As an introverted (& socially anxious) pandemic responder, it’s been hard for me to maintain social ties with folks beyond my family & colleagues — not because I don’t want to, but because I need time alone to recharge between seemingly endless Slack messages & Zoom meetings. 1/X
Due to the nature of the job, the amount of person-to-person interaction I’ve had professionally since January has been increasingly overwhelming for someone like me... So, by the time virtual trivia night comes around each week, I’m usually too Zoom’d out to join my buddies. 2/X
But here’s the thing: please keep inviting me. Please don’t cancel me & others like me. We love you & care about you, even if every spare minute we have right now is dedicated to trying to recuperate (& to grieve) in between the demands of our professional responsibilities. 3/X
To me at least, the best way that I can show that I care right now — for my friendships & for humanity — is by making my work (& my family) my very first priority (& by using what little “me time” I can get to take care of myself & restore myself in ways that work for me). 4/4

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More from @maiamajumder

12 Oct
Our paper on the role of environmental factors on #SARSCoV2 transmission (led by Canelle Poirier & @MauSantillana) has been peer-reviewed & is now live: nature.com/articles/s4159…

In our study, we find a lack of sufficient evidence for weather-mediated seasonality of #COVID19. 1/X
I’ll let @MauSantillana take the lead on fielding questions about this work, but I wanted to point out two things.

First, I use the term “weather-mediated seasonality” conscientiously. There are a lot of other phenomena that *also* mediate seasonality, like behavior change. 2/X
For example, I don’t doubt that congregating indoors – which will likely happen more often as winter emerges in the U.S. – may very well increase transmission risk in temperate climates.

These issues are extremely important, but they aren't what we’re studying in this paper. 3/X
Read 8 tweets
22 Aug
Recently, I've had a lot of folks ask me whether it'll be possible to safely visit loved ones during the 2020 holiday season. In the US, #COVID19 will likely still be with us then – but "merging bubbles" may be an option (if done responsibly).

Let's discuss what that means. 1/X
The concept of "merging bubbles" involves bringing two or more households together such that they can interact with each other (indoors and in person). In this thread, I'll be offering my own personal views on how to minimize #coronavirus transmission risk while doing this. 2/X
Let me preempt this by saying that the advice I offer today is based off of the best available knowledge at present and is subject to change between now and the holiday season. If it does, I'll be adding an addendum to this thread – so be sure to bookmark and check back then! 3/X
Read 24 tweets
13 Jun
Our latest preprint from the #COVID19 Dispersed Volunteer Research Network is now live at [biorxiv.org/content/10.110…]!

We use machine learning techniques to map the existing #coronavirus literature & identify research needs for #SARSCoV2 (compared to #MERS- & #SARS-CoV here).

1/N
(This study was led by @AnhvinhDoanvo in collaboration with @Money_qxl, @qramjee, @EvolvingEpitope, @angeldesaimd, & myself. It started off as one of our research hackathon projects in late March, & it's been such a delight to oversee its development over the last 11 weeks!)

2/N
Perhaps unsurprisingly to those familiar with scientific funding mechanisms, we find via PCA & LDA that #COVID19 studies (both preprints & peer-reviewed) have primarily been dominated by clinical, modeling, & field-based – as opposed to laboratory-based – research to date.

3/N
Read 6 tweets
13 Mar
OK, folks. I know things seem really scary right now. The landscape of our response to #COVID19 is changing by the hour, much like our knowledge about this disease (from a scientific lens) has changed by the hour over the last two months.

Please prioritize self-care, y’all. 1/N
For most of us, the best we can do is practice disease prevention (wash our hands, cover our coughs & sneezes, etc.) and (if we are in the position to do so) limit face-to-face time with others.

The latter (#socialdistancing) can #flattenthecurve, but it comes at a cost. 2/N
I’ve talked about some of these costs here on Twitter, wrt the consequences such measures — when institutionally mandated — may have on our most marginalized communities. That doesn’t mean we shouldn’t implement them; it means we need to do what we can to limit the harms. 3/N
Read 10 tweets
12 Feb
New analysis from me & @mandl suggests that preprints might have driven global discourse about #nCoV2019 (#COVID19) transmissibility prior to the publication of relevant peer-reviewed studies. Find our preprint here [ssrn.com/abstract=35366…] as well as an explainer thread below!
@mandl Assuming representativeness, we first collected Google search trend interest & MediaCloud news volume data on #nCoV2019 (#COVID19) transmissibility. We then curated relevant studies from Google Scholar & four popular preprint servers. (Discovery specs are noted in our preprint.)
@mandl After plotting search interest & news media volume, we overlaid when each of the relevant studies were published. We found that both search interest & news media RE: the transmissibility potential of #nCoV2019 (#COVID19) peaked before publication of the first peer-reviewed study.
Read 11 tweets
27 Jan
I've seen a few tweets recently about how R_0 is the mean of a distribution (via @nntaleb) and how its dispersion is important to understand (via @DFisman & @C_Althaus).

This is very true (for #nCoV2019 & otherwise), and it's why I posted this graphic last week. [THREAD] 1/x
@nntaleb @DFisman @C_Althaus As y'all may recall, R_0 is the *average* number of people a new case will infect in a fully susceptible population... But in a given population, the number of ways R_0 can be 2 (as in the above visualization) is essentially countless because *each person is different*. 2/x
@nntaleb @DFisman @C_Althaus We have different biology and different behaviors that impact our individual likelihood of passing on the infection in question. This is why some people may infect lots of people and some people may infect no one at all, yet yield an R_0 of 2 (see Scenario 2 in the viz). 3/x
Read 9 tweets

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