2/ For those following #H5N1 in CA, there have been positive farms there since late Aug.
@globaldothealth we're working w/ @ThinkGlobalHlth and @CFR_org to maintain a timeline of key events. This tracking allows us to better piece together signals. thinkglobalhealth.org/article/timeli…
3/ I'm concerned about the H5 wastewater signal because it lags far behind the uptick in farms and is better correlated with the rapid rise in human infections. thinkglobalhealth.org/article/timeli…
4/ Looking regionally at flu A and H5 in the wastewater, there's also a signal in San Jose. However, what we're seeing is largely contained to Turlock, which means there is time to act. Data from @WastewaterSCAN
5/ We urgently need sequence data from the wastewater to confirm it's of human origin and modeling to translate concentrations into estimation of case burden.
6/ I want to stress that these data don't mean human-to-human transmission is happening. We also don't know whether milk byproducts are being dumped in municipal wastewater, as has been seen in Texas.
But, this is *exactly* the kind of early warning signal we must act on!
7/ It's worth noting that the City of Turlock Regional Water Quality Control Facility states, "Nearly half of the flow comes from food processing and dairy industries."
8/ We know milk processors dumping byproducts into municipal has contributed to H5 signals in Texas, so it's worth taking that hypothesis very seriously in Turlock.
However, in Texas, we saw a *much* more rapid rise (like a step function) to concentrations 5x what we see in CA.
9/ Note that @WastewaterSCAN wasn't testing for H5 back in early 2024, but we can see from their publication that this influenza A signal was almost certainly H5. pubs.acs.org/doi/10.1021/ac…
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2/ Milk is pasteurized by heating it briefly to ~72 C (161F). This inactivates pathogens, but does filter the milk. As a result, there can be degraded genomic material from pathogens following pasteurization. PCR, as was done by the FDA, can detect these degraded genomes.
3/ Numerous peer-reviewed studies have found that pasteurization will inactivate influenza A virus, including #H5N1. ncbi.nlm.nih.gov/pmc/articles/P…
2/ As you may know, avian influenza doesn't readily infect humans (and doesn't transmit well from human-to-human) in part because of subtle differences in key cell surface receptors. journals.asm.org/doi/full/10.11…
3/ However, our eyes actually contain the bird-flu-friendly confirmation of the cell surface receptor. This is why eye inflammation is often a symptom of avian influenza infection in humans. thelancet.com/journals/lanin…
2/ Following a convening of @RockefellerFdn's Global Wastewater Action Group, we partnered w/ @MathematicaNow and surveyed representatives of wastewater monitoring programs in 43 countries (16 LMICs, 27 HICs) spanning six continents (when I said "all" I didn't count Antartica).
3/ In high-income countries, composite sampling at centralized treatment plants was most common, whereas grab sampling from surface waters, open drains, and pit latrines was more typical in low-income and middle-income countries.
1/ Data from @WastewaterSCAN shows that rates of SARS-CoV-2, RSV, and influenza have dropped precipitously from their winter peaks!
We still have a ways to go, but things are clearly headed in the right direction.
2/ Although for SARS-CoV-2 we've been hovering at peak levels for over a month and we need to see at least another month of continuously falling prevalence before we're back to more "baseline" levels.
3/ And note how *LONG* the RSV outbreak has been in the US.
We've been above 25% of the peak height for >3 months!
1/ For those concerned about #XBB15 and hospitalizations, I think the evidence is more mixed than many are admitting.
While it's true hospitalizations are up in states like MA where XBB.1.5 is common, they are up across the entire US, even in states w/ little-to-no #XBB15!
2/ If we plot daily XBB.1.5 prevalence at the state-level vs new adult hospitalizations for #COVID19, you can see there are some states (each color is a state) with weakly positive relationships, but this the signal isn't very strong.
3/ If we analyze these data using a mixed-effects regression model (with state as a random effect) there is a very weak, positive relationship, but XBB.1.5 only explains about 2% of the variability in hospitalizations on a log-scale!