1) PMC COVlD Dashboard, Feb 17, 2025 (U.S.)

🔥1 in 72 actively infectious
🔥Sustained high transmission
🔥30 states in high/very high transmission (CDC)
🔥3x the transmission of Feb 2021
🔥668,000 daily infections
🔥Only 1 in 28 cases reported 2) PMC COVlD Dashboard, Feb 17, 2025 (U.S.)

This is a mid-sized wave, meaning substantial transmission. Notice that transmission remains steady at high rates.

Expect steady or slightly declining transmission, unless the real-time data are retroactively corrected.

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.

https://twitter.com/TogetherWeMask/status/1889396341299699730

2) These are some tips for finding a well-fitting mask among common options in the U.S. and Canada.

https://x.com/michael_hoerger/status/1838798375451320821

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 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-…

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 #RapidTest 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.

https://x.com/michael_hoerger/status/1879764437575635160

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 #RapidTest 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).

📢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/

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.

https://x.com/michael_hoerger/status/1869463565394219071

PMC COVID-19 Dashboard, Dec 16, 2024
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🔹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
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🔹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.

PMC COVID-19 Forecasting Model, Oct 21, 2024
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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. PMC COVID-19 Forecasting Model, Oct 21, 2024
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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.

PMC COVID-19 Forecasting Model, Oct 7, 2024
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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 pandemic 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.

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. 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.

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 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

PMC COVID-19 Forecasting Model, Sep 23, 2024
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The U.S. continues to see an estimated 1.1 million daily COVID infections with 2.2% of the population actively infectious as we descend from the peak of a 9th Covid wave.

Transmission will remain very high the rest of 2024. PMC COVID-19 Forecasting Model, Sep 23, 2024
🧵 2/8

Looking at the year-over-year graph, note that we're in uncharted territory for this point in the fall. Expect a very high "lull" in early November before the winter surge sets in.

As we have noted previously, our current estimates are likely slight underestimates given elevated school-based transmission and the CDC data standardization process.

Kids: They are smaller, and make smaller "contributions" to wastewater. Basically, it takes more sick kids to produce the average amount of wastewater virus to equate to an average infection (mostly in adults). This issue evens out over time, but it means the model may underestimate during back-to-school periods.

CDC: They describe the details of their process for standardizing data over time. It's very strong, much better than what most localized wastewater orgs or WWS do. It's a bit Dunning-Kruger to question basic wastewater scientists in doing the most fundamental components of their jobs, and the data continue to correlate >.90 with other metrics like Biobot. In comparing with other data, I take their estimates as spot on, but could also see arguments that they may underestimate transmission by 0-5%. It's something we always keep an eye on. Trust, but verify.

Transmission is magnitudes higher than much of the public realizes, so we focus on the big-picture view rather than quibbling over such issues that tend to balance out over time.

I hear @jlerollblues is considering weighting a model based on estimates of the proportion of infections in children. That's actually tougher to estimate than it sounds at first glance. You should key an eye on his models too and the work he and others are doing with the WHN.

PMC COVID-19 Forecasting Model, Sept 9, 2024
🧵1/7

Nationally, we appear to have passed the peak of our late-summer wave, and it's not pretty.

At peak:
🔹>1.3 million daily infections
🔹2.8% (1 in 36) actively infectious
🔹Transmission higher than 90.5% of the pandemic

We are showing a peak around Aug 10, but as you look closely at the graph and in later Tweets, you'll see it was bimodal, with near-identical transmission on Aug 10 and Aug 24.

The CDC consistently corrects historical data, so in hindsight, we might expect the official peak date to flip to the 24th, or for the stats on the 10th to jump higher.

We had expected that Friday's data release might show this was the largest summer peak all-time (by the slimmest of margins), but the prior week's data were retroactively corrected downward by about 5%.

This is a common occurrence, which is why it's important to focus on the big-picture forecast (very bad transmission the remainder of 2024) as opposed to minute details.

Let's walk through the details in this Thread.... PMC COVID-19 Forecasting Model, Sept 9, 2024
🧵2/7

The current year-over-year graph on Covid transmission is troubling. We just had the worst August of Covid transmission in the U.S.

We are likely to have our worst September, worst October, and potentially worst November of transmission.

We expect to bottom out around 850,000 daily infections in early November, before the winter surge picks up.

These new monthly records for Covid transmission are the consequence of #LaissezFairePublicHealth, especially the 1-day isolation policy, but more generally that public health officials are not describing transmission frankly and the need for multi-layered mitigation.

PMC COVID-19 Forecasting Model, Sept 2, 2024
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We've headed from a false summit toward a larger peak in back-to-school transmission. Next week we'll know whether this is the largest or 2nd largest summer wave all-time. Expect 1.4-1.5 million daily infections at the peak.

This secondary peak is larger than anticipated previously, even when accounting for patterns of back-to-school transmission in prior years.

One, the West and South were peaking earlier, and the Northeast and Midwest have transmission picking up much faster than its falling off elsewhere. These are atypical regional differences.

Two, the 1-day isolation policy and general decline in school-based mitigation have unleashed transmission at higher levels than would be anticipated in prior years, even when accounting for the already-high levels of transmission as children went back to school.

Three, our model uses a combination of CDC and Biobot (former CDC contractor) data, using a 60% versus 40% mix for current case estimation. Biobot has not updated their data the prior two weeks without explanation. They were running much cooler than the CDC data, whether reflective of reality, or merely reporting delays. We have downgraded them to 20% in the model, and they will be downgraded further to 0% if they do not update their data this week.

You can take points 1 and 2 to account for the secondary and higher peak (shape of transmission), when models accounting for prior years would have suggested transmission slowing already, and point 3 accounting for a shift toward a slightly higher overall estimate of transmission. PMC COVID-19 Forecasting Model, Sept 2, 2024
🧵2/8

This graph shows year-over-year transmission. It clarifies the unusual shape of the transmission distribution. A "peak" or false summit, followed by a very slight decline in transmission (negligible), then a 2nd (true) peak.

PMC COVID-19 Forecasting Model, August 26, 2024
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We're in uncharted territory during the back-to-school period.

1.2 million people in the U.S. are getting infected per day.

1 in 41 people (2.5%) are actively infectious. PMC COVID-19 Forecasting Model, August 26, 2024
🧵2/8
The year-over-year graphs shows that we are experiencing much high transmission than during previous back-to-school periods.

This is a much higher and wider wave than Y1, Y2, and Y4 of the pandemic. Year 3's summer wave was very bad, but much earlier.

The 1-day isolation period, decline in most other mitigation, and a culture of denialism ("post pandemic," "Covid is over,") will fuel many infections among young people, teachers, school staff, and parents.

Expect absences at schools. Expect substantial workforce problems related to sick parents.

BREAKING: Version 2.0 of the PMC COVID-19 Forecasting Model, August 12, 2024
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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/ 🧵2/7

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.

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.

Slide Deck - Discussion of "Masking Policies at National Cancer Institute–Designated Cancer Centers During Winter 2023 to 2024 COVID-19 Surge"

x.com/i/spaces/1OdKr…

https://x.com/michael_hoerger/status/1818736089164923324

PMC COVID-19 Forecast, Jul 25, 2024 (U.S.)

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?
1/ 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.
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Long COVID Risk Tables

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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