James Hay
@jameshay.bsky.social
1.4K followers 590 following 24 posts
Research Fellow at the Pandemic Sciences Institute, University of Oxford. Using maths and stats to understand infectious disease dynamics, mostly viral kinetics and serology. https://hay-idd.github.io/
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Reposted by James Hay
dchodge.bsky.social
🚨 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
A serological inference package using reversible jump mcmc
The `serojump` package provides tools for fitting serological models to antibody kinetics data using reversible-jump Markov Chain Monte Carlo (RJ-MCMC). It enables researchers to model the dynamics of...
seroanalytics.org
jameshay.bsky.social
Closing date tomorrow!
jameshay.bsky.social
We're hiring a modelling postdoc at PSI Oxford for two exciting projects: 1) modelling the early immune responses to Nipah vaccination, and 2) joining the PRESTO team working on immunobridging in vaccine evaluation studies.

tinyurl.com/5abbxrjh

Get in touch for more info! Deadline 4th August.
Job Details
tinyurl.com
Reposted by James Hay
jameshay.bsky.social
Excited to share our paper on viral load dynamics of West Nile virus in mosquitoes! Key findings:

1. Variation in pooled Ct values from mosquito traps reflect underlying biological and epidemiological mechanisms.
2. WNV prevalence estimates are improved by using Cts rather than +ve/-ve pool status.
punya-alahakoon.bsky.social
New preprint!
We use pooled Ct values from mosquito surveillance to estimate West Nile virus prevalence—without binarising the data.
Joint work with Ian Marchinton, Joseph R. Fauver & James Hay.
Check it out: www.biorxiv.org/content/10.1...
Tracking West Nile virus dynamics using viral loads from trapped mosquitoes
West Nile virus (WNV) persists in an enzootic cycle between birds and mosquitoes. Human infections are incidental and usually sub-clinical with some cases of neuroinvasive disease. Mitigation relies on surveillance to guide decision making, including RT-qPCR testing pools of mosquitoes. Cycle threshold (Ct) values from these pools\---|semi-quantitative proxies for viral load\---|are binarised to estimate WNV prevalence in mosquitoes as a proxy for human risk. We showed that Ct value variation in Colorado and Nebraska (2022-2024) cannot be explained by laboratory factors, suggesting a role for biological and epidemiological mechanisms. We developed a multiscale model linking pooled Ct values to mosquito viral kinetics, bird-to-mosquito transmission, and seasonal force of infection. A novel method estimating WNV prevalence using pooled Ct values outperformed existing approaches at high prevalence. We demonstrate the importance of treating environmental viral load samples as quantitative, as binarising masks key information for arbovirus surveillance and risk mitigation. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust Early Career Award, grant 225001/Z/22/Z UNMC VCR
www.biorxiv.org
jameshay.bsky.social
We're hiring a modelling postdoc at PSI Oxford for two exciting projects: 1) modelling the early immune responses to Nipah vaccination, and 2) joining the PRESTO team working on immunobridging in vaccine evaluation studies.

tinyurl.com/5abbxrjh

Get in touch for more info! Deadline 4th August.
Job Details
tinyurl.com
jameshay.bsky.social
Excited to share our paper on viral load dynamics of West Nile virus in mosquitoes! Key findings:

1. Variation in pooled Ct values from mosquito traps reflect underlying biological and epidemiological mechanisms.
2. WNV prevalence estimates are improved by using Cts rather than +ve/-ve pool status.
punya-alahakoon.bsky.social
New preprint!
We use pooled Ct values from mosquito surveillance to estimate West Nile virus prevalence—without binarising the data.
Joint work with Ian Marchinton, Joseph R. Fauver & James Hay.
Check it out: www.biorxiv.org/content/10.1...
Tracking West Nile virus dynamics using viral loads from trapped mosquitoes
West Nile virus (WNV) persists in an enzootic cycle between birds and mosquitoes. Human infections are incidental and usually sub-clinical with some cases of neuroinvasive disease. Mitigation relies on surveillance to guide decision making, including RT-qPCR testing pools of mosquitoes. Cycle threshold (Ct) values from these pools\---|semi-quantitative proxies for viral load\---|are binarised to estimate WNV prevalence in mosquitoes as a proxy for human risk. We showed that Ct value variation in Colorado and Nebraska (2022-2024) cannot be explained by laboratory factors, suggesting a role for biological and epidemiological mechanisms. We developed a multiscale model linking pooled Ct values to mosquito viral kinetics, bird-to-mosquito transmission, and seasonal force of infection. A novel method estimating WNV prevalence using pooled Ct values outperformed existing approaches at high prevalence. We demonstrate the importance of treating environmental viral load samples as quantitative, as binarising masks key information for arbovirus surveillance and risk mitigation. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust Early Career Award, grant 225001/Z/22/Z UNMC VCR
www.biorxiv.org
Reposted by James Hay
chriswymant.bsky.social
Two postdoc positions to work on virus epi & evolution in response to vaccination, with both theoretical models + data analysis. Paris/Montpellier. With Sylvain Gandon, Sébastien Lion, François Blanquart, Katrina Lythgoe, & Troy Day
emploi.cnrs.fr/Offres/CDD/U...
emploi.cnrs.fr/Offres/CDD/U...
Portail Emploi CNRS - Offre d'emploi - Chercheur.e postdoctoral H/F en épidémiologie évolutive
emploi.cnrs.fr
Reposted by James Hay
christldonnelly.bsky.social
Please spread the word - a funded (home fees) DPhil (PhD) studentship available in @oxfordstatistics.bsky.social

