Lightnews — Scholar-powered news

Reposted by Juliana Taube

Jessica Davis @jessica-davis.bsky.social · 16h

We are currently accepting submissions for lightning talks to showcase the wide variety of tools and visualizations people have created. All formats are welcome (dashboards, animations, graphics, story-driven websites, etc.). See the website for more info!

1 1 2

Reposted by Juliana Taube

Greg Albery @gfalbery.bsky.social · 7d

I'm v excited to be recruiting a PhD student to work on badger behaviour and ecology! Starting date is March 2026; see the ad here, or message me for more details: www.gregalbery.me/s/March-2026...

10 8

Juliana Taube @julianataube.bsky.social · 11d

What is the purpose of inoculating the pigs in both isolators 1 and 2? Seems like the isolator 3 pigs could have been infected by either set of inoculated pigs, & this was a missed opportunity to test aerosol transmission distance limits/sensitivity.

Reposted by Juliana Taube

John M. Drake @jdrakephd.bsky.social · 16d

Newly expanded version of my guide to scientific writing -- known as the “15 steps” -- published in PLOS Computational Biology. Special thanks to Éric Marty for creating a fantastic visualization.

Check it out: journals.plos.org/ploscompbiol...

#ScientificWriting #PLOSComputationalBiology

1 21 37

Reposted by Juliana Taube

Mallory Harris, PhD @malar0ne.bsky.social · 16d

Hear more about it tomorrow at noon eastern in a webinar hosted by the @midas-network.bsky.social trainee network!
georgetown.zoom.us/meeting/regi...

1 4

Reposted by Juliana Taube

Juliana Taube @julianataube.bsky.social · 29d

Ever read a paper & think, "wow 🤩 that was well written"?

Learn how to write your own *wow* paper with @midas-network.bsky.social trainees on Thurs 9/25 @ noon ET.

We'll hear from @jdrakephd.bsky.social on strategies for crafting & communicating a good story.

🔗 georgetown.zoom.us/meeting/regi...

Flyer advertising upcoming MIDAS Trainee Network event on How to Write a Strong Manuscript. The event will take place on Thurs 9/25 at 12pm ET with a presentation from Dr. John Drake from the University of Georgia, followed by Q&A and discussion. Register at: https://georgetown.zoom.us/meeting/register/rTEZP0pSQIqlLv_EyquGgQ

6 5

Reposted by Juliana Taube

Juliana Taube @julianataube.bsky.social · 28d

Our paper on US contact patterns is now published in The Lancet Digital Health!

doi.org/10.1016/j.la...

Thanks to my brilliant coauthors @zsusswein.bsky.social, @vcolizza.bsky.social, & @bansallab.bsky.social for their help with this project.

Read on for an overview of our findings... 🧵

Screenshot of the paper titled "Characterising non-household contact patterns relevant to respiratory transmission in the USA: analysis of a cross-sectional survey"

1 6 13

Juliana Taube @julianataube.bsky.social · 28d

Thanks to the Delphi Group at Carnegie Mellon for sharing their survey data!

And thanks to you, dear reader, for making it this far! 😅

13/13

1

Juliana Taube @julianataube.bsky.social · 28d

We still need additional contact surveys in the US, esp. focused on
🚸 children
👭 assortative mixing patterns (when people interact w/ others w/ similar characteristics)
📅 individual behavior change over time

Our pandemic & baseline contact estimates are available at github.com/bansallab/re...

/12

GitHub - bansallab/resp_contact

Contribute to bansallab/resp_contact development by creating an account on GitHub.

github.com

1 1

Juliana Taube @julianataube.bsky.social · 28d

3️⃣ During the pandemic & at baseline, younger adults, men, & Hispanic & Black individuals have more contacts & are at greater disease risk

These geographic & social differences in risk can help target public health resources & surveillance 📢

/11

Figure showing contact by age, gender, race or ethnicity, and setting during the pandemic and at baseline

(A) Mean pandemic and baseline non-household contact rate by age. Each point represents a county-age category. Analysis was limited to counties with five or more responses per age category per week. Contact decreases with age.

(B) Mean pandemic and baseline non-household contact rate by gender. Each point represents a county-gender category. Analysis was limited to counties with five or more responses per gender category
per week. Contact is higher in men than women.

