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COVID-19, THE FEAR PANDEMIC, MODELING, SCIENCE, AND FREEDOM

by Philip Regenie
April 11, 2020

Inquire about our serology test kits

I am 67 years old this year and no spring chicken. I put off publishing this paper because I wanted to make sure it was accurate and that my writing put nobody at risk. Since the news of COVID-19 came out in early January I have been asking every single associate, friend, and person I run into, in stores, on the street, and on the phone if they personally know anyone that has COVID-19. That number to date is 2, with 1 death, cause unknown, but assumed to be COVID-19. I then ask them if they know anyone that has died in a car accident. That number is better than 50%. This is not scientific. It is a practical way to get the feel of my environment and if that environment supports the contention that between the two choices driving a car, or going outside your home, you should not drive the car. It also supports the contention that of the beds occupied in hospitals, when people were driving, more of them were filled with auto accident victims than COVID-19 victims; by a lot.

Today I was turned away from Target because I was not wearing a mask, or a burka, or a red armband. The edge between social responsibility and personal freedom is a slippery slope. If you are picking items up off the store shelf in the store there is a high likelihood that the viral load you are introducing others to is equal to or greater than the protection you are granting them by wearing a facemask.

Good fortune has allowed me to live a full life; I am healthy, and looking forward to tomorrow, except for one thing, the FEAR PANDEMIC. I may yet die in an auto accident, of a cold, pneumonia, sepsis, a fall in the bathroom, the flu, or COVID-19. That is life but life is for the living, not the dying.

People often prefer to ‘get pain out of the way’, treating pain in the future as more significant than pain now.

"Dread and the Disvalue of Future Pain - PLOS." 21 Nov. 2013, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003335. Accessed 11 Apr. 2020.

Read Article

I want to discuss how the institutionalized knowledge of our human reaction to a future claim of fear allows the population of the world to be controlled and manipulated to the benefit of a few at the expense of the many. I will discuss this by looking at the world’s response to COVID-19, the science behind mathematical modeling and it’s presumed accuracy, the actual numbers (as much as possible) during this flu season, and finally “How should we respond?”. At the end of this article I will summarize this article with your actual probability of contracting and dying from COVID-19 versus influenza.

Although I have only cited one article, there have been ongoing scientific discussions concerning the topic of fear since 1957 with repeated confirmed experiments and a field of knowledge that is well known. Let me say that again. The field of knowledge of fear is well known and has been understood and implemented for thousands of years. The summation of the above research up to the current date is the following statement about humans response to pain in general:

These participants preferred to experience the same pain sooner rather than later and were willing to accept more pain in order to hasten its occurrence.

"Dread and the Disvalue of Future Pain - PLOS." 21 Nov. 2013, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003335. Accessed 11 Apr. 2020.

Read Article

With this salient fact in mind I want to discuss COVID-19, the observable evidence from the beginning, the known lack of reliable evidence from the initiating source, the inaccuracy of data models based on not considering exogenous factors, the application of those models in order to determine public policy, and what it means to give an inch and take a mile in social contracts that determine the quality of your life and the quality of life for your children.

COVID-19

When did we know?

What was known about COVID-19 from our initial awareness? This is in fact one of the few points of information we can be sure of. The global community became aware of a viral outbreak in Wuhan, China in early October. I would like you to participate in this activity because I do not want you to take my information at face value. Let’s find out what we knew and when.

Instructions

Efficacy of hydroxychloroquine in patients with COVID-19 …
Despite Increase in COVID-19 Cases Outside China, WHO …

The efficacy for the use of hydroxychloroquine was published on October 7th, 2019 and the article discussing the increased number of cases outside China and the WHOs reaction was published October 15th, 2019.

When did we know?

Clearly world health organizations knew that COVID-19 existed on October 15th, 2019. What is most interesting about the dates is that the article date is February 24th, 2020 for the WHO article and the internal date on the efficacy of hydroxychloroquine was March 24th, 2020. The way that Google works is that when the search engine crawls an article it posts the datetime of the crawl to a file. We can’t say what happened other than the original file was found on the dates noted by the search engine.

What is the origin?

