Our new article is out documenting the burden of the #pandemic on food service workers.

What it boils down to is fighting an #AIRBORNE virus with droplet dogma.

I will walk you through some of the more shocking highlights.
1/
🧵 Background As an illustrative example of COVID-19 pandemic community-based participatory research (CBPR), we describe a community-academic partnership to prioritize future research most important to people experiencing high occupational exposure to COVID-19 – food service workers. Food service workers face key challenges surrounding (1) health and safety precautions, (2) stress and mental health, and (3) the long-term pandemic impact.  Method Using CBPR methodologies, academic scientists partnered with community stakeholders to develop the research aims, methods, and measures, and interpret...
During the BA.1 Omicron wave, for example, food service workers could often get free #HandSanitizer.

Yet, free high-quality masks (e.g., #N95) were hard to come by.

N95s prevent people from inhaling airborne virus into their lungs. Hand sanitizer does not.

2/ Survey Result, Statistic  Free hand sanitizer, well stocked 17 (73.9%) Encouraged to stay home when sick 16 (69.6%) Free soap, well stocked 15 (65.2%) Free gloves 13 (56.5%) HVAC (heating/air conditioning) system is well-maintained 10 (43.5%) Health insurance 9 (39.1%) Free cloth masks 9 (39.1%) Free surgical masks 9 (39.1%) Free COVID-19 testing 7 (30.4%) Paid sick leave 5 (21.7%) Free high-quality masks, e.g., N95, N99, N100, KN95, KF94 5 (21.7%) Dental insurance 4 (17.4%) Vision insurance 3 (13.0%) Mental health services/counseling 2 (8.7%) HEPA filters are provided in areas with many pe...
With a lack of #PublicHealth guidance, many in the food service industry have faced decision fatigue in handling C0VID-related issues.

Participants said co-workers struggle with what to do if sick (87%), when to return to work (83%), or whether to get a booster (61%).
3/ Estimate, whether any co-workers experience decision fatigue by area, No. (%)  What to do if possibly sick with COVID-19 20 (87.0%) What to do if a family member is diagnosed with COVID-19 19 (82.6%) How to interact with customers about showing proof of vaccination 19 (82.6%) When to return to work after COVID-19 19 (82.6%) What to do if a family member may have COVID-19 18 (78.3%) What to do if diagnosed with COVID-19 17 (73.9%) How to interact with customers who dislike vaccines 17 (73.9%) How to interact with customers who dislike masks 16 (69.6%) When a child should return to school aft...
Food service workers have expressed that C0VID has had a big impact on co-workers' #MentalHealth.

Anxiety, depression, and substance use lead the way. However, many respondents also noted a co-worker dealing with #bereavement, suicidal ideation, or violence (17-36%).
4/ Anxiety and worry 22 (95.7%) Depression and sadness 18 (78.3%) Substance use 16 (69.6%) Loneliness 11 (47.8%) Anger 11 (47.8%) Bereavement 6 (26.1%) Suicidal thoughts 4 (17.4%) Violence and abuse 4 (17.4%)
Shortly after the BA.1 Omicron surge, many in the food service industry were aware of a co-worker dealing with #LongCOVID.

"Fatigue" (often EXHAUSTION) led the way. Many knew a co-worker with prolonged loss of taste or smell, which are huge to that occupation.
5/ Awareness of someone personally in the food service industry dealing with a symptom or side effect > 3 months after getting COVID19 that the participant attributed to the virus (i.e., Long COVID symptoms)   Fatigue or overtired 15 (65.2%) Anxiety 13 (56.5%) Depression 12 (52.2%) Loss of taste 11 (47.8%) Difficulty sleeping 11 (47.8%) Headache 9 (39.1%) Attention difficulties 9 (39.1%) Loss of smell 7 (30.4%) Cough 7 (30.4%) Joint pain 6 (26.1%) Sick to one’s stomach 6 (26.1%) Pain 5 (21.7%) Difficulty breathing 5 (21.7%) Digestive problems 4 (17.4%) Weakened lung capacity 4 (17.4%) Weight l...
In the more recent days of the pandemic, food service workers emphasized that a key priority was reducing in-home spread of C0VID.

Experts you know from Twitter provided their guidance on reducing in-home spread. Here's a link to another resource.

