozen candidate materials for DIY face masks, it provided no insight into how easy of difficult it might be to breath through them.  I correct that shortcoming herein, and I make additional under-informed recommendations.

The fine folks at both the NIH and Smart Air tested the ease or difficulty of moving air through the various face mask materials that they tested for filtration efficiency. The NIH folks measured pressure drop across the material. The Smart Air folks measured the fan power required for a give flow rate through the material, and they provided their results as breathability scores ranging from 1 to 6.

Since most people don't relate particularly well to pressure drop, or have six fingers, I've converted both the NIH and Smart Air scores to a breathability score of 0 to 10, with the surgical mask at 5 and the most easy-to-breath-through material at 10. I provide a composite list of the results below, breathability winners on top.

Silk:  breathability score = 10
Pillowcase:  7
Scarf, 100% Cashmere:  6
Scarf, 100% Ramie:  6
T-shirt, 100% cotton: 6
Linen:  6
Cloth, floor cleaning, disposable, 3M: 6
Synthetic fiber, velvet: 6
T-shirt: quick-dry, 95% Polyester + 5% Spandex:  6
Bed sheet, brocade:  6
Scarf, wool, 100% merino: 6
HEPA Filter: 6
Polypropylene bag, non-woven: 6
Bandana, 100% cotton: 6
Neck warmer / snood, 100% microfiber polyester: 6
Cloth, dusting: 6
Paper towel, Scott’s blue shop towel: 6
Canvas, 0.45 mm thick:  6
Surgical mask:  5
T-shirt, 100% cotton (double layer): 5
Pillowcase (double layer):  5
Bra pad, muslin + sponge: 4
Nylon, 70D: 4
Dish towel / tea towel:  3
Pillowcase, antimicrobial:  3
Paper towel, brown, hand drying: 3
Bed sheet, 100% cotton, 120 thread:  3
Bed sheet, 100% cotton, 80 thread: 3
Denim, 10 oz, 0.7 mm thick:  3
3M N95 mask: 3
Coffee filter, CHEMEX: 2
Canvas, 0.75mm:  2
Nylon, 40D: 1
Coffee filter, HERO: 1
Canvas, 1.1 mm thick:  1
Dish towel, tea towel (double layer):  0
Vacuum cleaner bag:  0

So ...

All that you have to do to decide whether or not you should make your own masks, and settle on what material you should use (assuming you decide to go rogue), is look at two different sources of test data, for three different particle micron sizes, and somehow weigh those data against the breathability numbers provided above.

Or ...

You could just continue reading this august post, the one in which I have scored each of the candidate materials with another of my made up numbers, this time the soon to be famous Face Mask Index, or FMI. The Face Mask Index is merely the breathability score multiplied by the material filtration efficiency for 0.3 micron particles. (As an informative but boring aside, I had to adjust the NIH efficiencies for 1.0 and 0.02 micron particles based on 0.3 micron particle results for similar materials from the Smart Air data.)

The FMI results for each of the face mask designs / materials are presented below, in descending order of FMI. The winners are on the top; the losers are at the bottom. The list contains a few surprising results, but it is mostly populated with disappointing results.

Here we go.

HEPA Filter: FMI = 5.0
Surgical mask:  3.8
3M N95 mask: 2.9
T-shirt, 100% cotton (double layer):  1.5
Dish towel / tea towel:  1.4
Pillowcase:  1.4
Silk:  1.3
Linen:  1.2
Canvas, 0.45 mm thick:  1.1
Paper towel, Scott’s blue shop towel: 1.1
Pillowcase (double layer):  1.0
Coffee Filter, CHEMEX: 1.0
T-shirt, 100% cotton (double layer):  0.8
Synthetic fiber, velvet: 0.8
Pillowcase, antimicrobial:  0.7
Polypropylene bag, non-woven: 0.7
Bed sheet, 100% cotton, 120 thread:  0.7
Bed sheet, 100% cotton, 80 thread: 0.6
Canvas, 0.75mm:  0.6
Denim, 10 oz, 0.7 mm thick:  0.6
Bra pad, muslin + sponge: 0.6
Nylon, 70D: 0.5
Cloth, floor cleaning, disposable, 3M: 0.4
Cloth, dusting: 0.4
Bed sheet, brocade:  0.4
Scarf, wool, 100% Merino: 0.4
Scarf, 100% Cashmere:  0.4
T-shirt, quick-dry, 95% Polyester + 5% Spandex:  0.4
Scarf, 100% ramie:  0.2
T-shirt, 100% cotton:  0.2
Paper towel, brown, hand drying: 1.0
Bandana, 100% cotton: 0.1
Neck warmer / snood, 100% Microfiber Polyester: 0.1
Nylon, 40D: 0
Dish towel, tea towel (double layer):  0
Coffee filter, HERO: 0
Canvas, 1.1 mm thick:  0
Vacuum cleaner bag:  0

From a story-telling perspective, the most discouraging result is that I will not be recommending that people try to save themselves from the coronavirus by breathing through vacuum cleaner bags.

