Infection Control, Emergency Management, Safety, and General Thoughts

I have noticed some odd patterns in the US state data that stood out. In particular, I recall Ohio having a much higher proportion of pediatric hospitalizations than any other state. That left me wondering if other states that have reported pediatric hepatitis have had a higher proportion of pediatric hospitalizations as well, especially in comparison to states that have reported that they are not investigating any at this time. The statements for each state with hepatitis counts are taken from this website for TODAY, dated May 6, 2022.

I didn’t know exactly what I would find but my hunch was correct. I’m comparing states with pediatric hepatitis to those without. This can be done with the data itself, but I wanted to see if there was any visual differences that were obvious. It is quite clear that those states with a higher percentage of pediatric hospitalizations had a larger chance of having pediatric hepatitis.

The next piece I was going to add was a stringency index to see what kind of correlation there was with hospitalizations, but then I realized that I only had that data for nations, not US states.

Given my priority is prevention, the obvious piece was to look at how the lack of masks might be a part of the problem so I recreated all of the graphs with self-reported mask use rates and estimates of the use seen by others. The mask use is reflected on the right hand scale. Normally I would label these more carefully but I really wanted to get this information out, with the hopes of maybe influencing some policy as the BA.2 surge ramps up.

One thing to note is the consistently wide difference between self-reported mask use and what the same people perceived as use by others. Self-reporting is a known common problem in survey data in that people often want to make themselves look better in the data. This phenomena is known as social desirability bias. What is important to realize is that the true estimate is likely much closer to what is reported as percentages of the use by others (the yellow line).

Recall that the omicron BA.1 surge started around the end of November, which corresponds well with the rise in the pediatric proportion of hospitalizations in most states (again, except Ohio). That lends some support to hepatitis being associated with the omicron strains, although clearly it’s impossible to prove causation and may only be a temporal association. Given that pediatric hospitalizations were higher during this period though, it would seem to suggest that COVID, particularly omicron, is playing a causal role in the disease pathway.

Just a quick aside about Ohio. I’ve been watching pediatric hospitalizations there and have a very strong suspicion of what may be behind it. It could very well be due to a surface chemist by the name of Douglas Frank. He started a Facebook group at the beginning of the pandemic called “Dr. Frank Models” where he was going on about his expertise in modeling pandemics and predicting only about 1500 deaths in the US. Anyone that tried to counter his arguments were booted from his group, so it became an echo chamber of conspiracy theories as well as anti-mask and anti-vax propaganda. The group went through a couple of name changes and eventually was removed from Facebook. He claims that Facebook took it down, but I suspect that he and his admins did so because the evidence against his claims became so damning. It had grown to over 50,000 members. He relished in the attention and was always self-promoting himself to try to get news interviews. He is adept at making things look like advanced science and mathematics, but he’s really just playing off the lack of education among many of his followers who wanted something good to believe about the pandemic.

I also suspect that since he was doing all of this self promotion and getting on local media in Ohio as well as being asked to speak on the pandemic, many people in the state got on board with his false philosophy and that is why Ohio is in such bad shape when it comes to children there.

After his group disappeared, he went on to start claiming to be an expert on election fraud and is the “scientist” that is in one of Mike Lindell’s (My Pillow Guy) crackpot “documentaries” claiming that he has evidence for election fraud using a “6th degree polynomial” that has been repeatedly debunked. He parades Christianity and patriotism as a means to continue fooling his followers. His platforms of choice are now Telegram, Rumble, and other dubious sites.

His specific words about the pandemic and links to rebuttals to his election fraud conspiracy are available online if you are curious.

So what is the take home message if in fact this hypothesis is right concerning hepatitis? It means we aren’t doing a good job at protecting our children, or pretty much a lot of people in American society. The cases of pediatric hepatitis also suggest that there are other chronic conditions that are likely to show up among those with a history of COVID infection in the future. Many have already been emerging.

Here are my recommendations:

• Resume mask mandates in public spaces, particularly in schools, but also in workplaces.
• Educate the public on the need for respirators, not just cloth masks. Even better, get these in the hands of the public using the Defense Production Act and make them freely available to those who might not be able to afford them. Have stockpiles of them available in the appropriate sizes for children in schools as well.
• Improve ventialtion indoors by increasing the number of air changes per hour.
• Add HEPA filtration systems to air handlers and make Corsi-Rosenthal boxes readily available in schools and workplaces to help reduce viral particles in the air since this disease is airborne. In addition, promote their use as a means to keep family members safe if one is currently infected.
• Get the vaccine approved for those under age five.
• Encourage vaccine manufacturers to get variant- and subvariant-specific vaccines in the pipeline when they are emerging, such as omicron BA.4 and BA.5 in South Africa.
• Keep all of these measures in place until the END of the pandemic. The claims that we are in the “endemic phase” are simply false.

