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.
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.
The tsunami is just starting to sweep away those in it’s path on the beaches in the US. Head for high ground. This is the big one. This is not a drill.
A similar graph has been updated for each US state. See the dropdown list.
Here are a few items to provide more information on how to understand the pandemic. Be sure to read the one at the very end that is an online article.
The Great Influenza: The Story of the Deadliest Pandemic in History
This is the best starting point to understand the scale of the impacts of a pandemic and provides a multifaceted view of political, social, economic, health, and other consequences.
The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger
This may seem like a dry title, but Levinson covers the history of the shipping container in an interesting way that makes for an enjoyable read. Now that the world has experienced just how crucial this simple piece of the global trade infrastructure this has become, it is a very useful way to grasp how the world is still at risk.
Misinformation and Lies
This section is specifically to address some of the motivations and psychology behind what has turned the pandemic into a global catastrophe. Some people still don’t see it as such, but it should be quite clear early in 2022.
Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming
It’s very easy to see the manipulation that has been going on with information with the help of this book. This is particularly true where people often simply frame lies as “questions” to spread a false narrative.
People of the Lie: The Hope for Healing Human Evil
This is very pertinent not only to the lies being spread about the pandemic, but also around those that have led up to the insurrection on January 6, 2021 and the continued attempts to cast doubt on our electoral process. The final third of the book is of particular interest in that it addresses organizational evil through the author’s work for the military to find the root causes behind the Mỹ Lai massacre during the Vietnam War and the means to correct those problems.
How The Koch Network Hijacked The War On COVID – As Omicron surges, a shadowy institute filled with fringe doctors appears to be part of big business’ two-year strategy to legitimize attacks on pandemic interventions
This article is foundational to understanding the money behind the lies being spread behind the pandemic and the various individuals connected.
Omicron is starting to look like it could be most rapidly transmitted virus in human history. Again, rethink your holiday plans, and daily activities thereafter. In a matter of weeks, injuries from motor vehicle accidents that may have been treatable in the past may be triaged into the expectant category. Even routine procedures may not be available. Only travel if necessary, go to indoor public places if necessary, and always use a N95 or equivalent mask if you do need to be around others outside of your home.
I have been looking at the drop in case numbers in South Africa and pondering what might be driving them downward. It dawned on me that perhaps it was a false signal given the time of year, because I had seen a similar pattern in US data.
I did a search for holidays in the country and found exactly what I had been suspecting. Winter break in South Africa begins December 15th. A similar, but much smaller dip occurs about the same time last year. It’s very possible that it’s less easily distinguished because the case volumes were lower and the rates of case growth were slower.
South Africa
Something similar is seen in the US, but in a much more obvious way, partly due to the considerably higher case number. The drops are very noticeable for case counts at Thanksgiving and Christmas breaks.
United States
I interpret that as it may suggest two very important points.
Schools play a very important role in community spread.
The drop in cases in South Africa may be an artifact of schools closing for winter break.
Time will tell, but I don’t think any narrative the omicron peaks and retreats quickly is an accurate assessment. It’s simply too soon to know until the holidays have past.
People seem to be underestimating the risk of travel for the holidays. There is likely a great deal of both pandemic fatigue and temporal discounting going into these decisions, both of which skew critical thinking.
Delta
The delta surge is just starting to hit the US more broadly after being limited to smaller areas. This has led to significant impacts in hospitals in some of the hardest hit parts of the country, which has led to long wait times in the emergency room, patients being held in the emergency room because there are no beds available for them on nursing floors, and the cancellations of elective surgeries. The surge from delta alone will really start showing up in the numbers around Christmas as a result of Thanksgiving activities.
Omicron
Omicron has quickly thrown a new wrench into the works. There is still a lot to be learned about this variant, but that hasn’t stopped many with large voices from downplaying it without evidence to support their claims. It’s simply too soon to make meaningful claims about the severity of disease and the risk of death from this variant.