The most surprising result is the sequence of the top three items in the list. The HEPA filter wins because it has the best combination of filtration efficiency at 0.3 microns (83%) and breathability score (6). Though the N95 masks have the best filtration efficiency (96%), they get clobbered by their breathability score (3).

The disappointing result is that there are no great alternatives to the top three masks. All but the top three have such low filtration efficiencies that their breathability scores can't save them.

The most frightening result is that bandana score so low. I've seen many sites that mention bandanas as a viable face covering, and I fear those sites are doing more harm than good. Those poor people who take such bandana advice at face value might just as well wear their home made face mask around their neck, or put it in their back pocket. Either way, they will be taking it in the neck.

The bottom line is that you can't really do much better than just buying the reusable surgical masks, presuming you can get them. They have, after all, been working great for those 13 countries that began wide-spread use of face masks prior to 1 March 2020.

Not everyone will be able to acquire disposable surgical face masks, though. Given my ongoing efforts to free some of those wrongfully convicted, I'm particularly sensitive to those people we have behind bars, in close quarters, almost entirely reliant on others for potentially life-saving decisions regarding matters such as "which face mask, if any, should I wear?' In the one case consuming most of my wrongful conviction time and energy, I'm informed that the prisoners have been provided face masks, and that those face masks are seemingly made out of the same material as their bed sheets. That's pretty scary.

For those people who, for any reason, cannot get hold of N95 or surgical face masks, I recommend they somehow construct their own face mask using some combination of materials from the prioritized list below, using as much thickness as their breathing permits.

#1  HEPA filters
#2  coffee filters
#3  canvas
#4  paper towels
#5  denim
#6  bed sheets
#7  t-shirts

And my sincere best wishes to all those people in such dire straits.

Thursday, April 23, 2020

COVID-19 Prophylaxes: Face Mask Material, Filtration

In my previous post, I explained that the Prophylactic Index for widespread use of face masks (for those of us north of the tropics) is 12.7. I recommended that reasonable face mask requirements be part of any plan to relax social distancing rules. I said nothing about the efficacy of various types of face masks. I correct that shortcoming, and then some, in this post.

Clearly, some masks are better than others. I'll begin with the not-quite-a-party line. The N95 masks are the best, because they stop 95% of something or other that may or may not begin with the letter N. The N95 masks are generally cup-shaped, and they even have "N95" printed right smack dab on the nose. N95 masks, though, need to be properly fitted to be N95-worthy. Unfortunately, Mosts folks don't fit them properly, so the so-called N95 masks are actually N_something_less_than_95 masks, but that's neither as catchy nor as comforting.

Next apparently, in my under-informed opinion, are the so called surgical masks. They're the blue, pleated, nearly flat, paper (or paperish) masks so commonly seen among the people of the 13 countries that have such low infection rates. Good enough for a surgeon, I say, good enough for thee. Not according to the CDC, though. I attach their summary below. Click to enlarge.


The CDC helpfully advises that a surgical mask "Does NOT provide the wearer with a reliable level of protection from inhaling smaller airborne particles and is not considered respiratory protection."

The CDC should probably not share their wisdom with the non-N95-face-mask-wearing folks of Taiwan, since Taiwan's infection rate (as of 13 April 2020) was 17 per million while ours, here in the good ol' US of A, was a hundred times that, at 1,773 per million.

Instead, the CDC might want to consider several observational studies that show that surgical face masks provide the same level of protection against pathogens as do the N_not_quite_95 masks.

