Mike Hoerger, PhD MSCR MBA Profile picture
Dec 20, 2023 10 tweets 8 min read Read on X
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:

🧵 Top 7 Psychological Defense Mechanisms Used to Downplay COVID
#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.This is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
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#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.”This is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
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#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 maskThis is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
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#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

In-person only EDI events

Not testing because it’s just family

Mask breaksThis is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
#5 – Reaction formation – expressing artificial positive feelings when actually experiencing anxiety

“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”This is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
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#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”This is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
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#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 periodsThis is from a psychology book by Nancy McWilliams. I will post a link to a PDF of newer edition of the full book at the end of the thread. If someone has a better "ALT" trick, please educate me on this one.
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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.

Please let me know if there's a more accessible alt-text solution that you would prefer so I can do better next time.
isotis.files.wordpress.com/2016/07/mcwill…

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More from @michael_hoerger

Feb 11
1) A lot of high-risk/aware patients I talk to -- mostly getting cancer treatment -- tend to protect themselves from infection by staying home more.

In the work we do, we help patients to understand that a well-fitting high-quality mask can allow them to attend events safely.
2) These are some tips for finding a well-fitting mask among common options in the U.S. and Canada.

3) Here's a more comprehensive diagram of masks that fit most. Aside from #5, these are widely available.

*#5 (Aegle) was the first N95 widely available during the ongoing pandemic for <$1. Hard to find these days, but I gave some to students.
Read 7 tweets
Jan 28
1) PMC COVID Dashboard for the Week of Jan 27, 2025 (U.S.)

🔹1 in 108 actively infectious
🔹3.1 million weekly infections
🔹>150,000 weekly resulting Long Covid conditions Current Levels for Jan 27, 2025 % of the Population Infectious 0.9% (1 in 108) New Daily Infections 443,000  New Weekly Infections 3,101,000  Resulting Weekly Long COVID Cases 155,000 to 620,000  Monthly Forecast Average % of the Population Infectious 0.9% (1 in 106) Average New Daily Infections 452,933 New Infections During the Next Month 13,588,000 Resulting Monthly Long COVID Cases 679,000 to 2,718,000  Running Totals Infections Nationwide in 2025 18,779,000 Average Number of Infections Per Person All-Time, U.S. 3.60  There is more COVID-19 transmission today than during 41.9% of the pan...
2) We predicted the wave peak would be 0.8 to 1.3 million across various forecasts. We presently have it at 0.9-1.0 million, though retroactive corrections can change that. The WHN also runs an excellent model, with a peak estimated at 1.3 million.
whn.global/estimation-of-…
3) Approx 1 million daily infections is quite serious. This is a far cry from the various #nothingburger predictions, and the Monday morning quarterbacks who in hindsight minimize U.S. infections, Long Covid, & disability.

Perhaps they have social media revenue COIs. I don't.
Read 9 tweets
Jan 20
1) PMC COVID-19 Forecast for Jan 20, 2025 (U.S.)

If we are lucky, the 10th wave has peaked, likely in the 0.9-1.1 million daily infections range, barring significant retroactive corrections.

Over the next month, we should still see about 14 million infections, resulting in 700K to 2.8 million new conditions and enduring symptoms under the umbrella of #LongCOVID. This is simply your reminder than transmission remains high on the back on of a wave.

Regarding the peak, there were huge retroactive downward corrections, especially in Oregon. The CDC data originally showed one of the largest waves there all-time, and then corrected it to say a complete lull the whole time. Once the Biobot data get updated, we may see the peak date change by a week, or jump a bit higher than what you see in the main figure.

What you see in the far end of the forecast is unlikely to be a "high lull," but rather an average between a low lull versus a sustained post-peak haunch of lingering transmission. So, keep an eye on the data. If you're putting off a non-urgent medical appointment, we could get into relatively lower transmission in the next 4-8 weeks. What has me concerned is a sneak-peek of @jlerollblues's long-term forecast indicating a clear possibility of an earlier "mid-year" wave than usual, perhaps even in April. We're still getting pretty lucky on the viral evolution front, but the longer that persists, in absent of major policy change, the bigger the wave we could get. It's a very important time to stay tuned.

