A Scientific Look at COVID-19 Recommendations

In an effort to figure out the best response from here for the current pandemic, a dive into research is needed. Are the current strategies working? Should we maintain our lockdown course? Did we make any mistakes that we can learn from? What adjustments, if any, need to be made in our public policies and social recommendations?


To give a proper scientific look at these questions, we need to look at the current numbers to see what we can learn from them, where the projected numbers were derived, whether the current recommendations align with known science, other potential side-effects from our new protective policies that may have been overlooked, whether or not the recommendations seems to be working, then finally evaluate where to move from here. This will help us decide whether or not our suggestions and policies should be kept or adjusted.



Accuracy of Cases


Projections initially came from an “exponential curve” model, which showed pretty high numbers with both cases and deaths, which I will discuss shortly. How close were those numbers? According to a recent German study, “About 2 % of the people had one (a test) using the PCR method ... This means that 15% of the population in Gangelt are no longer infected with SARS - CoV - 2… Immunity to SARS - CoV - 2 has developed approximately 15%.” - (translated from German, and my parenthesis added).


Similarly, a new study out of California which randomly measured antibodies for COVID-19 in 3330 people noted, “These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases. The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases. Population prevalence estimates can now be used to calibrate epidemic and mortality projections.”



Those two previously mentioned studies agree with an article in the journal of Science which notes, “This estimate reveals a very high rate of undocumented infections: 86%.”



Those articles all align with what the head of the Civil Protection Agency, Angelo Borrelli said, “A ratio of one certified case out of every 10 is credible”


So if cases are drastically higher than what’s reported, then John P.A. Ioannidis, the co-director of Stanford University’s Meta Research Information Center may have been right when he said, “The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.” His projections showed that we were likely very off on the mortality rates.


Everybody knows there are unreported cases; the lack of widespread testing leaves huge gaps in the data; attempts to determine the possible scope of unreported and/or asymptomatic cases are imperfect; yet considering the findings in multiple places under varying circumstances, it appears more likely than not that the number of cases greatly exceeds the number of known cases, and that most are asymptomatic or very mild.



Accuracy of Mortality Rates


What do we know about the deaths caused from COVID-19 to date? Is the data accurate? According to the following sources, the greatest likelihood is that they haven’t been exactly honest with us.


Let’s start with the devastating death toll from Italy, “The Rome-based institute has examined medical records of about 18% of the country’s coronavirus fatalities, finding that just three victims, or 0.8% of the total, had no previous pathology. Almost half of the victims suffered from at least three prior illnesses and about a fourth had either one or two previous conditions.”


The average age of death was 79.5 years old, which is understandable housing the oldest population in Europe. The most difficult part of looking at numbers pertaining to deaths is separating emotions while thinking about the terrible tragedies. It does appear higher than a usual year for them, so later in this article I will explore other possible reasons for this.


It begs the question of whether everyone who died had a proper labeling for the cause. Oxford’s CEBM journal notes, Italy’s death rate might also be higher because of how fatalities are recorded. In Italy, all those who die in hospitals with Coronavirus are included in the death counts.” This is not an appropriate way to label deaths, though an argument could be made that COVID-19 stressed other illnesses. Therefore it would be appropropriate to label it as a comorbidity. We will see in their updated tallies later if other statistics, such as heart attacks or other causes, dropped at a similar rate of increase towards COVID-19.


But one would normally believe that the US wouldn’t do anything similar, however, “New York City is among a handful of places in the country, including Connecticut, Ohio and Delaware, that are beginning to disclose cases where infection is presumed but not confirmed,” and, “It is important to emphasize that Coronavirus Disease 2019 or COVID-19 should be reported on the death certificate for all decedents where the disease caused or is assumed to have caused or contributed to death.” I cannot agree with the CDC on this, since assumed is never good enough. How do we assume that they died of it based on symptoms? Their assumptions go too far by calling a death caused by COVID-19 when the person, 1) tests negative for COVID-19, 2) dies of a heart attack (or other cause), and 3) has been around someone who tested positive for COVID-19 within the last couple of weeks.


The CDC even states, “The flu, which is caused by influenza viruses, also spreads and causes illness around the same time as the common cold. Because these two illnesses have similar symptoms, it can be difficult (or even impossible) to tell the difference between them based on symptoms alone.” On that same page they state, “Other viruses that can cause colds include respiratory syncytial virus, human parainfluenza viruses, adenovirus, human coronaviruses, and human metapneumovirus.If it’s impossible to tell the difference between other flu-like illnesses, then why would they auto-label everything as “caused by COVID-19?”