The earlier of the two studies was reported way, way, way back in 2009 in JAMA (Journal of the American Medical Association) as Surgical Mask vs N95 Respirator for Preventing Influenza Among Health Care Workers. The key points of that study read:
"Context: Data about the effectiveness of the surgical mask compared with the N95 respirator for protecting health care workers against influenza are sparse. Given the likelihood that N95 respirators will be in short supply during a pandemic and not available in many countries, knowing the effectiveness of the surgical mask is of public health importance. 
"Results: Between September 23, 2008, and December 8, 2008, 478 nurses were assessed for eligibility and 446 nurses were enrolled and randomly assigned the intervention; 225 were allocated to receive surgical masks and 221 to N95 respirators. […] 
"Conclusion: Among nurses in Ontario tertiary care hospitals, use of a surgical mask compared with an N95 respirator resulted in noninferior rates of laboratory-confirmed influenza."
Allow me to translate the somewhat muddled portion. The surgical masks were "noninferior," meaning that they were "just as good."

Ten years later on, in 2019, the same exact Journal published the results of another, significantly larger study, under the nearly identical, noninferior title of "N95 Respirators vs Medical Masks for Preventing Influenza Among Health Care Personnel." The key points of that study read:
"Question:  Is the use of N95 respirators or medical masks more effective in preventing influenza infection among outpatient health care personnel in close contact with patients with suspected respiratory illness? 
"Findings:  In this pragmatic, cluster randomized clinical trial involving 2862 health care personnel, there was no significant difference in the incidence of laboratory-confirmed influenza among health care personnel with the use of N95 respirators (8.2%) vs medical masks (7.2%). 
"Meaning:  As worn by health care personnel in this trial, use of N95 respirators, compared with medical masks, in the outpatient setting resulted in no significant difference in the rates of laboratory-confirmed influenza."
Imagine that!  Still no difference. The N95 masks were noninferior to the surgical masks!

So what about home-made masks? Given how much as the CDC is troubled by even surgical masks, I assume that the thought of an uninformed citizenry sporting about in home-made masks would cause the Center for Disease Controllers to suffer severe functional dyspepsia. Nonetheless, I have found two studies that actually tested various materials to see which, if any, might work for the construction of homemade masks. I combine the filtration results of those two studies in this post. In the next post, we will consider the rather important issue of being able to breath through the various materials. In the post after that, the third in this august sub-series within an august series, we will consider the issue of sealing the masks to the face.

The first study was reported online in a 22 May 2013 National Institute of Health technical paper, "Testing the Efficacy of Homemade Masks: Would They Protect in an Influenza Pandemic?"

The second study is more recent, less formal, and way more entertaining. It is presented in a blog post, not a technical paper. That august post is entitled The Ultimate Guide to Homemade Face Masks for Coronavirus." It is the fine work of the fine folks at Smart Air. Their mission statement follows.
"Smart Air is a social enterprise and B-corp that combats air pollution by delivering cost-effective air purifiers and providing open-source data on air pollution.”
The emphasis is mine. In my opinion, those folks are providing the most timely, most informative, most useful, most clear, and most entertaining information about face masks, bar none. You can do a lot worse than reading through their entire list of blog posts about the coronavirus.

The Smart Air folks focused their studies on particle diameters near 0.3 micron, which is three one-tenths of a millionth of a meter, which is really really really small, clearly smaller than a breadbox, and approximately three times the the size of the COVID-19 virus.

The NIH folks focused on two microorganisms, one of them being the Bacillus atrophaeus, a black stained bacterium frequently used in biomedical testing, having a diameter ten times that of the coronavirus. Their second microorganism was the Bacteriophage MS2, a single-stranded RNA virus (as is the coronavirus) but only 1/5 as large as the coronavirus. I'll provide the NIH results for both particle sizes below, sorting the items by decreasing order of filtering efficiency, winners near the top, losers near the bottom.

Surgical Mask:  96% for 1 micron,  88% for .02 micron
Dish towel / tea towel (double layer):  97%,  N/A
Vacuum cleaner bag:  94%,  86%
Dish towel / tea towel:  83%,  72%
Cotton mix:  75%,  70%
T-shirt, 100% Cotton (double layer):  70%,  N/A
T-shirt, 100% Cotton:  69%,  51%
Pillow case, antimicrobial:  66%,  67%
Scarf:  62%,  49%
Pillow case:  61%,  47%
Linen:  60%,  62%
Silk:  58%,  44%

Now for the lengthier, more innovative, more relevant Smart Air test results for 0.3 micron particles