The question now is when and what did we know about the virus. Was it a mammal based virus or an avian virus? The first article published on this crawl by Google was on October 25th, 2019 by Z Li the same fellow who published the article above. What we learn is that the virus exists in both mammals and birds and is thought to have a common origin. This article has an internal date that matches the crawl of October 25th, 2019 validating the initial crawl and a change of dates on the first article.

The human coronavirus HCoV-229E S-protein structure and ....

25 Oct. 2019, https://elifesciences.org/articles/51230. Accessed 11 Apr. 2020.

Coronaviruses are enveloped RNA viruses found in mammals and birds (Graham et al., 2013; Su et al., 2016). They share a common ancestor thought to have originated in bats (Woo et al., 2012) and several genera that use a wide range of protein and carbohydrate receptors have evolved (Forni et al., 2017; Peck et al., 2015). Four coronaviruses, HCoV-229E, HCoV-NL63, HCoV-OC43 and HCoV-HKU1, circulate in the human population where they are responsible for approximately one-third of the common cold….

Read article

COVID-19 is an HCOV Virus

What is extremely important is to recognize that COVID-19 belongs to a family of viruses all having a common ancestry with common characteristics.  What might those characteristics be and why do they matter?  Those characteristics have to do with transmission, the robust nature of the virus, what temperatures the virus survives in and previous vaccines to prior family members.   We want to find our human knowledge base on HCOV viruses because we want to know if prior knowledge is applicable to the current situation.  For this search you can enter the following into the google search engine without any dates specified.

common characteristics of HCOV viruses

The answer to when we first started researching this family of viruses is cited in an article in 1990 by :Michael M. C. Lai:

In 1990 we knew that HCOV viruses targeted very specific tissue types:

| Coronaviruses, in general, have very restricted host ranges, infecting only cells of the:ir own host species. However, some cross-species infections do occur. For instance, BCV can infect bovine, human, and rat cells. In animals, coronaviruses also have restricted tissue specificity.

This is very important because it means that when they discovered that COVID-19 patients had airway issues they also knew that the virus was specific to that tissue type and the problem was more oxygen related than anything else.

How well does COVID-19 survive and where?

This is one factor that is being reported fairly accurately. It turns out that HVOC viruses have a wide range of survival characteristics, in medical terms called environmental stability, and that the data supports a fairly robust ability to handle both temperature and humidity ranges in the ambient environment. 

| Besides the absence of specific treatment and vaccine, HCoVs are now known to show a significant environmental resistance.

One of the unfortunate points of research with respect to viruses is that it often focuses on hospital environments, that is where the research dollars are, and their ambient temperatures which turns out to be around 20℃.  What I could not find is what effect would there be on the transmission rate of the virus in high temperature, low humidity environments.

Cleaning Solutions

The dominant word on the street is to wash your hands for 20 seconds with soap and water and when that is not available use a solution that contains 70% ethanol.  I have recommended this myself.  It turns out that the best agent to kill the virus every time it is 78% ethanol for 30 seconds.  The table below shows you that 30 seconds of exposure has a viral reduction factor of greater than 3.31 log(10), 1000 times, using off the shelf 70% ethanol and, 100,000 times, 5.01 using 78% ethanol.

I, too, did not know what a 5.01 viral reduction factor meant and so looked it up to be clear and here is an image from a pdf to explain it.

Interestingly, although washing of the hands using soap and water is recommended based on soap and water breaking down the hydrophilic membrane of COVID-19, finding concrete data on the reduction factor seen in the use of substances such as ethanol is much harder to find.  The reason for that is that soap and water interfere with the measuring process used to determine virosity reduction factors. Viral titers, by the way, is a numerical expression of the quantity of virus in a given volume. 

| A more recent study investigated the action of antiseptics-disinfectants on HCoVs 229E and OC43 with suspension tests and contact times of 5 min. The neutralization step was achieved by dilution in medium culture. The povidone-iodine (0.75% free iodine) caused a 50% reduction in infectivity of both of the viruses, which is not enough to claim a virucidal activity. Moreover, to obtain a 50% reduction in HCoV 229E titers, tenfold increase in concentration of povidone-iodine was required. Some other products (70% ethanol, soap or 5% bleach) were assayed but without success because they interfered with the biological viral titration assay [107].