6/ cleanaircrew.org/someone-in-my-…
COVID-19 Mitigation Professionals In-home mitigation Masks Wear N95 respirators (masks) or P100/N100 elastomerics if finances permit. Filtration Use HEPA filters or do-it-yourself (DIY) air cleaners called Corsi-Rosenthal boxes or SAFE air purifiers. Ventilation Open windows. Use fans to blow clean air in. Use fans to blow infected air out of isolation rooms. Isolation Create an isolation room at home. Family members testing negative stay outside as much as possible. The person who is ill should eat outside if possible. Testing and Treatment Testing Get PCR testing if possible. Use at-home ...
When explaining to other scientists that #CovidIsAirborne, I have found it helpful to quote Jha.


7/
Jha - C19 is "purely airborne"
Too often, restaurants continue to be superspreaders of C0VID and other airborne illnesses.

#ASHRAE indicates that they should be cleaning the air 28-40 times per hour. Instead, many are doing more like 0.8
8/ The new standards indicate that restaurants and similar establishments should clean the air approximately 28–40 times per hour, depending on occupancy level (more precisely, 40 cubic feet per minute per person [cfm/person] or 20 L per second per person [l/s/person]) to reduce airborne illness transmission, approximately double the historic standard of 15 air changes per hour in U.S. operating rooms. To put in context, many restaurants, bars, and similar establishments clean the air 0.8 times per hour [43], 35–50 times lower than the current ASHRAE air cleaning standards.
In fact, we clean fish tanks better than we clean the air in many dangerous settings like restaurants.
@sri_srikrishna
9/ As the engineer Devabhaktuni Srikrishna frequently notes, even fish get 4–6 full water exchanges per hour in properly running fish tanks [44]. Essentially, food service venues are among the highest-risk settings and have the lowest mitigation. The ASHRAE standards are a firm indication of the occupational hazards of food service work.

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More from @michael_hoerger

Oct 11
Biobot (blue) versus Verily (black) #wastewater data.

You'll see Verily data suggest the most recent wave (#7) has had considerably more transmission than Delta (#3). And that last winter (#6) was similar (or worse!) than the prior winter's BA.1 surge (#4).

Who wins?

1/ Graph of biobot and verily wastewater data. Both show similarly timed waves but vary in magnitude.
Here are the correlations among Biobot levels, Verily levels, & IHME true cases for the 1st of each month from Jan '21 to Apr '23.

Biobot correlates r=.94 (freakish) with IHME. Verily only correlates r=.67.

Either Biobot is much better, or Verily knows something we don't.
2/ Correlation table for biobot, verily, and ihme data
The CDC awarded Biobot's contract to Verily.

Once Verily brings on Biobot's former CDC-contracted wastewater sites, that should help. Case estimation will be easier if they fold in the historic data to more accurately represent the nation.

Forecasting is easy regardless.
3/
Read 5 tweets
Oct 2
PMC C0VID-19 Tracker, Oct 2, 2023

U.S. #wastewater levels are higher than during 58% of the pandemic:
🔹 1.56% (1 in 64) are infectious
🔹 >745,000 C0VID cases/day
🔹 37,000+ #LongCovid cases/day

Click the full Tweet for more details on each forecast...
1/5

Wastewater levels are up from 505 copies/mL last week to 512 copies/mL this week. Before one screams "SURGE!!!," let's dig into the details. That's about as small an increase as possible, and well within the range of data corrections to real-time reports.

The Real-time model (red line):
It assumes that real-time data reports are accurate. However, real-time data often get corrected. Biobot and the CDC are currently in a transitionary phase of modifying when and which sites report, so I take the real-time data with a grain of salt. If it is accurate, however, the model suggests a rebound in cases, peaking around Oct 11, before bottoming out around Nov 1, before cases begin rising again for the winter wave.

Alt Model #1, Turtle (green line):
The turtle model ignores the most recent week's worth of data from Biobot, treating it as unreliable. Thus, it assumes that rather than a small increase this week, levels are actually continuing to decline and that corrections to real-time reports will later reflect that. It's essentially saying that the bump you're seeing in the real-time model is just noise. Cases will stay almost completely flat the next 6 weeks, with an official low point around Oct 25, before cases begin rising again for the winter wave.