3M N95 mask:  96%
HEPA filter:  83%
Surgical mask:  75%
Coffee filter, HERO:  62%
Coffee filter, CHEMEX:  49%
Nylon, 40D:  49%
Canvas, 1.1 mm thick:  49%
Dish towel / tea towel:  48%
Paper towel, brown, hand drying:  33%
Paper towel, kitchen (double layer):  33%
Canvas, 0.75 mm thick: 31%
Denim, 10 oz, 0.7 mm thick: 29%
Bed sheet, 100% cotton, 120 thread: 24%
Paper towel, kitchen:  23%
Bed sheet, 100% cotton, 80 thread:  21%
Canvas, 0.45 mm thick:  19%
Paper towel, Scott's blue shop towel:  19%
T-shirt, 100% cotton (double layer): 15%
Bra pad, muslin + sponge:  14%
Velvet synthetic fiber:  13%
Nylon, 70D:  12%
Polypropylene bag, non-woven:  11%
T-shirt, quick-dry, 95% polyester + 5% spandex:  7%
Bed sheet, synthetic brocade:  7%
Cleaning cloth, floor, disposable:  7%
Cloth, dusting:  7%
Scarf, wool, 100% cashmere:  6%
T-shirt, 100% cotton:  3%
Scarf, light, 100% ramie:  3%
Neck warmer / snood, 100% microfiber polyester:  2%
Bandana, 100% cotton:  2%

Once again, I congratulate the fine folks at Smart Air for doing all the testing and making the results available to all at no charge.

The results are quite interesting, as far as they go. Without considering one's ability to breath through something like a vacuum cleaner bag or silk tent material, it's a wee bit early to make even an under-informed recommendation. For that, hang tight until the next post.

[Mea culpa note in reduced font size: I initially published this post on 23 April 2020. On 24 April, I discovered that I had posted the Smart Air test results for 1 micron particles rather than for 0.3 micron particles. The filtration percentages were substantially worse and the order ranking order was somewhat different. I published this corrected version on 24 April.]

Tuesday, April 21, 2020

COVID-19 Prophylaxes: Face Masks

On 29 February 2020, the US Surgeon General spoke down to those of us among the general public who would deign to wear a face mask. "Seriously people," he tweeted, "STOP BUYING MASKS! They are NOT effective in preventing general public from catching #Coronavirus, but if healthcare providers can't get them, to take care for sick patients, it puts them and our communities at risk."

He put the important part in ALL CAPS, so I guess he is to be believed.

On 30 March 2020, the World Health Organization repeated their mantra that face masks don't work, at least for those of us among the masses. "There is no specific evidence to suggest that the wearing of masks by the mass population has any potential benefit. In fact, there's some evidence to suggest the opposite in the misuse of wearing a mask properly or fitting it properly,"

Using my magnum databasus, I have compared the infection rate of those countries with widespread use of face masks prior to 1 March 2020 to those countries who had no particular use of face masks prior to that same date. You can probably guess some of the countries, and you can probably guess the impending results.

Based on my prowling of the internet (a.k.a the source of all knowledge), I concluded that the following countries were taking widespread advantage of face masks prior to 1 March 2020. I present the 13 countries below in decreasing order of infections per million residents, that value being in parentheses, that value being current as of 13 April 2020. The grand prize winner will be at the bottom.

Keep in mind that the average infection rate for the good ol' USA, land of the free and home of the "Seriously people, STOP BUYING MASKS!" US Surgeon General, was 1,773 as of that same date.

Singapore (499)
South Korea (206)
Hong Kong (135)
Macao (89)
Japan (60)
China (57)
Thailand (37)
Phillippines (25)
Taiwan (17)
Indonesia (17)
Sri Lanka (10)
Vietnam (3)
Nepal (0)

We can use these data to calculate my world famous PI, which stands for prophylactic index. The average infection rate for the 13 countries in the face mask study group is 87 infections per million. The average infection rate for the 178 countries outside the face mask study group is 455 infections per million. The PI for face masks is 455 / 87 = 5.2.

On a worldwide basis, those countries that follow the advice of the US Surgeon General and the World Health Organization are 5 times more likely to become infected than those countries who tell those leaders to piss off.

In fairness to the US Surgeon General, in a public act of probably enforced contrition, he later posted a video, starring himself, advising that not only should people wear face masks, they can make their own at home. He even published instructions. Good for him for swallowing the bitter pill.

In fairness to the the World Health Organization, it has been considerably less contrite with respect to its indefensible face mask position. In its position paper of 6 April 2020, the WHO tried to cover its organizational ass as best it could. The emphasis is in the original.