 

| Another recent study used MHV as the SARS-CoV surrogate, and carrier tests on Petri dishes. Antiseptic antiviral activity of common household disinfectants or antiseptics, containing either 0.05% of triclosan, 0.12% of chloroxylenol, 0.21% of sodium hypochlorite, 0.23% of pine oil, or 0.10% of a quaternary compound with 79.0% of ethanol, were investigated. All of them provided at least a 3 log10 reduction in viral titers within a 30 sec contact time, which is consistent with the previous results [13].

 

For now we will just have to take their word on the effectiveness of handwashing for 20 seconds using soap and water.  I personally do both.

Distribution Theory and COVID-19 (will you get it?)

The chances of a healthy person with no comorbidity being stricken seriously ill and dying from COVID-19 is extremely low. If you never get on a motorcycle and never cross the street your probabilities of being in a motorcycle accident are pretty small.  Still, I am sure, somewhere someone has been killed by a motorcycle that left the highway and entered their bedroom in the dead of night.  You probably never worry about dying in the middle of the night from a motorcycle entering your bedroom while you are asleep.  The question is, “Should you worry about dying from contracting COVID-19?” The answer to this question is non-trivial from the modeling perspective, but maybe trivial from the perspective of common sense.  Let’s look into it together and see what we find out.

Modeling

My first experience with modeling was in 1990 when Intel hired me to do systems analysis for them with regards to their manufacturing floor in Rio Rancho New Mexico.  I inherited a simulation program that simulated the movement of silicon wafers on the factory floor.  When the software was originally created Intel had a single line of memory wafers running through their factory and all the machines were dedicated to that line.  Pretty simple stuff.  When I was given the simulation in 1990 Intel had no fewer than 6 lines of business moving through their factory sharing machines with different numbers of wafers being introduced into each line.  This is a very complex simulation.  Each manufacturing line could have a thousand operations from start to finish and share machines.  This is called modeling and similar modeling techniques are used today to model the transmission of a virus and the total number of deaths that might occur.  Material movement through a factory is not very dissimilar to peoples flow through a website or virus flow through a population.  They all have factors in common and these days are pictorial represented by diagrams like below.

When I was going to college and taking advanced economics classes my professors warned us repeatedly to be aware that constraints often determine the viability of a model and that we cannot possibly know all the constraints and their effect on the economy.  Wise words over 45 years ago, and wise words today, no matter how powerful our computers.  That is because the computational model, even with AI, still requires a constraint model.  What is a constraint model? Constraint models are those factors that either multiply or limit growth or effect the flow of material within a system.  They can be exogenos, coming from outside the system, or endogenous, factors coming from within the system. Let’s take a look at that with regards to COVID-19 it’s spread and it’s lethalness. 

Constraints

Can you think of some constraints that might limit the transmission of COVID-19?  We have already discussed a few, washing of hands and surfaces and the solvent used to do so.  Some more obvious exogenous constraints might be population density, number of interactions per period of time, density of transmissive materials like metal and plastic, means of transportation, pollution, ambient temperature, food supply, and many more.  Some endogenous constraints might be genetic makeup, ADLs (activities of daily living) such as exercise levels, pre-existing conditions, age, smoking, drinking, sunlight exposure, hygiene, and eating habits.

You can see that even a cursory list creates a very complicated set of constraints even without considering  the general characteristics of transmission.  In fact, our ability to model the transmission and death rate of a complex global biological system is primitive and ineffectual, in contrast to what is being represented by the media and computer scientists.  We can tell you who might have COVID-19 or the common cold by what you are buying because we know that people with a cold buy antihistamines and look up COVID-19 10 times a day for 2 days, call their relatives, and search for remedies.

real-time objective measurement

How effective is modeling?


Read about why it's so hard

These numbers are extraordinarily misleading. The flu numbers come from many years across all population bases. The COVID-19 numbers come from a relatively small sample set, are skewed towards locations and populations that were deemed high probability targets for testing and countries that either exhibited low levels of sanitation or high numbers of seniors. Using this type of sloppy science and mathematics to quarantine all our futures is the worst kind of egoism conjoined with fear. May I suggest an accurate methodology so you can get it right. Select a general population that is a moderate cross section of demographics in 5 US cities of 10,000 people in each city. Test every man, woman and child. Perform statistical evaluation on these 5 sample sets and provide us with your conclusions.