If we fail to take these measures, the health, social, and economic consequences will be worse for each that isn’t in place.

Finally, if anyone is interested in doing a multivariate analysis of the data for publication, I would be more than interested in coauthoring that with you.

States with Pediatric Hepatitis Cases and Counts (if available)

Alabama (9) The first report the CDC issued on the mysterious hepatitis focused on a cluster of nine cases spread across different parts of Alabama.

Arkansas (1) Arkansas Department of health is “investigating one potential case, but more investigation is necessary,” a statement to TODAY read.

California (7) The California Department of Public Health told TODAY in a statement on May 5 that it has “received reports of seven young children in California with severe hepatitis since October 2021. We do not know yet if adenovirus played a role in these rare illnesses or if these cases are connected.”

Colorado (4) After asking health providers in late April to start reporting cases of pediatric hepatitis with an unknown cause, the Colorado Department of Health told TODAY in a May 5 statement that it received one reported case that dated back to December 2021. The child was under 10 and tested negative for adenovirus at the time.

Delaware (1) One case is being investigated in Delaware, NBC News reported in late April.

Georgia (handful) The health department in Georgia said it was also investigating a “handful” of cases as of late April, according to NBC News.

Idaho (2) The Idaho Department of Health is currently investigating two cases pediatric acute hepatitis of unknown origin, it told TODAY in a statement.

Illinois (3) The Illinois Department of Health announced in late April that it is investigating “three suspected cases of severe hepatitis in children under ten years of age, potentially linked to a strain of adenovirus. Two of the cases are in suburban Chicago and one is in Western Illinois.”

Louisiana (1) In late April, NBC News reported one case was being investigated in Louisiana.

Michigan (2) Two patients in Michigan meet the CDC’s criteria for being investigated as possibly part of the outbreak, a spokesperson for the Michigan Department of Health and Human Services told TODAY.

Minnesota (3) The Minnesota Department of Health is investigating “several cases” of hepatitis in kids with an unknown cause, local NBC affiliate KARE 11 reported in late April.

New York (handful) New York health officials are investigating a “handful” of cases of hepatitis in kids with an unknown origin, NBC News reported in late April.

North Carolina (2) Two cases of the rare liver inflammation were reported in school-age children in North Carolina, local NBC affiliate WCNC reported in late April.

North Dakota (1) A child in Grand Forks County is recovering at home after being hospitalized with hepatitis with an unknown cause, the North Dakota Health Department announced Thursday.

Ohio (6) Doctors at Cincinnati Children’s Hospital in Ohio told NBC News that they had treated at least six cases in kids between 18 months and 10 years old, all of whom were from Ohio. One child needed a liver transplant.

Pennsylvania (multiple) The Pennsylvania health department is investigating “multiple cases for a possible connection” to the ongoing pediatric hepatitis outbreak of unknown origin but has not confirmed any, according to a statement to TODAY.

South Dakota (1) The South Dakota Department of Health is investigating a case in a child under 10 years old in Brown County, it announced Wednesday.

Texas (cases) University of Texas Health San Antonio has “seen cases of a mysterious and serious liver disease in otherwise healthy children” in South Texas, according to a statement.

The Texas Department of State Health Services told TODAY in a May 6 statement that the state has no confirmed cases of pediatric hepatitis associated with adenovirus 41. “We are aware that some local health departments are investigating cases of pediatric hepatitis to determine if they are associated with adenovirus type 41,” it said, adding that the investigations are ongoing.

Wisconsin (4) As of late April, the Wisconsin Department of Health is investigating at least four cases of unusual hepatitis in children, including one death and one liver transplant.

States with No Confirmed Cases – This may mean that cases are under investigation.