Alt Model #2, Cheetah (orange line):
The cheetah model aims to correct for biases in real-time data reports. If last week's real-time report overestimated levels by 10% upon correction, it assumes this week's real-time report suffers the same bias. Last week's real-time report was quite accurate, so the cheetah model just looks close to the real-time model, same mini-peak, same valley, same rise in November for the winter wave.

Composite Model (black line):
This is the average of the three models. It's what's used for deriving all of the statistics reported. It basically suggests that cases will be mostly level at a high rate the next 6 weeks with minor fluctuations up or down. The composite model's take-home points are 1) continued high cases the next 6 weeks, 2) minimal fluctuation on a day-to-day basis during that time, 3) a low for the remainder of the year around Oct 25, and 4) a winter wave starting to pick up in mid-November.

Next Tweet, let's examine regional variation.
CURRENT ESTIMATES FOR September 27, 2023 Wastewater Levels (copies/mL) 512 New Daily Cases 745,000 % of Population Infectious 1.56% (1 in 64 people) New Daily Long COVID Cases  37,000 to 149,000   4-WEEK FORECAST FOR October 25, 2023 Wastewater Levels (copies/mL) 491 (-4% lower) New Daily Cases 714,000 % of Population Infectious 1.49% (1 in 67 people) New Daily Long COVID Cases  36,000 to 143,000
Regional variation suggests the need to pay attention for an increase unanticipated by the models.

The northeast is still rising steadily, and they are seeing 3-4x more FL 1.5.1 than other regions.

Caveat: this occurs during a time of Biobot/CDC reporting delays/issues.

2/5 Screenshots from Biobot. The northeast cases are still rising, whereas elsewhere, cases are basically flat, very minor uptick or downtick. Subvariant data suggest regional differences.
Zooming out from the 6-month & regional views to the full pandemic, note we're in a steady state of high transmission between the 7th & 8th waves of the U.S. pandemic.

We will likely see at least 1.4% of the population actively infectious every day for the rest of 2023.

3/5 There is more COVID-19 transmission today        than during 58.1% of the pandemic.  CURRENT ESTIMATES FOR September 27, 2023 Wastewater Levels (copies/mL) 512 New Daily Cases 745,000 % of Population Infectious 1.56% (1 in 64 people) New Daily Long COVID Cases  37,000 to 149,000   WEEKLY ESTIMATES FOR September 27, 2023 New Weekly Cases 5,200,000 New Weekly Long COVID Cases  261,000 to 1,043,000   2023 CUMULATIVE ESTIMATES AS OF September 27, 2023 Total 2023 Cases To Date 220,500,000 Total 2023 Long COVID Cases To Date  11,025,000 to 44,100,000
Read 5 tweets
Sep 25
PMC C0VID-19 Tracker, Sep 20, 2023

U.S. #wastewater levels are higher than during 58% of the pandemic:
🔹 1.55% (1 in 65) are infectious
🔹 >740,000 C0VID cases/day
🔹 37,000+ #LongCovid cases/day

Expect a high trough (600-750K cases/day) until a winter wave.
1/4
CURRENT ESTIMATES FOR September 20, 2023 Wastewater Levels (copies/mL) 509 New Daily Cases 741,000 % of Population Infectious 1.55% (1 in 65 people) New Daily Long COVID Cases  37,000 to 148,000    4-WEEK FORECAST FOR October 18, 2023 Wastewater Levels (copies/mL) 461 (-9% lower) New Daily Cases 671,000 % of Population Infectious 1.4% (1 in 71 people) New Daily Long COVID Cases  34,000 to 134,000
Let's zoom out from the 6-month view to the full pandemic.

The 7th U.S. C0VID wave has been huge, slightly smaller than Delta, & is now on the decline nationally.

We're seeing 5 million infections/week nationally, much higher than people realize, so continue with advocacy.
2/4 CURRENT ESTIMATES FOR September 20, 2023 Wastewater Levels (copies/mL) 509 New Daily Cases 741,000 % of Population Infectious 1.55% (1 in 65 people) New Daily Long COVID Cases  37,000 to 148,000   WEEKLY ESTIMATES FOR September 20, 2023 New Weekly Cases 5,200,000 New Weekly Long COVID Cases  259,000 to 1,037,000   2023 CUMULATIVE ESTIMATES AS OF September 20, 2023 Total 2023 Cases To Date 180,900,000 Total 2023 Long COVID Cases To Date  9,045,000 to 36,180,000
1.55% of the U.S. population is actively infectious with C0VID (Sep 20).

