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Casey Middleton, PhD

@caseymiddleton.bsky.social
167 followers 118 following 78 posts

Infectious disease modeling Views are my own

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Casey Middleton, PhD @caseymiddleton.bsky.social · 01/10/2026
Dear authors, If you submit a manuscript without line numbers, I can only assume you are actively trying to destroy my life. Hatefully, Reviewer #3 😭
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Casey Middleton, PhD @caseymiddleton.bsky.social · 28/09/2026
This is such cool work, and such a cool thread walking through the evolution of thinking in this area. Thanks for sharing!!
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Casey Middleton, PhD @caseymiddleton.bsky.social · 10/09/2026
Interested in creating community and finding collaboration opportunities in infectious disease dynamics? Join the GSIDD Connect Series! 📆 Friday, Sept. 11 🕐 1pm ET 🔗 www.gsidd.org/events
gsidd.org
Events | GSIDD
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Casey Middleton, PhD @caseymiddleton.bsky.social · 27/08/2026
Ten years ago, Beans was put up for adoption because a family member was abusing him. If you ask me, she should’ve divorced the man and kept the dog. Happy National Dog Day, Beanbo Doggins. You’re the coolest little dude I ever met!
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The Tennessee Holler @thetnholler.bsky.social · 29/07/2026
Literally this. They’re acting like people didn’t die. Like we all didn’t live through it and see it with our own eyes, and Trump wasn’t President at the time. It’s wild to watch.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
I'm excited to share a preprint that's been years in the making: the idea took shape during the COVID pandemic. Anecdotally, people would sometimes spread COVID at work but not at home, and vice versa. Why? One explanation would be if infectiousness is fleeting...
medrxiv.org
How bursty infectiousness shapes epidemic dynamics
An epidemic’s expected course is determined by the magnitude and timing of a typical person’s infectiousness — captured, in turn, by the basic reproduction number and the generation-time distribution....
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Karly Kingsley @karlykingsley.bsky.social · 29/07/2026
If you’re watching Fauci’s testimony remember that science isn’t an ideology. It doesn’t lie. It evolves. COVID-19 was a once-in-a-century pandemic caused by a brand-new virus, recommendations changed because the evidence changed. That’s not deception. That’s how it works.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
Some scientists can remember it all. I am not one of them! If you aren't either, join me in creating external memory systems. It’s not about perfection; it’s about habit. Find a system that prevents you from re-reading the same paper three times. Your future self will thank you. 📖 Happy reading!
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
When taking notes, I ask myself: • Summary: What key terms will I use (Ctrl+F) to find this paper in the future? • Deep Notes: What study designs (sample size, models, assumptions) will my team ask about? Tip: Always note if a key result or figure came from the Supplementary Materials!
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
The beauty of this setup? No clutter. I can instantly filter my library by a specific project or pathogen when I'm writing. If I need to dig into the weeds, I just open the page to see my detailed notes, without ruining my clean database overview (see example below).
Screenshot of Notion relational database with notes detailing the sampling scheme and modeling methodology for a SARS-CoV-2 viral kinetics study.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
Every entry in my database tracking a paper requires: • Title & DOI • Status (skimmed, read, etc.) • Summary (my main takeaway for MY work—not just the abstract!) I also use specific relational tags: • LaTeX citation key • Relevant projects • Pathogen studied and take detailed notes on key papers.
Screenshot of Notion Database with the following entries for each manuscript:
1. Name: a manuscript identifier with last author and year
2. Title
3. Summary
4. DOI
5. Status: Read / Skimmed / Abstract / Methods
6. Relevant projects
7. Pathogen
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
How to store these notes? While a spreadsheet works for small projects, combining high-level summaries with granular technical notes can get overwhelming. That’s why I love relational databases (like Notion or Obsidian). They keeps your library scannable, hiding the deep notes a click away.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
Your future self is the target audience. When taking notes, aim for two goals: 1️⃣ "I remember a concept. Which paper is it from?" 2️⃣ "I might use this finding. What were their exact methods?" A great system ensures you rarely have to open the original PDF a second time (if you read the paper fully).