Florida — no confirmed cases

South Carolina — no confirmed cases

States with No Reported Cases (others not listed had not responded)

Maine — not investigating any cases

Mississippi — not investigating any cases

Montana — not investigating any cases

Nevada — no reported cases

Oklahoma — not investigating any cases

Rhode Island — no reported cases

South Carolina — no confirmed cases

Vermont — no reported cases

Virginia — no confirmed cases

Wyoming — not investigating any cases

WHY?

I posted this on Twitter on April 9th. I realized that I should have written it more clearly. I was simply trying to stress that we were about to see another steep climb in cases like we had seen with BA.1 in December.

I use the prior three weeks of data to calculate the first derivative of COVID cases. It provides a means to assess the rate of climb or decrease in cases, and also allows for some projection forward. I’ve used ten days as a cutoff to have a reasonably good estimate of the future. The second derivative is simply a measure of the rate at which the first derivative is going. When they are both positive, that’s a bad sign and why I expected cases to start rising very steeply around April 19th. This is the image on the right from above.

The data table showing the raw numbers for the first derivative is below. It should be pretty clear that this indicates that we are entering a rapid rate as expected. There is obviously some variation on a daily basis, partly because some states don’t report on weekends and others only once per week. This poses a major challenge for keeping tabs on what is happening day to day in each county, metro area, or state.

When that derivative is plotted against cases, it’s quite clear that we are starting a surge in the US that will continue to grow faster over the next ten days.

There are other events that have aligned that will amplify this problem significantly. Every 33 years, Easter (Christian), Passover (Jewish), and Ramadan (Islam) occur about the same time. I’ve previously written how religion plays a role in the spread of outbreaks and/or pandemics. In addition, spring break was occuring in a number of areas near this time frame. That means somewhat of a perfect storm to create an additional acceleration in cases in about 4-6 weeks.

If that wasn’t enough, U.S. District Judge Kathryn Kimball Mizelle struck down the mask mandate on public transportation on April 18th. This was a terrible decision that will impact the health and safety of a number of people, particularly those who must use public transportation, which are often people of color, children under five who cannot be vaccinated, and those with compromised immune systems. This decision is almost analogous to removing airbags requirements from vehicles since they don’t prevent all traffic deaths. That will drive cases up in May as well.

Yes, a number of people use the wrong kinds of masks and/or use them incorrectly. We should be providing respirators to the public and teaching them how they should be used.

The longer we fail to take intervention measures, the more sickness and death that will occur, and the longer we will drag the pandemic out and risk even more variants. One of the major messages that isn’t being communicated enough is that death is NOT the only adverse outcome. Most people have heard of long COVID, but it doesn’t seem to register with people that there are considerably increased risks of chronic diseases from an infection. We are putting a very unfair burden on the future health of children.

The US is failing at mitigation. There are simple solutions that everyone should be taking. Wear a respirator around others. Get vaccinated. Improve ventilation in buildings. Avoid indoor gatherings with others outside of your household. In short, care about others.

“This is the true joy in life, the being used for a purpose recognized by yourself as a mighty one; the being thoroughly worn out before you are thrown on the scrap heap; the being a force of nature instead of a feverish selfish little clod of ailments and grievances complaining that the world will not devote itself to making you happy.”

George Bernard Shaw (1856 – 1950)

Be a mighty one.

The image on the top is the current CDC risk map. The one below is their old methodology. Both of them are available to view with current data. You can select the drop-down to get the first map (or zoom in) by selecting “COVID-19 Community Levels.” Use “Community Transmission” to get the old version. I’ll update this occasionally with the date that the images were pulled in the title.

What gives CDC? It really looks like you have sold out to business and economic interests. It’s going to take decades to make your agency trustworthy again. Your old adage had been “Be First, Be Right, Be Credible.” Was there so much belt tightening that it’s just “Be First” now?

If you want to protect yourself and those around you, get vaccinated (or boosted), improve ventilation, and wear a N-95 or comparable respirator.

Currently there is a technical problem with a platform I started using to automate moving graphs on this site but their support team is looking into it. In the meantime, I am going to manually post state case and variant charts. It’s my take that BA.2 is going to start another surge in the US in April. This might help individuals get an idea of when that could occur in their particular state.

My big fear is that a combination of the notion that the pandemic is over, inadequate testing and at home testing leading to insufficient data, and the lack of mask use by the public could combine to create another surge beyond what most people expect. Influenza has been climbing in the US, which started a few weeks later than usual, which closely correlates to the lifting of mask use across much of the US.