This will inevitably bloat the numbers, especially since numerous other viruses house the exact same symptoms of COVID-19; fever, cough, and achy muscles. Fun fact: influenza doesn’t ever cause throwing up. When a virus causes vomiting, it is often publicly inappropriately called the “stomach flu,” since the influenza virus never causes it. There are reports of B influenza causing it, but they are patients with renal disease, which commonly causes throwing up. Vomiting, when induced by a virus, is mostly due to a rotavirus or one of the noroviruses, which causes viral gastroenteritis (swelling of the stomach or intestines due to a virus), according to a study in the journal Viruses.


Another fun fact; influenza doesn’t “cause” death in anyone, but rather will make it so the person will not be able to fight off other infections that are roaming around. It is true that a high enough fever can cause death, but Seattle’s Children’s Hospital notes, “Fevers with infections don't cause brain damage. Only temperatures above 108° F (42° C) can cause brain damage. It's very rare for the body temperature to climb this high. It only happens if the air temperature is very high. An example is a child left in a closed car during hot weather.”


Studies generally label all causes of similar symptoms as flu since they are “flu-like illnesses.” According to the CDC, “Your health care provider may diagnose you with flu based on your symptoms and their clinical judgment or they may choose to use an influenza diagnostic test.”


So even if they aren’t positive you have the flu, you may be diagnosed with it, and even label the cause of death from it without a laboratory confirmed case. This creates a catalyst for incorrect data while making many people believe the flu is the only thing that causes death, when an article from 2014 in the journal of PLoS One notes, “Further, many patients present for medical care because of complications following an influenza infection, which can range from primary viral to secondary bacterial pneumonia, through pulmonary super-infections, exacerbations of chronic respiratory conditions, and even non-respiratory complications such as neurological complications, direct cardiac complications, or worsening of underlying cardiac conditions.” In fact, according to an article in the journal of Molecular Sciences, “More patients in the rhinovirus group developed pneumonia complications (p = 0.03), required oxygen therapy, and had a longer hospitalization period.” - (comparing it to influenza) - This means that the majority of “flu-like illnesses” that are diagnosed as the “flu” may actually be caused by rhinoviruses. These are the same methods described for COVID-19 labeling, which is less scientific than I personally wish to see.


This is further confirmed by a study funded by the CDC, “First, most adult patients with symptoms consistent with influenza infection are not tested for influenza. Those who are, generally receive rapid tests of only modest sensitivity. In addition, many influenza-associated deaths occur one or two weeks after the initial infection (when viral shedding has ended), either because of secondary bacterial infections or because the influenza has exacerbated chronic illnesses (e.g., congestive heart failure or chronic obstructive pulmonary disease).” Notice the specific usage of the word “association” related to the deaths, instead of labeling it as a cause.


In our current pandemic, cases with pneumonia caused by bacteria are prevalent, “Several patients with COVID-19 have been reported to present with concurrent community-acquired bacterial pneumonia. Decisions to administer antibiotics to COVID-19 patients should be based on the likelihood of bacterial infection (community-acquired or hospital-acquired)” Where did they pick up these bacterias that caused the pneumonia? In the CDC quote above, note that it may have been given to them at the hospital. When panic ensues, more people head to the hospital, which has the potential to increase the spread of bacterial pneumonia faster. This sheds light into potential reasons why a fear-induced lockdown state could actually increase the chances of people getting even sicker, but further reasons will be discussed later.



Demographics


Many people are sharing anecdotal stories of younger people being infected with the SARS-Coronavirus-2, while even showcasing a couple of cases where comorbidities led to death. Claims over social media have been that this is not just an elderly person’s virus due to these anecdotal evidences. According to the numbers given, these anecdotal instances are far outliers, “The average age of deceased and COVID-19 positive patients was 79.5 years (median 80.5, range 31-103)” & no health professionals under the age of 50 have died.”


Also among the social media hysteria, many have claimed that this is a really credible threat, many times referring to someone who they personally know that contracted COVID-19. The newest numbers available as of April 18, 2020 show that 97% of known active cases in the world are mild, according to Worldometers.