- Philip Regenie

It is very important to recognize that although Igor has a PHD and is a neuroscientist he is not a data analyst and he represents a company with a vested interest, Merck, a drug company.

What other models and data have been used as a basis for global decision making?  Before we start it is important to know that there are compilations of viral propagation rates compiled in various sources one of which is here in a git repository.  In December the primary method of modeling for COVID-19 was basic proportional extrapolation based on Chinese numbers.

covid19 NA medRxiv ascertainment rate NA proportion China

This is, in fact, high school mathematics and involves no simulation or modeling at all.  It assumes the data is accurate and the rest of the world has identical conditions to a manufacturing town in northern China.  We can do some very easy calculations to see if Wuhan matches our base rate based on some simple data points like average life expectancy, deaths due to cancer, etc.  The problem with this mentality is it assumes the reported numbers are accurate and verifiable, which they are not.  To give yourself an idea of where and how the data is achieved go to this Wikipedia page and ascertain from the publishing sources whether you believe that the life expectancy throughout China is greater than 80 years old based on their published data or whether you believe global data here.  The point is that there is a significant discrepancy.  

According to the The Lancet on infectious diseases:

| Indeed, it is not clear whether early cases of COVID-19 were from infection by animal or human, and data are limited and unreliable. In this case, models fitted by early data probably produce results divorced from reality.

I queried time frames for modeling analysis, between October and the end of December, between January and February, between February and March.  Amazingly, a lot of the modeling is by China.  Doesn’t that seem a bit suspicious? 

Do the search yourself

As a matter of point, a large percentage of the searchable material on modeling the transmission and death rate comes from Chinese generated research and papers.  Their estimates are orders of magnitude off given the current global state of deaths and confirmed cases of COVID-19.  The modeling agency being listened to by the United States is IMHE, Institute for Health Metrics and Evaluation.  Here are the most recent charts revised down from estimates of 500,000 deaths to 60,000.

So, What have models missed?

Ask yourself or any of your friends what major lifestyle difference is there between northern Italian families, asian families and United States families.  If the first thing that jumped into your mind is that their households tend to be multi-generational then you came to the same conclusion I did. On top of that, their ability to pay for extended hospitalization is limited.  How does this effect the existing models?  Is this the only constraint that is different?  I suggest that RO the rate of transmission, is different depending on societal context and that in standard healthy society the rate of transmission is actually much higher than previously assumed.  In the United States we sequestor our old in senior communities and our sick in hospitals.  Their level of interaction with the general public is significantly less than any other sector of our society with the exceptions of jails.  RO has a variable rate depending on population density such as rural Kansas, societal interaction such as senior communities, and visitors such as travelers from abroad much like the gif below.  On the left side of the membrane the green dots bump into each other much less frequently because there are fewer of them, this is similar to rural America or the traffic in and out of senior communities.  On the right side green dots run into each other much more frequently like in a big city or with young adults.  The orange represents allowable traffic from abroad or into a senior community.  The smaller circles represent travelers or workers and their interaction with the community.  This type of modeling describes a large array of circumstances and allows us to visualize frequency of interaction.

Although this image is not entirely accurate, because the number of interactions on the large circle side would decrease and not increase it is easy to see that collisions which are the transmission rate are less frequent.  Here is one key to the reason the models have been so deficient.  The other key is that if RO is higher than 2 and Americans were introduced to the virus sooner and a much greater percentage of the population has already been exposed to COVID-19.  According to the New York Times 430,000 chinese have traveled to the United States since October. What if the infection rate is much higher in the general population and much lower in sequestered population.  How can we model that? We might start with looking at actual data and seeing that we are over the hump in New York.  Since October this is the 6 month mark.  If the actual RO was around 2.5 the entire population of the United States, without the sequestration, would have been exposed to COVID-19.  Here is a simple simulation you can play with.  These numbers are paired down so that the initial infection number roughly estimates a portion of the 430,000 entering the total US population and it taking 6 months to infect everyone.  You can multiply the results by 3.5 to arrive at the real number at any point during the simulation.  The RO for COVID-19 has been estimated to be between 2.2 and 2.5.  Using the model below you can see that a majority of the population will have been introduced to the virus in 6 months.

