My #atscience talk about Lea is now online! If you've only ever heard me talk about NLP, here's a chance to hear me rant about #atproto (the protocol you're reading this on!), social media for researchers, and the free internet.
And if you're interested in helping us build Lea, please reach out!
Parenting insight, day 168: sucking the snot out of someone else’s nose is actually oddly satisfying?
I haven’t been quiet in my life, so I’m not about to start now.
Thank you, @microbetv.bsky.social, for giving me a microphone 🗣️
#ScienceSky #NutritionSky #AgSky listen to Gastropod!
On this strange, strange night, this helped:
On this public health week, I can’t think about anything but this. These threats, if realized, would be a public health crisis, a humanitarian crisis, a moral and civilizational crisis, on the grandest possible scale. I’m calling my representatives; we need to take a different course.
ATTENTION ASV TRAINEES! The NOMIS & Science Young Explorer Award is a great opportunity to showcase your research and practice your science communication. Details below
www.science.org/content/priz...
Public health is more than just systems. It’s the foundation for healthier communities everywhere.
This National Public Health Week, help us celebrate and expand access to equitable health care around the globe.
Learn more about how you can get involved: https://www.pih.org/advocate
For more information, see our preprint, and you can explore more scenarios via our Shiny app: kisslerlab.shinyapps.io/ag-epi-model/. We welcome feedback! Email stephen.kissler@colorado.edu with constructive comments, typos, clarification questions, etc. etc.
www.medrxiv.org/content/10.6...
This work spun out of a group rotation project with the IQBiology program at CU Boulder - if you're a prospective student & this work sounds interesting, check them out! Work was co-led by Katie Bardsley, Luis X. de Pablo @lxdepablo.bsky.social, Emma Keppler Canada, Naia Ormaza Zulueta & Zia Mehrabi
Nevertheless, our findings show that respiratory outbreaks pose a substantial risk to agricultural worker health and food production. Surveillance in the general community may lag behind and significantly under-represent disease burden in agricultural workers.
There are of course many limitations: data on agricultural workers are sparse. We only examined household size and crowding as predictors of disease spread, and we only looked at harvest-related production losses. Our region-level analysis could mis-represent the experience of individual farms.
Plot of the estimated percent production impact (vertical axis) vs. epidemic peak day (horizontal axis) for strawberries, iceberg lettuce, and oranges, shown as three colored lines. When epidemics peak over the summer, the impact is high for strawberries and iceberg lettuce; when epidemics peak over the winter, the impact is higher for oranges.
We also looked at impacts on three labor-intensive California crops with different harvest seasons: lettuce, strawberries, and oranges. A poorly-timed outbreak (May for lettuce & strawberries, Jan for oranges) could lead to tens of millions of dollars in lost production.
Epidemic curves depicting simulated outbreaks in agricultural workers vs. the general community across six U.S. regions. Time in days is on the horizontal axis and proportion infected is on the vertical axis. Bold lines demonstrate that, at the region level, outbreaks reach higher peaks and peak earlier in agricultural workers than in the general community. Thinner lines show that there is substantial variation in outbreak timing and intensity across counties within regions.
This translates into substantially more intense outbreaks among agricultural workers: For R0=1.5, peak prevalence was 23-45% higher depending on the region, final outbreak sizes were 15-28% higher, and outbreaks peaked 5-12 days earlier in agricultural workers.
Two-panel figure with six histograms in each panel depicting how household size and the proportion of crowded households differs between agricultural workers and the general community across six U.S. regions. Agricultural workers have systematically larger household sizes and their households are substantially more crowded than the general population.
Agricultural worker households are substantially larger and more crowded (>1 occupant per room) than the general community: in California, for example, about 8.3% of households are crowded overall, but 33% of agricultural worker households are crowded!
We built a household-structured disease transmission model to simulate respiratory virus spread among agricultural workers and the general community, accounting for household size and crowding. We compiled data from the US Census and National Agricultural Workers Survey across six U.S. regions.
But until now, we've lacked a way to project how respiratory infections in agricultural workers might play out in new contexts - be it a bad outbreak of seasonal influenza or a novel respiratory pathogen. Here, we aim to bridge this gap.
Recent cases of H5N1 influenza in dairy workers underscore how this threat has not disappeared...
www.cdc.gov/mmwr/volumes...
We already know respiratory virus outbreaks pose a major risk to food system workers: COVID-19 sickened >59k meat processing workers and >300,000 farmworkers, leading to major food chain disruptions.
pmc.ncbi.nlm.nih.gov/articles/PMC...
journals.plos.org/plosone/arti...
New manuscript! Agricultural workers in the U.S. face huge health disparities, and living conditions in particular make agricultural workers more vulnerable to respiratory infections. But just how much more vulnerable - and how might outbreaks impact food production? tinyurl.com/ms7tv2pf
Ultimately, we envision a world where diagnostic tests and testing guidelines are tuned to both clinical and public health needs, and where testing empowers outbreak control through both informed individual decision-making and evidence-based policies.
Our research for this article made clear that testing-based interventions can fail from many angles – but, if successful, they can be a huge asset for outbreak control. Avoiding the many possible pitfalls will require scientific innovation, cross-sector collaboration, and institutional reform.
Testing can and should be a cornerstone of outbreak response, but we've often lacked the basic infrastructure and conceptual frameworks to successfully deploy testing-based interventions. In the article, we outline some potential ways forward and highlight many exciting efforts already underway.
(6) How should we allocate testing resources during fast-moving outbreaks? (7) And then, how can we measure the impact of these decisions, especially when testing is deployed alongside other interventions?
(4) How can we remove regulatory barriers in a system that lacks a framework for evaluating tests based on their public health value? www.science.org/doi/10.1126/... (5) And once tests are widely deployed, how can we avoid diagnostic escape, where a pathogen evolves to evade detection?
(2) How can we account for how people *actually* seek testing and respond to test results? What unintended outcomes might happen – like reduced vaccination rates among those who tested positive for COVID-19? www.pnas.org/doi/10.1073/... (3) Relatedly, how can we maintain privacy and avoid stigma?
This is great progress! But there are still major hurdles, like: (1) How should we balance cost, sensitivity, specificity, and turnaround time when designing testing protocols? High sensitivity isn't the end-all, be-all of testing - it can even be counterproductive. www.science.org/doi/10.1126/...
Diagnostic tests are often seen as clinical tools, but testing has also long played a role in public health. Testing is crucial for screening, surveillance, and antibiotic stewardship. Recent campaigns like "U=U" for HIV and asymptomatic COVID-19 testing have made a major dent in disease spread.
