"Acute infections in vaccinated and unvaccinated people feature similar proliferation and peak Ct, but vaccinated individuals cleared the infection more quickly. [BUT] Viral concentrations do not fully explain the differences in infectiousness..." 1/6 medrxiv.org/content/10.110…
New pre-print from the labs of @yhgrad & @NathanGrubaugh using data from the NBA, confirms that peak viral load is similar between vaccinated/unvaccinated cases, but clearance is faster in vaccinated. 2/6
However, and here is the *really* important part, viral concentrations did not fully explain differences in infectiousness between pre-Alpha, Alpha, and Delta variants. 3/6
What does this mean? 1.) masking & testing are critical even for vaccinated individuals to control #COVID19, 2.) the number of secondary infectious is probably lower for breakthrough infections, which makes vaccination all the more important, 4/6
3.) we desperately need contact tracing data on breakthrough infections, and 4.) we need more longitudinal studies like this globally. 5/6
I'm staying on @Northeastern as an affiliate assistant professor & would not be in a position to take on this incredible responsibility w/out the support from so many amazing individuals at NEU, especially Prof. Vespignani (@alexvespi) and the wonderful members of @NUnetsi.
I owe a huge thanks to countless other mentors, colleagues, and friends, but want to specifically call out @MOUGK, @johnbrownstein, and the entire @globaldothealth team. Working with you on improving #COVID19 data over the past 18 months has been the honor of lifetime.
For the past year, we've been building an open data platform for tracking epidemics and curating a global repository of #COVID19 cases. Today, with support from @Googleorg & @RockefellerFdn, I'm proud to introduce @globaldothealth. 1/13
We have an amazing team of engineers, academics, technologists, and entrepreneurs; many of whom have volunteered hundreds of hours helping us build Global.health. You can learn about them and their incredible work here: global.health/about/ 3/13
Building from foundational work in math. epi. and network science, we show how super-spreading creates havoc for pandemic risk predictions based on R0 alone and then derive a method for correcting the predictions. 2/10
This paper includes what I think is the most intuitive explanation for how higher moments in the distribution of secondary infections affects epidemic risk that I've read (@LHDnets & @all_are wrote the following lines). 3/10
Regardless of what happens, 48% of voters in US supported hate, greed, and anti-science. Until we accept and address these persistent issues, we cannot progress as a country.
Since the trolling has started, here’s my logic. In the 10 months leading up to this election, the actions of our incumbent president directly *caused* the deaths of >200k Americans and counting & wiped 12 trillion dollars from our economy.
Anyone voting for him must have an even stronger motive. The only ones I can think of are hate, greed, and anti-science. I file taking away a woman’s right to choose under hate and anti-science.
The intensity of #COVID19 epidemics is heavily influenced by population structure. Our new paper analyzing high-resolution case, population, & mobility data from China and Italy is out today in @NatureMedicine. Co-led w/ @MOUGK & @EvolveDotZoo. 1/15 nature.com/articles/s4159…
Using case data from the "Open COVID-19 Data Working Group" (github.com/beoutbreakprep…), paired with high-resolution population and mobility data, we showed that epidemics are sharper in lower-density areas and broader and longer in big cities. 3/15
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