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Mike Hoerger, PhD MSCR MBA Profile picture
@michael_hoerger
Jan 23 • 7 tweets • 3 min read • Read on X
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Let's talk #DeathTrajectories 🧵

One of the biases in #PublicHealth policy is the focus on acute COVID deaths. It's a lagging indicator and only covers 1 of 5 common death trajectories.

For COVID, people imagine the upper left. Get COVID, then a quick death.
1/7 Figure shows a line graph where an individual has a sustained level of physical functioning, gets COVID, and dies shortly thereafter. This was a good model of acute COVID deaths in the early pandemic. It's also useful for considering accidents, homicide, suicide, and sometimes catastrophic health events like heart attacks.
This is another common death trajectory. You see this a lot with serious cancer diagnoses.

However, you can see it with COVID too. Someone was doing well, gets COVID, and then experiences a decline over 1-2 years. It may cause or aggravate another health condition.
2/7 Line graph. Shows high level of physical functioning, stable over time. Then, someone gets COVID and it causes or aggravates health problems, leading a decline toward death over 1-2 years.
This is a 3rd common death trajectory, often typical of organ failure. You can see someone get COVID, and somewhere down the line it causes or aggravates organ damage.

Dips in functioning are common, often with rebounding improvement, but sometimes a steep decline.
3/7 Line graph characteristic of organ failure. Someone has 4 big dips in physical functioning, rebounds and improves to just below the previous baseline, until finally there is a big dip that leads to death. Pattern is predictable, but the number of dips before death is not, so there's a chronic state of uncertainty surrounding how severe a dip in functioning will be.
This is a 4th common death trajectory. Someone has a low baseline for physical functioning. It's sustained for a long time and only declines gradually before death.

Here, COVID may increase the steepness of each minor decline or accelerate the entire process.
4/7 Line graphs shows low baseline physical functioning, but further declines are relatively slow. COVID could accelerate the decline.
Each of these stereotypical trajectories can be superimposed upon one another. In this 5th trajectory, it's a combo of trajectories #2 & #4.

Big decline in functioning, lower baseline, then a long tail. I worry we're going to see more of this with COVID.
5/7 Ling graph: Someone has a high level of functioning, gets COVID, and over 1-2 years sees a pretty steep decline in functioning. This make a new low baseline, but a long timeline before death.
Once people understand #DeathTrajectories, it's easy to see why a primary focus on hospitalizations or acute deaths is inappropriate at this stage of the pandemic.

Many of the deaths will take 3-15 years, with a lot of years of life lost (YLL). Focus on transmission.
6/7
These are some useful sources for learning more about death trajectories.
7/7



rcemlearning.org/modules/the-dy…
ruralhealth.und.edu/assets/3101-12…
csupalliativecare.org/wp-content/upl…

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More from @michael_hoerger

Mike Hoerger, PhD MSCR MBA Profile picture
Dec 20
x.com/i/article/1870…
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
Mike Hoerger, PhD MSCR MBA Profile picture
Dec 16
PMC COVID-19 Dashboard, Dec 16, 2024
🧵1 of 8

🔹1 in 64 (1.6%) actively infectious in the U.S.
🔹750,000 new daily infections and rising
🔹Highest % increase in transmission in nearly 3 years
🔹10th wave is the "silent surge," coming on late out of nowhere

The video will walk you through each of the graphs on the dashboard and covered in this thread.

Info for new readers:

For those unfamiliar with the PMC model, find full weekly reports for the past 14+ months 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, and NBC. See pgs 11-13 at the above link.

We will have a pre-print out in the next month 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.
PMC COVID-19 Dashboard, Dec 16, 2024
🧵2 of 8

🔹10th wave taking off (U.S.)
🔹5 million infections expected this week
🔹>250,000 post-infection conditions (#LongCovid) expected to develop from this week's infections
🔹Higher transmission than 73% of the pandemic

Info for new readers (as noted in Tweet 1):

For those unfamiliar with the PMC model, find full weekly reports for the past 14+ months 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, and NBC. See pgs 11-13 at the above link.