One source that may have driven that thinking was a study from the largest health system South Africa. However, in that study the average age of patients was 34, which is known to be a much lower risk than for older age groups, thus presenting a very skewed view. The data paints a slightly different story.
When looking at admissions to different nursing unit types, the close curve between COVID incidence and hospitalizations cannot be denied. It’s also worth noting that omicron represents 98% of COVID cases in the country currently, so it is safe to assume that the majority of these are due to omicron. In addition, it should be noted that the downward curve at the very right likely represents incomplete data reporting, which is known to lag for days.
There are also claims being made that there aren’t omicron deaths. The fact is that there is a long interval between when a case is reported and when a death is reported. Some of the deaths are starting to show up in the data..
The influenza season also normally starts to grow quickly about this time of year. Thanksgiving and Christmas are likely two major times of long distance spread across the US. Last year, influenza activity was low, due to high influenza vaccination rates, a lower severity season in the southern hemisphere (theirs precedes that of the northern hemisphere), and people were taking additional precautions for COVID, such as social distancing, masking, and frequent hand hygiene. In addition, by keeping the numbers low early on, that prevented exponential growth of influenza cases.
There is no mask usage data for this exact time last year, but one can safely assume that it was likely as high as the highest point around the beginning of 2021. Mask use has decreased considerably since, part of it due to the bad messaging that those vaccinated did not need to wear masks, which accounts for the very large dip in use. It is worth noticing the correlation between the drop in mask use and the rise in cases as well. Mask use by state can be found be found on the graphs for the respective states at the top of the page.
The best way to think of the situation is that there are two separate pandemics superimposed on each other, delta being quickly followed by omicron. The wildcard is H3N2, which is beginning a sharp climb in the US.
H3N2 caused an influenza pandemic in 1968 leading to an estimated 1 million global deaths. That seems small compared to the 5.4 million recorded deaths from COVID, which is likely a gross underestimate. However, there are some troubling signs with H3N2 this year.
The influenza vaccine was formulated to protect against H3N2, but a new variant of it emerged (Darwin) from Australia that appears to evade vaccine. This is causing an epidemic of influenza in South America. What is troubling about that is it is occurring outside of their normal influenza season. This suggests a significant year for influenza, so we can at least expect two superimposed pandemics and an epidemic.
Decision Analysis and Temporal Discounting
Christmas is rapidly approaching and changing plans might be a difficult decision. Hopefully this simple version of a decision analysis tool will help. First though, the behavioral psychology or behavioral economics concept of temporal discounting needs to be addressed.
Temporal discounting is simply the tendency of people to value something in the future less than something in the present, although they may have exactly the same value. This is a particular problem in American society, as evidenced by how the average household carries over $6000 in credit card debt. This shows a clear tendency to pay much more for something to have it now (because of the high interest rate on a credit card) versus waiting to pay for it in full.
One tool of decision analysis is known as a two by two table. I will avoid the technical terms to get to the point. The top contains the two unknown possibilities (the virulence of omicron) and the left contains the choices of gathering or not. Inside the table are some of the various outcomes.
There are two tables to take into account temporal discounting. I’ve highlighted the words in red that differ between them. It contains three general categories of outcomes to consider.
The risk of infection should be relatively obvious. Social costs in the short term tend to focus on those directly involved in the decision, however, in aggregate the social cost could be considerably higher for the same group of individuals because of how the actions of the rest of society affect them. The same is true for economic costs. Those two variables are a perfect example of how the decisions of others can affect them directly. Much of the narrative of people who aren’t taking precautions focus simply on the short term risk of infection for them as an individual.
When thinking through this framework, it becomes easier to understand the differences between the different combinations. Obviously continuing with plans if omicron has high virulence is a very bad choice and the delaying plans is the best choice.
It’s worth a quick mention of the social and economic costs that seem to be overlooked. If a family member becomes ill and requires hospitalization, there is a very large economic impact on the family. If the family member dies, the economic and social impacts increase even further.
