Monitoring BA.2 in the US

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..









District of Columbia





















New Hampshire

New Jersey

New Mexico

New York

North Carolina

North Dakota





Puerto Rico

Rhode Island

South Carolina

South Dakota







West Virginia



The International Rise of Omicron BA.2

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.


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.


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.


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.




Denmark surge on surge on surge?


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



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.


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.





Malta doesn’t have BA.2 data, but I bet it started about the start of the year.



Russia isn’t have a rapid climb currently but I’m assuming people would want to know because of the war. Expect cases to climb with BA.2 shortly.




United Kingdom



New Zealand

North America

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

United States


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.

Wrong Way

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.


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.

Misinformation and Lies

Madurodam, The Hague, Netherlands

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.


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).


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


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.


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


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.

India Alert 2

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.

India Alert

Flag of India - Wikipedia

The official COVID death toll in India currently is 481,770. However, this number is likely many times lower than the actual toll. A paper was published in July last year that estimated the death toll to be between 3.4-4.9 million.

Most people can probably remember some of the awful images of people trying to find oxygen and the massive numbers of funeral pyres in April and May of last year when the country was officially peaking at 400,000 cases/day, although that’s likely a gross underestimate as well. Most of that surge was due to delta, which is also thought to have originated in the country.

Something alarming presented itself in the data from India. When looking at the epidemic curve, it doesn’t appear that there is anything very concerning right now. Part of that is due to the massive scale of the big surge there last year However, I have used the first derivative of the epidemic curve to identify rapid growth or slowing of cases (the red line). It’s also useful to project about 10 days out the rate of case growth or slowing. It may not look like much now, but it matches the rate of growth in the earliest part of the delta surge.

When zooming into the epidemic curve, it becomes readily apparent that there is an exponential curve starting in India.

The proportion of omicron found from sequencing samples in India also suggests that this curve is the start of the impact of omicron there. It should be noted how much faster omicron spreads in other countries causing a much steeper curve, so this is a veery ominous warning sign.

This is also very concerning given how small a proportion of the population has received either one or two doses of vaccine in India.

Not only is this a potential catastrophic disaster for the people of India, but the repercussions of it will be felt throughout the world.

Most Americans are completely unaware the role India plays in supplying generic drugs to the US. In addition, almost 70% of the active ingredients India uses in manufacturing the pharmaceuticals originate in China. At one point, the combination of the supply chain from the two countries was about 80% of the US generic pharmaceutical supply. While that number has come down considerably, the US is still very dependent on the production from both countries.

India is also the major supplier of COVID vaccine to the world, particularly in less developed countries. Any impacts on vaccine production in India will be felt globally.

The more people who become infected with COVID, the more likely the emergence of another variant of concern that could start an entirely new wave of infections which may not have coverage by any vaccine. One only needs to look at the impacts of both the delta and omicron variants to understand the potential scale of impact on India. The population size and density makes this a very significant risk.

One thing that has been very notable is how politically divisive the pandemic has become in many parts of the world. The pandemic is one area where we need to come together not only as a nation, but as a global community to use evidence-based science and medicine to mitigate the impacts of the pandemic. The big test for the US will be starting this month. If we can’t start caring for each other as countrymen, there seems little chance that we will be able to do so as part of global community.

Disability during a Pandemic

May be an image of 1 person and text that says 'The true measure of any society can be found in how it treats its most vulnerable members. Mahatma Gandhi'

My friend Crystal Evans wrote a personal account of what it is like to have a disability during a pandemic. The notion that has been pushed that we can easily protect those at high risk of disease or with complex medical needs is simply a lie. Our country has not been able to do that with healthy people. How on earth does anyone who supports the Great Barrington Declaration think we can do that well with those who need support?

Here are Crystal’s words.

As a medically complex individual who relies on medical supplies and the healthcare system to stay alive, I live a side of the pandemic many of you have the privilege NOT to experience.