We are simply not collecting enough data or reporting it frequently enough to have a clear picture of what is really happening. Some states have moved to reporting case data weekly. During a time of exponential growth, a delay of a week can cause considerably more spread without the availability of data to make policy decisions. COVID cases are starting to climb in the US and that will become even more apparent as the weekend data becomes available this evening.

We are steaming full ahead through iceberg filled waters while in dense fog. I’m afraid that this won’t end well..

Alabama

Alaska

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

District of Columbia

Florida

Georgia

Hawaii

Idaho

Illinois

Indiana

Iowa

Kansas

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

Missouri

Montana

Nebraska

Nevada

New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Puerto Rico

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming

It’s very disheartening to watch mask mandates and other controls be eased just as the US is moving into what likely will become a surge of BA.2. Some have made the argument that because it is so similar to BA.1, that should reduce risk. That may be true to some extent, but it is also likely that because of the messaging that seems to have grabbed hold of much of the US population that the pandemic is over, masks and distancing have been tossed aside. I suspect that will have a greater impact. In addition, the boosters that many have had were given months ago, so that protection is likely waning as well.

I will also add that what I say about each country is just my thinking which could admittedly be wrong. It’s difficult to really know without complete data as well as more information about other factors that could be influencing spread, but right now I’m mostly concerned about the role of BA.2.

The biggest surges are currently occurring in SE Asia and Europe. It would be helpful to have a visual of cases as they relate to both covariants and to vaccination. Unfortunately, no educational or government institution has done so publicly but I have pulled that data together to do so.

First, if you haven’t seen my work before, I’ll quickly describe the two graph types. Both have the number of new cases plotted on the left y-axis over time on the x-axis. This is known as an epidemic curve. The one with vaccines is the percentage of the population over time that has had one, two or three doses, is plotted on the right y-axis, and should be relatively easy to interpret.

The other plots the epidemic curve against the variants or covariants. Only the major ones get their own line, the rest are lumped together as “other.” These lines represent the samples that have been taken for genetic sequencing and provides and graphs the percentage of each sequenced sample as a percentage of all sequenced samples over time. That percentage is on the right y-axis.

One thing that I am seeing consistently is that both BA.1 and BA.2 seem to drive case climbs about two months after each particular subvariant starts becoming more dominant. I am generally excluding countries that don’t have variant or hospitalization data. The combination of both was my initial criteria for tracking a country more closely, but I have added a few that did not meet that for various reasons.

Africa

South Africa is a little puzzling although the wide, tapered base is likely some of the impact of BA.2 It is entirely possible that the high number of BA.1 cases provided some protection, but it is difficult to know.

Asia

India has been very puzzling all along. Given their population density, one would expect an even bigger catastrophe. I think that the first wave was mitigated by the hard control measures that were put into place. The second and third waves (delta and omicron) however fall at roughly the same time of the year and during the dry season. I have argued since before the pandemic that humidity plays a role of transmission of respiratory viruses. My argument is that in dry air, some of the aerosols have small enough droplets that desiccate, leaving the virus particle suspended, hence pushing transmission further to the airborne end of the transmission continuum and further from the droplet transmission end. Those two surges lend support to that.

What remains to be seen is the interaction between BA.1 and BA.2 I would argue that with both emerging at the same time and BA.2 becoming dominant quickly, that this might be the only surge that India sees with this particular subvariant.

Indonesia appears to be a little bit behind the US. There is an obvious surge from BA.1 which looks to be slowing. The question remains around what will happen there with BA.2. They would be wise to watch other countries that have gone through both.

Israel will also be interesting because of their high uptake of vaccine. Cases are just starting to climb there as well.

Japan also has relatively high vaccination rates. What I note in their epidemic curve is how much wider it is related to omicron. I’m attributing this to what I had said earlier about a two month lag for each variant. The initial climb is what is expected, but the widening at about the 70,000 case mark aligns well with a BA.2 surge while BA.1 was falling.

Malaysia shows a similar pattern to Japan, but BA.2 started climbing earlier, thus pushing that widening closer to the peak of when the BA.1 trend was heading downward.

Singapore further supports this two month argument. In this case though, the BA.2 surge started DURING the BA.1 surge, hence the entire curve is widened at the outset.