Schools and in-person work remain extremely risky.

If in the U.S., schedule a #booster. Go remote. #MaskUp. Read up on and improve indoor air quality. Avoid indoor dining. #RapidTest.
3/4 Number of People  |  Chances Anyone is Infectious  1	1.6% 2	3.1% 3	4.6% 4	6.1% 5	7.5% 6	8.9% 7	10.4% 8	11.8% 9	13.1% 10	14.5% 15	20.9% 20	26.8% 25	32.3% 30	37.4% 35	42.1% 40	46.5% 50	54.2% 75	69.0% 100	79.0% 150	90.4% 200	95.6% 300	99.1% 400	99.8% 500	>99.9%
Read 4 tweets
Sep 14
PMC C0VID-19 Tracker, Sep 13, 2023

U.S. #wastewater levels are higher than during 64% of the pandemic:
🔹 1.8% (1 in 57) are infectious
🔹 >800,000 C0VID cases per day
🔹 >40,000 #LongCovid cases per day

Let's look at the good and bad of where we're heading....

The "Good" News:
Peaking. Transmission is slowing down a little and forecasted to decline further over the next month. If the Biobot data hold up against data corrections, the late summer wave peaked on August 30 at approximately 931,000 cases per day. By October 11, we expect to be closer to 700,000 cases per day. Less morbidity and mortality are always good news.

Modeling. Our models generally predicted that the peak would be sometime between Aug 23 and Sep 14, even amid some noisy wastewater data from Biobot. We regularly predicted that wastewater levels would fluctuate between 400-700 copies/mL from August to October, even amid much scolding and taunting from people who believed it would be much better or worse. The current forecasts show much convergence. The composite (black line) forecast is comprised of a real-time forecast (assumes Biobot's real-time wastewater data are accurate), turtle forecast (ignores the most recent week's Biobot data), and cheetah forecast (corrects the most recent week's Biobot data), all of which are quite similar at the moment. At present, this looks like a win for forecasting, with more improvements on the way. Good forecasts can help people make better predictions in an uncertain world.

Dissemination. The final bit of good news is that people are using this data to inform friends, their workplaces, and schools. In this regard, the time to act is NOW. People who do not follow wastewater are watching the anecdotal evidence in their lives pick up (personal reinfection, friends and coworkers infected, more masking, near-misses at schools, etc.). As reality glimmers through denial, now is the time to show people the data that validates what they are subjectively experiencing (still amid much second-guessing and gas-lighting). Anyone "flipped" toward watching the data will be much better prepared for the winter.

The Bad News:
High valley. Although a decline in transmission is always good, we're headed toward what is likely a very high valley in October at 700,000 infections per day, and then the picture will likely get much worse in November, December and January, unless we see an unanticipated level of vaccinations.

Grim Implications. In the "good" times of October, we will still see 1.5% of Americans (1 in 69) actively infectious at any given moment. Our low-end estimate suggests 35,000 new Long C0VID cases from such infections. That's grim.

Media narrative. The psychological dynamics are bleak. Expect the news media to focus on the fact that transmission is going down (the "good" news) instead of the much bigger picture that it's leveling off at very high rates (the very bad news). The over-optimism will likely undermine vaccinations and masking. The narrative should be "plan to take multi-layered precautions like using masks, getting boosters, and using remote options through January," but will more likely be "cases are headed back down."

#VaxUp #MaskUp #Ventilate #HEPA #CorsiRosenthalBox #remotework

1/
CURRENT ESTIMATES FOR September 13, 2023 Wastewater Levels (copies/mL) 579 New Daily Cases 843,000 % of Population Infectious 1.76% (1 in 57 people) New Daily Long COVID Cases  42,000 to 169,000   4-WEEK FORECAST FOR October 11, 2023 Wastewater Levels (copies/mL) 479 (-17% lower) New Daily Cases 697,000 % of Population Infectious 1.46% (1 in 69 people) New Daily Long COVID Cases  35,000 to 139,000
Let's zoom out from the 6-month view to the full pandemic.

If things holds, the 7th U.S. C0VID wave appears to have leveled off as slightly smaller than Delta, still a huge wave. 🌊

We're seeing about 6 million infections/week in the U.S., much higher than people realize.