Those are only the known numbers from confirmed cases, however, as shown above the cases could be up to 85 times more than the confirmed, while the death rates have been overblown.



Accuracy of Initial Projections


This leads us now to question the original projections that came from the exponential curve model, quoted by the WHO director general, “Globally, about 3.4% of reported COVID-19 cases have died. By comparison, seasonal flu generally kills far fewer than 1% of those infected.”


Overall most of the information we obtained from the WHO from China during the preliminary stages of the outbreak were false, most of which came from Bruce Alwayrd, a Canadian epidemiologist and senior WHO advisor. Some light may have been shed as to why his info was wrong during an interview with a Taiwanese reporter, Yvonne Tong. During the exchange the WHO official ignored her question asking about Taiwan’s membership into the WHO, followed by him claiming that he didn’t hear her. When she said she will repeat the question, his response was to just ask a different one. After she asked again anyway, he hung up on her. After calling him back and asking how Taiwan has done with the virus, he inappropriately referred to Taiwan as part of China. See the video here. For these reasons it doesn’t appear that we can trust any of the numbers that he has given us pertaining to China.


Further terribly wrong projections were touted previously by Charles Ornstein while interviewing Dr. Fauci, “But if 1 in 12 people age 70-79 who get the virus and 1 in 7 people age 80 or older who get the virus die, and the virus spreads to 20%, 40% or 70% of the population, we’re talking massive death tolls, the likes of which we have never seen before in our lives.”


The Washington Post even put out an article explaining how to flatten the curve of this pandemic while projecting, “If the number of cases were to continue to double every three days, there would be about a hundred million cases in the United States by May.”



Mask Effectiveness


Now I will dive into one of the aspects of the “reducing the curve plan” that the CDC is currently promoting. They state to wear a cloth facemask when out in public around other people, such as when at a grocery store. In order to determine if this is good or bad advice, we need to know the size of the virus, the size of cross stitching in fabrics, and the size of water molecules that the virus is likely attached to.


The SARS-CoV-2 is a large sized virus (approximately 120 nm in diameter),” according to a study. A nanometer is 1/1,000,000th of a millimeter. To give an approximate example, this virus is roughly 10,000 times smaller than a gnat. So the virus by itself is very small, 0.12 of a micron.


Will a mask made from sheets or clothes help stop the spread of SARS-Coronavirus-2? The first study comparing cloth masks to surgical ones was done while finding, “The results caution against the use of cloth masks… Moisture retention, reuse of cloth masks and poor filtration may result in increased risk of infection.”


So the science doesn’t support using the CDC’s recommendation of cloth masks, but what about the N95 respirators they want to have reserved for health care workers? “CDC does advise health care workers working with SARS patients to wear a special mask called an N-95 respirator. But even these masks offer limited protection from coronaviruses. The name of the mask says it all. The “95” means the mask, if properly fitted—and that “fit factor” presents a big if—can filter out particles down to 0.3 microns 95 percent of the time. (A human hair is roughly 100 microns in diameter.) Human coronaviruses measure between 0.1 and 0.2 microns, which is one to two times below the cutoff.”


As stated earlier specifically the SARS-Coronavirus-2 is 0.12 microns, falling into the smaller size of coronaviruses. The larger ones from the past have shown the same results, “Since the coronavirus is an extremely small virus, it can pass through the pores of both the surgical mask and N95 respirator.” This is similar to influenza in size, which, The main way that influenza viruses are spread is from person to person via virus-laden respiratory droplets (particles with size ranging from 0.1 to 100 μm in diameter) that are generated when infected persons cough or sneeze.” This affirms the idea that water molecules are too small for even an N95 mask to prevent the spread.


According to another study, “The N95 filtering face piece respirators may not provide the expected protection level against small virions. Some surgical masks may let a significant fraction of airborne viruses penetrate through their filters.”


This gives evidence that even those masks won’t prevent this virus from getting through. A study confirms that it is possible,A physician who cared for the wife of the initial case patient in Taiwan developed clinical features that met the criteria for a probable SARS case and was confirmed to be infected with the SARS coronavirus by the laboratory. He was considered as being infected by a direct line of droplet spread when the SARS patient had episodes of coughing while sometimes partially sitting up during the performance of a chest ultrasound and while supervising the intubation despite using a N95 respirator.”