What about those who have been sequestered.  How long does it take them to get the virus?  Once again that has to do with RO which we know is much lower because of the number of people coming in contact with them.  Let’s see how much lower.  Before going into the actual interaction numbers, the data on senior loneliness is staggering with an incredible impact on their health which far exceeds the impact of COVID-19.

| Participants (N = 173) aged 20 to 79 years reported their social interactions at five random times throughout the day for one week.

The actual interactions with people from the outside on a daily basis drops to 1.2 to 1.6 per day for people over 70 years of age in the United States.  We use 105,000 with respect to the 750,000 used in our initial simulation as the total population basing this on 14% of our population being over 65 or approximate 50 million.  We also recognize that travelers from Wuhan are probably not racing to visit people in senior communities so we lower the number of initial infections to be equal to our contact distribution which is 20% of the standard 5 a day.  This is very generous in terms of actual numbers.

After 180 days or 6 months the simulation predicts 862 deaths multiplied times our factor of 500, for 50 million seniors in the United States over 65,  and we arrive at 45,920 deaths close to the current estimate of 60,000.

What is the conclusion of this modeling?

If we use simple modeling with common sense observational data we arrive at numbers that match our current physical experience.  More importantly, we arrive at some surprising conclusions:

    1. It is quite possible that most Americans have already been in contact with the coronavirus
    2. There was a latency to the death toll because most of our comorbidity population is sequestered
    3. Simulations are easily manufactured, can be very wrong no matter the good intent, and probably should be weighted less importantly than the use of common sense

The world took action that quite probably was:

    1. Too late in the cycle
    2. Did not take into account cultural parameters
    3. Instituted policies that were counter to their goals

Any consideration of sequestration should have been applied to those people who had comorbidity, other life threatening diseases, and not the general population.


What About You Being a Carrier and Respecting Others?

The moral principle of this question rests heavily on what is the desired outcome?  My desired outcome is the highest probability of survival of a population in the short term and long term. There are three factors that play into this:

    1. Fatality rate based on all infected individuals
    2. Herd immunity build rate
    3. Inoculation

We actually do not have a general population fatality rate and quite probably do not have fatality rates for age ranges.  There are many reasons for not having accurate data; some controllable and some not.  Death certificates are not a science but a social construct and are clearly not accurate forms of judging the cause of death for many reasons:

    1. Autopsies are rare and inaccurate
    2. Social rules can mandate cause
    3. Laymen fill out more than 40% of the certificates without medical knowledge 

| In cases where a definite diagnosis of COVID–19 cannot be made, but it is suspected or likely (e.g., the circumstances are compelling within a reasonable degree of certainty), it is acceptable to report COVID–19 on a death certificate as “probable” or “presumed.”

We are completely in the dark with respect to the mortality rate of COVID-19 and will be until testing for antibodies is done on entire population groups.  Antibody testing is a point-of-care (requires a medical practitioner) test for antibodies associated with COVID-19, takes between 3 and 7 minutes, and has an agreement of 97.19% ensuring that out of every 100 people tested showing antibodies present, 97 of them have had COVID-19 exposure.  When these tests are administered to thousands with known deaths CONFIRMED to have died from COVID-19 (not presumed) within that population group, the death rates will be established.  Until that point in time we must extrapolate from existing data. To do this, we should take worse case scenarios based on actual data. Let’s extrapolate the worst case conditions based on New York City. I gathered these numbers to date from a query using google “new york city deaths covid 19”  and got these results:

New York City 139,385 18,018 10,657

The total population of New York City is more than 8.39 million.  If we look at deaths attributed to COVID-19 for the last month, the peak of the crisis, and extrapolate those deaths throughout the year then we would get 90 thousand deaths by the end of the year with a population of 8.39 million or 1%.  Of course this number is ridiculous for many reasons the greatest of which is that those most susceptible will die off quickly and the rest of the population will build up resistance. In fact, this inescapable conclusion is represented in the research article Global mortality associated with seasonal influenza epidemics: New burden estimates and predictors from the GLaMOR Project

It is really important to compare COVID-19 deaths with deaths from the flu so that we can get a measure of the lethal nature of the disease.  It turns out that deaths due to the flu based on research are anywhere from 294,000 to 518,000 worldwide per year.  Here is a chart of the current deaths worldwide from COVID-19.