We will have a pre-print out in the next month 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.Current Levels for Dec 16, 2024 % of the Population Infectious 1.6% (1 in 64) New Daily Infections 748,000 New Weekly Infections 5,236,000 Resulting Weekly Long COVID Cases 262,000 to 1,047,000 Monthly Forecast Average % of the Population Infectious 2.5% (1 in 41) Average New Daily Infections 1,178,167 New Infections During the Next Month 35,345,000 Resulting Monthly Long COVID Cases 1,767,000 to 7,069,000 Running Totals Infections Nationwide in 2024 242,424,000 Average Number of Infections Per Person All-Time, U.S. 3.50 There is more COVID-19 transmission today than during 73.3% of th...
PMC COVID-19 Dashboard, Dec 16, 2024
🧵3 of 8

Areas of the U.S. depicted in darker red have higher transmission, as of 9 days ago. The map uses CDC data and is simply the CDC "cool blue" map recolored in more traditional red, which is best practices.

The line graph shows transmission increasing by region.

CDC map:
cdc.gov/nwss/rv/COVID1…

CDC regional graph:
cdc.gov/nwss/rv/COVID1…

Info for new readers (as noted in Tweet 1):

For those unfamiliar with the PMC model, find full weekly reports for the past 14+ months 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, and NBC. See pgs 11-13 at the above link.

We will have a pre-print out in the next month 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.Map shows highest transmission in Arizona, New Mexico, Minnesota, and Massachusetts. Estimated Percentage Actively Infectious PMC Model Raw CDC Data (9 days old) National 1.6% (1 in 64) 0.9% (1 in 113) Northeast 0.8% (1 in 132) 0.4% (1 in 234) Midwest 2.4% (1 in 41) 1.4% (1 in 73) South 1.3% (1 in 74) 0.8% (1 in 132) West 1.5% (1 in 66) 0.9% (1 in 118)
Read 8 tweets
Mike Hoerger, PhD MSCR MBA Profile picture
Oct 22
PMC COVID-19 Forecasting Model, Oct 21, 2024
🧵1 of 4

Every indication is that the 10th U.S. Covid wave is on the way. Within 2 weeks, expect transmission to be meaningfully higher.

Current estimates from PMC:
🔹1 in 115 actively infectious
🔹Higher transmission than during 43% of the pandemic
🔹Nearly 3 million weekly infections

These estimates are high in the absolute sense, but low relative to the recent summer wave and likely winter surge.

The CDC data show transmission increasing in the Northeast, and a slowing of the decline in transmission elsewhere. Biobot data also show flattening transmission. The raw CDC and Biobot wastewater data are delayed >1 week. Walgreens shows positive cases, testing, and positivity ratios flattening and is delayed only 1 day.

For those unfamiliar with the model, find full weekly reports for the past 14+ months 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 (median) 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, and NBC. See pgs 10-11 at the above link.GRAPH Shows forecasted entry into a 10th Covid wave. TABLES Current Levels for Oct 21, 2024 % of the Population Infectious 0.9% (1 in 115) New Daily Infections 414,000 New Weekly Infections 2,898,000 Resulting Weekly Long COVID Cases 145,000 to 580,000 Monthly Forecast Average % of the Population Infectious 1.3% (1 in 76) Average New Daily Infections 627,600 New Infections During the Next Month 18,828,000 Resulting Monthly Long COVID Cases 941,000 to 3,766,000 Running Totals Infections Nationwide in 2024 225,097,000 Average Number of Infections Per Person All-Time, U.S. 3.46 How Does ...
PMC COVID-19 Forecasting Model, Oct 21, 2024
🧵2 of 4

These graphs show the forecast for changes in transmission over the next month.

The first graph shows year-over-year transmission. The 2nd focuses on the most recent year. Within a month, expect to see 0.7 to 1.0 million daily infections, if the assumptions of the model hold.

If lucky, we could get a slightly longer "lull" than what the model shows. The model likely underestimates the true value for the recent summer peak because many children were infected while going back to school, in fact, the highest peak at that time period all-time. Wastewater underestimates child infections ("contributions" correlate highly with body weight, so it takes four ill 50 lb kids to show up as one ill 200 lb adult). In underestimating the peak, transmission also fell more rapidly than anticipated post-peak. To the extent we underestimated the magnitude of the peak, there may be fewer than anticipated infections the next two weeks and a longer lull.

If you look at the first graph, however, you will see a clear patterns of escalating transmission in November, so it's more a matter of how quickly the situation will worsen than whether it will worsen.

During this relative "lull," it's an excellent time to stock up on high-quality masks, get vaccinated, upgrade the quality and quantity of air cleaners, re-stock on rapid tests, and encourage others to do the same.Two graphs, described in Tweet.
PMC COVID-19 Forecasting Model, Oct 21, 2024
🧵3 of 4

Regional differences suggest that the NE may already be rebounding in transmission. Transmission declines are slowing elsewhere.