Caveats: No data from Biobot in weeks (20% model weight). The California wildfires and pending severe storms in the Deep South are wildcards for transmission. School-based transmission could pick up, but to get a higher peak, transmission would need to pick up much faster in the South and West than in the Midwest and North (unlikely).

In the report, I note that PMC will persist even if the CDC drops or scales back their surveillance program. Also, the most two recent "odd" waves have helped clarify how to handle historical data, and a minor update to the model should help with future atypical waves. If time permits, we will fine-tune those changes further, but there are always more battles on the Covid front than we're able to fight. We also provide a link and light commentary on our recent pre-print showing what our current case estimation model for estimating present/prior daily infections has performed well, and why a lot of other models (BNO, JP, CDC) are underestimates.

Info for new readers:

For those unfamiliar with the PMC model, find full weekly reports for the past 1.5 years at pmc19.com/data

The models combine data from IHME, Biobot, and CDC to use wastewater to estimate case levels (r = .93 to .96) and forecast levels the next month based on typical levels for that date and recent patterns of changes in transmission the past 4 weeks.

Our work has been cited in top scientific journals and media outlets, which are fully sourced in a detailed technical appendix at pmc19.com/data/PMC_COVID…

Examples include JAMA Onc, JAMA-NO, BMC Public Health, Time, People, TODAY, the Washington Post, the Institute for New Economic Thinking, Salon, Forbes, the New Republic, Fox, CBS, NBC, and CNN. See pgs 11-13 at the above link.

#MaskUp #VaxUp #CleanTheAir #RapidTestCurrent Levels for Jan 20, 2025 % of the Population Infectious 1.1% (1 in 87) New Daily Infections 547,000  New Weekly Infections 3,829,000  Resulting Weekly Long COVID Cases 191,000 to 766,000  Monthly Forecast Average % of the Population Infectious 1.0% (1 in 102) Average New Daily Infections 466,700 New Infections During the Next Month 14,001,000 Resulting Monthly Long COVID Cases 700,000 to 2,800,000  Running Totals Infections Nationwide in 2025 15,281,000 Average Number of Infections Per Person All-Time, U.S. 3.59  How Does Risk Increase with More Social Contacts? Number of People | Ch...
2) Here is the issue of Oregon I noted, with the "disappearing surge" in the CDC data. By that, I don't mean a surge that declined quickly. I mean, the CDC saying there was a huge surge in OR and then saying it was a lull the whole time. Baffling.

3) It's an important time to reflect that we have never had a federal Covid response commensurate with the magnitude of this $14-billion problem in the U.S.

3/11/20-1/19/21 = 290K infections/day (91 million total)

1/20/21-1/19/25 = 759K infections/day (1.1 billion total)

10 waves and >1 billion estimated infections in 5 years.

We have never had a well-conceived multi-layered mitigation strategy, and the strategy we have had has often underachieved due to insufficient operational management.

This places society at greater systemic risk from repeat-infection Long COVID. The approach is unreasonable to people with primary immunodeficiencies, cancer, organ transplants, kidney disease, type 1 and 2 diabetes, Long COVID, pregnancy, and many other conditions. Upwards of 2 million older adults in the U.S. are in early retirement, with the labor participation rate still well below pre-pandemic levels, and older adults almost wholly accounting for that presently. The children that were pretended to be magically shielded from Covid are not doing well on the cumulative infection front either.

I do not see that changing. I hope the many scientists and public health officials biting their tongues the past 4 years now feel liberated to speak up on Covid. Note that state and regional organizations and individuals were a big reason why transmission was better under control in year 1 of the pandemic.

Note that our statistics are estimated "true" cases based on the PMC model, not reported cases, which are vast undercounts (ascertainment bias). See the first Tweet for info on our model, including our website, which contains hundreds of pages of reports (pmc19.com/data), or read our recent pre-print showing the high accuracy of our case estimation model, to the extent that is ascertainable (researchsquare.com/article/rs-578…). To believe the true infection estimates are lower than these figures, one would have to suspend cognitive reasoning and merely assume transmission happens at vastly lower rates in the U.S. than those documented through the most-rigorous testing-based program in Europe.figure showing the 10 Covid waves (U.S.)
Read 5 tweets
Jan 6
1) PMC COVID-19 Dashboard, Jan 6, 2025 (U.S.)