“Wearing a medical mask is one of the prevention measures that can limit the spread of certain respiratory viral diseases, including COVID-19. However, the use of a mask alone is insufficient to provide an adequate level of protection, and other measures should also be adopted. Whether or not masks are used, maximum compliance with hand hygiene and other IPC measures is critical to prevent human-to-human transmission of COVID-19.”

Their new position, if I may be so bold as to state it clearly for them, is that face masks do work but only if you wash your hands.

Do we really need the WHO to tell us to wash our hands? Whose mother didn't tell him or her repeatedly to wash his or her hands?

In the same position paper, the motherly WHO immediately followed its new, brilliant "wash and wear" position with the following gobbledygook.

"Studies of influenza, influenza-like illness, and human coronaviruses provide evidence that the use of a medical mask can prevent the spread of infectious droplets from an infected person to someone else and potential contamination of the environment by these droplets. There is limited evidence that wearing a medical mask by healthy individuals in the households or among contacts of a sick patient, or among attendees of mass gatherings may be beneficial as a preventive measure. However, there is currently no evidence that wearing a mask (whether medical or other types) by healthy persons in the wider community setting, including universal community masking, can prevent them from infection with respiratory viruses, including COVID-19."

If I may be so bold, I'll try to clarify their gobbledygook for them. Here we go. Masks work if you are around people who are known to be sick, or if you are in a mass gathering of people not known to be sick, but they may not work when you are otherwise out in the public, rather or not everyone else is wearing a mask.

Sorry. That was the best I could do for them. It was like trying to put lipstick on the swine flu.

Here, back in the good ol' US of A, we are considering how soon and to what extent we should ease our semi-lockdown. Given the effectiveness of face masks in reducing the spread of the coronavirus, we should absolutely condition relaxation of the social distancing restrictions on adoption of appropriate face mask requirements.

To stress this point more fully, I'll end this post by recalculating the face mask PI based on just those countries north of the tropics. Recall that in my previous post, those countries north of the tropics have far, far higher infection rates than countries further south. Face masks, therefore, will likely provide those in the northern latitudes even greater relative benefit.

Here we go:

South Korea: 206 infections per million, as of 13 April 2020
China: 57
Japan: 60
Taiwan: 17
Nepal: 0

Average for northern latitude, face-mask-wearing countries: 68.1
Average for other northern latitude countries: 863.9
Increased safety factor for wearing face masks: PI = 12.7

As the contrite US Surgeon General might now tweet: ""Seriously people, START WEARING MASKS!" 

Sunday, April 19, 2020

COVID-9 Prophylaxes: Geographic Location

I provide my introduction to this COVID-9 Prophylaxes series in my magnum postus. Here I began detailing the results of my observational studies. Be aware that we are in search of one or more COVID-9 prophylaxes. In other, less suggestive words, were are in search of preventative measures to keep us from becoming infected.

Using my august magnum databasus, I analyzed the per capita infections of 191 countries. Though I have 208 countries in my database, I exclude from my analysis those 17 countries with fewer than 50,000 residents. Those small countries (in population, not necessarily in size) show extreme variations in per capita infection rates; some are extremely high and some extremely low. Rightly or wrongly, I attributed those extremes to the problem of small sample size, and I thereby excluded them without further study.

I obtained death and infection numbers for each country from the now world-O-famous worldOmeter site. I updated those numbers not long before writing this post. They are current as of 13 April 2020.

I obtained the population for each country from a different worldOmeter web site.

Since I'll be reporting infections per million people on a country by country basis, those two sets of data are all I need to get started. I will identify my other sources of data as appropriate.

I'll be reporting potential prophylaxes based on their Prophylactic Index, which I cleverly abbreviate as PI. The Prophylactic Index is a term I simply made up. It allows me to score potential prophylaxes by a single number. The PI is simply the ratio of two infection rates. More specifically:

PI = infections per million of control group / infections per million of potential prophylactic group.

The larger the PI, the more effective the potential prophylactic might be. As a hypothetical example, presume that one country with Possible Prophylactic #1 has 100 infections per million while every other country in the group has 1000 infections per million. In that hypothetical, PI = 100 / 10 = 10. In other words, the data suggests that Possible Prophylactic #1 residents will suffer 10 times fewer infections than everyone else, everything else being equal. Alternatively, those who do not (or cannot) take advantage of Potential Prophylactic #1 are 10 times more likely to contract the virus, everything else being equal.