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
Throughout your scientific career, you will read hundreds of dense manuscripts. 📚 As the literature stack piles up, remembering exactly where you saw a specific methodology or finding can be tough. Here is how I use a relational database to keep track of it all (and et al.). 👇
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Carl T. Bergstrom @carlbergstrom.com · 04/07/2026
1. A bit of evolutionary biology. I'm really intrigued by a new perspective piece from Steve Frank that explores connections between how natural selection creates systems that generalize and recent work in machine learning about the surprising capabilities of massively overparameterized systems.
academic.oup.com
Generalization as the great leap in evolvability: insights from machine learning
Abstract. Natural selection encodes learned information in the genome. Learned solutions may be tuned specifically to past challenges, failing in altered e
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IDDjobs @iddjobs.org · 03/07/2026
PhD position (Melbourne, Australia) Mechanistic modelling of the epidemic dynamics of vector-borne zoonotic diseases in Australia with Oliver Eales, Freya Shearer, Marya Potorek at University of Melbourne More details: iddjobs.org/jobs/2561
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Dan Larremore @danlarremore.bsky.social · 08/06/2026
Calculations on the containment of Bundibugyo ebolavirus with @caseymiddleton.bsky.social: www.medrxiv.org/content/10.6... We estimate: 1. Symptom screening misses 68%–86% of infected travelers. 2. A 7d post-travel quarantine misses 26% of infections. → 18%–23% missed by combined measures. 1/
Limitations of cross-border containment strategies
for Bundibugyo ebolavirus, by Casey E. Middleton and 
and Daniel B. Larremore
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Casey Middleton, PhD @caseymiddleton.bsky.social · 04/06/2026
Today's academic birthday gifts: ✨ Opinion piece rejected. 🏅 Tiny change required for resubmission of preprint. 🏆 Mandatory Disclosure of External Professional Activities due. Another day in paradise 🌴😎
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Your Local Epidemiologist @ylepidemiologist.bsky.social · 04/06/2026
6/ We eliminated measles 26 years ago. We eradicated New World Screwworm 60 years ago. Keeping diseases at bay requires sustained investment, trained people, and ongoing engagement.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 01/06/2026
Last night, we celebrated two birthdays: Cecil Earnest Middleton, born May 23, 1941 Casey Elaine Middleton, born June 4, 1996 Highly recommend celebrating your birthday with your granddad ✨
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Helen Branswell 🇨🇦 @helenbranswell.bsky.social · 20/05/2026
This is such a moving piece. If your interested in #Ebola — the damage it causes, the trauma families and communities and responders face during and after an outbreak — please read it. www.statnews.com/2026/05/20/e...
statnews.com
I saw Ebola as both doctor and patient. I wish people cared more about the Africans I worked alongside
“As another Ebola outbreak unfolds, we cannot repeat the mistakes of the past,” writes Krutika Kuppalli.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 20/05/2026
Absolutely. We derive how the bias in ARR-based leaky VE estimates increases with the number of exposures. Practically, this means that using ARR to estimate the leaky HR is okay for rare diseases, short study durations, etc. But we should make this point explicit in the text before final print!
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
#EpiSky #IDSky #MedSky
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
This work would not have been possible without my co-authors @eales96.bsky.social, @jmccaw.bsky.social , and Freya Shearer. We hope this work brings us one step closer to more accurate models of vaccination. There are many limitations and areas for future work here. See paper for details!
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
These predictions have real-world implications, and it is a modelers' responsibility to ensure parameter sources match their definitions. 🫵 So, I challenge you to check the VE source(s) next time you review a modeling paper. Are they using the proper statistic for the assumed vaccine mechanism?
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
⚠️ Our work demonstrates that modelers must consider both the vaccine mechanism and the empirical VE statistic when parameterizing models. Using a mismatched empirical VE estimator leads to inaccurate model predictions, including herd immunity threshold targets, predicted disease burden, and more.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
🤷‍♀️ We have shown that the adjusted parameterization approach improves model accuracy, but does it change model predictions? Well, predicted herd immunity thresholds are drastically lower under adjusted parameterization! *Note that this work is not meant to establish real-world vaccination targets.