(C) Mean pandemic and baseline non-household contact rate by race or ethnicity. Each point represents a state-race or ethnicity category. Analysis was limited to states with ten or more responses
per race or ethnicity category per week. All racial and ethnic categories are non-Hispanic unless labelled otherwise. Other denotes individuals who reported their race as American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or other, or as falling in multiple categories. Contact is lowest in Asian respondents, and highest in individuals reporting other or multiple race categories.

(D) Mean pandemic and baseline non-household contact rate by setting. Each point represents a county-setting. Analysis was
limited to counties with ten or more responses per setting per week. Contact is highest at work, followed by shopping, then social settings.

1 1 2

Juliana Taube @julianataube.bsky.social · 28d

2️⃣ Contact patterns vary across US counties regardless of disease 🌎

Based on population density, we expected urban counties 🏙️ to have higher contact rates than rural ones 🚜

This is true at baseline, but not during the pandemic, when urban areas were more responsive to gathering restrictions

/10

Figure showing spatial heterogeneity and urban–rural gradient of pandemic and estimated non-pandemic contact

(A) Mean number of non-household contacts per person per day for each county relative to the national mean (8⋅7 contacts per person per day) during the COVID-19
pandemic (Oct 1, 2020, to April 30, 2021). There was high spatial heterogeneity in contact, even within states, which was fairly consistent across time. Counties shaded in grey did not have a sufficient sample size to estimate contact.

(B) Map of inferred mean number of non-household contacts per person per day for
each county relative to the national mean (10⋅9 contacts per person per day) in a non-pandemic scenario. Spatial heterogeneity in contact remains high, although which counties have values above and below the national mean has shifted compared with the pattern observed during the COVID-19 pandemic.

(C) The mean contact rate (non-household contacts) for each county decreases with increasing urbanicity during the pandemic, but increases with urbanicity during inferred non-pandemic times.
Only counties with ten or more responses per week each week (from Oct 1, 2020, to April 30, 2021) are included. NCHS class describes the urbanicity of the county, with 1 indicating a large central metropolitan area and 6 representing rural, non-core areas. NCHS=National Center for Health Statistics.

1 1 2

Juliana Taube @julianataube.bsky.social · 28d

If contact rates aren’t changing much over time, then increased winter respiratory infections may be driven more by where contact is happening (indoors 🏢 vs outdoors ⛰️ ) rather than by an increase in overall contacts

See @zsusswein.bsky.social's paper for more on this: doi.org/10.7554/eLif...

/9

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

Fine-grain mobility data empirically quantify the propensity for human mixing to be indoors across the US and improve understanding of the relationship between the physical environment and infection r...

doi.org

1

Juliana Taube @julianataube.bsky.social · 28d

1️⃣ Early in the pandemic, contact varied over time 📆

However, contact and COVID-19 incidence were anti-correlated during this period (when disease went ⬆️, contacts went ⬇️)

Thus, after controlling for disease, there was no longer any systematic variation in contact over time

/8

Figure showing contact dynamics observed over time during the COVID-19 pandemic and estimated non-pandemic contact dynamics, by county

(A) Mean number of daily non-household contacts for individual counties over time during the COVID-19 pandemic. Contact is presented as a Z score relative to each
county’s mean to allow comparison between time series despite the large range of mean contact values across counties. Each line represents a county and is coloured by mean contact relative to the national mean. The black line shows the Z score of the centred 3-week rolling average of national COVID-19 case incidence for context.
Counties had similar contact dynamics over time: most counties had higher contact during the summer of 2020, and all had lower contact during the winter of 2020–21. Counties in which contact decreased in the summer of 2020 were typically in states that had a higher incidence of COVID-19 during that time.

(B) Mean contact rate (non- household contacts) in the absence of disease (baseline; slate) was effectively constant over time, compared with observed contact during the pandemic (teal), across a diverse set of counties. We controlled for disease using a linear regression model that predicts contact from national case incidence, state and county policy data, and county vaccination coverage. This analysis is restricted to Oct 1, 2020, to April 30, 2021, to encompass a full wave of COVID-19. Shaded areas represent 1 SD above and below the fitted contact value or estimated non-pandemic value.