We compare the PMC map in standard red against the CDC map using the same data in cool blue.Estimated Percentage Actively Infectious* National 0.9% (1 in 115) Northeast 1.0% (1 in 101) Midwest 0.9% (1 in 114) South 0.8% (1 in 130) West 0.9% (1 in 106)
Read 4 tweets
Mike Hoerger, PhD MSCR MBA Profile picture
Oct 7
PMC COVID-19 Forecasting Model, Oct 7, 2024
🧵 1/4

540,000 daily infections during the "lull" between the 9th & 10th U.S. Covid waves.

🔹1.1% (1 in 88) actively infectious
🔹19 million anticipated infections the next month
🔹Higher transmission than half the pandemicGraph: 10 waves of the pandemic Tables/stats: Current Levels for Oct 7, 2024 % of the Population Infectious 1.1% (1 in 88) New Daily Infections 541,000 New Weekly Infections 3,787,000 Resulting Weekly Long COVID Cases 189,000 to 757,000 Monthly Forecast Average % of the Population Infectious 1.3% (1 in 74) Average New Daily Infections 643,433 New Infections During the Next Month 19,303,000 Resulting Monthly Long COVID Cases 965,000 to 3,861,000 Running Totals Infections Nationwide in 2024 220,311,000 Average Number of Infections Per Person All-Time, U.S. 3.44 How Does Risk Increase wi...
PMC COVID-19 Forecasting Model, Oct 7, 2024
🧵 2/4

Year-over-year, we have seen the steepest drop in transmission all-time on the back end of a summer/fall wave.

Similar transmission the next month, a very high lull. Expect transmission to accelerate in mid-Nov.Two graphs Year-over-year transmission. A very high late-summer wave, the highest all-time during Aug/Sep, followed by the steepest decline on the back of a summer wave, all-time. Forecast. Note similar transmission over the next month, 40-60% of the summer wave's peak. Expect transmission to pick up considerably in mid-Nov in anticipation of a winter surge peaking around New Year's Eve
PMC COVID-19 Forecasting Model, Oct 7, 2024
🧵 3/4

Relative to the Northeast, transmission is 2x higher in the Midwest/South & 2.5x higher in the West region.

However, the CDC regions mask extreme variation within region. The 7 highest-transmission states are far north.Map: Transmission highest in Washington state, Oregon, Montana, Minnesota, Vermont, New Hampshire, and Maine. Line Graph: Shows the regional statistics summarized in tweet.
Read 4 tweets
Mike Hoerger, PhD MSCR MBA Profile picture
Sep 30
PMC COVID-19 Forecasting Model, Sep 30, 2024
🧵 1/5

COVID transmission remains extremely high, but we're entering a "lull" in the U.S. sooner than anticipated.

Among all summer/fall waves, the 22% 1-week drop in transmission is steepest all-time.

Details:

In Friday's data release, the CDC retroactively corrected the prior week's numbers downward 6% (for Sep 14). This is a big correction, bigger than average, but nothing nefarious. The most recent week's numbers show an additional 1-week decline in transmission of 22% (from Sep 14 to Sep 21) on top of that 6% correction (for Sep 14), so the transmission estimates have fallen quickly.

Looking back, we estimate that the 1-week drop of 22% is the largest decline on the back end of any summer/fall wave in the U.S.

*If* these numbers hold against future retroactive corrections, it means people have about 5 weeks of similar transmission from today through Nov 7.

Why might we have seen a record decline in post-peak transmission?

Several hypotheses:

1) Reporting Error: The 22% decline could be driven in part by errors in real-time reporting. These average is 5%, based on our analyses of Biobot wastewater data. We do not have long-term data on the accuracy of Verily/CDC real-time reports versus retrospectively corrected values. In the updated graphic, we have added 95% confidence intervals for the real-time values based on Biobot data, which show that 95% of real-time errors fall within 8.33%. Note that the dotted lines do not show 95% confidence intervals for the forecast, merely how the best estimates would change if a large error in the real-time reports of +/- 8.33%. It’s possible that next week the data will get corrected upward, and the forecast will more resemble the top dashed line. In the next Tweet, we show the forecast for our old (Biobot-based model); it’s still showing a slower decline, but they update their data about 5 days slower than the CDC, so it is unclear whether it’s a big real-time reporting error at the CDC or just that the CDC is ahead of the game.

2) Unprecedented School Transmission: This is the largest wave during the August back-to-school period. It’s possible transmission disproportionately affected school children and their families, and in being more targeted than typical transmission, the wave went down faster that what is normative thus far in the pandemic.