You can see that tripling the numbers to exaggerate deaths for the entire year puts the total of worldwide deaths from COVID-19 at 531,000, the same number of deaths worldwide from influenza. 


YOUR ODDS OF DYING FROM COVID-19 ARE THE EXACT SAME AS YOUR ODDS OF DYING FROM THE FLU


What is Viral Load and Why Does it Matter?

Our body has two mechanisms of defense against viral infections, innate and acquired.  These two systems in combination allow us to first stall the attack, innate, of a virus by limiting reproduction and then kill it, acquired, after specific immunities are built up over a 2 to 3 week period.  It is an incredible system and you can read about it here. Viral load is the number of virus particles found in a given amount of body fluid or on a surface.  

Viral load is important because a small number of particles entering your body produce an initial innate reaction and over a 2 to 3 week period build up immunities.  

| Viruses are not poisons, within the cell they are self-replicating. That means an infection can start with just a small number of articles (the ‘dose’). The actual minimum number varies between different viruses and we don’t yet know what that ‘minimum infectious dose’ is for COVID-19, but we might presume it’s around a hundred virus particles.

An extremely large viral load entering your body provides the virus with a tremendous kick start possibly overwhelming your body’s ability to get to the acquired immunity stage of the disease.

| In general with respiratory viruses, the outcome of infection – whether you get severely ill or only get a mild cold – can sometimes be determined by how much virus actually got into your body and started the infection off.  It’s all about the size of the armies on each side of the battle, a very large virus army is difficult for our immune systems army to fight off.

It should be obvious that if you can control the amount of exposure to a small limited viral load you provide your body with the timing required to become immune. It is for this reason that population sequestration can have negative effects introducing large viral loads possibly from people coughing on others who have no antibodies to ward off the disease.

Conclusions

It is not the response to a crisis, but the lack of a crisis that matters. There is no crisis if resources exist and can be provisioned effectively at a cost that society can bear.  We must move towards a distributed model of health provisioning with remote monitoring and home visits and reserve sufficient rooms and expertise where necessary to handle demand no matter what the crisis is. 

We cannot predict and should not attempt to, events based on a potential fear of pain. Our predictions will always be validated and the results of our actions will always be harmful. Planning is important and should be carried out  by planning for a positive future and not away from a fictitious future pain.  In an industry like the medical industry that means we plan for emergencies and build reserves into our system so that we always hold a percentage in reserve for things like pandemics.  In the current scenario with COVID-19 a reserve of 10% would have been sufficient.  

“What about our medical infrastructure and the stress we have put on it?”  This is an extremely valid point.  The question is, “Why is our infrastructure limited in its response to a pandemic?”  Are we capable of fending off the demand of gun wounds, auto accidents, falls, cancer, and sepsis?  Why is it then, that we are incapable of fending off something that is responsible for less than 10% of the current total resource requirements?  

Economic sectors of our economy have a responsibility to our national welfare.  This pandemic is a perfect example of how a single poorly orchestrated sector can hold the whole economy hostage.  The medical sector has a self imposed limitation on the supply of doctors, nurses, housing, and medical staff.  The limitations on supply come from our social policies, medical, legal, and pharmaceutical institutions and a one size fits all health system that houses  alcoholics, the homeless, and drug abusers in emergency wards.  We have restrictions on supply through restricted entry degree programs, torte law, patent law, and FDA approval.  More importantly, our medical sector answers to institutions such as the CDC whose consideration is not just our national welfare but also the methods used to attain that welfare.