South Korea has been hit particularly hard after doing so well through the pandemic. That is a testament to just how easily the omicron variants are spread. Again, using the same two month assumption, I interpret this as the BA.2 surge starting just as the BA.1 surge was nearing its peak. The question is if BA.2 has peaked or if there is more climb ahead (or at least a slowed decline) since it is a much smaller percentage of sequenced cases so far.

Europe

With the rationale I’ve laid out in the Asia section, I think it will be very easy to see the same patterns in European countries and in Oceania. I’m only going to post images for countries with the worst spread right now and will only comment on anything exceptional.

Austria

Belgium

Czechia

Denmark surge on surge on surge?

Estonia

Finland has a very clear delta wave followed by BA.1 and BA.2 widening it.

France

Germany

Greece is particularly interesting because there is a bulge in the BA.1 wave that seems to coincide with a brief uptick in delta percentage rates.

Iceland doesn’t report variant data anymore, but the waveform of the epidemic curve since the beginning of the year suggests that they have had both their BA.1 and BA.2 wave, with BA.2 starting just as BA.1 was starting to recede.

Ireland

Italy might look like an exception to the pattern, but given that there wasn’t a curve to the proportion of BA.1 but shot up suddenly, this may be due to insufficient testing.

Latvia

Liechtenstein

Luxembourg

Netherlands

Portugal

Slovakia

Slovenia

Switzerland

Oceania

Australia

New Zealand

North America

Canada is just about to start the BA.2 surge, right on schedule.

United States

Summary

As I got about halfway through copying these, I realized that a good test of my two month premise would be if I could look strictly at the vaccination graph and estimate about when each omicron subvariant would have started climbing when I pulled up the covariant graph. The assumption held.

I have spent all of my free time the last couple of weeks working on coding to automatically get the graphs posted. However, I think I stumbled across a bug in the service I’m using that prevents an automatic upload. In total, there about 1400 different charts posted on the site. I could go through manual updates, but I’m going to see if the company can figure out how to fix it.

My next step is to post the case/covariant graphs for each US state. I will do that as soon as I see the next covariant data update where I pull that data. I’m hoping that is tomorrow.

One of the most puzzling things during the pandemic is the repeated idea that as soon as cases start to fall, people start assuming that it is over. Sadly, this is far from the truth. The sudden reversal of various mandates such as those related to wearing masks and restrictions on indoor gatherings will accelerate the next surge. Worse, this leads to a further erosion in confidence by the public, which ends up costing more in the terms of health, lives, and the economy.

While it is true the the surge of the omicron subvariant BA.1 seems to be done, BA.2 is right around the corner for many areas. Sadly, US politicians don’t seem to have learned any lessons yet about observing what is happening in other countries. The fact is that BA.2 has barely made a showing yet in most countries is illustrated below with a couple of examples.

South Africa is a particularly interesting one to assess. Prior waves had generally smooth declines in cases. That is not the case with the recent one. There is a bulge about the time that BA.2 started becoming a larger proportion of sequenced isolates and the decline is ending much sooner at a higher baseline.

The other big difference in South Africa is seen in the slope of the cases (black dotted line) in comparison to the covariants. The valley of the final slope curve is much more narrow and sharp, also in conjunction with the rise of BA.2. The slope line is also settling again near zero, meaning that the baseline case rate is stabilizing at a higher level than the past.

Denmark is a particularly good warning. It’s very easy to see the BA.1 surge starting in December but then a surge-on-a-surge in January as BA.2 becomes dominant.

New Zealand is a prime example of the impact of BA.1 and BA.2 hitting almost simultaneously. It’s rising so fast there that it’s difficult to see the peak, so I’ve also included a graph with the slope of the case curve added.

So where does that put us in the US? BA.2 is only just starting to emerge. It’s more easily spread than BA.1. When that is combined with the widespread relaxation of administrative controls and outright banning of them in some jurisdictions, it spells a bad spring for the US.

The smartest thing to do is to be fully vaccinated and wear a N-95 or comparable respirator when in public spaces. The notion that the pandemic is over is simply wrong. Most likely we have years to go with a higher and higher rate of transmission with each subsequent variant of concern.

ADDENDUM (2/25)

I’ve looked through a number more countries this evening and have seen similar patterns. I wont provide much for narrative other than a header indicating one of three patterns that I’ve place in what I believe to be an order of progression as BA.2 becomes more prevalent.

1. Sharp Reversal of Case Slope as Early Indicator in Estonia and Sweden. It’s a little difficult to see this in the Estonia graph since it is just starting.
   

