James Hay

Closing date tomorrow!

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.

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.

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.

Enhanced testing can substantially improve defense against several types of respiratory virus pandemic Mass testing to identify and isolate infected individuals is a promising approach for reducing harm from the next acute respiratory virus pandemic. It…

Our latest paper www.sciencedirect.com/science/arti... shows that mass PCR testing could control a range of potential respiratory pandemics at low societal cost. We also explore logistics. Even a prototype would be really nice now with H5N1. @jameshay.bsky.social 1/9

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...

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...

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.

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…

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...

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!

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…

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...

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.

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.

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.

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.

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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

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:

Ideally. Let me know if you get it running without one!

If you encounter bugs/difficulties, flag an issue on GitHub and I'll help get things running!

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

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...

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.

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.

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.

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.

This new paper brings these pieces together: we fit serosolver to our massive dataset of over 70,000 HI titers, summarizing antibody profiles against 20 A/H3N2 strains for 1,130 individuals from Guangzhou, China.

An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver Author summary Antibody levels can determine previous exposure to a pathogen and how likely individuals are to be infected in the future. However, antibody concentrations change over time, and some pa...

We then developed this method into an R package, serosolver: journals.plos.org/ploscompbiol.... Around the same time, Bingyi Yang analysed and summarised the serodynamics of our massive serological study from Guangzhou, China: journals.plos.org/plospathogen...

Timescales of influenza A/H3N2 antibody dynamics Author summary It is challenging to determine the true extent of influenza infection and immunity within a population, because a person’s immune response to a specific influenza strain depends both on...

Previous work led by @adamjkucharski.bsky.social showed how to decode these infection histories using panels of antibody measurements against multiple influenza strains combined with mathematical models journals.plos.org/plosbiology/...

Pathogens like influenza and SARS-CoV-2 are antigenically variable, meaning your immune system is regularly exposed to new variants. Consequently, your antibody repertoire builds up over your lifetime, reflecting your history of infection/vaccination with different viruses.

TLDR: we inferred infection histories for 1,130 individuals using 70,000 antibody titers measured against influenza A/H3N2 strains isolated between 1968 and 2014. This enabled us to study the long-term epidemiology of influenza.

Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course medRxiv - The Preprint Server for Health Sciences

New preprint: Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course

www.medrxiv.org/content/10.1...