Where must we as a nation fall on these issues?  We must side with foresight and not an emotional reaction to a resource shortage.  We need to understand that the resilience of our society is not based on a small cadre of excellence, our doctors and nurses,  but on the depth of our team.  We must lower the barriers to entry into and out of the medical profession so that our supply is sufficient.  We must decentralize administration and enhance our remote health measurement systems, called remote monitoring,  as well as establish remote health care both through medical practitioners using online methods of communication and sensory data, and when required, use household visits.  To accomplish this we as a nation need to  lower the regulations and costs for institutions to enter and stay in the medical care market.

A major commentary left out in the previous paragraph is the commentary on healthcare load based on our poor quality national food supply and our tendency to sequester seniors in isolation.  Both of these social constructs impact our healthcare system much greater than the pandemic.  There are excellent resources for occupancy rates in hospitals.  Bed occupancy rates in New York city were between 79.7% and 89.7% in 2018.

| Much information is unavailable. While a March 19 report estimated that New York City hospitals had a 74.2% occupancy rate, hospitals haven’t been disclosing their occupancy or their data on intensive care units and life-saving ventilators. If you have information, reach out to help Bloomberg News cover the coronavirus story.

OCCUPANCY OF NEW YORK CITY BEDS ON MARCH 19TH, WAS 5% LESS THAN 2018 AVERAGE WITH A RESERVE CAPACITY OF MORE THAN 25%. ALTHOUGH A CLOSE CALL, VERY FEW ADDITIONAL RESOURCES WERE REQUIRED TO MEET THE SURGE IN DEMAND.

There is a crisis.  It is in multigenerational homes and housing communities with residents having comorbidities. This has always and will always be a two way street, companionship versus higher probability of being introduced to infection.  COVID-19 poses very little additional risk above that experienced during normal flu seasons. It is recommended that voluntary sequestration of those whose health is questionable be recommended during high levels of transmission. 

Personnel Recommendations

We recommend that you:

Manage your exposure and viral loads so that you build up immunity in small controlled intervals. 

      1. Try not to touch your face
      2. Keep your hands in your pocket
      3. Limit touching objects in the environment
      4. Stay a reasonable distance, at least 6 feet, from people who cough or breath with their mouth open
      5. Avoid anyone who looks extremely ill and ask them to sequester themselves
      6. Bring a bottle of hand sanitizer or a solution with 70% ethanol or greater with you
      7. When you do touch surfaces use the sanitizer
      8. Wash your hands frequently with soap and water
      9. Treat people with respect and kindness
      10. Get back to work

PLEASE HELP GET AMERICANS BACK TO WORK

BUY AND TEST YOUR WORKERS FOR IMMUNITY WITH SEROLOGICAL TEST KITS

Find out how to protect those you serve

PLEASE HELP SUPPORT AMERICA BUILT ON THE THE PROPOSITION THAT ALL MEN ARE ENDOWED WITH THESE UNALIENABLE RIGHTS

LIFE, LIBERTY, AND THE PURSUIT OF HAPPINESS

References

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 “The human coronavirus HCoV-229E S-protein structure and ….” 25 Oct. 2019, https://elifesciences.org/articles/51230. Accessed 11 Apr. 2020.

“Coronavirus: Organization, Replication and Expression Of ….” http://www.accessdunia.com.my/coronavirus-organization-replication-and-expression-of-genome/. Accessed 11 Apr. 2020.

 “Human Coronaviruses: Insights into Environmental … – NCBI.” 12 Nov. 2012, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3509683/. Accessed 11 Apr. 2020.

 “The human coronavirus HCoV-229E S-protein structure and ….” 25 Oct. 2019, https://elifesciences.org/articles/51230. Accessed 11 Apr. 2020.

 “Modelling COVID-19 transmission: from data to … – The Lancet.” 1 Apr. 2020, https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30258-9/fulltext?rss=yes. Accessed 11 Apr. 2020.

 “430,000 People Have Traveled From China to U.S. Since ….” 4 Apr. 2020, https://www.nytimes.com/2020/04/04/us/coronavirus-china-travel-restrictions.html. Accessed 11 Apr. 2020.

 “The average coronavirus patient infects at least 2 others ….” 17 Mar. 2020, https://www.businessinsider.com/coronavirus-contagious-r-naught-average-patient-spread-2020-3. Accessed 11 Apr. 2020.

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