With that dreadfully boring explanation now in hand, you get your first peek at the results of my observational study. It shows the prophylactic effect of living at various latitudes. Here we go:

Tropics (81): 6.1
South of Tropics (20): 5.3
North of Tropics (90): 0.1

Holy Cow!

To understand my feigned amazement, I offer the following, thrilling descriptive narrative. The number within the parentheses represents the number of countries in the potential prophylactic group. Using the Tropics as an example, the study compared the 81 tropical countries against the remaining 170 countries in the study. The non-weighted average infection rate for the 170 non-tropical countries was 6.1 times greater than the non-weighted average infection rate for the 170 tropical countries. The PI (prophylactic index) for living in the tropics is therefore 6.1.

With that shorthand in hand, we can move much more quickly.

Looking at the latitude results reveals that those of us living north of the tropics are 10 times more likely to become infected than are those living south of us. It's not immediately clear why that should be so, but that's what the data say.

One possibility, which we will consider carefully beginning with the next post, is that the apparent latitude effect is actually some sort of weather effect. Lots of smart people north of the tropics are arguing that the virus will abate when the summer arrives, due to increased temperature and humidity. Previous flu seasons, after all, tended to abate in the summer months. Why should the COVID-19 virus behave any differently?

If they be correct, those smart people living north of the tropics, then we can expect that the results will flip come northern hemisphere summer, which will also become southern hemisphere winter. Then the northern hemisphere folks will be only 1/10th as likely to contract the infection as their southern hemisphere antipodes.

I know what's coming in the next few posts though, and I fear that the smart NL folks might be in for a rude awakening.

I'll spend the remainder of this post summarizing the PI values for each geographic region on earth, excluding the arctic and antarctic. I'll allow you to begin speculating on what the results might mean.

Africa (55): 14.1
Oceania (6): 3.3
Latin America (20): 3.1
Asia (47): 2.6
Caribbean (17): 2.0
North America (4): 0.5
Europe (42): 0.1

Finally, Africa gets a break.

North America and Europe take it in the neck.

Friday, April 17, 2020

Surviving the Coronavirus: Part 11

Magnum Postus Edition

Magnum Opus is Latin for "Great Work." I'll allow you to guess at what I mean by Magnum Postus.

This is my 616th post for this august blog. The statistics page tells me that this blog is approaching nearly a million page views. That's chump change compared to the top blogs, but it's substantially more than most non-august blogs. I was going to write a post, seemingly within the next few months, bragging that this blog had just surpassed a million page views. Now it just doesn't matter. Who cares about such things? I don't.

I have usually written of freedom wrongfully denied and of needles wrongfully injected. I have been pretty much ineffective. After failing to prevent the execution of someone I believed absolutely innocent, I walked away from this blog. I returned, and I walked away again. Now I am back once again, considerably older and a tiny bit wiser, still grasping at some faint hope that I might help, in some faint way, to resolve a serious problem.

Based solely on the number of page views, my magnum opus is Johnny Frank Garrett and Bubbles the Clairvoyant. Some 37,000 pairs of eyes have zoomed across that article. It was certainly the title, much less certainly the contents, that attracted the viewers.

I sincerely hope that more eyes will soon fall on this post, and be amazed, and excited, and hopeful. Based on what I intend to relate herein, I consider this post to be my magnum postus. I urge you to stay tuned, to read to the last word, for new information on how we can get ourselves out of this deadly pandemic mess.

I am deadly serious.

As I have explained since the beginning of this coronavirus series, we will not be completely out of this deadly pandemic mess until we reach herd immunity. We will reach herd immunity only after a certain percentage of us are immune to the virus, either naturally (due to having survived the infection) or artificially (due to having been vaccinated).

The percentage required for herd immunity can be easily calculated from the starting reproduction number, a.k.a the Ro, R zero, R naught, whatever. The best estimate for the coronavirus Ro has, until recently, been somewhere between 2.28 and 2.5. That Ro range would indicate that we will be completely out of this deadly pandemic mess only after 56% to 60% of us have somehow gained immunity.

Unfortunately, the long term picture may be substantially bleaker than even a 60% threshold for herd immunity. The authors of High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2 argue, persuasively in my discouraged opinion, that the Ro is more likely 5.7. If they are correct, then 82% of us will need to somehow become immune before we cross the threshold to that herd immunity wonderland.