2/ There is more COVID-19 transmission today than during 63.5% of the pandemic.  CURRENT ESTIMATES FOR September 13, 2023 Wastewater Levels (copies/mL) 579 New Daily Cases 843,000 % of Population Infectious 1.76% (1 in 57 people) New Daily Long COVID Cases  42,000 to 169,000   WEEKLY ESTIMATES FOR September 13, 2023 New Weekly Cases 5,900,000 New Weekly Long COVID Cases  295,000 to 1,180,000   2023 CUMULATIVE ESTIMATES AS OF September 13, 2023 Total 2023 Cases To Date 175,200,000 Total 2023 Long COVID Cases To Date  8,760,000 to 35,040,000
Nearly 2% of the U.S. population is actively infectious with C0VID (Sep 13).

Schools and in-person work remain extremely risky.

If in the U.S., schedule a #booster. Go remote. #MaskUp. Read up on and improve indoor air quality. Avoid indoor dining. #RapidTest.

3/ What's the COVID Risk in an Office or in a Classroom?  Number of People   |   Chances Anyone is Infectious  1	1.8% 2	3.5% 3	5.2% 4	6.9% 5	8.5% 6	10.1% 7	11.7% 8	13.3% 9	14.8% 10	16.3% 15	23.4% 20	29.9% 25	35.9% 30	41.4% 35	46.4% 40	50.9% 50	58.9% 75	73.7% 100	83.1% 150	93.1% 200	97.2% 300	99.5% 400	>99.9% 500	>99.9%
Read 4 tweets
Sep 11
If your county has Biobot #wastewater data, here's information on how to estimate the percentage of people in your area who are actively infectious.

Quick guide. 🧵

Here are a few examples to start.
1/
County, Biobot Wastewater Level (copies/mL), % Infectious Middlesex, MA (near Boston)	1296	3.9% Orange, FL (Orlando)	1100	3.3% Honolulu, HI	1030	3.1% Washington, DC	821	2.5% Shelby, TN (Memphis)	794	2.4% Dallas, TX	611	1.9% Nassau, NY (near NYC)	547	1.7% Palm Beach, FL (near Miami)	478	1.5% Los Angeles, CA	474	1.4% Clark, NV (Las Vegas)	422	1.3% San Diego, CA	350	1.1% Montgomery, PA (near Philadelphia)	350	1.1%
If you're not mathematically inclined, use this table. Look up your county's #wastewater levels (on Biobot only, not other sites), and simply convert it to the % infectious estimate.

If wastewater levels are 1000 copies/mL, 3% of the county is infectious with C0VID.
2/ Biobot Wastewater Level (copies/mL), % Infectious 100	0.3% 200	0.6% 300	0.9% 400	1.2% 500	1.5% 600	1.8% 700	2.1% 800	2.4% 900	2.7% 1000	3.0% 1100	3.3% 1200	3.7% 1300	4.0% 1400	4.3% 1500	4.6% 2000	6.1% 2500	7.6% 3000	9.1% 4000	12.2% 5000	15.2%
To precisely convert Biobot #wastewater levels to the % infectious, just take the Biobot levels and divide by 328. You'll get a percentage.

For example, national levels are at 641 copies/mL. 641/328 = 1.95. So, 1.95% of the U.S. is infectious.
3/ Formula (Long Version)			 Biobot Wastewater Level (copies/mL)	641		 Biobot Wastewater to Cases Multiplier	1455		Multiplier for Biobot to IHME case estimate conversions (uses a trimmed mean, most estimate in the 1000-1600 range) Average Infectious Window	7		https://doi.org/10.1016/S2213-2600(22)00226-0 Decimal to Percentage Multiplier	100		Turns decimals to percents U.S. Population, April 1, 2023	334,565,848		Final day of IHME data, source from https://www.census.gov/popclock/ Local Percent Infectious	1.95	%	=B2*B3*B4*B5/B6 			 			 Formula (Easy Version)			 Biobot Wastewater Level (copies/mL...
Read 4 tweets
Sep 9
PMC C0VID-19 Tracker, Sep 6, 2023

U.S. #wastewater levels are higher than during 70% of the pandemic:
🔹1.95% (1 in 51) are infectious
🔹Nearly 1 million C0VID cases per day
🔹Causing >40,000 #LongCovid cases per day

Let's look at these wildly divergent forecasts for the next 4 weeks.

