“many public health programs are not collecting the data needed to show the [#COVID19] pandemic’s impact on poor and minority people.” Fantastic piece by @tsimonite in @WIRED on how inequality and racism also *cause* public health data bias in the US. wired.com/story/covid-hi…
As we demonstrated in our recent @PLOSCompBiol publication, these data biases further the inequality by obscuring those communities most in need and often hardest hit by epidemics.
However, when considering new public health surveillance systems designed to close these data gaps, we must also acknowledge and *address* the decades and decades of racism and violence that have *caused* so many communities to be at higher risk during epidemics. #racismnotrace
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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!
2/ Our work builds from a network-science-based approach to modeling gene networks, called the Pathway Framework, which Chia-Hung developed during his @NUnetsi dissertation. You can read more about that model here:
3/ In this paper, we show how a single locus population genetic model can be generalized to an arbitrarily complex genotype using basic probability theory.
Connecting these results to the pathway framework allows us to derive a pop-gen-style model for gene networks.