As a clinical health psychologist, I notice that many people are using psychological defense mechanisms to downplay the risk of COVID.
These are my Top 7 examples:
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#1 – Denial – Pretending a problem does not exist to provide artificial relief from anxiety.
Examples:
“During COVID” or “During the pandemic” (past tense)
“The pandemic is over”
“Covid is mild”
“It’s gotten milder”
“Covid is now like a cold or the flu”
“Masks don’t work anyway”
“Covid is NOT airborne”
“Pandemic of the unvaccinated”
“Schools are safe”
“Children don’t transmit COVID”
“Covid is mild in young people”
“Summer flu”
“I’m sick but it’s not Covid”
Taking a rapid test only once
Using self-reported case estimates (25x underestimate) rather than wastewater-derived case estimation
Using hospitalization capacity estimates to enact public health precautions (lagging indicator)
Citing mortality estimates rather than excess mortality estimates. Citing excess mortality without adjusting for survivorship bias.
#2 – Projection – When someone takes what they are feeling and attempts to put it on someone else to artificially reduce their own anxiety.
Examples:
“Stop living in fear.” (the attacker is living in fear)
“You can take your mask off.” (they are insecure about being unmasked themselves)
“When are you going to stop masking?”
“You can’t live in fear forever.”
#3 – Displacement – When someone takes their pandemic anxiety and redirects their discomfort toward someone or something else.
Examples:
Angry, seemingly inexplicable outbursts by co-workers, strangers, or family
White affluent people caring less about the pandemic after learning that it disproportionately affects lower-socioeconomic status people of color
Scapegoating based on vaccination status, masking behavior, etc.
“Pandemic of the unvaccinated”
Vax and relax
“How many of them were vaccinated?” (troll comment on Covid deaths or long Covid)
Redirecting anxiety about mitigating a highly-contagious airborne virus by encouraging people to do simple ineffective mitigation like handwashing
“You do you” (complainers are the problem, not Covid)
Telling people to get vaccinated or take other precautions against the flu or RSV but not mentioning Covid
Parents artificially reducing their own anxiety by placing children in poorly mitigated environments
Clinicians artificially reducing their own anxiety by placing patients in poorly mitigated environments
Housework to distract from stress
Peer pressure not to mask
#4 – Compartmentalization – Holding two conflicting ideas or behaviors, such as caution and incaution, rather than dealing with the anxiety evoked by considering the incautious behaviors more deeply (hypocrisy)
Hospitals and clinicians claim to value health/safety but then don’t require universal precautions
Public health officials claim to value evidence but then give non-evidence based advice (handwashing over masking), obscure or use low-value data over high-quality data (self-reported case counts over wastewater), etc.
Getting a flu vaccine but not a Covid vaccine
Interviewing long Covid experts who recommend masking in indoor public spaces but then going to Applebee’s
Masking in one potentially risky setting (grocery store) but not masking in another similar or more-risky setting (classroom)
Infectious disease conference where people are unmasked
Long Covid and other patient-advocacy meetings where only half the people mask
“It’s good I got my infection out of the way before the holidays”
“I had Covid but it was mild”
Anything quoted in Dr. Jonathan Howard’s book, “We Want Them Infected: How the Failed Quest for Herd Immunity Led Doctors to Embrace Anti-Vaccine Movement”
Herd immunity (infections help)
Hybrid immunity (infections help)
“It’s okay because I was recently vaccinated”
“Omicron is milder”
“Textbook virus”
“Building immunity”
#6 – Rationalization – Artificially reducing Covid anxiety through a weak justification.
Examples:
“I didn’t mask but I used nasal spray”
“I don’t need to mask because I was recently vaccinated”
“It finally got me.”
“You’re going to get Covid again and again and again over your life.”
“It’s not Covid because I don’t have a sore throat.”
“It’s not Covid because I took a rapid test 3 days ago.”
“It’s not Covid because I’m vaccinated.”
“Airplanes have excellent ventilation.”
“I’ve had Covid three times. It’s mild.”
“Verily was cheaper.”
“Nobody else is masking.”
