Tomorrow I'm speaking @yale_eeb on "Network Theory and COVID-19." My goal is to pull a thread across the 10+ papers we've written on the topic & convince you that #COVID19 became a pandemic because the world does not understand complex systems. h/t to my host @big_data_kane. 1/13
First, building from foundational work in math. epi. and network science, we showed how super-spreading creates havoc for pandemic risk predictions and then derive a method for correcting the predictions. 2/13
Second, how de-coupling the risk of infection from transmission breaks the friendship paradox, which most (non-mass-action) herd immunity thresholds rely on & can mean that backwards case investigation is more important than forward contact tracing. 3/13
Forth, that real-time mobility data during one of the largest human population disruptions in history--i.e., the cordon sanitaire of Wuhan, China--revealed the complex epidemiological dynamics of #COVID19. 5/13
Fifth, the way in which human behavior and policy interventions (or lack thereof) interacted to shape mobility patterns and physical distancing during lockdowns. 6/13
Sixth, why visits to places like churches and parks reveal the epidemiological risks associated with heterogeneous policies regarding non-pharmaceutical interventions. 7/13
Seventh, using data from >300k US survey responses to show how mask wearing affects COVID-19 transmission (pre-print and formal tweets coming soon). 8/13
Eighth, why, now, more than ever, understanding the non-SEIR dynamics of #COVID19 and the *potential* role of environmental transmission is critical. 9/13
Ninth, that classic ecological theory on crowding predicts the intensity & duration of #COVID19 epidemics and that hierarchical structure in mobility and social networks may be a critical driver of this pandemic (as predicted by network scientists). medrxiv.org/content/10.110… 10/13
Lastly, how an international consortium has compiled, what Steven Johnson in The New York Times Magazine, describes as what "may well be the single most accurate portrait of the virus’s [#COVID19's] spread through the human population in existence." 11/13
I'm excited & also nervous. Nervous because the work we've done stands on the shoulders of so many giants (including one my PhD advisors @meyerslab, which is where the title comes from) & it may take an hour just to acknowledge collaborators/funders. 12/13 ncbi.nlm.nih.gov/pmc/articles/P…
And excited because so many of us are convinced that the work of complex systems and network scientists matters more than ever. I can't wait to tell the world why and it starts tomorrow @Yale. 13/13
• • •
Missing some Tweet in this thread? You can try to
force a refresh
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!