I have a genetic neuromuscular disease, and for me, infections can result in disease progression. In December 2015, I got what would have been “just a cold” for many – which turned into bronchitis, but because I had underlying neuromuscular disease, I lost remaining respiratory function and have been ventilator dependent ever since. The first 4 years post-tracheostomy were generally manageable, but when COVID hit, the dynamics of being medically complex changed everything.

Since April 2020, I’ve been dealing with ventilator supply shortages and medical supply rationing. I’ve dealt with painful airway infections as a result of prolonged use of ventilator supplies, and have spent months to navigate health insurance battles for covering alternative solutions as supplies are scarce. Tracheostomy tubes are now among medical supplies in a shortage. And tracheostomy groups are full of younger people ending up trached after long ICU stays with COVID.

I’ve spent the past month with barely any home care coverage because everyone is ending up quarantined after getting exposed to COVID. And too many people currently need COVID tests for PCAs to quickly access appointments for them. Many of my friends are in perpetual home care coverage crisis too – having to weigh the risks of exposure vs lack of assistance for basic Activities of Daily Living.

Please don’t assume that at-risk people can simply “stay home” to avoid the virus. While you’re out living your life, you’re also risking exposing healthcare workers many of us depend on. I haven’t left my house at all in over 6 weeks. Yet I’ve had exposures in my home by healthcare workers, without actually going anywhere.

Managing our underlying conditions is harder now than ever. The hospitals we used to turn to when we were sick have become some of the least safe environments for us. As a vent user, the unit that has nurses trained to care for me has become a COVID unit. As a result, many of us need to manage care in our homes for issues that was previously hospital-level care. I had sepsis 3 times early in the pandemic due to supply chain issues, but was the one managing my own round-the-clock IV meds, and medical needs. I’ve drawn my own blood dozens of times in the past 2 years to pass off to community paramedicine to take it to the lab.

Many of my friends with underlying conditions can’t get outpatient care or VNA care for basic disease management because the healthcare system is overwhelmed. I’ve also seen several friends with disabilities die directly because of these home care coverage shortages.

The compassion America had for frontline workers in Spring 2020 is long gone. People who don’t feel they are at risk have long moved on with their lives, with little regard to what the healthcare workers continue to be up against.

I’ve seen the incredible amounts of stress home care nurses and VNA therapists are under throughout the pandemic – trying to keep those who are at-risk stable in the community to protect us, and to save those hospital beds for patients who desperately need them. Home Care Agencies and VNAs are short staffed, the workers are exhausted, and at multiple points of the pandemic several have been in tears in my home.

Some of the ongoing nursing staffing issues I’ve dealt with stem from nurses getting long COVID, leaving them unable to work or having to reduce their hours. I’ve also seen 2 of my home care nurses lose their husbands to COVID.’

While healthy people might see increases in local case numbers, but not feel personally impacted or assume it’s “fear mongering,” those of us who are high-risk depend on that local data as it may mean re-thinking basic daily healthcare.

For us to stay healthy during COVID surges might mean eliminating certain services to reduce our contacts – like physical therapy, homemaking, reducing PCA coverage, avoiding in-person errands altogether, and only accessing care via telemedicine. Those of us like myself with inborn errors of metabolism may have to re-think meal prep and how to access medical diets if our kitchens are inaccessible.

I am one of the 19% of American’s living with a disability. While my ventilator and wheelchair make my disability visible, 10% of American’s are living with an invisible disability, and many of these individuals are also immunocompromised or high-risk with COVID. You can’t tell just by looking at someone what their risk factors are. We are surrounded by high-risk individuals in our community, as well as those who live with or care for high risk family members. They are people of all ages. – our children’s classmates, our colleagues, our neighbors, fellow customers in local businesses.