“Nobody else is testing.”
“My roommates don’t take any precautions, so there’s no point in me either.”
“I have a large family, so there’s no point in taking precautions.”
Surgical masks (they are actual “procedure masks,” by the way)
Various pseudo-scientific treatments used by the left and right
Handwashing as the primary Covid public health recommendation
Droplet transmission as a thing
Public health guidance that begins with “data shows” (sic)
Risk maps that never turn deep red
5 expired rapid tests
“Masks recommended” instead of universal precautions
“Seasonal”
#7 – Intellectualization – using extensive cognitive arguments to artificially circumvent Covid anxiety
Examples:
Unending threads to justify indoor dining
Data-rich public health dashboards that use low-quality metrics and/or don’t change public health recommendations as risk increases
The entire justification for “off-ramps”
Oster, Wen, Prasad
Schools denying air cleaners because it “could make children anxious”
Schools not rapid testing this surge because it “could make children anxious”
The mental gymnastics underlying the rationales for who can get vaccinated, how frequently, or with what brand
Service workers told not to mask because it could make clients uncomfortable
“What comorbidities did they have?”
“The vulnerable will fall by the wayside”
Musicians and others holding large indoor events
5-day isolation periods
Here's a link to the full book, a newer edition than what I own. The information on defense mechanisms begins on textbook page 100.
BREAKING: Version 2.0 of the PMC COVID-19 Forecasting Model, August 12, 2024
🧵1/7
The U.S. now tops 1.3 million daily infections. 2.8% of the population (1 in 36) are actively infectious.
Deep Dive on Version 2.0 of the Model...
Welcome to version 2.0 of the PMC Model. The “C” in PMC is for Collaborative, and the work to improve this model is grounded in feedback from readers like you over the past year. Thank you for your support.
What’s New?
In short, the new model has substantial data quality improvements by combining multiple data sources for estimating transmission in unique ways that will hopefully increase forecasting accuracy, provide a truer representation of what has happened and is happening during the pandemic, and linkages to some statistics you will find helpful in day-to-day decision making.
Here is a deeper dive into the changes (skip to next section if desired). The new model is designed to provide a “true” picture of what has happened during the pandemic. It integrates three main data sources: the IHME true case estimation model, Biobot SARS-CoV-2 wastewater surveillance data, and the current CDC NWSS SARS-CoV-2 wastewater data. IHME provided a comprehensive case estimation model through April 1, 2023. Biobot was the CDC wastewater subcontractor through last fall and continues to do extensive non-CDC wastewater work. The CDC NWSS data are currently subcontracted with Verily, a subsidiary of Alphabet, which is the parent company of Google. Over the past year, we have seen Biobot scale back their public data and visualizations, and Verily has made steady improvements in their work with the CDC.
We previously relied solely on Biobot for forecasting and a Biobot-IHME data linkage for case estimation. It was a Biobot-heavy model. The current model is not tied strictly to any data set, but rather the PMC’s best estimate of the truth, a true-case model that uses multiple data sources in the spirit of IHME’s original work in this area. Essentially, we link all three data sources, which have been active over different points of the pandemic to derive a composite “PMC” indicator of true levels of transmission. The indicator is weighted based on which data sources were available and their perceived quality at each point in time. We scale this composite PMC indicator to the metric the CDC uses when helpful for comparisons with their website, and scale it with the true case estimates of the IHME otherwise, as true cases are more relevant than arbitrary wastewater metrics.
A great feature of the model is that it continues to integrate real-time data from Biobot and the CDC. From the perspective of Classical Test Theory, this is a huge advantage, as it provides a much more reliable indicator of what is currently happening with transmission. Both sources often make retroactive corrections for the most recent week’s data, sometimes sizable, and pitting the two indicators against one another reduces measurement error on average, which offers vital improvements in forecasting.
What are the Biggest Improvements in the Model?