Real-time Model: If you assume Biobot is reporting accurate real-time wastewater data each week, follow the red line. This says we have peaked on our late summer wave. That would be great news in terms of less morbidity and mortality. The problem is that real-time reports have been prone to error lately, more often than not underestimating wastewater levels, and then corrected later.

Alt Model 1 (Turtle): The turtle model moves slow, like a turtle. It assumes the most recent week's data from Biobot are useless and ignores them. By ignoring the most recent data, it will be slow to detect a quick change in transmission, like a peak. It basically expects "more of the same" for a little longer. See green line.

Alt Model 2 (Cheetah): The cheetah model moves fast, like a cheetah. It assumes that if last week's Biobot wastewater data underreported levels by X% that this week's current real-time data are also underreporting by that same percent. Last week's real-time data were corrected upward by 15%, which makes a huge difference in forecasting whether we're leveling off or on a steep incline. The cheetah model has us getting up to 1.4 million cases/day, so this is a good model for a worst-case scenario. See yellow line.

Composite Model: This is the average of the three models. It's what we use in the red box for estimating cases 4 weeks from now. It's a good estimate if trying to cite a point estimate to coworkers (e.g., "The U.S. will see about 1 million new cases/day the next several weeks). However, from a forecasting perspective, it's less useful because the underlying models are so divergent. See black line.

Big-Picture Framing
The current state of the pandemic is extremely bad. Expect approximately 1 million new U.S. cases per day the next several weeks. Less if we're lucky, and more if we're not. As a psychologist, I would characterize denial about the current C0VID wave to peak in the next couple weeks. Most people believe "the pandemic is over" and we're "after C0VID." Expect further gaslighting for now.

#MaskUp #VaxUp #Ventilate #HEPA #CorsiRosenthalBox #remotework

1/
PMC 6-month view of wastewater levels, case estimates, and near-term forecast.   CURRENT ESTIMATES FOR September 6, 2023 Wastewater Levels (copies/mL) 641 New Daily Cases 933,000 % of Population Infectious 1.95% (1 in 51 people) New Daily Long C0VID Cases  47,000 to 187,000   4-WEEK FORECAST FOR October 4, 2023 Wastewater Levels (copies/mL) 716 (12% higher) New Daily Cases 1,042,000 % of Population Infectious 2.18% (1 in 46 people) New Daily Long C0VID Cases  52,000 to 208,000
Let's zoom out from the 6-month view to the full pandemic.

Given current levels and forecasts, we're in a wave of transmission similar to the winter of 2020-21 or Delta. 🔥🔥

178 million infections & >8 million #LongCovid cases in 2023 thus far.

2/ There is more COVID-19 transmission today        than during 70% of the pandemic.  CURRENT ESTIMATES FOR September 6, 2023 Wastewater Levels (copies/mL) 641 New Daily Cases 933,000 % of Population Infectious 1.95% (1 in 51 people) New Daily Long COVID Cases  47,000 to 187,000   WEEKLY ESTIMATES FOR September 6, 2023 New Weekly Cases 6,500,000 New Weekly Long COVID Cases  327,000 to 1,306,000   2023 CUMULATIVE ESTIMATES AS OF September 6, 2023 Total 2023 Cases To Date 178,600,000 Total 2023 Long COVID Cases To Date  8,930,000 to 35,720,000
With about 2% of the U.S. population actively infectious with C0VID, school and in-person work remain extremely risky.

Offer remote activities. #MaskUp. #VaxUp again when allowed. Read up on and improve indoor air quality. Avoid indoor dining. #RapidTest frequently.

3/ Number of People,	Chances Anyone is Infectious 1	2.0% 2	3.9% 3	5.7% 4	7.6% 5	9.4% 6	11.2% 7	12.9% 8	14.6% 9	16.3% 10	17.9% 15	25.6% 20	32.6% 25	38.9% 30	44.6% 35	49.8% 40	54.5% 50	62.7% 75	77.2% 100	86.1% 150	94.8% 200	98.1% 300	99.7% 400	>99.9% 500	>99.9% 25	38.9% 30	44.6% 35	49.8% 40	54.5% 50	62.7% 75	77.2% 100	86.1% 150	94.8% 200	98.1% 300	99.7% 400	>99.9% 500	>99.9%
Read 4 tweets

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