SIR-predicted herd immunity thresholds under standard parameterization and adjusted parameterization. HITs are lower under the adjusted parameterization.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
We also extend their adjusted parameterization approach to a heterogeneous vaccine effect model. The improvements, or reductions, in model accuracy from this adjusted parametrization depend on how well our model captures the true distribution of individual-level vaccine-derived protections.
Heatmap showing model prediction error when the distribution of vaccine-derived protection is assumed correctly or incorrectly by the model, under the adjusted parameterization. Error is 0 when the model assumes the correct distribution. If the model assumes protection is leaky and it is truly all-or-nothing, or vice-versa, error is high.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
✨ In hopes of changing this, we use a simulation study to show that the Shim & Galvani method makes leaky model predictions more accurate. While the adjusted parameterization reduces error across all scenarios, it performs best when solved using parameters that mirror the context of the VE study.
Line plot showing that adjusted parameterization improved model accuracy even when the modeled vaccine coverage is not aligned with the vaccine coverage at the time of the VE study.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
😨 But what if only ARR-based VE estimates are available, and we think the vax is leaky? Lucky for us, Shim & Galvani introduced an approach to adjust model parameterization in this scenario. However, few modelers have adapted this approach, despite its publication in 2013. doi.org/10.1016/j.va...
doi.org
Redirecting
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
Thus, modelers must consider both the assumed mechanism of action and the statistic used to empirically estimate VE when parameterizing models. 🛑 If a manuscript models both all-or-nothing and leaky vaccine assumptions, the same VE source should not be used for both!
Preferred VE measurement depends on vaccine mechanism. All-or-nothing: cumulative attack rate ratio. Leaky: hazard ratio.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
Halloran, et al. show that controlling for exposure (e.g., challenge studies) or using hazards ratio (HR) estimators can more accurately recover direct protection for a leaky vax. However, HR estimators may appear to increase over time for an all-or-nothing vax. doi.org/10.1093/oxfo...
Figure showing how various study statistics estimate VE depending on vaccine mechanism. For leaky vaccines, ARR-based estimates underestimate the true effect, while hazards-based and challenge studies are unbiased, For all-or-nothing vaccines, hazards-based estimators overestimate vaccine effect.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
What does this mean for modelers? ARR-based VE estimates generally do not align with leaky model parameters. Statistically speaking: risk ratios != hazard ratios when risk is not low. Plugging these VE estimates into models leads to an underestimate of vaccine effects. 🤔 So what can be done?
Schematic showing that using ARR-based VE estimates to inform leaky model parameters will lead to an underestimate of the modeled population-level vaccine effect.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
Lewnard, et al. derive this bias for TND studies. However, all ARR-based VE studies will be prone to this bias if multiple exposures occur. This bias also exists when a vaccine provides heterogeneous protection (i.e., varying levels of leaky protection across recipients). doi.org/10.1093/aje/...
ARR-based VE estimates change with the number of exposures for leaky and heterogeneous vaccines, but not all-or-nothing. More exposures leads to lower VE estimates.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
What is VE? Empirical studies often define VE as 1 - ARR, where ARR is the relative attack rate in vaxed vs. unvaxed folks. Smith, et al. showed that ARR-based VE estimates may appear to wane over time for a leaky vaccine, even when true protection is constant. doi.org/10.1093/ije/...
VE estimates using attack rate ratios for leaky vaccines appear to wane over time. For all-or-nothing vaccines, VE approaches the true protection level.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
🚨 Disease modelers: Do you use empirical VE estimates interchangeably to inform vaccine effect parameters? Me too, and it’s distorting our predictions. VE estimates often do not align with model parameters. We discuss these pitfalls & solutions in our new preprint: arxiv.org/abs/2605.18571 🧵👇
arxiv.org
Incorporating vaccine effects into epidemiological models: common pitfalls and solutions
Incorporating vaccination into mathematical models appears deceptively simple: models integrate vaccine-derived protections, such as reduced susceptibility to infection, using parameters informed by e...
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Adam Kucharski @adamjkucharski.bsky.social · 05/05/2026
My four year old: I do number maths. Adults do maths with letters they don’t know. Me: Actually, yes, they do! Him: Why don’t they know all the letters yet? I do.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 04/05/2026
Do you think hypothesize that this same logic would apply to non-scholars contributing meaningfully to a given field? Armchair experts -> real contributors?
