James Hay
@jameshay.bsky.social
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/
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!
December 7, 2024 at 9:59 PM
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!
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.
November 22, 2024 at 7:36 PM
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.
November 22, 2024 at 7:33 PM
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.
Ideally. Let me know if you get it running without one!
April 9, 2024 at 9:39 AM
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!
April 9, 2024 at 9:08 AM
If you encounter bugs/difficulties, flag an issue on GitHub and I'll help get things running!
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...
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
April 6, 2024 at 8:33 AM
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...
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.
April 6, 2024 at 8:32 AM
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.
April 6, 2024 at 8:32 AM
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.
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.
April 6, 2024 at 8:32 AM
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.
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.
April 6, 2024 at 8:32 AM
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.