2. Bulges forming in the downward trend of BA.1 cases in Portugal and Spain. It also makes sense that these two nations would be having a similar experience with BA.1 and BA.2 given their geographical proximity.

3. Cases stabilizing at a new HIGHER baseline until BA.2 causes the next surge in Greece, Ireland, and Norway.

It feels almost impossible to stop the flood of lies and misinformation around the pandemic, no matter how hard one tries. It helps to understand how some of these get started and this Twitter thread is a perfect case study.

https://threadreaderapp.com/thread/1492586980735082497.html

2/7/21 Note: I had someone claim that I was being selective by only showing the first set of studies. I honestly hadn’t realized that I missed some since I was writing late. This will be completed, but I have added the second group image with the same criteria used. That one only has one study worth digging into. I’ll do the same with the third group when I have time.

Ivermectin continues to be pushed for COVID when there isn’t clear evidence to indicate that it provides any benefit. Hill et al (2022) indicate that “the results suggest that the significant effect of ivermectin on survival was dependent on largely poor-quality studies.” They also go on to describe a number of fraudulent studies that have greatly influenced the outcomes of some meta analyses and conclude “These instances suggest that the data available to the support the use of ivermectin for COVID-19 are not reliable” and have illustrated how much difference removing completely fraudulent to those with high risk or some concerns change the outcome considerably. They also suggest that publication bias (see Publication Bias: A Brief Review for Clinicians) is also overestimating the benefit of ivermectin.

There is a website that people often use to try to claim that it works, but clearly they don’t understand statistics. It uses 77 studies as of this writing to try to promote ivermectin.

This is a screenshot of that website with the studies highlighted with different colors. Someone without much background in science or statistics might look at the column on the right and quickly conclude that these studies indicate how well ivermectin works. In fact, that graph is intended to completely misrepresent the data.

The third column in the table has a pair of numbers in [brackets]. They indicate a range from low to high known as the 95% confidence interval, often referred to as the 95% CI, or in this table the [CI]. Without going into a deep discussion on what these values mean, there is only one important fact about the studies shown with the red highlight – they include the value of 1.00 in that range. When that is the case, it indicates that there is no statistical difference between the study group and the control group, or to put it in simple English, there is no evidence that the treatment worked any better than not using the treatment. Without even opening those individual studies, one can see that over half don’t show evidence for efficacy.

This is also clearly stated as “If the confidence interval crosses 1 (e.g. 95%CI 0.9-1.1) this implies there is no difference between arms of the study” on a website where you can learn some very basic biostatistics.

The other important concept that can rule out studies in this table is the sample size. It’s simply the number of participants in each arm of the individual studies. This gets quite a bit more technical but it is related to another concept known as statistical power, which is a measure of how effective a study is at determining differences between the two arms of the study. This is a number that can be calculated, but with experience, it becomes relatively easy to quickly identify those that have too small of a sample to really provide any meaningful conclusions. A slightly more complicated but still readable discussion of this can be found here.

As a quick example, assume you have an opaque jar with 100 different marbles, 25 of each color – red, blue, green, and yellow. If one doesn’t have knowledge of the composition of colors in the jar and was asked to provide an estimate of the proportion of each, obviously they will get a much closer estimate of those percentages by taking 50 marbles out instead of 5. That’s a very basic way to understand a little bit of the relationship between those two concepts.

This is an analogy of the above table. The ones that remained after eliminating the red ones are highlighted yellow because they didn’t sample enough marbles to prove their hypothesis.

That leaves only FOUR of the initial 77 studies to assess for validity (highlighted green in the table).

Borody

This study doesn’t appear in a peer reviewed journal nor could I find it on a preprint server. Typically I would look at the date it went on a preprint server as the next step. If it has been a long time, that generally means that no reputable journals have accepted it. The fact that it’s not in either location is a MAJOR red flag. A little further digging explained why.

“Doctor who advocated Covid-19 therapy including ivermectin applied for patent on same unproven treatment
Exclusive: Australian professor Thomas Borody’s failure to widely declare that he could potentially profit from treatment he is promoting raises ethical concerns”

de Jesús Ascencio-Montiel

Guess who is behind the website where the screenshot came from. Yes, de Jesús Ascencio-Montiel. Given that he is clearly misrepresenting data, it puts into anything he is behind into question. In fairness though, this one has been accepted for publication in what at first glance looks to be a legitimate journal from Elsevier, the Archives of Medical Research. One of the easiest ways to determine if a journal is legitimate is to assess if it uses a pay-to-publish model. If it is, it can be discarded.