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Casey Middleton, PhD @caseymiddleton.bsky.social · 29/04/2026
Today's micro-feminist win: changed my email signature from Thanks so much, to Thanks, Please hold your applause 🏅
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Casey Middleton, PhD @caseymiddleton.bsky.social · 28/04/2026
I had to go all the way into the Supplement to figure out how they were really defining "false positive" 🤦‍♀️ Ahhhh, so you mean "likely not infectious"
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Casey Middleton, PhD @caseymiddleton.bsky.social · 24/04/2026
This is such an insane solution to the problem. I am absolutely gobsmacked right now.
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Adam Kucharski @adamjkucharski.bsky.social · 24/04/2026
Claude has started refusing to give citations. Yes, this technically means fewer hallucinations. But it doesn’t mean that AI is now hallucination-free in the way a careful researcher is.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 09/04/2026
This is so clever — I’d laugh if it wasn’t so sad 😵‍💫
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Mallory Harris, PhD @malar0ne.bsky.social · 09/04/2026
For years, Jay Bhattacharya has been upset at the MMWR for publishing findings that run contrary to his predetermined conclusions (e.g., on immunity after covid vaccination) and his political agenda. Now he's in a position to prevent those findings from being published.
Tweet from Jay Bhattacharya in February 2022: I've long respected the CDC's MMWR as a source for important epidemiological facts. I am sad to see that the CDC has used the journal to publish articles to support its predetermined conclusions (e.g. on immunity after covid recovery), rather uphold its pre-covid high standards
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Joshua Weitz @joshuasweitz.bsky.social · 19/02/2026
"This is not reversing the damage. It is spending more than we spent on WHO to create an institution that’s unlikely to survive and will certainly accomplish only a fraction of what we did by working together with the entire world.” - Atul Gawande, @agawande.bsky.social 🎁 wapo.st/4tKRwyM
wapo.st
After leaving WHO, Trump officials propose more expensive replacement to duplicate it
HHS proposes spending $2 billion a year to re-create systems the U.S. accessed through the WHO at a fraction of the cost, according to officials briefed on the matter.
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Casey Middleton, PhD @caseymiddleton.bsky.social · 27/01/2026
#idsky #episky #sciencesky
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Mallory Harris, PhD @malar0ne.bsky.social · 27/01/2026
In a new preprint, we combine modeling with school-level vaccine data to contextualize the risk of 'breakthrough infections' and impacts on the ongoing US measles outbreak. #idsky #episky www.medrxiv.org/content/10.6...
medrxiv.org
Interpreting Breakthrough Infections Given Assortative Mixing of Partially Vaccinated Populations
Declining vaccine coverage across the United States has increased the risk of outbreaks of vaccine-preventable diseases. Even when vaccines have low primary failure rates, conventional epidemic theory...
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Casey Middleton, PhD @caseymiddleton.bsky.social · 26/01/2026
🏅 Concurrent work from Z. Zhang, C. Boyer, & @mlipsitch.bsky.social explore alternative approaches, including generalized additive models (GAMs), to infer titer-protection relationships from TNDs. Be sure to check out their paper if this work is relevant to you! doi.org/10.1101/2024...
doi.org
Use of the test-negative design to estimate the protective effect of a scalar immune measure: A simulation analysis
Background The relationship between antibody levels (more generally, a scalar measure of immune protection) at the time of exposure to infection (so-called exposure-proximal correlates of protection) ...
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Casey Middleton, PhD @caseymiddleton.bsky.social · 26/01/2026
🎯 The scaled logit model, introduced by A. Dunning for case-control studies, provides this flexibility. We demonstrate that the scaled logit model can accurately infer a wide range of titer-protection relationships by fitting only one additional parameter. doi.org/10.1002/sim....
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Casey Middleton, PhD @caseymiddleton.bsky.social · 26/01/2026
Using simulated studies and mathematical arguments, we show that logistic regression is ill suited to recover biologically plausible titer-protection relationships in TNDs. ⚠️ This finding is unique to TNDs, which rely on exposure odds ratios (ORs). Thus, we need a model with more OR flexibility!
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