I have also argued from the beginning of this series that, until an effective vaccination is available and widely applied, we need to seriously distance ourselves from one another, also that we need an effective treatment, toot sweet. With a Case Fatality Ratio (CFR) of 0.66% (the current best estimate) and an R zero of 5.7 (the most recent best estimate), the virus, if left to its own devices, will take 82% x 0.66% = 0.54% of us. In the US alone, with our 330 million souls, the pandemic would claim 1.8 million.

Social distancing does delay the spread of the infection. It buys us time until vaccines can be developed, tested, and distributed. The vaccines will take a year or so, however, and the adverse effects of social distancing (on the economy and on our basic infrastructure) can be deadly in themselves. We need a means (oxymoron trigger warning!) to quickly ease back into a sustainable way of life while we await the vaccines. That is why I've stressed the need for a treatment, with chloroquine (or some variation thereof) being my unprofessional, not worth a nickel, top pick for most likely to succeed.

What I did not fully appreciate, however, is that there might be one or more prophylactic preventative measures that could act as a temporary pseudo-vaccine. Any such prophylactic preventative measure would be far better even than an effective treatment. Much better to avoid contracting the disease than to burden the system by suffering through an infection.

Social distancing is actually a prophylactic measure. It keeps us from infecting others or being infected ourselves. It does not, however, allow us to go about our lives as if we've been vaccinated. It is the non-disruptive pseudo-vaccination preventatives prophylaxes in which I have most recently become interested in, and in search of.

I believe that I have pseudo-stumbled across not just one, but three, effective prophylaxes that we can rapidly adopt, that will allow us to get back to work as a society, that will spare an untold number of lives. In this post I will present the evidence on which I base my outrageous claim. In this post, and many to follow, I will explain that each measure is being effectively used, either consciously or unconsciously, in multiple other countries. In this post, I will present what I believe to be a safe, low-cost approach for getting our lives back in order while, at the same time, saving more lives than we will thankfully ever count.

That is why I consider this post to be my magnum postus. Not because of its magnus writing, but because it reveals a simple, life-saving path forward.

To identify potential COVID-19 prophylaxes, I wrote my own database. It is an exceptionally simple database, at least for those accustomed to writing databases. Nonetheless, it has become my magnum databasus.

Because what I believe I have learned is too extensive for even one of my posts, which tend to be a bit on the lengthy side, I will use this post to summarize my conclusions. I will follow with a new series of posts to provide detail.

Focusing on countries north of the tropics, my results reveal:

Finding #1: Countries that began widespread use of face masks prior to 1 March 2020 have 11 times fewer infections from the COVID-19 virus compared to those countries that have delayed widespread use of face masks.

Finding #2: Countries suffering malaria at a rate in excess of 0.1% of their population have 66 times fewer infections from the COVID-19 virus compared to those countries with a lower rate of malaria.

Finding #3: Countries suffering tuberculosis at a rate in excess of 0.1% of their population have 83 times fewer infections from the COVID-19 virus compared to those countries with a lower rate of tuberculosis.

Based on the results just summarized, my conclusions are:

Conclusion #1: Widespread use of face masks provides society with a powerful prophylaxis against the COVID-9 virus.

Conclusion #2: There is at least one malaria medication, already in use prophylactically in some countries, either intentionally or unintentionally, that is an effective prophylaxis against the COVID-19 virus.

Conclusion #3: There is at least one tuberculosis medication, already in use prophylactically in some countries, either intentionally or unintentionally, that is an effective prophylaxis against the COVID-19 virus.

Based on my conclusions just summarized, I offer the following recommendations:

Recommendation #1: Face masks should be required for all people who venture out and may come near anyone else. If we adopt this recommendation, we can safely ease social distancing measures.

Recommendation #2: To identify specific medications that can act as safe, effective prophylaxes against the COVID-19 virus, we need quick-turnaround observational studies that are superior and more detailed than mine. If we identify and adopt even one such prophylactic medication, then we can safely eliminate nearly all social distancing measures.

Details to follow in subsequent posts.

Monday, April 6, 2020

Surviving the Coronavirus: Part 10

Time for Cautious Optimism Edition

This will be the shortest post of this august series so far. I'll provide a little extra white space to allow the cheering to die down.


I just checked the number of infections in the U.S. The curve seems to be noticeably rounding.  I checked the doubling period. It jumped to 9 days. I've updated my chart and I present the current version below.  Behold.


If the doubling period continues to increase, I will gladly miss my prediction that we will have a million infections by the end of the month. We will still go over a million infections, by a substantial amount, but perhaps not this month.