Social optimisation of public-facing digital tools for health protection and trial frameworks for non-pharmaceutical interventions
www.stats.ox.ac.uk/research-stu...
Research Studentships | statistics
www.stats.ox.ac.uk
jameshay.bsky.social
Hello influenza enthusiasts! You may be interested in our recent publication linked below. We used multi-strain serology to figure out who got infected with which A/H3N2 influenza strain and when, allowing us to reconstruct epidemiological patterns back to 1968 stratified by time, age and location.
jameshay.bsky.social
Reconstructed influenza A/H3N2 infection histories using multistrain serology, paper out in PLOS Biology plos.io/3YIDQpt! We inferred lifetime infections and antibody levels for 1130 individuals in Guangzhou, China, giving insights into long-term influenza incidence and immunity.

Thread below:
Reposted by James Hay
jameshay.bsky.social
Are you interested in analysing serological data for infectious disease epidemiology? Check out our new review article on serodynamics! With Saki Takahashi at JHU and Isobel Routledge at UCSF. (See next comment if you're into serology modeling) www.sciencedirect.com/science/arti...
Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data
We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as ser…
www.sciencedirect.com
jameshay.bsky.social
One thing I'm particularly proud of is showing that virtually all serodynamics models and data, from the basic serocatalytic model through to complex time-since-infection models, are described by a common data-generating process. This is in the Appendix so please check it out!
jameshay.bsky.social
Are you interested in analysing serological data for infectious disease epidemiology? Check out our new review article on serodynamics! With Saki Takahashi at JHU and Isobel Routledge at UCSF. (See next comment if you're into serology modeling) www.sciencedirect.com/science/arti...
Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data
We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as ser…
www.sciencedirect.com
jameshay.bsky.social
Right, we wanted to see if there was a simpler approach - is there enough signal without needing to write down all the convolutions? I think the convolution framework is still promising and may be the most robust approach, but with variants and immunity, it's hard to get it right after 2020.
jameshay.bsky.social
Sorry about that! It's ready to go, but waiting for clearance to share the data before making the repo public. Should hopefully be live before too long.
Reposted by James Hay
jameshay.bsky.social
New study tracking epidemic dynamics using cycle threshold values (a proxy for viral load) from routine hospital and community testing. This is the next step of our work from 2021 showing that the population distribution of Ct values over time is related to SARS-CoV-2 epidemic growth rates.
jameshay.bsky.social
New study tracking epidemic dynamics using cycle threshold values (a proxy for viral load) from routine hospital and community testing. This is the next step of our work from 2021 showing that the population distribution of Ct values over time is related to SARS-CoV-2 epidemic growth rates.
Reposted by James Hay
mrc-outbreak.bsky.social
NEW EPISODE #ScienceInContext! This week Dr @eonore.bsky.social speaks with Dr @jameshay.bsky.social of the Big Data Institute about influenza: who, where and when influenza infections are likely to occur using antibody profiles and individual infection histories 👇
youtu.be/Ux6yDWO_c8I
YouTube
Share your videos with friends, family, and the world
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jameshay.bsky.social
Reconstructed influenza A/H3N2 infection histories using multistrain serology, paper out in PLOS Biology plos.io/3YIDQpt! We inferred lifetime infections and antibody levels for 1130 individuals in Guangzhou, China, giving insights into long-term influenza incidence and immunity.

Thread below:
jameshay.bsky.social
Ideally. Let me know if you get it running without one!
jameshay.bsky.social
If you encounter bugs/difficulties, flag an issue on GitHub and I'll help get things running!
jameshay.bsky.social
This has been a massive saga over many years. Although the data are cool enough on their own, the modelling work also tackles many challenges on serodynamics modeling in general.

Code and data here: github.com/jameshay218/....
 
Serosolver package: github.com/seroanalytic...
GitHub - jameshay218/fluscape_infection_histories: Code and data for the Fluscape infection histories manuscript
Code and data for the Fluscape infection histories manuscript - jameshay218/fluscape_infection_histories
github.com
jameshay.bsky.social
Given the rise in multiplex antibody assays and technologies like PepSeq and PhIP-Seq, modeling methods like these will help us to understand the mechanisms and consequences of how immunity builds over the life course to pathogens like influenza and SARS-CoV-2.
jameshay.bsky.social
An exciting output of our inference is the well-known relationship between antibody titer and probability of infection. Using our method, we can understand not just serological patterns, but also immunity patterns using these multi-antigen serology panels.
jameshay.bsky.social
We find:
1. Serology-based attack rates are high, at around 18% infected per year.
2. Influenza A/H3N2 infection rates are highest in children, decrease with age and plateau in adulthood.
3. Incidence rates are highly correlated at this small spatial scale.
jameshay.bsky.social
Fitting serosolver gave us estimates for: 1) each individual’s sequence of lifetime influenza infections; 2) incidence at a fine spatial scale; and 3) parameters of an antibody kinetics model describing boosting, waning, cross-reactivity and measurement error.