· Accuracy in Real-Time Data – In integrating two active surveillance data sources, the real-time data will be more accurate. The biggest predictor of next week’s transmission levels, and the shape of how transmission is increasing or decreasing, accelerating or decelerating, is the current week’s real-time data. If the real-time data are off by 5% or 10%, the big-picture take on the forecast will still be reasonable, but a more precise estimate allows for greater accuracy in estimating the height and timing of waves.
· Regional Statistics – We are already integrating some regional data. Like you, we miss the vast and high-quality regional data and visualizations Biobot provided. We are hoping to take back some of those advantages through the new model and will improve them over time.
· Credibility – Although Biobot and CDC have unique strengths and limitations, a clear strength of adding the current CDC data set is that many people prefer to defer to the credibility of the CDC. The PMC model can be characterized fairly as a “CDC-derived case estimation and forecasting model,” which should lend more credence with those who are not deep enough in the weeds to evaluate the data as critically and prefer appeals to authority. We also provide some statistics that will allow you to draw more useful inferences from the CDC website.
What’s the Same in the Current Model?
The analytic assumptions underlying the forecasting model remain the same. It uses regression-based techniques common across all industries, using a combination of historic data (median levels of transmission for each day of the year) and emerging data from the past four weeks to characterize how transmission is growing or shrinking. Holidays and routine patterns of behavior that map on well to a calendar are “baked in” to the historic data. “New variants” and atypical patterns of behavior are baked into the data on recent patterns of transmission. It’s a top-down big picture model.
What are the Biggest Drawbacks of the New Model?
· Disruptions in Longitudinal Comparisons – You will notice some inconsistencies between the current and prior model that use additional data to form more accurate estimates, which is sometimes frustrating. A few examples. In the early pandemic, we estimated cases linking Biobot to IHME case estimates. Biobot transmission estimates were a bit “hotter” than others during that time period, the IHME estimates “cooler.” Our composite model depicts each of the first 4 waves somewhat smaller, which we believe provides a better picture of the “truth” as we can estimate it, but it is annoying psychologically to re-envision what has happened. This also throws off some of the big-picture statistics; for example, as of August 12, 2024, we estimate that Americans have had about 3.3 infections on average. A few months ago, we estimated nearly 3.5, so this is consistent with “cooler” picture of early-pandemic transmission. Presently, the CDC transmission estimates are running much hotter than those of Biobot, leading to estimates of a larger and earlier peak in the present wave. We would have preferred the CDC re-up with Biobot at the potential contract renewal to promote continuity in the data, but these sorts of changes in model estimation are the expected consequences of such a transition.
· Constantly-updating Historical Data – The CDC updates all of their historical estimates of transmission frequently, any time a new site comes on board, and twice annually to standardize the data longitudinally. This can sometimes create weird issues, where transmission is going up, but real-time values are lower than what was reported in real time the prior week because recent data were corrected downward. It will also throw off some of the helpful statistics we provide. These are minor nuisances, but be aware of them in case you spot something that seems strange.
· Documentation of Accuracy – We have excellent data on the accuracy of the prior model and will submit a report for publication shortly. All prior reports are publicly available. Many report quick facts on longitudinal accuracy, international comparisons, use in news articles, and references to use in peer-reviewed scientific journal articles. We cannot document the real-time accuracy of the new model yet, but know that when using historical data, the model accounts for 98% of the variability in wastewater transmission 1-week into the future, which is 2% higher than our prior model. The vast majority of forecasting errors have been and will continue to be based on inaccuracies in the real-time data wastewater surveillance companies report, and the model changes reduce those issues. We hope you will trust our history and that the methodologic changes represent improvements.
What Improvements Should We Expect in the Future?
There are many improvements we hope to roll out in the future. These include changes based on your feedback, the addition of confidence intervals in some of the graphs, and regional forecasting models. We may incorporate additional data sets if they can improve real-time estimates of current transmission.pmc19.com/data/
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Our graph of year-over-year transmission shows we have likely never had such high COVID transmission in mid-August.
Many classrooms will have a >50% chance someone is infectious. Expect K-12 schools and universities to be hotbeds for COVID outbreaks unless they are using serious multilayered mitigation.
