Delighted to share the (preprint) output of @mhairijan.bsky.social's Oxford visit:
SARS-CoV-2 Neutralising Antibody Profiles Reveal Variant Specific Antibody Dynamics and Regional Differences in Infection Histories in Malawi. dx.doi.org/10.2139/ssrn...
The final piece of my PhD work is now published in Science Translational Medicine! We present a new framework to jointly infer epidemiological and antigenic parameters from multi-pathogen population serological studies🦠
www.science.org/stoken/autho...
Totally agree! We did a quick analysis and some scenario models a few weeks ago and came to a similar conclusion -- fast and early but not unprecedented. An early start may also lead to earlier susceptible depletion.
zenodo.org/records/1770...
Similar from WHO: www.who.int/emergencies/...
Great take. We looked at the data a few weeks ago and concluded that other than starting early, this year's dynamics are comparable to previous bad seasons. Our scenario models suggest a peak in infections (not hospitalisations which lag 1/2 weeks) in early/mid December.
zenodo.org/records/1770...
🧵 Is it a super flu year? Who knows, but I think the current reporting is stupid.
A pissed off thread using data.
Firstly - here are today's headlines and some from the last 3 years... spot the difference. 1/10
WHO Disease Update Notification:
Seasonal influenza
🦠Since August 2025, there's been a rapid increase of A(H3N2) J.2.4.1 subclade K driving early & prolonged seasons
🦠Vaccine helps against hospitalisation, unclear about milder impact
🦠K induces "normal" disease spectrum
www.who.int/emergencies/...
Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets journals.plos.org/ploscompbiol...
Many thanks also to @stevenriley.bsky.social @scauchemez.bsky.social @neher.io
@eales96.bsky.social, and Ben Cowling, Freya Shearer and Juliette Paireau for helpful discussion.
(18/18)
Coauthors: @punya-alahakoon.bsky.social @mishkendall.bsky.social @mghafari.bsky.social @chriswymant.bsky.social @christophraser.bsky.social and Alex Greenshields Watson, Rob Hinch, Luca Ferretti and Jasmina Panovska-Griffiths not on bsky.
(17/18)
Thanks to the @christophraser.bsky.social team for immense work in a very short time, particularly to @punya-alahakoon.bsky.social and Alex Greenshields-Watson who made great contributions under pressure.
(16/18)
Caution is needed – we looked only at data from England, whereas other countries might see quite different trends (e.g., bsky.app/profile/cmye...). Historical experience with early H3N2 seasons is also worth bearing in mind: pmc.ncbi.nlm.nih.gov/articles/PMC... (1989/90 season).
(15/18)
Overall, the K clade seems to have higher growth than dominant strains in prior seasons, but not total immune escape (antibody escape != all immune escape). For England, the earlier season might have interacted with half-term to dampen the peak burden (but not necessarily overall burden).
(14/18)
Our plan is to update this analysis again with cleaner data as things continue to unfold. The most current data are presented by UKHSA here: www.gov.uk/government/s...
(13/18)
Another key limitation is the omission of vaccination. The K clade seems to have drifted away from this season’s vaccine strains, but preliminary data from the UKHSA suggest that vaccine efficacy is in line with previous seasons. www.eurosurveillance.org/content/10.2...
(12/18)
This analysis was done quickly with many limitations (see preprint!) – we merged imperfect ILI+ datasets from past seasons, combined H3N2 and other subtypes, made many assumptions for our scenario model, and some growth trends might be masked by smoothing introduced through the models.
(11/18)
Also compatible but perhaps less plausible are a 10-20% higher R0, or an earlier seed date. In almost all scenarios, an earlier and faster epidemic growth rate leads to depletion of susceptibles before the Christmas period with a dampening effect due to the half term school holiday.
(10/18)
The scenarios suggest substantial immune escape is unlikely given current trends. Only a small drop in population immunity is needed to generate an early peak and faster growth. Current data are compatible with small levels of immune escape in all ages, or greater escape in children.
(9/18)
However, it’s tricky to translate immune escape from antibodies in the lab to overall immune escape in human populations, where other immune mechanisms will play a key role.
(8/18)
A key concern for the season has been immune escape of the new clade. Antibodies generated in response to infection from other recent H3N2 viruses, including the vaccine strain, do not appear to recognise K clade viruses very well: youtu.be/jKlthy3YYNQ?....
(7/18)
Alex Greenshields-Watson put the model online as a webtool for others to explore: hay-idd.shinyapps.io/ModelFluUk-H.... *We particularly encourage users to tailor scenarios for Scotland, Wales and NI, as the season timing has been quite different to England so far.*
(6/18)
We developed an age-stratified SIR model, visually calibrated to 2022/23 data (an H3N2 season). We ran scenarios varying intrinsic transmissibility, loss of immunity overall or in children, and the season start date. We accounted for changes in contacts over school holidays and Christmas.
(5/18)
Rapid spread in children – the usual culprits driving seasonal flu transmission. Compared to the past 2 seasons, there has been a larger difference in growth rate between <14 yos and adults. Half term caused a drop in growth and may have acted as a circuit break for the early season.
(4/18)
Case numbers and epi dynamics don’t look too unusual when you align the curves by date of peak growth rate (* to date*). The flu season has started very early but is otherwise not completely unprecedented (at least in terms of symptomatic cases).
(3/18)
We analysed public data sources (Resp DataMart, RGCP, WHO FluNet) from England to compare growth rates and reproduction numbers to previous seasons. So far, peak growth rates have been higher than the past 10 seasons, but Rt estimates are in line with the upper end of previous seasons.
(2/18)
H3N2 preprint: there are concerns of a severe incoming influenza season due to the drifted H3N2 K clade. We at @psioxford.bsky.social analysed epi data and ran scenario models to see what we could discern about K clade transmission dynamics: zenodo.org/records/1770....
(1/18)
In Texas' public health region 6/5S, which includes Houston, the number of emergency department visits for influenza is already almost on par with the *peak* of last year.
Now out in @natcomms.nature.com Kudos to @tylim.bsky.social and @jameshay.bsky.social for a huge effort and thanks to all the collaborators for their hard work. See the final version here: www.nature.com/articles/s41...
New study shows that shifts in SARS‑CoV‑2 Ct value distributions can help nowcast epidemic trends, revealing both strong potential and key real‑world limitations for Ct‑based surveillance. From CCDD alumni TY Lim & @jameshay.bsky.social and faculty @yhgrad.bsky.social at bit.ly/4o37EaN
🚨 New paper out in PLOS Computational Biology! 🚨
We're excited to share our new paper, serojump, a new probabilistic framework and R package for inferring infections and antibody kinetics from longitudinal serological data.
📄 Full paper: tinyurl.com/re7du3t2
R package: seroanalytics.org/serojump
Closing date tomorrow!