📈1 in 49 people actively infectious
🔥Nearly 1 million daily infections
🎲About a 50-50 chance someone has COVID in a large class if typical risk and no testing/isolating
🏥300,000+ new Long Covid conditions per week

The infections are likely minor underestimates. AZ and OR did not report this week. They were surging, so the lack of data brings down the average. As well, the model gives 80% weight to CDC wastewater data and 20% weight to Biobot, but Biobot took the week off, so this is dependent on observed changes in the CDC data.

It would be wise to add multiple imputation into the model to account for all the non-random missingness during surges, but I won't likely get to that anytime soon.

The peak is looking more and more like 1.4 million daily infections, but I wouldn't be surprised if it's earlier than shown and more like 1.3 million, based on the pattern of retroactive data corrections last winter. If the real-time data really stink, it could come in closer to 1.0-1.1 million. To top 1.6 million, we would probably need some serious immune escape that at present I just don't see happening. However, in past winters, transmission was declining nationally in early/mid January, and back-to-school is a wild card.

Info for new readers:

For those unfamiliar with the PMC model, find full weekly reports for the past 1.5 years at pmc19.com/data

The models combine data from IHME, Biobot, and CDC to use wastewater to estimate case levels (r = .93 to .96) and forecast levels the next month based on typical levels for that date and recent patterns of changes in transmission the past 4 weeks. Our work has been cited in top scientific journals and media outlets, which are fully sourced in a detailed technical appendix at pmc19.com/data/PMC_COVID…

Examples include JAMA Onc, JAMA-NO, BMC Public Health, Time, People, TODAY, the Washington Post, the Institute for New Economic Thinking, Salon, Forbes, the New Republic, Fox, CBS, NBC, and CNN. See pgs 11-13 at the above link.

We will have a pre-print out in the next week or so documenting very compelling evidence for the validity of using wastewater to estimate case rates. Forecasting is challenging in the context of the current viral evolution, but the real-time estimates of cases are impressively accurate to the best we can evaluate it.

#MaskUp #VaxUp #CleanTheAir #RapidTestCurrent Levels for Jan 6, 2025 % of the Population Infectious 2.1% (1 in 49) New Daily Infections 980,000  New Weekly Infections 6,860,000  Resulting Weekly Long COVID Cases 343,000 to 1,372,000  Monthly Forecast Average % of the Population Infectious 2.7% (1 in 38) Average New Daily Infections 1,272,833 New Infections During the Next Month 38,185,000 Resulting Monthly Long COVID Cases 1,909,000 to 7,637,000  Running Totals Infections Nationwide in 2025 5,468,000 Average Number of Infections Per Person All-Time, U.S. 3.55  How Does Risk Increase with More Social Contacts? Number of people |...
2) PMC COVID-19 Dashboard, Jan 6, 2025 (U.S.)

We're in the 10th wave of the pandemic (1st graph), and transmission this year has picked up atypically late, while coming on strong (2nd graph).Two graphs, summarized in tweet
3) PMC COVID-19 Dashboard, Jan 6, 2025 (U.S.)

Note that sputtering in the West's rise is likely an aberration, as surging OR and AZ did not provide data this week.

Read 5 tweets
Jan 3
📢Clean Air Advocates📢

I recently learned of a new strategy to get more clean indoor air to people's homes. I don't believe I've heard anyone mention this on here, but please add if you have made inroads.
1/
Last August, I was surprised to learn that Entergy, our regional energy company, was giving away free HEPA filters to customers.

This was surprising to me. Why would an energy company do this?
2/
Apparently, most jurisdictions in the U.S. have regulations that require a portion of consumers' energy payments to go toward an energy efficiency fund.

These are often used for discounts on thermostats but occasionally for Energy Star appliances.
3/
Read 4 tweets
Dec 20, 2024
2) This is one of the better scenarios I noted, with national levels coming in at about 3.33. Unfortunately, the rise was a little lower than anticipated only because transmission slowed in the west. Not uniform, so lots of uncertainty.

3) Transmission remains much higher than people realize. Many will get caught off guard by a seemingly #SilentSurge. This is in part because the CDC spent the past month downplaying numbers in misleading graphs.

Read 8 tweets

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