Testing Problems
Some may think that relying on testing and their vaccination will provide adequate protection. However, we know that omicron can evade vaccination, although the vaccine does reduce the risk of serious illness. There is a major problem with testing. “FDA finds 3 COVID-19 tests that fail to detect the omicron variant.” False negative tests could cause a disaster among people who think that they had tested negative.
Recommendations
It would be best to consider delaying holiday celebrations until more is known. The rate of spread of the omicron variant is incredibly alarming. The best plan would be to limit contact to those in your immediate household.
If you choose to travel, wear a respirator in any indoor (and possibly outdoor) venues. It may be worth doing around those you are planning to visit as well for the reasons listed above. It may also be helpful to look at some new reports from the federal government. There isn’t a single page with a link for each state, but they are easy to find using the search term “State Profile Report (SPR)” and the name of the state you want to find.
I started putting the data together to monitor the impact of Omicron in the UK but in the process saw a really good example of exactly how the vaccine is working. I want to get the data from a couple of other countries put together so there won’t be much description with one exception.
I’ll only add one quick comment before turning over the narrative. I multiplied the number of cases by 10 as a means to make the data of three different scales readily visible to show how they compare to each other. I had posted this in my Facebook group and my friend Dale Harrison wrote an excellent summary. Here’s what he had to say:
WHAT VACCINATIONS ARE DOING!
The graph below is from the UK and shows how well vaccination correlates with suppressing deaths.
The blue line is cases, the black line is deaths, and the red line is a normalized curve showing vaccinations. The 2nd graph shows total 1-dose vs 2-dose vaccinations plotted against deaths.
This same effect is being seen everywhere total vaccination rates rise above about 70%.
There is a COMPLETE de-coupling of cases and deaths!
This is the strongest population evidence for just how effective the vaccines are…and for the very limitations of the vaccines.
If you were to plot this for a sterilizing vaccine (like measles back in 1962), cases and deaths would stay locked in sync all the way down to a near-zero final baseline.
This graph REALLY shows in what aspects the vaccines are and are not effective.
Vaccination primarily prevents the onset of Covid pneumonia, which is the key precursor for both hospitalization and death.
In fact, the death rate AFTER hospitalization is essentially identical for the vaccinated and the unvaccinated. What the vaccine does is KEEPS YOU OUT OF THE HOSPITAL!
But what the vaccine DOES NOT do is keep you from:
1. getting infected 2. getting sick 3. transmitting the virus to others.
This graph is the PERFECT illustration summarizing all these key points about the vaccines!
Get vaccinated…get boosted…wear a mask. No one is perfectly effective, but each provides part of a layered defense.
Omicron (SARS-CoV-2, B.1.1.529) was identified on November 24, 2021 in South Africa (SA). It’s been somewhat downplayed as being “mild,” although it’s too early to make that claim. It’s already clear that it is quickly transmitted and that the COVID vaccines may not be as effective. Some data is available now to get a better sense of what this variant could mean for the rest of the world.
First, it’s worth looking at the scope of previous waves of COVID in SA. The scale on the left side of this graph is for the blue portion of the graph, which is known as the epidemic curve. It also has a dark blue line representing the seven-day moving average, which is the way that many media outlets display the data.
The gray part of the graph is the deaths in the same format, but the values for that are on the right axis.
These are cases and deaths for ALL variants of COVID in SA. The most important characteristic in this graph though is to note the somewhat gradual way prior waves climbed to their peaks. The difference with the current wave at the far right is that it is almost to the same height in only a couple of weeks, whereas the other waves took almost two months to reach that same height.
The other important part to note is that generally speaking, increases in cases don’t start showing up as increases in deaths until 2-3 weeks later. In the second wave, that doesn’t appear to be the case, but that is likely due to this being the entire country in one graph and a number of reasons that could explain this pattern.
The next graph is the same blue epidemic curve, but now contains something from calculus known as the first derivative. This particular derivative uses the past three weeks of data to calculate the rate of growth or slowing in cases and is the red line in this graph.