Not every high risk individual can be effectively vaccinated – some immunocompromised people may never develop antibodies post-vaccination. Others may have drug reactions or other risks with vaccination due to their underlying disability. Dec 8 the FDA announced EvuShield, an antibody for high-risk individuals, but last week stated that the US Government only purchased enough for 10% of the 7 million adults in the US who are eligible.

Over the past 2 years, the COVID-19 pandemic has made it clear how devalued the lives of seniors and people with disabilities are by many in our communities. To dismiss the virus as “only people who are 65+ or with underlying conditions are at risk” is incredibly ableist. We are people too. We have jobs, we have families, we have dreams, accomplishments, and goals – just like anyone else. Our age or disability shouldn’t make us worth less than any other person.

Please remember – statistics aren’t just statistics. There is a person behind each case, a family behind each death, a life changed by each Long-COVID infection. But empathy seems to be gone, because the people statistically impacted the most by the pandemic are disabled, elderly and/or people of color. Too many people are more focused on their “rights” and their “freedom” no matter how it affects those in their community.

When you blow off the virus as “just a cold” or “the flu” it’s dismissive to the families of nearly 850,000 American’s who have died from the virus, to the thousands who have spent weeks in the ICU, as well as those who are living with Long-COVID. It shouldn’t matter what the person’s age was, or if they had underlying conditions. They are people who have lived in our community, whose lives matter.

Omicron Severity

The false narrative that the omicron variant is nothing worse than a cold persists. The data from South Africa now makes it clear that is not true.

First, it’s important to understand that omicron is the dominant variant in the country, as shown by the red line on this graph plotted against the incidence of COVID cases (blue) over time. Cases are measured on the left axis, the percentage of a variant on the right.

The next graph provides cases against the three categories of hospitalization in the country. Minor colds don’t cause major climbs in general hospitalization (green line), especially during what is their summertime.

In the next graph, I’ve removed general ward admissions to provide a better visualization of their high care (yellow – what might be thought of as a step down unit in the US) and ICU care (red). A mild illness does not drive admissions for that level of care either.

The immunization data is a little harder to see for the country, but the curves for first and second doses and be connected to see the curves.

The ICU/high care data for the country suggests that recent delta infection or immunization prevent severe illness., hence the lower amount of these services as a ratio to cases than in prior waves.

Take omicron seriously. It spreads with amazing speed and clearly causes much more severe illness than many people believe. Wear a N95 or similar mask. Don’t share indoor air space unless necessary. Improve ventilation and/or use HEPA filters.

The country is in for something really quite unimaginable to most people.

Omicron, the United Kingdom, and Hospitalization

Flag of the United Kingdom - Wikipedia

The best indicator for hospitalizations caused by the omicron variant in the US is the United Kingdom. There is still a narrative that the variant causes less significant disease, however it’s still to soon to make that call.

The default view of cases against hospitalization and ICU use isn’t very helpful to draw any conclusions.

Omicron represented only 9% of samples in the UK on 12/13. That date is marked by the vertical red line. This graph changes the scale and focuses on general hospitalizations in relation to cases. This suggests that most hospitalizations at this point were due to delta, which had been the dominant strain. It’s also been relatively clear through the pandemic that hospitalizations lag cases by 1-2 weeks in most places.

When changing to viewing cases against ICU beds, it’s also clear that there is a lag, but in this case it’s about 2-3 weeks, which would make sense. People are admitted to a regular floor and their condition declines, requiring ICU care.

This 1-week lag between hospitalization and ICU use when looking at the curves of the occupancy of each set on different scales, although delta did seem to show a bit of an exception to that in the UK, which can be seen in the data since summer.

The fact is that it’s simply too early to draw any conclusions on the severity of omicron using UK data. A clearer picture should be available for hospitalizations in about a week, and ICU use in about two.

In the meantime, the best thing to do is wear a N95 or similar mask, increase ventilation with fresh air, avoid indoor spaces with anyone outside of your household unless absolutely necessary, and get the full series of three vaccine doses.