It easily passes through the N95 masks, and even more easily passes through the cloth masks most people are wearing. Most people do not likely wear them properly. Will the masks block some of it? Possibly some of the viruses that cling to water that stopped on your mask, but the evidence shows this is not enough to prevent a contagious person from spreading it with a high viral load.


So is it harmful to wear a mask even if it doesn’t help spread this or other small viruses? It actually may be harmful due to the false sense of security it offers. When a prominent organization such as the CDC promotes it as an effective tool in preventing viral spreading, that boosts confidence in the wearer. They may feel as if they no longer need to cover their mouth during a cough causing a large viral load to escape. Another issue may be the incorrect notion that they are now able to scratch an itch on their face with the hand they just used to open a door that was infected with the virus. Touching the nose through a cloth will not prevent a virus from easily passing through the very large cross stitches compared to its size.


Perhaps proper coughing, sneezing, and after-blowing-nose hygiene should be promoted over these antiquated mask-wearing recommendations, as well as staying home while sick. Better community information could be given pertaining to the highest viral load periods (highest contagious days).



Potential Effects Due to Lockdowns


We have discussed the inaccuracies of cases and mortality statistics, while discussing ways that infections may actually be getting worse due to the current recommendations. In order to fully look at the impact of the lockdown, we must also look at any potential negative effects caused by it. From there we can evaluate if the positives outweigh the negatives from a scientific standpoint.


Some of the potential negatives could be an increase in suicides, homicides, domestic violence, child abuse, and other violent behavior. Poverty tends to breed desperation. Desperation leads to increases in all of those.


Due to the lockdown of our society there have been many businesses that have had to lay people off, or even close permanently. Here is a graph showing how suicides were the highest during the Great Depression, quickly falling after the economy had picked back up again;


Image from Impact of Business Cycles on US Suicide Rates, 1928–2007


“Figure 1a shows that the overall suicide rate generally increased in recessions, especially in severe recessions that lasted longer than 1 year. The largest increase in the overall suicide rate occurred during the Great Depression (1929–1933), when it surged from 18.0 in 1928 to 22.1 (the all-time high) in 1932, the last full year of the Great Depression.”


The CDC even concurs, “The overall suicide rate generally rose in recessions like the Great Depression (1929-1933), the end of the New Deal (1937-1938), the Oil Crisis (1973-1975), and the Double-Dip Recession (1980-1982) and fell in expansions like the WWII period (1939-1945) and the longest expansion period (1991-2001) in which the economy experienced fast growth and low unemployment.”


Due to these previous examples, Glenn Sullivan, Ph.D. projected, “2021 could see more than 54,000 deaths by suicide (versus about 48,000 in 2018). The 6,000 excess deaths—which I fervently hope do not occur—would be additional victims of the coronavirus emergency and its economic impact.”


The reason for such an increase in deaths could be due to anxiety caused by the crisis, or rather from the appearance of a crisis like that which we have never seen. A study was created to, “develop and evaluate the properties of the Coronavirus Anxiety Scale (CAS), which is a brief mental health screener to identify probable cases of dysfunctional anxiety associated with the COVID-19 crisis.”


According to a study, “Like previous epidemics and pandemics, the unpredictable consequences and uncertainty surrounding public safety, as well as misinformation about COVID-19 (particularly on social media) can often impact individuals’ mental health including depression, anxiety, and traumatic stress.”


Those may not only lead to an increase in suicides, but potentially homicides as well. Specifically looking at this pandemic, “actions such as social-distancing, sheltering in-place, restricted travel, and closures of key community foundations are likely to dramatically increase the risk for family violence around the globe… An increasing risk of domestic violence-related homicide is also a growing concern – reports continue to surface around the globe of intimate partner homicides with ties to stress or other factors related to the Covid-19 pandemic.”


This shows strong evidence that the negative side effects from locking down society may be very damaging, while creating many problems that were not considered before hastefully locking down everything. This virus is far less of a problem than the original projections alluded, while many less will likely circum to a catastrophic level compared to that which was first believed. The mask-wearing recommendations are a-scientific and need to be reversed, but we still need to address whether or not the social distancing and lockdowns were the best move in preventing this disease from spreading too quickly.



Social Distancing and Lockdowns Effectiveness


First it is imperative that we realize that these social distancing and lockdown efforts were never ultimately supposed to prevent the spread entirely, as a study in the journal Science notes that we would need to keep our lockdown until 2022 to do so. Another article from the same journal notes, “If the novel coronavirus follows the pattern of 2009 H1N1 pandemic influenza, it will also spread globally and become a fifth endemic coronavirus within the human population.” The point was instead to prevent the spread from getting too bad where hospitals would be overrun. A look into if the numbers and data support this is now necessary.