🔹Indoor air quality that meets ASHRAE Standard 241 (if they have never heard of this or cannot explain how they are meeting the standard, they likely are not meeting the standard).
🔹Surveillance testing.
🔹Free on-demand testing.
🔹Universal masking.
This is uncharted territory in terms of such low mitigation coupled with high transmission with school starting. The possibility of a slightly larger wave than what we forecast remains.
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Let's zoom in on the current wave. We're at our highest level of transmission since the winter surge, with 1.3 million daily infections.
Note, our model now combines Biobot and CDC data. Biobot still has the peak coming in early Sept, and so did the CDC until a huge spike this week.
By including two data sources, it helps counterbalance against errors in their real-time reporting, but we could still see some volatility in the size and date of the peak at this point.
Of course, different locations peak at different times.
You'll note that Aug 12 appears in the "forecasted" zone. That's because even wastewater data experience lags in reporting.
Just plugged today's CDC numbers into my new forecasting model (releases Mon). My initial reaction was "Jesus Christ. That's bad. That's really really bad."
If you live in the West in particular, it's currently about as bad as last winter. About 1 in 23 infectious out West.
Those of us modeling have been talking about the late-summer wave -- all year -- as a given.
The 1-day isolation policy, the lack of a twice-annual updated vax, & the vilification of masks are emblematic of #LaissezFairePublicHealth. A wintery summer surge is the result.
I hope the present numbers are revised downward, but there is no reason to suspect that. In my view, the current estimates are as likely to be overestimates as underestimates.
The U.S. is hovering around 900,000 daily infections.
Nearly 2% of the U.S. population is actively infectious with COVID. Ultimately, such infections are expected to result in >40,000 new daily #LongCovid cases.
#YallMasking?
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In the rising 9th U.S. Covid wave, we have higher transmission than 69% of the pandemic, lower than during 31% of the pandemic.
In a deck of cards, imagine the J, Q, K, & Ace as days with higher transmission, all other cards the days with lower transmission. 2/
How many people will you interact with this week? Here are the chances at least one of those people is infectious with Covid.
20 people? --> 1 in 3 chance
100-300 on an airplane? --> 85-99% chance
Wear a well-fitting high-quality mask (respirator) to avoid breathing virus. 3/
You probably saw this week's NEJM article on #LongCOVID. We did a special section on it in this week's PMC COVID-19 Forecasting Report (pgs 6-8).
THREAD of tables. 🧵🔢
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Details:
Our model continues to provide estimates of Long COVID cases that will ultimately result from each day’s infections.
We provide a credible interval that 5-20% of infections will result in Long COVID.
This week, Al-Aly and colleagues reported in the New England Journal that in the more recent era of the pandemic, vaccinated individuals have a 3.5% chance of developing Long COVID from a particular infection.
They focused on medically documented new serious health conditions. We continue to view 5% as a useful lower bound for two reasons.
Long COVID chances were higher in unvaccinated individuals in their study, and there were no analyses based on time since last vaccination.
With many Americans still unvaccinated and many not vaccinated in the past year, the true estimate for a 2024 infection could well surpass 5% for a medically documented new serious health condition.
Moreover, Long COVID is a heterogeneous condition, and many cases are likely not medically documented, especially at the less debilitating end of the spectrum.
The following tables show the risk of ever developing Long COVID from an infection assuming 3.5%, 5.0%, and 20.0% rates.
These statistics document the seriousness of Long COVID with Americans getting infected nearly once a year (average of 12.5 months by our estimates).
However, it is also important to know that some effects are enduring, and others more likely to improve, so many with Long COVID will improve.
Many will also have repeated bouts of Long COVID, likely with different phenotypes.pmc19.com/data/
If you assume 3.5% of people get Long COVID per infection, the risk grows sizably with reinfections, which are happening nearly once per year. Avg of 9 infections/American the next decade.
In the previous Tweet, we note how 3.5% is an obvious underestimate.
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Based on that 3.5% estimate, a more realistic low-ball estimate of serious long COVID is 5-7%, given that not all serious new health conditions are documented in medical records & rates are higher among those unvaxxed or not recently vaxxed.