1 1 1

Juliana Taube @julianataube.bsky.social · 28d

I know what you're thinking

"These survey data are from early in the pandemic, and it's 2025... 🙄 Is this even relevant?"

We got you! 🫡

By controlling for the effect of cases, vaccines & pandemic policies on contact, we inferred baseline contact patterns in the US 🎉

So what did we find?

/7

1

Juliana Taube @julianataube.bsky.social · 28d

Here, we tackle these questions using a large survey in the US from June 2020 to April 2021 📝

Contacts are defined as either:
🗣️ conversations >5 mins long w/ someone <6 ft away
or
🫂 physical contact

We analyze the average # of contacts in each county for each week in the study period

/6

1

Juliana Taube @julianataube.bsky.social · 28d

Hopefully, you're convinced that contact patterns are important by this point.

Yet we know relatively little about them in the US! 😧

🗺️ Do contact rates differ across counties?
☃️ Are contact rates ⬆️ in winter, driving resp. infections, like flu?
👵 How do contacts vary with age, gender, & race?

/5

1

Juliana Taube @julianataube.bsky.social · 28d

For this reason, we model disease spread on networks, where some people have more connections than others.

And theory suggests that the people with more contacts are more likely to get infected 🤒

Thus, understanding contact patterns can help us better estimate individual & population risk!

/4

Figure from Bansal et al. (2007) showing that infected individuals have higher degree (more contacts) than susceptible individuals (uninfected) in a model of disease spread incorporating variation in contact patterns

1

Juliana Taube @julianataube.bsky.social · 28d

Initially, disease models relied on the homogeneous mixing assumption, where each person has the same number of contacts 👯‍♀️ #twins

But from past surveys (eg POLYMOD) we know that there's actually lots of variation in # of contacts across individuals & this heterogeneity affects disease spread! 🤓

/3

Histogram showing that the number of contacts reported by individuals in the POLYMOD study is heavy-tailed, meaning most people have relatively few contacts (~10) but a few have a lot of contacts (> 75).

1

Juliana Taube @julianataube.bsky.social · 28d

Interactions between individuals (contacts) provide opportunities for respiratory infectious disease transmission 🦠

By studying these interactions and incorporating them into mathematical models, we can better predict how far and fast diseases can spread 📈

/2

1

Juliana Taube @julianataube.bsky.social · 28d

Our paper on US contact patterns is now published in The Lancet Digital Health!

doi.org/10.1016/j.la...

Thanks to my brilliant coauthors @zsusswein.bsky.social, @vcolizza.bsky.social, & @bansallab.bsky.social for their help with this project.

Read on for an overview of our findings... 🧵

1 6 13

Juliana Taube @julianataube.bsky.social · 29d

Ever read a paper & think, "wow 🤩 that was well written"?

Learn how to write your own *wow* paper with @midas-network.bsky.social trainees on Thurs 9/25 @ noon ET.

We'll hear from @jdrakephd.bsky.social on strategies for crafting & communicating a good story.

🔗 georgetown.zoom.us/meeting/regi...

6 5

Reposted by Juliana Taube

MIDAS Network @midas-network.bsky.social · Sep 10

MIDAS Trainee Network Hosts Event on How to Write a Strong Manuscript.

Register: georgetown.zoom.us/meeting/regi...

2

Reposted by Juliana Taube

Greg Albery @gfalbery.bsky.social · Sep 8

Celebrating the publication of our big collaborative spatial-social meta-analysis of density-dependent transmission effects, out now in Nature Eco Evo! doi.org/10.1038/s415... (or rdcu.be/eD6eB)

1 22 43

Reposted by Juliana Taube

Demetre Daskalakis @drdemetre.bsky.social · Aug 28

Enough is enough. The health of the country is in danger. Read our resignation letters: insidemedicine.substack.com/p/breaking-n...

Breaking News: Read three top CDC officials' resignation emails.

The end of an era.

insidemedicine.substack.com

280 2.4K 5.3K

Reposted by Juliana Taube

MIDAS Network @midas-network.bsky.social · Jul 29

Join the MIDAS Network Trainee Committee for an interactive workshop.

Register: tinyurl.com/MIDASFigures

1