I wish we were monitoring (and reporting on) statistics about Long COVID the way we are about deaths.

Death isn't the only clinically relevant outcome of COVID, and our public health surveillance systems need to reflect that. I know this is a challenging task—but taking into account existing (patient-centered) definitions of Long COVID, point estimates of its prevalence, & risk factors for its emergence, I think it should be possible to produce (spatiotemporally dynamic) nowcasts for public awareness.

Even if you’re not at high risk of hospitalization from omicron, the people you come in contact with might not be so lucky—& overburdened hospitals translate to poorer outcomes, no matter what you’re in for… Including accidents, heart attacks, & other common non-COVID ailments. If you’re feeling under the weather (& can afford to do so), please get tested if possible & stay home if you test positive. Wear a well-fitting mask (KF94/KN95/etc.) while in (public) indoor spaces. Try to socialize outdoors when feasible, & if you must be indoors, ventilate!

I’m an outbreak epidemiologist.

Much like 2020, I spent 2021 responding to COVID.

But I also met a creative milestone:

In 2021, I wrote my first novel—about a lady plague doctor in 17th century Mughal India.

(High-stakes science meets court intrigue & romance!)

THREAD 1/

https://twitter.com/maiamajumder/status/1394325567206473730

This is a big achievement for me—something I never dreamed I could do. But last year, I committed to making more space for my creative pursuits, & I’m proud of myself for seeing it through.

11 readers have given me feedback so far, & I've been truly moved by their reviews.

2/

As more folks get #vaccinated, the % of folks who get sick with #COVID19 & happen to be vaccinated will increase. This is expected (at rates that my team & others are monitoring), & it doesn't mean that #vaccines aren't working; rather, it reflects the realities of probability. A few of y’all have asked for clarification about the math here. I may get around to doing a more comprehensive thread, but for now, the easiest way to understand this is in the extreme situation where 100% of Population A is vaccinated & a sick person visits from Population B.

2020 has been a tough year for pandemic responders & scientists, but I am extraordinarily proud of my team.

Here’s a thread of quote-tweets that compiles some of our #COVID19-related work since January 2020.

Please join me in celebrating my amazing trainees & colleagues!

1/X On January 23, with @mandl:

https://twitter.com/maiamajumder/status/1220501403057229824?s=20

2/X

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

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

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

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

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

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.)

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

https://twitter.com/maiamajumder/status/1220802808976486406

@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

We've updated our transmissibility assessment for #nCoV2019! R_0 estimates (based off of publicly reported confirmed cases through 1/26/20 & subject to change) remain ~stable, now ranging from 2.0 to 3.1.

Pre-print will be updated soon: ssrn.com/abstract=35246…

See thread below.

https://twitter.com/maiamajumder/status/1220501403057229824

On January 24, this study [thelancet.com/pb-assets/Lanc…] provided onset data for the first 41 cases & back-dated the start of the outbreak to December 1 (instead of December 8, as we'd thought before due to the WHO [who.int/csr/don/12-jan…]). Our estimates now include these data.

New pre-print by myself & @mandl:

Early basic reproduction number estimates for #nCoV2019 range from 2.0 to 3.3 (based off of publicly reported confirmed cases through 1/22/20 & subject to change) [ssrn.com/abstract=35246…].

Short explainer & several caveats in the thread below. @mandl The basic reproduction number (R_0) is a measure of transmissibility that aims to describe the average number of people a new case *in a fully susceptible population* will infect. (Most of the time, this number isn’t actualized thanks to interventions as simple as hand-washing.)