Our new pre-print shows that people are excited to use N95s during COVID surges when given them for free w/helpful information on their evidence & how to use them. 1/ psyarxiv.com/f76vw
You might remember when we posted about the study back in December!
I hope you find the results useful. We tried to make the figures relatable, even if you're not reading science articles every day. Please let me know if you have questions! 2/
We gave out 2,500 #N95 masks in 5 packs to our racially & socioeconomically diverse community. Many used N95s for the 1st time. People treated us like we were giving them kidneys. So grateful!
97% used at least 1 N95 & 40% used all 5 within 1-mo!
3/
We called our #N95 giveaway study "Project #Bandura," named for the famous #psychologist who studied social learning.
Well -- people learned, socially! >80% of participants told others about N95s. Several enthusiasts shared w/dozens of ppl! 4/
Cost was the biggest barrier to sustained #N95 use!
Still, ppl planned to buy an average of >10 more N95s in the near future (2x what we gave them).
More interesting, if FREE, 88% would use N95s again. Once you pop, you can't stop! 5/
We only gave away 2,500 N95s. It was labor intensive b/c we included educational materials, had a follow-up survey, went through IRB, etc.
However, it validates @amandalhu's 17,000 mask giveaway, those of cities and fed govt's.
As a #psychologist, there is 1 big truth I believe w/my heart. If you talk to most ppl in a way that's reasonable, about 97% will feel empowered to take actions to improve their lives & communities.
As true for COVID as anything else. We just need more courageous leadership. 7/
With N95s extremely popular during #COVID surges, what's the national strategy?
The U.S. had a stockpile and started giving them out for free (after my psychology research team 🤣). There are no known plans to buy and distribute more.
🚩🚩🚩
As a vigorous defender of #CDC data, their switch from using normalized to non-normalized COVlD wastewater surveillance data today harms data quality.
"Normalizing" means accounting for basic confounders like rain levels. It is a choice to use worse data.
1/5🧵
Historically, the CDC data have correlated near-perfectly with similar metrics, such as Biobot's wastewater estimates (still active) or the IHME true case estimates (through mid-2023).
The changes reduce those correlations. It's like going from an A+ to a B.
2/5🧵
You can readily see the loss of data quality in the PMC "whole pandemic" graph (preview shown, subject to change) with choppier waves, caused by the CDC adding extra noise to the data and applying retroactively from BA.1 Omicron to present.
U.S. CDC numbers just released. Good news (for those not in Louisiana). "Only" a 5% national increase.
2025 has closely tracked with summer 2023 transmission. A 12-13% increase would have been expected based on those numbers. That said...
real-time data have been prone to retroactive corrections. This is frustrating, of course, because it leaves people making decisions based on data that are only of good quality when 2 weeks old.
If we saw a 12% increase this week, I'd say look at 2023 for a glimpse...
at the future. Instead, I would consider these plausible scenarios:
🔹Wave still similar to 2023
🔹Later wave with schools more implicated
🔹Something temporarily much better
COVlD is surging in 7 states, according to the CDC.
🔹Hawai'i (Very High)
🔹California (High)
🔹Nevada (High)
🔹Texas (High)
🔹Louisiana (High)
🔹Florida (High)
🔹South Carolina (High)
2. PMC COVlD Dashboard, July 21, 2025 (U.S.)
Western surge:
🔹California: 1 in 63 actively infectious, much higher in LA & Bay areas
🔹Hawai'i: 1 in 35 actively infectious
🔹Nevada: 1 in 63 actively infectious
These are wastewater derived estimates, not from individual tests
3. PMC COVlD Dashboard, July 21, 2025 (U.S.)
Southern surge:
🔹Texas: 1 in 56
🔹Louisiana (New Orleans): 1 in 65
🔹Florida: 1 in 66
🔹South Carolina: 1 in 71
Again, wastewater estimates (wise indicator), not individual testing (low-quality data).
We estimate 1 in 148 Americans are actively infectious. This equates to 2.3 million infections/week, expected to result in >100,000 new #LongCOVID conditions & >800 deaths.
A room of 100 people is a coin toss of an exposure.
2) PMC COVlD Dashboard, July 14, 2025 (U.S.) 🧵
Transmission (red) is closely tracking the path of 2 years ago (yellow). However, the incoming data are spotty. >20% of CDC states have limited/no data, & Biobot hasn't reported in weeks.
Could be MUCH worse or slightly better.
3) PMC COVlD Dashboard, July 14, 2025 (U.S.) 🧵
Our model formalizes the mathematical assumptions in those predictions. If transmission follows what we know in terms of how waves grow or slow generally and historical patterns, this is what we'd expect.
The spottiness of the current real-time data reduce precision substantially. Retroactive corrections can make the forecast jump around from better to worse from one week to the next. Expect the worst. Hope for the best.