3) Laissez Faire Public Health: Public health guidance has weakened (e.g., 1-day isolation policy, not strongly pushing additional mitigation), which likely pushed the peak of the 9th wave higher, which could have led to a faster-than-usual resolution. The model accounts for these changing dynamics reasonably well, but with the school issue noted in hypothesis #2, it is possible the weakened public health guidance disproportionately hit a subset of the population, which altered the back side of the wave.

4) Missing Data: There are no widespread state-level instances of missing data, as is often the case. It is possible that specific areas did not report this week, and if there is a bias toward higher transmission in those places, the numbers will get retroactively corrected upward. This is one example of the issues that contribute to #1.

5) Politics: There is no evidence to suggest the CDC is modifying transmission data for political reasons. We put deep trust in the fundamental scientists doing the critical work translating wastewater into meaningful downloadable data. The inferences, agency graphs, and guidance can be influenced by politics, but the data are sound, given the limitations noted under #1.

Overall, if the data hold or anything reasonable within the ballpark, which is likely, this means a more prolonged and slightly lower lull than previously anticipated. Those putting off medical appointments and other risky activities may see this as a slightly broader window (today through Nov 7) to get things done. Note that even under the most optimistic forecasting scenarios, transmission remains very high in absolute terms, even if low in relative terms.Graph #3 from the PMC dashboard. Shows a steep decline in transmission based on the most recent CDC numbers.
PMC COVID-19 Forecasting Model, Sep 30, 2024
🧵 2/5

Note that while the CDC data show an unprecedentedly steep decline, Biobot data suggest more gradual changes, along the lines we have been forecasting the past several weeks.

This graph shows a resurrected version of our discontinued model 1 (Biobot-only) forecast.

I would not be surprised if the CDC data get retroactively corrected upward a little bit (this is common, not nefarious), AND the Biobot data go down a bit faster (they lag the CDC data by about 5 extra days). There is regional variation between the data sets, which means that BOTH models could actually be accurate without corrections.

Nonetheless, anything in the ballpark of these two forecasts suggests a "lull" from about now through Nov 7. It's a very high lull, but people find these dates useful for risk-based planning.Biobot-based model suggests a much more gradual decline, with transmission still bottoming out in early November.
PMC COVID-19 Forecasting Model, Sep 30, 2024
🧵 3/5

The United States is coming down from a 9th Covid wave and will soon transition into a 10th.

We are still #DuringCOVID.

The peak will likely be around New Year's Eve, though some models suggest slightly later.

A 3-month forecast is extremely volatile. I mention it now because people are already making travel plans.

Hospitals should formalize their plans for requiring universal masking if they have not already done so. Many have missed the mark during the ongoing summer/fall wave due to reliance an outdated zeitgeist of "respiratory virus season" that treats cold, flu, and Covid at interchangeably equal. Covid peaks twice annually and is more disabling long-term. Nonetheless, even hospitals relying on outdated models will be more likely to take the forthcoming wave more seriously.

As we have published, universal masking during Covid waves is a key indicator of healthcare quality. Yes, this is true based on 2024 data. Unfortunately, it's mostly only the very best-of-the-best health centers. Consider printing and mailing this article to places where you receive care.
jamanetwork.com/journals/jaman…Graph #1 from the PMC dashboard, shows we are coming down from a 9th wave, with a 10th wave about to start.
Read 5 tweets
Mike Hoerger, PhD MSCR MBA Profile picture
Sep 24
As an expert in psychological assessment who has testified in court on cognitive assessments I've conducted, people should interpret carefully the new eClinicalMedicine paper on Covid-related cognitive changes.

Quick 25-pt thread⚡️
🧵1/25 Main figure from the paper. It has a lot of information but actually communicates very little that is meaningful. They authors suggest changes in general cognitive skills (A) and curiously mix this with figures showing more viral load among people infected with virus (B). The top half of A is virtually uninterpretable. The bottom half is vastly overstated. I think people see the big findings in B and think a lot is going on cognitively, when it's just about virus.
The field of clinical psychology has developed, implemented, and evaluated normed broadband tests of cognition for the past 119 years.

These are highly specialized instruments with carefully selected tests to cover the breadth of key areas of cognition.

2/25
The study did not use one of the well-established, normed, broadband tests of cognition. Instead, it used a novel app-based hodgepodge of tests with little empirical history.

People should be very cautious in interpreting results. There is no vast literature on the tests.
3/25
Read 25 tweets

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