We can visually tell when a symptomatic person has a high viral load (therefore we know to stay away from them), but what about all of the reports of barely or asymptomatic (pre or post) people spreading the virus? Assuming that someone does not have enough of a viral load to create symptoms leads to the probability that presymptomatic people would not spread enough viral load to cause an infection in another person, though if they do, the newly infected will have the best chances of fighting the virus due to this low viral load. “Hence, it is possible, but remains to be demonstrated, that SARS-CoV-2 transmission from indolent or mildly symptomatic persons to naive individuals generally occurs at a relatively low viral load (lower than if the infection stems from severely affected patients), which then might have higher probabilities to induce immunity instead of severe and sometimes lethal infection.”


This is antithetical to the numerous suggestions to the contrary. Combining this with the likelihood that many more have created an immunity to this prior to the lockdowns, as the German and California studies confirmed, lends credence to the high possibility that the lockdown may actually prevent this immunity from being given to the masses. In other words, for the greater good of the masses, we should not be locked down if this is true.


I created a chart using April 18, 2020 numbers (when I wrote this), while only including countries with at least 10,000 confirmed cases, other than China’s since, as discussed earlier, their numbers cannot be trusted thanks to where we received the information.


Data as of April 18th from worldometers.info/coronavirus/ -





A few of the most notable things from this chart; 1) all of the countries that have the highest number of confirmed cases per million people have issued a shelter-in-place order, which included business lockdowns, 2) most of the countries that have the lowest number of cases per million have not issued a full shelter-in-place order, including no business lockdowns, 3) the countries that issued less stringent lockdowns have less cases per million people, and 4) the countries more spread out and in colder climates seem to be doing better even with a shelter-in-place orders.


Some reasons why a shelter-in-place order could have an opposite of the intended outcome include many of the same reasons that cold season is more prevalent in winter months; decreased exercise, more time inside spreading illnesses with family members, worse indoor air quality, decreased vitamin D exposure. One author wrote the following in the journal of PLOS Pathogens in 2018, “Here we explore the concept of an epidemic calendar, which is the idea that seasonality is a unifying feature of epidemic-prone diseases and, in the absence of control measures, the local calendar can be marked by epidemics”


Here are some journal articles supporting the idea that exercise will help fight against cardiovascular disease and viral infections in the winter; “This finding may have important implications; it seems that the risk of cardiovascular diseases appears to be the greatest during the winter months, particularly in elderly people,” and, “This article has provided evidence to support the hypothesis that moderate intensity exercise reduces inflammation and improves the immune response to respiratory viral infections.”


Poor indoor air quality seems to be a reasonable explanation why people staying inside will end up with a higher infection rate, thus offering evidence to why countries without the lockdowns are doing better than those with them. Here are two science journals explaining that, as well as offering evidence of vitamin D’s role in preventing infection; “The results support the hypothesis that elevated levels of HAP exposure are associated with increased reported respiratory and non-respiratory symptoms in adults,” (HAP = Household Air Pollution) and, “People should be informed about the importance of proper housing ventilation and the potential benefits of increased outdoor activity in natural UV light. Furthermore, adequate vitamin D status may be required particularly in winter to decrease infection rates.”


These collectively offer a valid rationale why stay-at-home orders with city-wide lockdowns are likely the opposite of the best solution. Considering the increased suicide, homicide, and other violence associated with depressions and high unemployment numbers, it would appear that the best thing any country can do at this point is; admit to, then learn from our mistakes, while adapting to this new outlook for current and future outbreaks.



New Guidelines


The advice should be revised to;

  • Stay home when you are sick and for the post-symptomatic time recommended by health scientists

  • Cover your mouth with your arm (never hand) when coughing or sneezing

  • Wash immediately following sneezing or blowing your nose into a tissue

  • Wash your hands before eating

  • Don’t touch your phone after washing, before eating

  • Create physical distance when in the vicinity of obviously infected people


Public officials should immediately do everything in their given powers to 1) calm the public, 2) share these new guidelines, and 3) reverse the lockdown. Measures to date were an understandable attempt at a science experiment gone wrong, which no one could have foreseen.



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