The best analogy to the red line is to think of it as the accelerator or brake on a car. There is a black dotted line running across the graph at zero on the right axis. When the red line is near this line, there is relatively little change in the number of cases at a given time. When the red line goes above the black dotted line, cases rates are accelerating, and the higher the red line is above the dotted line, the faster that acceleration is happening, similar to flooring the accelerator.
The opposite is also true. The lower the red line is below the dotted line, the faster cases are slowing, similar to stomping on the brake when the red line is at the very lowest.
One benefit to this approach is being able to project a short time into the future using this derivative. Just like with a car, the faster it is accelerating, the longer it takes to slow down. I’ve found that this three-week derivative can project about 10 days into the future with reasonable accuracy, which is represented by the yellow highlight on the far right side.
If you look at where the red line is vertically above where the blue epidemic curve ends and compare it to the peak of the red line in the other waves, you can see that with current data, the rate of new cases is already at the highest it’s ever been in SA. That rate is likely to keep climbing over at least the next week.
The red line in the next graph is the exact same derivative as in the previous one. The black line is what is known as the second derivative. The simplest way to explain it is a measure of how fast the change of the rate is changing. This can be used to identify if the first derivative (red) will continue on its current trajectory, climb faster, or start to slow. If you look at the yellow area again, you can see that the second derivative remains close to zero currently, which indicates that the rate of growth of cases isn’t changing anytime soon.
The next graph has the same blue epidemic curve, but now has a number of colored lines representing each province in the state, again measured on the right side of the graph. These show the number of hospital admissions in each province.
The province of importance with omicron is Gauteng (the red line). This is the one that contains Johannesburg, Pretoria, and Soweto. Again, at the far right, look how quickly hospitalizations are climbing in the province. that provides a great deal of evidence that omicron is NOT mild. It’s also apparent that some of the other provinces are starting this climb as well.
Further evidence that this increase is due to omicron having become the dominant strain in the country. Omicron had not been detected in any sequenced samples for the period ending 11/1/21. By 11/15, it was 25% of samples, 11/29 it was 85%, and for the period that will end 12/13, it’s currently 100%. This is well visualized at covariants.org, which is the source for the following graph. It shows the proportion of any particular covariant over time. Omicron is the purple area at the far right.
The final graph that supports this interpretation has the same background epidemic curve. The right axis on this graph is hospital admissions and discharges. The green line is admissions, the red is discharges. Again, there is about 2-3 weeks between these curves. The reason that the numbers are different between the curves is the number of deaths.
Again, looking at the far right of the graph, it’s far too early to detect deaths. Most likely, the earlier decedents did not have their samples sequences, so it is reasonable to assume that there are deaths from omicron, but they simply have not been detected yet.
So what is the takeaway? This could easily be far worse than delta, which we are really just starting to feel the impact of in the US and will really start to climb around Christmas. The best thing to do to understand this variant is to continue to monitor the impact in SA but also to watch the United Kingdom closely, because that is likely to be the next area most greatly impacted by the variant.
As an individual, get your booster, wear a mask (preferably a respirator), and don’t spend time indoors with anyone outside of your household. Of course it’s the holidays, but is it worth making it the last one where you might see loved ones alive? This is incredibly serious and we are failing in our response as a country. Do your part for the good of those around you.
12/19/21 Here’s an excellent additional Twitter thread on omicron showing why it will likely be worse that what we have seen from other variants in the US.
Today was the best view of what’s coming in the US since before Thanksgiving. Most of the data should be in from the Thanksgiving weekend now. Unfortunately, the first and second derivatives both suggest that we are about to see the fastest rate of growth in cases since this began late next week. The rapid climb of the positivity rate also supports this.
I doubt that will be readily available in the data though due to home testing and labs becoming backlogged again. This is primarily delta. If omicron is as virulent or if it evades vaccine, we are in for a world of hurt. If either of those are true, we should be treating omicron as an entirely new pandemic superimposed on the current one. Maybe this will finally awaken those who have been in denial, but I have my doubts.
