Last week I told the @BostonGlobe models showed a #COVID19 peak in 1-2 weeks (i.e. later this week or next). The data are consistent with a peak happening now, but, the Globe picked a different forecast for their headline. 🤦♂️ 1/6 bostonglobe.com/2022/01/12/nat…
Wastewater data are peaking in Boston. However, as @davidlazer mentions we’re still *way* above record highs & hospitalizations will continue rising after cases peak. So things will get worse healthcare-wise. Masking & testing are vital now. #wastebeforecase@BiobotAnalytics 2/6
Cases are falling in DC, but because of surging hospitalizations that will continue to rise, the mayor has rightly implemented new emergency measures. 3/6 nbcwashington.com/news/coronavir…
For more accurate forecasts, I’d follow groups like @utpandemics and @alexvespi. However, recent forecasts from most teams submitting to the #COVID19 Scenario Modeling Hub are consistent with an earlier peak than is quoted in the Globe. 4/6 covid19scenariomodelinghub.org
h/t to @jkbren for pointing out that the models shown in the Globe have deaths & hospitalizations peaking on about the same day, barely a week after cases. This is highly implausible. Deaths are just now peaking in S. Africa ~1 month after cases, consistent with past waves. 5/6
“Scarpino of the Rockefeller Foundation said Friday that the models he’s reviewed suggest the peak in cases is coming in the next one to two weeks, with the Northeast, from Washington, D.C., to Boston, leading the way, with cases then declining sharply.” 6/6
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How fast will #Omicron drop after peaking? Turns out, it's not so obvious. A thread on the shape of epidemics. 1/25
You might think that if an epidemic curve goes up quickly, then it will come down quickly. We (@LHDnets, @all_are, & me) did too. And, it can! For example, replacing sick workers can drive accelerating disease spread and decline. 2/25 nature.com/articles/nphys…
Our intuition is that this worker replacement effect is a BIG deal for the isolation time debate. The tl;dr of the @NaturePhysics paper above is that the local social network matters A LOT & how exactly we replace workers matters A LOT. More soon on this. 3/25
1. The "worried well" have no symptoms, no known exposure, etc., but are freaked out because of the news around #Omicron. There's not much data on this group, but--based on our experience with H1N1 in 2009--we should assume demand is going up. 3/15 sciencedirect.com/science/articl…
"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
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