The one-day jump from 6 to 9 days might be a one day anomaly. Alternatively, it may be more evidence that we are indeed flattening the hell out of the curve. I'll check again tomorrow. Assuming that I'm not one of the one-in-a-thousand of us infected, I might provide another update.

Saturday, April 4, 2020

Surviving the Coronavirus: Part 9

Good News! Bad News! Good News! Edition

The good news is that I was wrong, oh so glad to be wrong, about when we would reach a quarter million infections in the good ol' USA.

In Part 6 of this august series, the Don't Die! Edition, I reported that the number of infections in the US was 53,478 as of Tuesday, 24 March 2020. As a pop quiz in exponential growth, I challenged readers to guess how many cases there would be in a week, as of Tuesday, 1 April 2020. I predicted somewhere between a quarter million and a half million.

The good news is that I was wrong, oh so glad to be wrong. According to the wonderfully named worldOmeter, the number of infections as of Tuesday, 1 April 2020 was "only" 215,003.

The bad news is that I was wrong by only two days. According to the now world-O-famous worldOmeter, as of Thursday, 3 April, there were 277,161 cases in the good ol' USA. As of today, this moment, as I write type this, at 1612 GMT, at 9:12 AM Pacific Daylight Savings Time, there are 291,545 cases.

Would anyone like to guess how long before we get to a million US infections? I predict we'll exceed that dramatic but no-more-meaningful-than-any-other number by the end of this month, with a few days to spare. I sincerely hope that I will once again be proved wrong, and this time by a wide margin.

The good news is that the doubling time is increasing. The doubling time is the number of days required for the number of cases to double. If there are 10 cases on day 3 and 20 cases on day 5, then the doubling time is 2 days. Similarly, if there are 100 cases on day 30 and 200 cases on day 32, then the doubling time is 2 days. Small doubling times are bad (for pandemics, not stock portfolios). Very small doubling times are very bad.

What we want to see are very large doubling times. When the doubling time reaches infinity, the number of infections has reached its peak, and the numbers will begin to drop.

We can, and I will, extract approximate doubling times from the daily infection numbers. Before I do that myself, I will present a quick mathematically based (don't tell anyone) summary on how you might do it yourself.

TRIGGER WARNING! Look away for a few lines if you are offended by math.

We'll call the doubling time Td.

We'll call the number of cases on a given day Nt (for Number today) and the number of cases on the previous day as Ny (for Number yesterday).

We'll call the rate at which infections are increasing Ri. We can approximate Ri by taking the average of two infection levels (one day after the other) then using that average as the divisor for the difference in infections between the two days. In other words:

     Ri =  (Nt - Ny) / [(Nt + Ny) / 2]

Now the easy part. For a good approximation of the doubling time, apply the incredibly simple equation below:

     Td = 0.7 / Ri

That's it. That's how I will use the worldOmeter data to calculate doubling times below.

If, in the extremely unlikely chance that you would like a more thorough discussion, see the Wikipedia article on doubling times.

TRIGGER WARNING CANCELLED!  You can now look back.

Below, I present the trend in doubling times for USA coronavirus infections in the good ol' USA on a daily basis, based on the good ol' worldOmeter data, in a handy dandy Excel chart.


On 13 March, the Feds declared the pandemic to be a national emergency. Governors began mandating social distancing practices, slowly at first, then more aggressively. Ten days later (or so) the doubling time for coronavirus infections began increasing from around 2.5 days up to nearly 6 days as I write type this sentence. The doubling time will continue to increase, but we have a long, long way to go.

There is no way that the coronavirus will not extract a heavy toll on our country, and on other countries. We need to be aggressive about social distancing, particularly the geezer community among us. We need to get masks and gloves and other protective gear to those who are most exposed: the medical personnel and other first responders, the grocers, those who work in Amazon warehouses, those who deliver our goods. We need to develop and distribute an effective treatment quickly, regs be damned, at least reasonably damned. An effective treatment will allow us to relax, but not remove, the social distancing measures until our society reaches herd immunity with a combination of infection survivors and vaccinated citizenry. That herd immunity, approximately 60% of the population, might be more than a year away.

This is not the time for us to squabble, to deny reality, or to blame others for our own national circumstance. There will be plenty of time to return to those well-worn comfy shoes after we have won this war.

This, instead, is the time for each of us to point at ourself and ask "How can I help rather than hinder?"