The Archives of Medical Research is a tricky journal in this regard. It uses both the traditional publishing model and the pay-to-publish model, which means one must assess each individual article. It’s pretty easy with his. Open access means pay-to-publish. Look at the bottom right of the image.

de Jesús Ascencio-Montiel can be discarded.

Mayer

Mayer doesn’t show up in preprint or published locations either. Discard it. It’s garbage.

Merino

The title of this should be a dead giveaway that it’s not a useful analysis. “Ivermectin and the Odds of Hospitalization Due to COVID-19: Evidence from a Quasi-experimental Analysis Based on a Public Intervention in Mexico City.”

Once again, it’s another that doesn’t appear to have been published and can only be found on a preprint server. This is a good example of why it’s helpful to look at the posting date on the server. It was placed there on May 4, 2021. Given how long ago that was, it’s a very safe bet that this will never be accepted for publication either.

Soto-Becerra

This is another preprint that has not been published or peer reviewed in over four months. It was posted on October 8, 2020.

Summary

With some very basic knowledge, one can easily rule out the validity of studies. Out of these 77, not even one needed to be evaluated from a research methodology and science standpoint by reading it, which is considerably more challenging.

Stay away from ivermectin as a treatment for COVID or anyone who is pushing it. They are either scientifically illiterate, acting from a basis of politics and not science, or are financially profiting in some way.

Just five days ago I wrote about my fears of what is about to happen in India. The situation is finally getting a little bit of press, but not enough.

The official death toll in India stands at 483,000. However, the actual mortality is considerably higher. In one study, three different methodologies were used to estimate excess deaths in India from COVID up to June 2021, which was in the tail end of that wave. These estimates ranged from 3.4-4.9 million excess deaths due to COVID.

In only five days time, the data paints a frightening picture as the omicron variant begins to spread in the country. One can now easily see this exponential growth of cases currently compared to the graph from five days ago.

The red line in this graph is the slope, or can also be referred to as the first derivative. It’s a measure of how quickly cases are growing or slowing. and the distance from zero as represented on the right axis is directly proportional to the rate of growth or slowing. It was that inflection on the graph a few days ago that caught my attention much more than the epidemic curve itself because the massive scale of the last big wave dwarfed the current incidence of cases.

The next piece to look at is how quickly that 1st derivative is growing. That allows for a comparison between the last wave and the current one to see if there is much difference. Just by eye, it looks as if the current wave is considerably steeper. The 2nd derivative is useful to assess that assumption.

The graph below has that same 1st derivative (the red line) but the scale for it is on the left. The 2nd derivative is in blue and is on the right.

The question that this can answer is whether the rate of the rate of acceleration is even faster this surge. To help understand how this works, look at the red highlighted box on the right. The bottom left corner of it is aligned with the start of the climb of the first derivative. The top right corner is aligned with the current height of the first derivative. The height is what is important for the comparison.

The yellow highlighted box is the same height as the red one. The bottom left corner starts at the corresponding start of the earlier surge on the 1st derivative. The next thing to do was to widen that box to find the point at which the 1st derivative was the same height as seen in the red highlight. It’s obviously quite a bit wider.

Each of those boxes has been placed with their left edge corresponding to Sep 1, 2021 on the date scale at the bottom and they overlap. Think of each as being pushed to the left edge of the dotted line surrounding them.

If you look at the now orange box (from overlapping red and yellow), you can see that this current rate of the rate of acceleration (not a typo) was reached in about two weeks, whereas in the prior surge, it took about 5-6. That is what we would expect with the omicron variant because of it’s much higher rate of spread.

This can also be seen by the higher reproduction rate of cases this surge compared to the prior one.

That could easily spell a major disaster for India. Currently, it’s estimated that 63% of the population has had one dose of vaccine and only 45% has had two. The epidemic curve against vaccinations is in the graph below.

I have already discussed to some extent how this is really bad for not just India, but the entire world. Until we have N95 or equivalent masks, adequate testing, and a fully implemented mass vaccination program globally, this cycle is likely to continue due the much higher chance of new variants arising as the virus has more chances to mutate and replicate. We are YEARS from being out of the woods from COVID.